Written by: Mark Cowan - CDO, Put It Forward

February 26, 2019

But Will it Work? The Pressure for Marketers to Drive Pipeline Conversion

It has become increasingly commonplace for marketers to rely on analytics to guide their department planning.With all of these  insights, the real value is in the probability determination of interpreting the data to make sound strategic decisions.

Said in another way - is what I’m hoping to achieve actually going to work?

In this post we’re going to look at how to determine the true value of your MQL’s before you do anything.

Marketers spend a lot of time and energy trying to get the right message inserted at the right point in a conversation to influence. They also spend even more time in crafting out the overall narrative that we’re all a part of every day.  This is important work because it impacts how we perceive the world.

However it has a major flaw -  it presupposes how you as a person are going to react.  That you’re at a particular level of needs awareness and are ready to advance to the next level in some way.  

So what does the marketer do knowing that the individual may or may not be ready for the gift they’re about to receive.  Simple: they rely on the law of large numbers and play the game accordingly by engaging bigger audiences. The result is some level of correlation between a large group of people and individuals who shake out at the end to become customers.

Everyday Put It Forward is in the middle of this conversation, believe me, we see it all by definition of the capabilities we bring starting with integration through to orchestration all the way up to predictive outcomes through machine learning.

We’ve been fortunate to engage with some of the brightest and well meaning people in marketing all over the world with our integration technology.  They’re passionate and committed to being market makers and generating demand.  However when we see their marketing stacks which on average these days is around at least 35-39 discrete applications from marketing automation to CRM to content to events and so forth - one thing stands out as interesting. They’re all the same - functionally.  The difference is in how the technology is applied as well as the operational maturity. I can also say there are a number of non-obvious correlations about a companies marketing stack and their ability to deliver value but that is for another discussion.

So what happens when we ask the question - but will it work?  Meaning will you get more or better MQL’s than before - and how do you know they will be better?.

When I ask this question the answers I usually get are “we have correlation established in our analytics platform” or “yes we know our customers” or “it’s self evident in our conversion attach rates”.  Which begs the following question - “So why if it works does an average of 97% of your engagements result in no or dead air?”

Now if I’m a marketer whose entire value proposition is boiled down to the number of MQL’s I generate and the quality of these leads, this number would scare the heck out of me. I’d know there was a major problem in spending all of this time and effort to identify my audience, craft a strong narrative and build high value content.

What if though, you could gauge the probability of what an individual was going to do at the next step of the conversation?

Suppose you could add to this by determining when they were going to take that step. This is the fundamental difference between analytics - did it work? and predictive propensity modelling through machine learning- but will it work? Yes that’s a meaty phrase but let’s take a closer look into this because it’s just likely the future of marketing one way or another.   Daily people ask us, what does Put It Forward do that’s so special and we reply with unlocking value from data or a variation thereof.

What we really mean though is that when data moves it’s representing an exchange of some kind and in that moment is where the highest value is.  After that the data sits at rest and it’s value decays quickly. This is precisely why analytics while interesting are helpful only in the past tense.

Every application in the marketers tech stack is meant to create data at rest - yet even greater value is achieved when the data is in motion.

What Put It Forward does is tell a marketer not only will it work but exactly who it’s going to work on and more importantly at what moment in time.  Then triggers the right sequence of events to respond like having an account exec call a person or customer support reaching out to keep them as a customer.  

Now everyone is from the kingdom of prove it to me when we tell them the following.  We will take your current audience and tell you not only who is going to become a customer but when.  Then we can tell you how to get the rest of them as your customer. This has resulted in 90% accuracy in determining who is going to convert in advance not just once but at every engagement step.  It’s also resulted in a 40X conversion rate consistently for those who decide to Put It Forward.

Sure you’re paying attention now and yes there’s more I’d like to show and share with you.   Please feel free to connect with me directly or fill in the fields below and press the button for us to connect with you.

Thank you and I look forward to learning more about your story.

Written on December 11, 2023.

Learn how to develop a model for Conversion Rate Optimization and increase ROI.

6 minute read

If you think your marketing team is facing a few hurdles, you are not alone. Marketing teams, big and small, face different challenges. They have to adapt to changes in their industry, their customer’s behavior, and even marketing trends.

Nowadays, marketing teams focus on attracting traffic toward websites, believing that this traffic will naturally be turned into qualified leads. However, the case is more complex for demand generation, lead generation, and demand capture. With the recurring market changes and differences in customer demand, more than driving traffic is needed to guarantee a consistent conversion. For that, marketing teams need to turn their sights on new solutions, such as conversion rate optimization tools.

In this article, we will explain why conversion rate optimization is important, describe the possible strategies related to it, and explain how to calculate conversion rates. Therefore, if you are a marketer or an analyst, we strongly encourage you to read on.

What’s Conversion Rate Optimization

Conversion rate optimization is a set of actions and strategies introduced to boost the percentage of users of your website that perform a desired action – convert. It may be introduced both to increase sales (e.g., e-commerce stores) or to boost the number of users who leave their contact information (e.g., sign up for a newsletter). In simple terms, it’s aimed at turning more visitors into customers.

Why Is Conversion Rate Optimization Important

With the growing customer acquisition costs, attracting more and more users to your website is simply expensive. By optimizing conversion rates, you can gain more customers with fewer resources needed, achieving a higher marketing ROI.

How to Calculate Conversion Rate

There are three different ways of approaching conversion rate calculations: by traffic, by net new customers, and by lead goal. Let’s take a look at each of these separately.

By Traffic

In general, you can calculate the conversion rate by traffic by dividing the number of conversions by the number of visitors and multiplying the result by 100 – this way, you get a percentage score. However, just in this case, you can opt for two solutions:

• Calculating the conversion rates for particular subpages (those which allow users to convert).
  
  
• Calculating the conversion rate of your whole website.

By Net New Customers

In this case, you must divide your net revenue goal by your average sales price. The result is the number of new customers that you need to acquire.

By Lead Goal

To determine your lead goal, you need to take the number of new customers acquired in the previous calculation and divide it by your lead-to-customer close rate. 

This way, you can use these three formulas to calculate the most important factors for your business: conversion rates, visitor-to-lead rates, or even visitor-to-customer rates.

Conversion Rate Optimization Best Practices

There are several conversion rate optimization best practices and strategies that you can leverage to improve your visitor-to-customer ratio. But, as a foreword, we need to mention that the most effective ones involve real-time insights delivered by automation and often the use of tools using AI for marketing and sales. Nevertheless, let’s look at the particular actions that you can undertake to give your conversions a boost.

Automate Customer Communication

The truth is that it’s simply impossible to achieve outstandingly high conversion rates – many people simply don’t enter your site to convert, forget about the products in their cart, or are simply only at the beginning of their sales journey. But, it doesn’t mean that you can’t turn them into customers.

One of the conversion rate optimization best practices to improve this particular aspect is to create lead magnets to be able to communicate with the users. Sending e-mails with offers, posting relevant content (segmented to match your target groups), boosting engagement through social media, retargeting/remarketing - all of this will help you get some of the users to return to your site and finally convert. But, why do you need automation for that?

The case here is simple – for this conversion rate strategy to work, you need tools that can create personalized content quickly and efficiently, i.e. automatically. It’s impossible to follow up with each lead with personalized offers manually, but with AI-driven automation tools, it’s an entirely different story. These are capable of using the data that you gathered and analyzed on each potential client and preparing a message adjusted to the particular person or company's needs.

Automate Lead Scoring

An automated lead scoring solution is also among your must-have conversion rate optimization tools. Most leads simply won’t turn into sales, and that’s natural. Only 3% of your target market is in an active buying motion. While 97% are in research or have not identified the problem to solve. So, you don’t want to waste time on pursuing them. Automated lead scoring helps you avoid that.

With platforms like Marketo, Eloqua, or HubSpot you can track prospects across your sales funnel and grant (or deduce) points depending on their actions – with little effort from your team. You can set the score, which will be considered as “ready for a conversation, or ready for a demo, or ready for a sale,” and whenever a user achieves it, they will be passed to the sales teams seamlessly, and you’ll save vast amounts of time, accelerating the sales cycle.

Automate Customer Data Analysis

Bear in mind that for the first two strategies to be effective, you need to base your insights on the right customer data, which is complete and gathered across different teams. For that, you need a proper data integration system that will collect all the information that you have about your clients in one place, from which your conversion rate optimization software will be able to access it.

With this data stored and analyzed in one place, all your other platforms (e.g. Marketing and Sales Technology Stack) will have access to the most up-to-date information about the clients, enabling you to conduct the most effective, personalized targeted marketing strategies. In addition, with real-time insights organizations will be better equipped to maximize their sales productivity. 

Develop a Conversion Rate Model with Put It Forward

At Put It Forward, we build conversion rate models with our Intelligent Automation Platform. With us, you can:

• Choose the audience segment to focus on – Select the attributes of the perfect audience for the AI predictive models to focus on. This way, you’ll get the best possible results and see what target groups you need to center your attention on.
• Use “what if” scenarios – Test different models to optimize revenue creation, churn, or customer engagement. See how each of the models will scale along with your business.
• Validate results – See the results of each iteration and test them before using the model in the market. This way, you will validate the sought-after results and use the models more effectively.

Summary

So, why is conversion rate optimization so important? Because it enables you to improve customer acquisition, customer lifetime value, and sales productivity without the need to use up too many resources to boost your website traffic. To do so, you first need to calculate your conversion rates to traffic, net new customers, and lead goal. Then, you can use several strategies, including:

• Customer communication automation.
• Lead scoring automation.
• Customer data analytics automation.

With all of that in place, you will start closing more leads without the need to attract even more visitors to your website. Don’t know how to start? Check the Put It Forward Conversion solution.

Did you find this article insightful? You may also want to read other resources from our blog, especially our article on measuring true marketing ROI.

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Written on November 20, 2023.

Learn how to leverage customer insights from intelligent automation for competitiveness.

6 minute read

In today's fast-paced business landscape, staying competitive is more challenging than ever. Sticky inflation, elevated interest rates, and rising geo-political pressure in Europe and the Middle East, challenges are mounting. In addition, with rapidly changing customer preferences, evolving technologies, and intense competition, companies must adapt constantly. 

One critical tool that can make all the difference in this quest for competitiveness is a customer insights tool. Customer insights tools provide a unique perspective into what your audience wants, how they behave, and what they expect from your products or services. 

Understanding Customer Insights

Customer insights are more than just data. Insights is the new oil. They are the actionable information derived from data that helps you deeply comprehend your customers. 

Organizations today produce tremendous amounts of data from surveys, feedback, social media analysis, and market research, packed with valuable insights into customers' behaviors, needs, and preferences. However, this customer data is stored in disparate systems, giving rise to siloed data and leaving teams data-rich and insight-poor. 

Think of the possibilities when these datasets are analyzed. It can produce real-time insights and help teams make informed decisions, create more relevant products, provide a superior customer experience, and improve employee retention. 

Operate at the Edge with Customer Insight

1. Improved Product Development: The first and most direct benefit of customer insights is their role in product development. Understanding your customers' data, needs, and preferences allows you to tailor your products or services to match their expectations. By actively involving customers in the development process, you can ensure that your offerings are aligned with market demands.
2. Enhanced Customer Experience: In a world where every organization is after differentiation - customer experience, insights can be invaluable. Knowing what your customers like, dislike, and expect enables you to deliver a seamless and satisfying customer experience. This, in turn, fosters loyalty and positive word-of-mouth marketing. Equipped with the ability to analyze integrated customer data to surface insights, front-line employees can deliver enhanced experiences and further analyze these insights to foster predictive insights. 
3. Effective Marketing: Insights can refine your marketing strategies. You can create more targeted campaigns and personalized messages and choose the right channels to reach your audience with the right offer. By speaking directly to your customer's interests and constantly analyzing customer data, you can increase the effectiveness of your marketing efforts.
4. Competitor Analysis: Understanding your customers also means understanding what they like about your competitors. Analyzing your competition's weaknesses and strengths through customer insights allows you to identify opportunities for differentiation and improvement.
5. Innovation and Adaptation: organizations must be more agile given today's dynamic business environment. Customer insights act as an early warning system, allowing you to foresee changes in customer behavior and preferences. This enables you to innovate and adapt before your competitors do.

Collecting, Integrating, and Analyzing Customer Insights

1. Customer Interviews, Surveys, and Feedback: Surveys are a straightforward way to gather customer opinions and preferences. Whether conducted through email, website pop-ups, or social media, surveys help you obtain direct feedback. Capturing this data and integrating it with other data streams with an intelligent automation platform allows teams to quickly identify trends and common pain points and recommend your next best action. In-depth customer interviews can also be integrated into the platform to leverage these personal interactions to reveal underlying motivations and emotions that drive customer behavior.
2. Website Analytics and Social Media Monitoring: Website behavior is another goldmine most organizations leverage in one dimension rather than integrating with other data across the customer journey to understand better how to engage. Social media is a goldmine of customer data. Without the proper tools to integrate and analyze the data, they remain data. Customer reviews are another potent source to help understand the strengths and weaknesses of your products and services from the customer's perspective. However, these customer data inputs are stored in disparate systems. For organizations to outperform their competition, data needs to be integrated using an Intelligent Automation Platform where data is constantly analyzed, and insights from the data are delivered in real-time. With the intelligent automation platform consistently analyzing this data and looking for patterns and trends, your teams can identify trends and track customer/buyer sentiment in real-time, allowing your go-to-market teams to explore expansion opportunities faster and with more insight.

Put It Forward's Intelligent Automation Platform, with advanced data analytics and artificial intelligence tools, sifts through large datasets to deliver predictive insights. Identify patterns, correlations, and hidden insights in your data, enabling more informed decision-making.

Leveraging Customer Insights from Intelligent Automation for Competitiveness

1. Segmentation: Divide your customer base into segments based on common characteristics, such as demographics, behavior, or preferences. This allows you to tailor your marketing and products to specific groups.
2. Personalization: Using insights from personal data and personal information allows go-to-market teams to tailor recommendations, content, and communications to match each customer's preferences and needs.
3. Continuous Feedback Loop: Customer insights are not static. With Put It Forward's Intelligent Automation Platform, you have a continuous feedback loop in place. With the motion - collect, integrate, and analyze, your teams can act on new insights to stay ahead of evolving customer demands.
4. Innovation and Adaptation: Customer insights can guide product updates, service enhancements, and the development of entirely new offerings.
5. Empower Your Teams: Share customer insights across your organization. This ensures that all departments, from marketing to product development, are aligned with customer needs and expectations.

Key Takeaway 

In a world where customers have more choices than ever, staying competitive is a continuous challenge. To remain relevant and thrive, customer data must be collected, integrated, and analyzed continuously. 

These insights are the keys to understanding customer needs, personalizing experiences, and innovating with market trends. Companies that embrace the Intelligent Automation Platform to generate customer insights use them to inform their strategies, stay competitive, and adapt to ever-changing market conditions. 

Ultimately, it's not just about data; it's about real-time insights to understand, respond and exceed customer expectations.

Interested in what Put It Forward's Intelligent Automation Platform can do for you in terms of customer insights? Get a demo and let the expert team help you!

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Winning and retaining customers is an expensive investment. That is why it is crucial to invest primarily in those customers who are profitable for the organization in the long term. It is important to understand customers, engage them in the right channels, and tailor offers to their context and needs. This can only be achieved by drawing on customer-related metrics, and the primary metric is the customer lifetime value (CLV).

Looking to the future, how does predictive customer lifetime value impact the revenue generation strategy? By reading this article, you will be able to understand how to use predictive customer lifetime value to gather historical data, get more sales, and increase customer retention. 

7 minute read

Collecting the right data throughout the customer journey

To estimate the current and future value of customers, organizations should collect and analyze tons of customer data and their journey.

These four data categories are essential to measure customer demand and build a successful buying funnel:

• Transaction data such as shopping timeline, product information, prices, method of payment, delivery, or returns, is supplied by the customer data platform and the connected financial systems.
  
  

• Demographic data such as gender, age, occupation, and place of residence, is condensed into customer profiles in order to better predict future shopping behavior and personalize marketing actions.
  
  

• Engagement data such as responses to campaigns, and external online data, helps to get deeper insights about the customers’ preferences or purchasing behavior.
  
  

• Behavioral data such as customer experience, net promoter score (NPS), and sentiments, helps to measure customer loyalty and satisfaction with the brand and products.

Predicting customer lifetime value

There are a number of analytics models to calculate customer lifespan. Business leaders can choose the best model that will suit the business they operate, the historical data they have, and the quality of insights they want to access.

• Descriptive model: Insights into the past

Descriptive analysis helps business leaders and professionals learn from past behaviors, and understand how they might influence future outcomes. It calculates CLV using historical consumer data and identifies behavioral patterns of customer groups mostly through simple manual analysis. These insights can only serve as an initial indicator of CLV for potential decisions.

• Predictive model: Insights into the future outcomes

Customer lifetime value predictive analytics is focused on understanding the future and provides organizations with actionable insights. This kind of analysis uses historical data patterns to determine future CLV and forecast what might happen. Using it, business leaders can access highly accurate predictions to make effective decisions. 

• Prescriptive model: Insights into the possible outcomes

Prescriptive analysis helps to check out a number of different possible outcomes, providing insights to business owners. Organizations benefit from customer lifetime prediction using machine learning as it makes recommendations to quantify the effect of future decisions. Using it, organizations can choose the actionable scenario that will bring the best possible outcomes.

How to leverage predictive CLV and drive revenue

Predictive CLV helps organizations better manage their budgets for customer acquisition and customer retention, and lets them easily calculate the return on investment of each customer. Customer lifetime value predictive analytics is aimed to measure not just sales revenue for each customer but how profitable they are.

By understanding how to predict customer lifetime value, organizations can quantify the total expected profit that a customer can bring within a specific period. They can plan the expenses, time, and effort that can be put in, proportional to the expected profits.

The marketing strategies can also become more effective when they focus on CLV prediction because they will become aware of the expected profits. As a result, business leaders can allocate marketing budgets accordingly because there is no guesswork involved in preparing them.

With a high-quality and accurate prediction of CLV, business leaders can identify the most profitable customers and invest in acquiring and retaining them. All this helps to find the key to unlocking continued and consistent profits.

Value of predictive CLV for product development and marketing strategies

Customer lifetime value is a great metric to measure and optimize product development and marketing strategy. Calculating the predictive customer lifetime value gives a chance to know what products high-value customers purchase, what products should be marketed the most, what future products should be developed, and what products should be bundled together or should be used for up-selling.

Previously, organizations have been segmenting customers by simply grouping them into similar cohorts based on features they believe are relevant. Today, the approach to building a successful product development strategy has changed and is more focused on calculating the predictive customer lifespan and comparing this metric with the number of units sold.

Organizations can define groups of products with high accuracy:

•  Developing Products: High CLV, Low Units Sold

That can mean a newer product, and so boosting marketing spending will hopefully drive more unit sales while keeping the customer CLV high.

• Champion Products: High CLV, High Units Sold

Organizations can examine what exactly sets this product above the rest, and use this to create future product offerings that strive to replicate the success of champion products.

• Lagging Products: Low CLV, High Units Sold

The best strategy will be to reduce the marketing spend for these products and to see if smaller, more targeted efforts can help raise the product’s CLV. Also, organizations can bundle them with champions or develop products.

• Disappointing Products: Low CLV, Low Units Sold

For products with low CLV and low units sold, it’s necessary to stop selling. The disappointing product offering is diverting resources from more profitable deals.

Empowering customer relations by predicting CLV

Today, the customer relationship strategy is very important as it makes customers feel satisfied while using the products and services. All this directly impacts the speed of sales growth.

Predicting customer lifetime value helps to model a customer’s purchasing behavior, predict customer retention, forecast future revenue, and many more:

• Improve Customer Loyalty

Calculating CLV prediction, organizations can analyze the customers who make repeated purchases over time. If organizations can sustain it over the expected customer lifespan, they can earn loyal customers and brand promoters.

• Build Better Customer Relationships

CLV insights help organizations create hyper-targeted interactions to build long-term relationships with customers rather than just satisfy them. With value-based segmentation, it’s possible to focus on segments with a higher probability of customer retention to improve the revenue further with personalized interactions.

• Predict & Reduce Customer Churn

Customer lifetime value predictive analytics helps organizations see early signs of customer churn and take necessary actions to retain them. By combining predictive CLV with customer experience score and purchase history, business professionals can see which churned customers hold the potential to bring in higher profits if they are successfully retained. It also helps to identify customers with high order value and highly predictive CLV who haven’t purchased for some time.

Enhancing marketing and sales efforts based on CLV prediction

Predicting customer lifetime value helps businesses develop strategies to acquire new customers and retain existing ones while maintaining profit margins. The Predictive CLV opens more insights for organizations and helps:

• Know Who Will Become Most Valuable Customers Next Year

Predictive CLV shares insights about who the most valuable customers were and currently are. It helps to understand how much the customers are likely to spend in the future over a certain time frame.

• Improve Customer Segmentation Strategy

By calculating the CLV prediction, organizations can find the segments that can bring in higher profits and double down on efforts to increase the customer retention rate of these high-value customers. This accurate segmentation helps focus on low-level customers that may not have a high average order value but can provide more profits over the long term.

• Optimize Marketing Strategies

CLV prediction helps to answer important questions with high accuracy: which marketing channel will be the most effective for a customer, how much revenue should be attributed to each marketing channel, and how much money should be spent on each channel.

• Reduce Customer Acquisition Cost

Predicting customer lifetime value helps organizations target only those potential customers whose lifetime value makes it worth the user acquisition cost. The prediction insights help reduce acquisition costs for new users and optimize the return on investment (ROI).

Key takeaways

Customer lifetime value prediction is a great way to get valuable insights about customer acquisition, marketing efforts, and future financial outcomes. Monitoring predictive CLV is essential for business leaders to forecast the monthly or annual revenue from different customer types. 

The predictive capability gives organizations the precise information they need to provide a great customer experience, grow the number of deals, and decrease customer acquisition cost. This will give more focus to growing more profitable relationships with current and future customers. 

Business leaders and professionals regularly use the predictive analysis of CLV to build long-term and positive relationships with customers, grow sales, and reduce acquisition costs. Are you looking for an effective solution to calculate predictive CLV for your business? Reach out to the Put It Forward team to discuss your specific needs.

Written on 04 November 2022.

Marketing has always been about trying to understand what the customer wants. With recent advancements in big data, data analytics, and machine learning, predicting customer behavior has become achievable. This technological expansion has given marketers more powerful tools to predict customer preferences and buying patterns.

6 minute read

If marketers could accurately make customer behavior predictions, it would make life easier for everyone. Machine learning and propensity models are two tools that brands can use to anticipate and optimize the impact of their marketing tactics. Recently, propensity modeling has become a popular tool for predicting an individual's likelihood of taking a specific action, like purchasing within a certain timeframe.

Grand View Research reports that the predictive analytics market will continue to grow at a CAGR of 23.2% through 2025 as more companies leverage predictive modeling for better decision-making in their marketing endeavors.

So, what is predictive modeling? Predictive modeling is a method of using statistical techniques to make predictions about future events. This type of modeling is often used to drive customer predictions in marketing. It is designed around how to predict customer behavior. For example, predictive modeling can make customer purchase behavior predictions from payment datasets to determine which customers will most likely purchase a product within the next month.

Predictive analytics customer behavior modeling is based on the idea that past behavior is a good indicator of future behavior. This means that if we can identify the factors influencing a customer's purchase decision, we can use this information to predict future behavior.

Information on your ideal customers – for example, their buying habits, attitudes, and demographics – is necessary to create and carry out marketing programs that will work well. This data can help develop targeted promotional campaigns, as well as other programs aimed at increasing customer retention, loyalty, or boosting sales. This knowledge, made possible by propensity modeling, comes in handy when creating a prediction model of a new customer's behavior profile.

What is the Best 'New' Customer?

The best new customer for your business is an individual whose behavior profile closely matches your existing customers who have registered a high lifetime value (LTV). You'll need to build a propensity model to find your best new customers. Customer behavior prediction models forecast the likelihood that someone will perform a specific action--such as buying something, signing up for a newsletter, or leaving (churning)--based on their past behavior.

By analyzing patterns in data, you can understand how likely someone will take the desired action. You can then calculate a propensity score - a probabilistic estimation of the likelihood that a customer will perform those actions. Using this score, you can grade that customer's value and use this data to predict who among your potential leads could become your newest best customer.

Steps to Find Your Best 'New' Customer with Propensity Models

So, how can you leverage propensity score models to find your best ‘new’ customer? Here are four steps you can follow:

Analyze your current customer base

Take a close look at your existing customer base to figure out:

• Who they are - the age and gender of individual consumers or industry and business size for corporate customers.
• What they think and believe - their interests, opinions of you and your product.
• How they behave - their historical data, including which products they buy, where, when, and how they pay.

With this information, you can create a behavior profile for your ideal new customer.

Define what makes a customer valuable

Before anything, you must figure out what characteristics make a customer valuable to your company. For example, is it someone who buys high-margin items or only pays the sticker price? Is it better to have customers that order fewer oversized items or multiple small ones? Is a customer's value measured by whether or not they cancel or amend orders?

Does your business prefer customers who purchase and pay on time without needing reminders or extensive after-sales service? Identify the attributes most important to your brand and give each attribute different levels of importance.

Choose the criteria for building the propensity model of customers that will bring you revenue

To find these individuals, use predictive modeling to score each customer according to how likely they are to exhibit the desired behavior. The model will analyze all available data on your customers and potential customers to generate a propensity score.

Create the propensity model for the best 'new' customer

This will help you identify new prospects more likely to behave like your best customers. You can use the model to score new leads and prioritize those most likely to turn into high-value customers.

Integrate Into Operational Processes and Update or Create Versions

Once the propensity model is created for a particular outcome the outputs need to be integrated into the operational data flows so they can be acted upon. This will help you achieve scale with the insights. Then create different versions for other outcomes or segments and look to update the model based on the needs of the day for greater returns and more scale.

Using Propensity Modeling for the Best 'New' Customer Profile

Once you have identified your best "new" customer profile, you can use propensity modeling to score new leads and prioritize those most likely to turn into high-value customers.

Good propensity score models must be:

• Dynamic - Models should always consider the latest data to advance with trends, adapt, and learn.
• Scalable - Models shouldn't just be designed for one use only; they should be capable of producing a range of predictions. 
• Adaptive - Data pipelines need to regularly ingest new data, validate it, and deploy changes so that models stay up-to-date.

You can deploy one or more of these propensity score model types:

• Model for propensity to convert/purchase 
• Model for propensity to engage
• Model for propensity to churn 
• Model for customer lifetime value prediction using machine learning

The propensity to convert/purchase model lets you know which customers will be more inclined or less likely to buy your services or products or carry out some target action like subscribing to a newsletter. After figuring out which of your customers are willing to purchase from you, you can send them customized offers.

The propensity to engage model lets you analyze the likelihood of your leads and customers taking proactive action. For example, it could be a score that displays which website visitors are most likely to click on an ad.

The propensity to churn model allows you to discern which potential and current customers may leave your company. By measuring this estimation, you can then decide whether or not a re-engagement campaign would be beneficial to keep them as a customer.

How to predict customer lifetime value: Lifetime Value: Total Gross Revenue - Total Cost

Predicting customer lifetime value (CLV) using the customer lifetime value model estimates the total amount of money a customer will spend on your business during their lifetime engagement with your brand. You can use this information to see which marketing campaigns produce customers with the highest CLV so you know where it's worth investing more in the future.

You can merge distinct propensity models in certain situations to make more intelligent campaign choices. For example, you could offer a bigger discount to customers who are likely to churn but have a higher lifetime value.

Use Case: Find A High-LTV New Customer with Predictive Modeling

Acquiring customers involves a lot of expenses (like advertising, promotions, discounts, etc.) But sometimes, some customers cost more money than they're worth in terms of lifetime value. You need to be able to identify these behavior patterns and segment your customers so that you can take appropriate action. You can do this by building machine learning predictive models that will predict your customers' lifetime value.

These steps should help you build an effective predictive model:

1. Define an appropriate time frame for Customer Lifetime Value calculation (e.g., number of months, years)
2. Identify the parameters you are going to use to predict the new customers with the highest LTV (e.g., avg. number of purchases, avg. order value, the frequency of purchases)
3. Train the predictive model and calculate LTV
4. Finalize and run the predictive model. Apply clustering and have three segments (low LTV, mid LTV, or high LTV). The number of segments will depend on your business dynamics and goals.
5. Check the successful results for the high LTV segment. Confirm that this predictive model works for your business.

With this predictive model in hand, you'll be able to segment the leads by the probability to make recurring purchases, and prioritize those most likely to turn into high-value customers.

Conclusion

By understanding how to apply predictive modeling, you can more intelligently segment your customers, personalize campaigns, and allocate resources for maximum ROI. Predictive analytics models are a must-have for a successful decision-making process. They help to reduce risks and costs and define the audience, message, and expected outcome before execution.

Predictive modeling can be applied to many key parameters that impact the final outcome for your business (e.g., purchase, average order value, acquisition cost, LTV). The models are beneficial in defining which audience segment will bring the highest outcome to the business and engage them to close deals.

Written on December 6, 2023.

Learn how to build a successful lead generation and demand capture strategy.

6 minute read

Marketers need to embrace the 95:5 rule. Only 5% of the market is actively in buying mode while 95% are actively researching. Thus marketers need to be able to balance their approach to demand gen, lead gen, and demand capture.

Customers expect personalized, omnichannel experiences delivered in real-time. Thus, to attract them, businesses require a well-thought-through plan. A successful lead generation strategy should be concerned with much more than just clients – it ought to begin within an organization with the teams involved in the process. So, how to generate sales-qualified leads? - we explain it in this article.

What Is a Lead Generation Funnel

A lead generation funnel is the initial part of a sales funnel – the route that a potential client travels from learning about a product or service to purchasing it.

The lead generation funnel starts with the moment an individual becomes aware of your brand attention to the moment they decide whether they want to buy the product or services. It consists of three main stages:

• Top of the funnel (ToFu) – This is the phase in which a company tries to build awareness and acquaint as much target audience as possible with the brand.
• Middle of the funnel (MoFu) – In the middle of the funnel, the most work is done. This is when organizations target potential customers with ads and other marketing materials to convince them to purchase the product or service itself.
• Bottom of the funnel (BoFu) – At this stage, the targeted potential customer decides whether to purchase what you offer or to walk away. In the second case, it is also possible to introduce retargeting strategies to put the potential customers back into the funnel and attempt to convince them to convert once again.

Successful Lead Generation Strategy

As it is visible from the particular parts of the lead generation funnel, a successful lead generation strategy leads directly to an increase in sales. Therefore, it is crucial to prepare these tactics thoroughly, and constantly improve them. How to build a strategy to generate leads effectively? Here are the key steps:

Connect Customer Data

In order to achieve a higher level of personalization and build a truly omnichannel customer experience, businesses need to integrate their data. Sales, marketing, and customer service teams all collect information on the customers, but they often do not share it, thus creating data silos and sending inconsistent messages to the customers themselves. An integrated system solves this problem and enables businesses to deliver better experiences to their potential and current customers.

Personalized Engagement

Nowadays, customers expect personalized treatment. They want to know how a product will solve their problems and make their lives better. Thus, to engage them and make them more likely to convert in the end, a lead generation strategy should involve high personalization.

Businesses need to create detailed marketing personas and break them down into potential client types to address the needs of each customer as accurately as possible. With integrated data – the first step – this is fairly straightforward – all the information gathered on a potential customer is in one place, waiting to be utilized to the fullest.

Automate Time-Consuming Operations

It is impossible to turn every lead into sales, and the more leads a company acquires the more sales it will make. For that reason, it is important for organizations to actively bring new customers into the ToFu. And, this can be accomplished with automation.

Many manual tasks performed by marketing teams are time-consuming, yet they do not bring much value to a business. With automation, it is possible to eliminate this problem and give marketers more time to plan their strategies. Additionally, this also lets organizations approach many more potential customers, even with limited resources, since no human is capable of processing as much data and completing as many actions at the same time, as AI or scripts.

Optimize Buyer Journey

The buyer's journey needs to be adjusted to the needs and expectations of the potential customers. How to generate sales leads with it? It’s crucial to look at every step separately:

• ToFu – At this stage, people seek answers to their problems. Thus, instead of approaching them with heavy marketing materials, organizations need to focus on showing their proficiency in the related field and providing their target audience with knowledge and their brand identity.
• MoFu – This is when businesses need to convince the customer that their product or service is the best. The focus in this step should be put on advertising, with a clear enumeration of the benefits, price, and features of the products. It is also a good practice to use reviews and testimonials in the middle of the lead funnel.
• BoFu – No matter the result, at this point, organizations need to show that they understand the decision. Those who decide to purchase the product or service need to be guided through sales quickly and effortlessly; those who decide to give up should be shown understanding – even if a business is planning to introduce a retargeting campaign, it must not be done too soon since it will only be intrusive.

Use Intent Analysis Models

Often, a particular individual goes through the lead generation funnel but gives up not because they were unsatisfied with what they were exposed to, but rather because they never wished to convert. At other times, a potential customer has a clear-cut commercial intent from the very beginning and needs to be pushed through the funnel faster. Understanding when these situations happen is the key to any effective lead-generation strategy.

To do that, organizations need to introduce intent analysis models and incorporate them into their marketing tools. This way, the target buyers will be provided with the most suitable materials, increasing the chances of successfully closing the lead.

Get Real-Time Engagement Insights

Real-time engagement insights are excellent for understanding how the lead generation funnel works – which aspects perform better than others, but also when the content is more effective. Thus, any organization wishing to put a successful lead generation strategy into life should incorporate real-time engagement analysis solutions into their tech stack.

How Sales and Marketing Benefit from Automated Lead Generation

Automated lead generation is an effective way to collect valuable leads thus increasing sales. How exactly may an organization benefit from such a solution?

• High lead quality – Based on data, automated lead generation models make better decisions – they leverage all the information they have to automatically spot the leads that are truly worth pursuing, thus boosting the overall lead quality.
• More business opportunities – With an automatic system, it is possible to collect more leads than manually. This gives businesses a higher number of opportunities to expand and acquire new clients.
• Accelerated sales flow – Automated means quick. By pushing potential customers through the three stages of the lead generation funnel automatically, businesses can close sales more quickly and with less effort.
• Reduced manual work – High-paid marketing and sales specialists should spend time on work that truly matches their skills, not manual, repetitive, and low-value tasks. With automated lead generation, this is no longer a problem, since such jobs are performed by the system.

Generate More Leads with Put It Forward

Put It Forward Lead Generation solution can help your business thrive and acquire more high-quality leads while saving your resources. How can you benefit from using our platform?

• Accurate lead scoring – Marketing Qualified Leads (MQLs) are the most important element that you are looking for when running your promotional campaigns. With our data-driven intelligent platform, your marketing and sales teams will immediately know which leads are more promising and their number will in general be higher – you should experience an approximately 100% - 200% increase in MQLs.
• Connected customer experience – Due to our data integration solutions, your business will be able to deliver consistent messages across all channels, thus enhancing customer experience and boosting their satisfaction levels.
• Personalized engagement – With integrated data, personalizing your content and engaging customers with matters that they truly care about will be effortless.
• Automated acquisition – Due to the use of intelligent automation, AI, deep learning, and other technologies, our platform is capable of guiding your potential customers from their very first impression to the sales, increasing the pipeline by up to 80% and giving you more resources to distribute to more burning issues.
• Real-time customer insights – Our platform offers real-time customer insights as well, to grant you full control and overview of the effectiveness of your marketing campaigns, thus letting you squeeze the maximum ROI and reduce acquisition costs by 30%.

Key Takeaways

How to generate leads efficiently and build a successful lead-generation strategy? By connecting customer data, personalizing engagement, automating time-consuming tasks, optimizing buyer journeys, and analyzing available data. This is exactly what Put It Forward solutions are capable of – get a demo now and see how we can help you generate more high-quality leads.

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Written on 09 December 2022.

Put It Forward's customer 360 analytics platform provides advanced analytics capabilities, enabling businesses to identify patterns in customer behavior and personalize their marketing strategies for maximum impact.

6 minute read

In the age of digital transformation, businesses are striving for digital maturity by using tools and technologies that improve their customers’ experiences. With the right tools in place, understanding your customer's journey has never been easier — or more valuable. A 360-degree view of your customer is essential for long-term business success and growth. This data will help you better understand the people you serve, deliver improved customer experiences, and set you up for future success.

What Is a 360 Degree View of the Customer?

A 360-degree view of a customer starts with data collected from multiple data sources, including website visits, purchases, and engagement. All this data can be used to create an aggregated profile that gives businesses a comprehensive understanding of their customers' behavior – from first contact to purchase and beyond. This provides companies with invaluable insights into their customers' journey, empowering them to personalize customer experiences and increase customer loyalty.

Therefore, we can define a 360 degree customer view as an aggregation of all available customer event data. It is founded on the idea that companies obtain a more complete understanding of customers when they consolidate data from the various customer touchpoints throughout the customer journey. The 360-degree view of the customer gives a company an all-encompassing look at what their customers experience and feel throughout every stage of their journey.

The 360 view of customer data is steadily gaining traction even though only 5% of enterprises have figured out how to leverage a 360-degree customer view for growth. According to a recent Harvard Business Review survey, 45% of respondents said their organizations hope to achieve a unified customer view across all channels.

Challenges of Customer Data Analysis

The biggest challenge with customer data analysis is comprehensively understanding the customer's journey. This can be difficult due to the sheer volume of data that needs to be analyzed and understood - from website visits and purchases to interactions on social media or mobile devices. Additionally, it can often require manual integration of different types of data, which can be time-consuming and costly.

What's more, collecting and analyzing data manually is error-prone and leads to duplicate records. Finally, marketers and sales teams often find themselves lagging behind when it comes to receiving up-to-date insights, which can prevent them from reacting promptly to customer behavior. Businesses must analyze data from various sources to understand their customers more deeply. This often requires using data management, storage, and analytics tools, as well as machine learning (ML) and business intelligence (BI) platforms.

Benefits of 360-Degree Customer View

A 360 customer view is key for successful customer experience management. Some of the most notable benefits that companies can expect when leveraging a 360 degree view of customers include:

• A complete and accurate picture of each customer, sales, marketing, and operational data.
• Viewing a customer's interactions with your brand, both in the present and past.
• Understanding your customers' behaviors and needs at certain moments in time.
• Fast, automated customer insights that allow you to react quickly to changing customer trends and personalize your campaigns for maximum impact.
• Unforgettable brand experiences that lead to repeat purchases and increased loyalty.

How to Build a Powerful Customer 360 View with Put It Forward

Put It Forward is a powerful 360 view customer platform that lets companies quickly and easily build a 360-degree view of their customers. Put It Forward automatically collects, cleanses, and stores customer data from multiple sources, including web analytics, CRM systems, marketing automation tools, and more. The data is then made available in real-time through an intuitive, customer 360 view dashboard, allowing marketers and sales teams to instantly view customer profiles and respond quickly to changing customer trends.

Put It Forward also provides advanced analytics capabilities, enabling businesses to identify patterns in customer behavior and personalize their marketing strategies for maximum impact. With Put It Forward's 360 degree customer view data models, companies can gain a deep understanding of their customers and deliver unforgettable experiences.

Here's a rundown of what you can achieve with Put It Forward's 360-degree view of your customers:

- Automatically collect and unify all customer data points

- Build the customer activity reporting with no code efforts

- Separate the real customer signals from all of the noise

- Access the valuable business insights in one dashboard

- Drive responses to various channels of communication in real-time

- Easily integrate with your organization's other BI, ML, and data management tools.

Use Case: Engage Customers in a Single Voice

The need: One of our clients was interested in improving their customers' digital journey by bridging the multiple points of engagement and creating a single customer view. They also wanted to know how to keep these customers loyal even when competitors were trying to poach them.

The solution: Put It Forward allowed for a unified customer 360 view architecture by combining multiple identifiers and integrating real-time visitor activity into the analytics platform. This created a single view of customers across multiple touchpoints, allowing the company to retarget with laser precision and engage with customers across multiple touchpoints.

The result: The solution helped the company enhance customer engagement by:

• Building a unified view of the customer and their activity across platforms, from marketing to analytics
• Having a complete 360-degree view of customers’ activity throughout the buyer journey.
• Combining 3rd-party customer information with proprietary data to create effective 1-1 engagement

Key Takeaways

Customer 360 analytics enables businesses to understand customer engagement at every stage of the journey. By creating a single customer view, it provides cohesive customer engagement data for every stage of the customer journey, allowing companies to optimize their sales and engagement processes.

Sharing customer insights and data across an organization increases efficiency, removes friction, and results in a better understanding of customer trends. Put It Forward's 360 view solution collects and analyzes customer data and shares valuable, up-to-date insights. Try it in action to connect many disparate point solutions and reduce the considerable time and cost of the effort.

Today's customers demand better experiences from the brands they interact with. To improve your customer experience, you will need to build an effective strategy. Such a strategy should create long-term relationships with customers by addressing their specific needs throughout their purchase journey.

Customers today are prioritizing experiences over everything else when making purchase decisions. As such, finding a way to close the customer experience gap — that difference between the expectations of your customers and how well your company believes it is meeting those expectations — is more critical than ever if your business is to stay competitive.

The customer experience strategy can be effectively applied to both B2B and B2C scenarios based on the market feedback. Moreover, marketers are increasingly stepping outside communication channels to develop D2C experiences.

Today's consumers demand better experiences from the brands they interact with; according to a recent Salesforce Research survey, 80% of consumers consider the experiences a company delivers as significant as its products and services. As much as 86% of buyers would rather pay more for a product or service if doing so can guarantee them a better customer experience.

Companies that will create better customer experience objectives will reap bountifully; research shows that enterprises with yearly earnings of $1 billion or more earn as much as $700 million every year within three years of substantial CX investments.

To improve your customer experience, you need to build a compelling customer experience strategy. This article will explore a CX strategy, its challenges, best practices, and benefits to your business. 

How to Improve Customer Experience Strategy and Harness Its Challenges

What is a customer experience strategy? Let's start by defining customer experience (CX). Customer experience is not only the overall impression of your company; it's all about your brand, products, and services. Customer experience takes a holistic approach beyond a company's customer service department; it involves everyone in the organization. Some of the factors that are typically at play in great customer experiences include:

• Does your product or service's performance blow customers away?
• Are they satisfied by the attention they receive from your customer support reps when helping them solve a problem?

Unlike customer service, customer experience doesn't focus on one specific interaction with a customer at a given time. Instead, it considers the entire customer journey and seeks to build long-term customer relationships.

On the other hand, a customer experience strategy refers to all the actionable plans a business has set toward delivering an excellent, meaningful experience for its customers in all their interactions across various channels.

A CX strategy must incorporate all touchpoints in the customer journey, from awareness and discovery to purchase and customer loyalty. Moreover, it should create long-term customer relationships by addressing their specific needs throughout this journey.

Challenges when creating a customer experience strategy

There are several challenges businesses face when developing a compelling customer experience strategy. These include:

Personalizing customer experience at scale

There is a big difference between providing personalized experiences to each customer and delivering a one-size-fits-all experience. The former requires businesses to understand their customers enough to anticipate their needs and provide relevant experiences that add value. On the other hand, the latter is the mass production of experiences with little or no customization, leading to customer frustration and, ultimately, churn.

Responding in real-time to customer needs

Businesses need to respond quickly to customer needs as they arise. This requires real-time data collection and analysis so that they can make the necessary changes to their CX strategy on the fly.

Creating consistent omnichannel experiences

Customers today interact with businesses across various channels, including in-person, online, over the phone, and through social media. Customers today want a consistent customer experience from companies across multiple channels.

Forrester reports that companies with robust omnichannel engagement strategies record a 10% year-on-year growth, a rise in close rates of 25%, and a 10% higher average of their order values. Businesses must create consistent experiences across all these channels so customers can quickly move from one to the other without feeling disoriented or frustrated.

Managing customer expectations

With the rise of social media, customers today are more informed and have higher expectations than ever before. They are quick to voice their displeasure when their expectations are not met. It would be prudent for businesses to manage customer expectations by setting realistic ones and consistently delivering on them.

Measuring the right metrics

There are a lot of metrics businesses can track based on their customer experience objectives. Companies need to identify the metrics that matter the most to their trade and focus on those. Some of the most helpful CX metrics include customer satisfaction (CSAT), net promoter score (NPS), customer effort score (CES), and first contact resolution (FCR).

How to improve customer experience strategy

There are several ways that business enterprises can solve these and other challenges to improve their customer experience management. They include:

Use omnichannel communication to analyze customer journeys across digital and live channels

Businesses need to understand how customers interact with their brands across channels. They can use various tools, such as heat maps and session recordings to analyze customer journeys and identify areas where they may be getting stuck.

Understand your customers' most preferred channels to get in touch and react faster to their requests

Customers today expect businesses to be available on their preferred channels. Companies can use customer data to understand which channels their customers prefer, make sure they are available, and promptly respond to customer requests.

 Predict customer behavior and cover demand on time

Businesses can proactively address problems and deliver relevant experiences by predicting customer behavior and needs. Data analysis can help companies identify trends and patterns to take the necessary steps to improve their CX strategy.

Automate processes to improve efficiency and reduce costs

Businesses can automate processes such as lead capture, data entry, and marketing campaigns. Automating these processes can help companies improve efficiency and reduce costs by freeing employees' time to focus on more critical tasks.

Build data connectivity and save time communicating with the right customers at the right time

Businesses should connect data from various sources, such as CRM systems, social media platforms, and customer surveys. They can then use it to segment customers and deliver the right message to the right customer at the right time.

Measure CX across various channels and operate faster with customer data

Collect customer feedback across channels and use it to identify areas where you can improve customer experience. Use customer data to decide how to allocate resources so that you can operate faster and enhance the customer experience.

Get a singular view of each customer

Use customer data to get a 360-degree view of each customer. This will help you understand their needs and how best to serve them.

Receive insights, analyze patterns and behaviors, and understand buyer's values and triggers

Use customer data to receive insights into customer behavior. Your business can develop more effective marketing and sales strategies by understanding a buyer's values and motivations.

Customer Experience Strategy Best Practices

What makes a great customer experience? The following are some of the best practices for creating a customer experience strategy:

1. Deliver real-time data updates - Customers today expect businesses to be able to provide them with up-to-date information. Make sure you have a system that can provide them with real-time data updates.
2. Supply personalized content at the top of your sales funnel - Use customer data to segment your customers and deliver personalized content to them at the top of your sales funnel.
3. Streamline customer communication - Customers should be able to get in touch with your business quickly. Set up a system that allows them to quickly and easily communicate with you.
4. Create an omnichannel customer experience - Make sure your customer experience management techniques are consistent across all channels; your customers expect nothing less.
5. Evaluate customer engagement - Find out how engaged your customers are and what you can do to improve their engagement.
6. Analyze customer feedback - Use customer feedback to identify areas where you can improve your customer experience.
7. Incorporate a "customer first" mentality in your organization - Make sure that everyone in your organization is aware of the importance of customer experience and is working towards providing an excellent experience for your customers.

The Benefits of Providing Great CX

There are many benefits of providing a great customer experience, including:

• Increased brand loyalty - Customers who have a positive experience with your brand are more likely to be loyal to your brand.
• Increased sales - Creating positive customer experiences will undoubtedly lead to customers making more purchases from your brand.
• Improved customer retention - The way to retain customers is to ensure they have a positive experience when interacting with your brand and its products or services.
• Reduced customer churn - You can expect lower customer attrition levels as you invest more in customer experience.
• Increased customer lifetime value - You can increase your customers’ lifetime value by ensuring all the interactions customers have with your brand and product or service are positive.

Customer Experience Strategy: Case Study

Consider this scenario: 

A large insurance company that sells life insurance policies develops a new customer experience strategy to improve interactions with its customers. It decides to leverage various technologies, including process automation, data consolidation and management, and data analysis.

The company’s customer experience strategy includes the following components:

• Process automation - The insurance company automates various processes, including quote generation, policy application, and claims processing. This allows it to provide a faster and more efficient customer experience.
• Data consolidation and management - Consolidating customer data from various sources and managing it in a central repository gives the company a single view of the customer, which helps to improve customer service and support.
• Data analysis - Leveraging data analysis to get a 360-degree view of every customer. This customer data profiling helps the insurance company better understand customer behavior and preferences..
• Effective collaboration between many departments while working with multiple customers -  The insurance company puts in place processes and systems that allow for effective collaboration between its various departments, making a consistent and coordinated customer experience possible.
• Prediction of customer behavior to build the sales strategy -  The insurance company uses data analysis to predict customer behavior. This helps the company develop its sales strategy and target its marketing and sales efforts more effectively.

Here's an example scenario of a CX strategy in action for the insurance firm:

A customer service agent is talking to a potential customer on the telephone. They are notified that the customer they’re talking to just requested a quote on a life insurance policy a few minutes ago directly through the insurer's website.

The customer service agent seamlessly modifies that quote and sends it back by text with personalized options for the customer. The customer goes ahead to finalize their purchase on their mobile phone while getting the right advice in real-time from the agent.

The insurer can streamline an otherwise complicated process of managing the same customer across different channels through process automation, data consolidation, and data analysis.

How Intelligent Automation Can Enhance Your CX Strategy

To create a customer experience strategy that works, you need to ensure that you're using the right tools. Intelligent automation is one of the most powerful tools to help with this.

Intelligent automation revolves around:

• Automated tasks and workflows - building effective collaboration between employees, and getting extra time for revenue-focused tasks
• Consolidated data - getting easy access to the data, faster reaction to customer requests from all communication channels
• 360-degree view of each customer - making quick analysis of customer profile, customer journeys and engagement steps
• Prediction of customer behavior - getting insights to build only successful sales strategies, and decrease the customer churn.

Here are some ways in which intelligent automation can help:

• Improving the quality and speed of responses - respond to customer queries faster and more accurately
• Advanced data analysis and AI prediction - analyze a big volume of data and make predictions of future outputs
• Integrated experience across all touchpoints - build omnichannel customer experience to make cross-sells and get new deals.

As a result, the business can update customer requests and records with ease and in real-time, improving the customer experience it provides.

Key Takeaways

Creating a great customer experience is essential for organizations today.  Follow these key takeaways  while creating a customer experience strategy:

• Consumers are demanding better experiences from the brands they interact with
• A customer experience strategy must incorporate all touchpoints in the customer journey, from awareness and discovery to purchase and loyalty
• Providing an excellent customer experience will increase brand loyalty, reduce customer churn, and bring new deals Intelligent automation is one of the most powerful tools to build an effective CX strategy.

Put It Forward's Intelligent Automation platform provides integrated data services in a single tool. It helps organizations to grow their businesses using process automation, data integration, and advanced data analytics coupled with predictive AI. 

Leverage the Put It Forward's data platform to enhance your CX strategy. 

Do you want to get a free consultation with Put It Forward experts? Please share your request, and we’ll contact you.

Written on October 20, 2023.

Learn how to build a churn prediction model and drive positive outcomes.

6 minute read

• We are living in an age when the costs of customer acquisition have reached enormous levels. Therefore, it is even more crucial to retain customers. Churn prediction is one of the ways to achieve that. But, how to build a churn prediction model, and how can it enhance the growth of your business? Learn that, by reading this article.

What is Churn Prediction?

Before we answer how to predict churn, we need to underline what churn prediction is. We will divide it into two sections: revenue and customer churn predictions.

• What is customer churn prediction? This is the act of analyzing which customers are about to leave your company, based on their behavior. It is done to react and prevent customer churn.
  
• What is revenue churn prediction? Revenue churn prediction is based on calculating how big the financial losses generated by customer churn will be. It focuses on the income.

Why is Predicting Churn Important?

Churn prediction allows you to react before the negative changes happen. It is a way to reduce the costs of customer acquisition by ensuring that your customers stay loyal. In general, predicting customer churn has three main advantages:

• Identifying at-risk customers. Prevention is better than cure, and retention is better than acquisition. Predicting customer churn allows you to understand which clients are on the verge of leaving your company and react to that, saving you the resources that you would have to spend on customer acquisition otherwise.
  
• Identifying customer pain points. Predicting which customers are about to churn is also an excellent way to understand why they want to do that. Being able to accurately forecast specific customer’s churn allows you to learn about their pain points and develop your products or services further, to reduce future churn.
  
• Identifying strategies to lower churn and increase customer retention. By predicting customer churn, your organization is capable of testing retention strategies. This way, it is possible to choose the most effective one and prevent high churn in the future.

What You Need to Build a Prediction Model

If you want to learn how to predict customer churn, you need to begin by building a proper prediction model. Yet, for it to work, you must collect appropriate data and input it into your customer churn prediction software. What exactly do you need?

• Customer success metrics. Measurable information from customer satisfaction surveys, Net Promoter Scores, etc.
  
  
• Metadata. The date on your target group, for instance, age, gender, or education for B2C clients or company size, type of industry for B2B clients.
  
  
• Engagement metrics. The levels on which your customers are engaged and willing to perform additional activities (for instance, interacting with your website offer and contact forms).

Designing Churn Prediction Workflow

What is a churn prediction model without an organized workflow? An inefficient model. In order to create a system that will be truly effective, you should follow five steps:

• Defining problem and goal. Getting the wrong insights from churn prediction will get you nowhere. Therefore, it is crucial to decide what you want to collect and why.
  
• Establishing data sources. CRM systems, customer surveys and feedback, analytics services – all of these may be the source for your model. You need to select which ones you want to use before proceeding further.
• Data preparation, exploration, and preprocessing. Since your model will be using machine learning, you need to adjust the data you have to it. Change it into a format that your system will be able to use, and get rid of the unsuitable data in the process.
  
• Modeling and testing. No product is perfect on day one, neither will be your model. Before you begin to rely on it, test its effectiveness and introduce the required updates.
  
• Deploying and monitoring. Finally, you may send the best version of your model to work. Integrate it with your systems and control how it works.

Calculate your Churn with Put It Forward

If you desire to truly elevate your business and are looking for a dependable tool to calculate your churn, opt for Put It Forward Revenue Churn Calculator. Thanks to the advanced churn predicting model, our tool is capable of calculating two crucial metrics for your organization:

• Expected revenue. To understand how much your company is growing and earning each month or year and plan future investments.
  
• Potential losses. To realize how revenue churn affects your business and prevent these losses.

Key Takeaways

Churn prediction is crucial for the finances of any organization – it allows the teams to react quickly and decrease the number of customers leaving your business. To conduct it, you need to implement a churn prediction model that will suit your specific business needs. 
Was this article helpful? Consider reading: How to Create a Customer Experience Strategy.

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Written on October 2, 2023.

Learn how to measure a true marketing ROI and best practices to drive efficiency. 

6 minute read

What has been a gamble years before, is now based purely on data – yes, we mean evaluating marketing returns. It is so thanks to the possibility of calculating marketing ROI, the return on investment from a company’s campaigns. But, how to do this correctly, how to measure a true marketing ROI? We will explain this here, so read on if you wish to discover the answer!

What is Marketing ROI?

Marketing ROI stands for marketing return on investment. It is a calculation of both the costs of a marketing campaign and the revenue generated by it. The aim of measuring it is simple: to discover how effective the acquisition efforts of your marketing team are.

Depending on the organization, marketing ROI may include numerous costs related to launching a successful campaign. It does not only have to encompass the basics: paying for advertising space, creating graphics, or recording a video. A marketing ROI may include all possible and measurable resources, e.g. spending and indicators of marketing performance.

Steps to Measure Your Marketing ROI

Measuring marketing ROI correctly is a multistep process – it cannot be done ad hoc, without proper planning. So, what are the steps to measure your marketing return on investment? Here they are:

Determine KPIs

The circumstances and context in which every business operates may vary. Therefore,t organizations might focus on different factors when determining their aims. In order to measure your marketing ROI correctly, you should choose the most valuable metrics for your business  and set realistic KPIs. The range of choice includes:

• Cost per lead (CPL)
• Customer lifetime value (LTV)
• Customer acquisition costs (CAC)
• Conversion rates
• Purchase frequency (PF)
• Average order value (AOV)
• Customer average lifespan (CAL).

Tempting as it may be, remember that setting too many KPIs can do more harm than good. Thus, you should select only the 1-3 critical metrics that are the core for your marketing team.

Identify the attribution model

Attribution in marketing is all about one thing – identifying which touchpoints lead to conversions. Being able to find the most crucial ones lets you select the right attribution model – the set of rules that determine how credit for sales and conversion is assigned to particular touchpoints. You need to make a data-driven decision here, and select one of the following attribution models:

• First touch attribution: full credit goes to the first touchpoint
• Last touch attribution: full credit goes to the last touchpoint
• Time decay: the closer the touchpoint is to conversion, the more credit it receives
• Linear: equal credit for all touchpoints
• Multi-touch: considering several touchpoints and assigning credit depending on a logic specific to the particular business.

Determine what metrics to measure

Finally, you need to select the metrics that you want to measure in your MROI. These are the same ones that were present in the first step – setting KPIs. The number and kind of metrics that you choose may depend on several factors, such as:

• Market conditions
• Stage of your marketing campaigns
• Stage of your business
• KPIs
• Your business’s financial goals.

In general, the formulas can be as simple as LTV/CAC, or complex, multifactorial like (total revenue - total cost of products sold - marketing investment)/[marketing investment] x 100. It all depends on the abovementioned circumstances – sometimes simpler is better, and at other times you need complex measurements to calculate your ROI correctly.

How Automation Increases Marketing ROI

Knowing all about evaluating marketing returns, let’s delve into what we are experts at – automation. Marketing automation is a proven way to increase the marketing ROI. According to Nucleus Research, it returns an average of $5.44 for every dollar spent. What is more, it is estimated that investing in such a solution returns the initial costs in only 6 months.

Marketing automation software enables marketing teams to manage leads and customers more strategically. It has a wide array of functions – such software can even track leads and automatically retarget them to increase conversion rates even further. Thus, it should not be surprising that marketing teams quickly see measurable benefits of investment into automation – they simply work more efficiently.

There are numerous advantages of implementing automation to increase the marketing ROI. Here is our insight into the most crucial ones:

Increased efficiency

Sales and marketing processes naturally include a lot of repetitive tasks, which can be done quickly and effortlessly by implementing automation. As a result, your staff may focus on making true value – doing the creative work that matters the most. The capabilities of marketing automation software include:

• Scheduling automated e-mails/social media posts
• Triggering content
• Sending reminders.

There is no need for you and your team to conduct all the simple, repetitive, and low-value tasks. The efficiency is much higher, and you can achieve more even with a smaller team. Marketing automation also raises the potential to grow – your employees focus only on high-value business tasks and can accomplish more.  

Raised quality of customer service

The key to success in this digital age is building an omnichannel customer experience. Yet, to do so, you need to collect data. And, with marketing automation, you have it at your disposal.

One of the key benefits of marketing automation is the fact that it unifies all touchpoints, giving you a full insight into your audience and delivering relevant, personalized content. This, on the other hand, leads to better customer satisfaction and a higher likelihood of conversion. Let’s look at an example.

Imagine that you are searching for automation solutions for the customer service department. After visiting the site of a potential provider, you will receive a follow-up email with a link to content that resonates with your needs. If you decide to download it and fill out a form attached to it, your data will get updated. As a result, you will be provided with customer service automation solutions tailored to your team’s needs.

If that impresses you, let’s put a reminder – all of this will be done automatically. There is no need for extra effort from the marketing team, while a personalized offer is delivered when a customer needs it.

Key Takeaways

Measuring true marketing ROI requires some preparation – you first need to set the right KPIs, then choose the right attribution model and finally decide on the most crucial metrics to use for the calculation. If you wish to increase your marketing return on investment, opt for marketing automated solutions – they are a proven way to elevate the profitability of your marketing campaigns. By using them, you can increase the efficiency of your team,  enhance customer satisfaction, and optimize ROI.

You might also read: The Fundamental Guide to Account Based Marketing

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Written on November 3, 2023.

Learn how to supercharge your GTM strategy with an integrated tech stack.

6 minute read

• Understanding customers and providing the best customer experience is a must-have for the success of your business. This is why you require a proper, well-thought-through go-to-market strategy. In this article, we will focus on one particular aspect of it – an integrated tech stack. How does it help you optimize your efforts? Learn by reading on!

What’s the Go-to-Market Tech Stack

A go-to-market-tech tech stack is the tools and technologies used to successfully promote and sell your offer at the market. It consists of a number of solutions (software, platforms) that are used to aid marketing, sales, customer engagement, and customer service. Typically, the tech components of a go-to-market strategy include

• customer relationship management (CRM)
• marketing automation software
• customer success management software.

How Intelligent Integration Optimizes GTM Strategy

Intelligent integration is an excellent way to optimize the go-to-market strategy and make full use of all the tech stack exercised in a campaign. Real-time data integration, having all the tools available in one place – it all comes with a plethora of benefits, such as

• Gathering customer data into a single source. Forget about data silos and the inability to access all the information you need quickly and easily. With intelligent integration, all the data will be stored in one place, making it available for all teams to use it to the fullest.
• Automating customer processes and workflows. With an intelligent integration, you can automate many actions, thus reducing the time your teams spend on low-value, repetitive tasks and giving them more time for revenue-generation work.

• Identifying new customers and targeting them with personalized campaigns. Storing your data in one place lets you make more data-driven decisions. As a result, your organization is capable of finding new target groups more effectively and serving them personalized engagement offers.
  

• Tracking customer engagement and tracking customer lifetime value. Integrating various tools and resources gives you a better understanding of your customers’ lifetime, letting you find possible bottlenecks and eliminate them.
  
• Analyzing customer behavior and providing personalized customer experience. Having insight from all the platforms at once lets you predict customer churn, discover pain points, and comprehend your customers’ preferences. This way, you can approach every customer individually, with personalized advertisements, materials, and product choices.

Why Invest in Tech Stack Integration

Marketing, sales, and customer success tech stack integration may be quite a game changer. Why exactly should you invest in it?

• Aligned business plan and goals. Every team puts different efforts into life and has different goals. They should be guided by one general aim of the company, yet with data discrepancies it is often difficult to create a unified strategy. Tech stack integration is a solution to that – all the data is in one place, so the goals can be fully aligned.
  
• Reduced time to market. Integrating your tech stack means that all the processes are aligned and move smoothly between the teams. As a result, the time to market is much quicker.
  
• Decreased likelihood of extra costs. With everything in one place, you can make accurate predictions. This way, you know for sure that the estimated budget will indeed be enough.
  
• Enhanced ability to react to changes and customer desires. In a non-integrated setting, changes take a lot of time. One team spots a shift and has to inform the other teams. And each of these has to come up with solutions and then these need to be aligned. When integrated, the process takes significantly less time, and customer experience increases significantly.
• Guaranteed regulatory compliance. Many industries have one additional feature that they must pay attention to – regulations. With an integrated system, it is much easier to monitor all the marketing, sales, and customer success activities and control whether they meet the requirements.

Benefits of Integrated Tech Stack

An integrated tech stack should be the aim of every business – it is an example of how you can majorly improve your go-to-market strategy. What benefits does it have in particular?

• Automated engagement. Time is your most valuable resource, and with automated engagement, you spend less effort on doing routine engagement campaigns.
  
  
• Accelerated lead generation. Not every lead transforms into sales, therefore it is crucial to get as many quality leads as possible. With an integrated tech stack, the whole process is much quicker, thus allowing you to reap the harvest of an increase in lead worth pursuing.
  

• Omnichannel experience. Currently, the boundaries between different channels are slowly diminishing. Customers are likely to view products online, while being in a physical store, and check the app before purchasing something on the website. With a tech stack integration, it is possible to quickly transfer data between all these channels and provide customers with a truly unified experience.
  
  
• Real-time insights. Finally, an integrated tech stack gives you real-time access to all the information you need, from every of the teams working on your go-to-market strategy. As a result, you can operate with up-to-date information while implementing a strategy.

Key Takeaways

A go-to-market tech stack is used by your organization to provide a personalized customer experience and drive more efficient growth. Using intelligent integration is an excellent way to integrate your go-to-market tech stack and facilitate the engagement and acquisition processes. From better, quicker, and data-driven decisions, to building an omnichannel customer experience and improving customer retention – all of these are possible with intelligent integration of your tech stack.

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CMI's Enterprise Content Marketing research shows that 34% of businesses use account-based marketing and 21% plan to incorporate it.

This marketing strategy helps create what traditional ads lack; personalization. It allows businesses to gather data on their target customers to create personalized messages and increase engagement.

Read on for a guide that begins with ABM basics and then provides the more advanced methods that will lead you to success.

What Is Account-Based Marketing

Account-based marketing or ABM is an approach used by sales and marketing teams.

They target specific accounts and identify which ones are the most important. They gather data on the most high-priority accounts and use it to create customized messages.

There are several different forms of ABM that all follow a similar process. Choosing the right type and how to use it depends on the needs of your business.

Forms of ABM 

One-to-one account-based marketing selects and targets 5-10 top-tier accounts. Despite being the most common approach, it's resource-heavy and best for businesses that want to narrow their focus.

One-to-few selects larger segments of accounts, known as clusters. They're categorized based on challenges, relationships, and other factors. It's the most cost-effective method.

One-to-many account-based marketing uses several types of software to gather data. It's able to work on a large scale and personalize marketing information for each account.

The ABM Process

Stakeholder and sales-marketing alignment begins when key stakeholders buy in. Each one must be analyzed to see what it needs and how it fits into the business. 

Sales and marketing alignment is an essential part of making an account-based marketing strategy work. The sales and marketing team must work to achieve the same objectives and focus on the same target accounts.

Account selection and definition involve determining what an ideal account looks like and which potential accounts match this description. The decision should be objective but data-driven. 

Data and insight provide the sales and marketing teams with information that helps them decide how to reach their target accounts. The types of information it may involve includes:

• Transaction and engagement history
• Engagement history
• Pain-points
• Priorities
• Media habits
• Buyer journeys
• Connections and contacts

The program, proposition, and content step is where the sales and marketing team create personalized content and messaging for their target accounts. They use the information gathered in the data and insight step to do so.

Account-Based Marketing Tools

The right ABM technology helps you manage and use your data more efficiently. Types include martech, intent data, adtech, and programmatic ABM.

Search for tools that automate each step of the process. Find ones that can handle sentiment analysis, data gathering, and other daily tasks of account-based monitoring. Each one will save you time and money.

Budgeting for ABM

Approximately 24% of marketing budgets go towards account-based marketing programs. They're one of the most popular and important investments a business can make.

Create a budget for each account based on its priority level. This keeps you from spending too much on low-value accounts.

Who Should Use ABM

Account-based marketing is perfect for:

• High-ticket products or services
• High-value, limited-scale marketplaces
• Sales teams with a high value-per-deal ratio
• Business with a long list of target companies or team-led buyer journies

ABM helps larger companies target their most valuable accounts. Small businesses may think they don't need it, but they can still benefit from it.

ABM helps you find similar small businesses to work with and analyze your data more efficiently This can increase your ROI by helping you develop personalized marketing and engagement and prioritizing sales follow-up.

How to Succeed at ABM

A successful ABM marketer must have several essential skills, which only 1% of marketers believe their team has. You'll need to be a strategic, long-term thinker. 

Develop the Right ABM Strategies

Knowing the basic steps of the ABM process isn't enough to make it work for you. You've got to develop plans, categorize and cater to your targets, and work with the sales team.

Plan for every part of the process. Develop a budget, get buy-in upfront, and set your timeframes before you begin.

Categorize your contacts based on whether they're social, content, or ego-led. This will help you determine which types of messages will connect with each of them.

Find out what each account needs and show how you can provide it. Your ads should speak to them on an emotional level to show how your products or services are aligned with their values and can solve their problems.

The sales and marketing team need to work together to solve the same goals.

Create Households

Creating households involves defining the relationships between each of your accounts. You can look at the individuals themselves, group digital devices that share IP addresses, or create custom households based on behaviors.

This process allows you to categorize your account. You'll enjoy better engagement if you create customized messages for each household based on their shared characteristics.

Use Customer Data Platforms

A customer data platform, abbreviated as CDP, stores customer data in a centralized database. It identifies each account with a unique data trace known as a digital footprint.

Marketers are responsible for managing all the information in a customer data platform. It's the ideal tool for account-based marketing for several reasons.

The average business uses 17 different applications to house customer data, but a customer data platform creates a centralized database. This makes all attempts to access customer data faster and more efficient.

47% of new business data has one or more errors, and 57% of marketing efforts suffer from this disparity. This applies to account-based marketing as well. A customer data platform provides clearer, more accurate data.

Focusing on customer experience leads to increases in revenue of up to 80%. This is in part because excellent experiences cause 49% of customers to make impulse purchases.

Use Sentiment Analysis

Sentiment analysis uses AI to analyze customer feedback to determine how they actually feel and if their responses are genuine or sarcastic. It can analyze audio, video, and text.

Sentiment analysis has several applications, including everything from predicting ROI to determining if a driver is drunk based on their usual startup procedures. It's also useful for analyzing data in ABM campaigns.

Use Lead Scoring

Lead scoring ranks each lead on a value scale. Businesses can use the score to determine the priority of each one.

There are several types of leads. They include:

• SALs or sales accepted leads
• SQLs or sales qualified leads
• MQL or marketing qualified leads

An MQL is one of the most valuable types because they have the highest level of interest in your products or services.

There are several ways to measure a lead, including:

• Overt qualifications from responses to forms
• Implicit qualification from behavioral and demographic scoring

Once you've measured a lead, send it to CRM to be evaluated by the sales team.

Lead scoring makes the sales process more efficient, reduces lead response time, and increases conversion rates.

Measure Your Success

To measure your success with account-based marketing, you'll have to look at account engagement and conversion.

Account engagement involves how and how often the individuals within your accounts are engaging with your content. Measure how often they share your content, click on your emails, and attend your events or meetings.

Conversion measures how this engagement translates to sales and involves more than just ROI. Look at how many deals you close and how valuable they are. Also, consider the length of your sales cycle.

You can also measure your success during the sales process. Quantify how valuable your prospects find their ABM experience and how their opinion influences your sales. Look at coverage, engagement, impact, and influence.

ABM Examples

There are several account-based marketing examples you can learn from.

A well-known one-to-one ABM example comes from the story of a company that found out the CEO of their prospective client loved comic books.

They created a custom one just for him, mailed it to him, and published it online. He set up a meeting hours later and officially became their client.

A useful one-to-few example comes from a company that gathered target client's positive reviews. They sent them in a burro backpack with a personalized note. Their efforts led to a 36% increase in response rates.

A recent study proves the usefulness of one-to-many ABM. 

Target accounts were separated into two groups. Half received customized ads, and the other half received more generic messages.

The personalized half had 60% higher email open rates and 29% higher reply rates.

These examples show how to use the different types of account-based marketing. You need to customize your marketing efforts to ensure they meet your client's needs and make them feel valued.

Account-Based Marketing Benefits

77% of B2B marketers believe account-based marketing improved the success of their target accounts.

ABM can improve your sales process. It leads to shorter sales cycles, better pipelines, and lower acquisition costs.

ABM increases marketing re-engagement and creates additional upsell and cross-sell opportunities.

At least 87% of businesses say ABM increases ROI more than any other type of marketing. 27% of businesses also saw an increase in their C-level engagement.

ABM leads to better sales and marketing alignment. 

Where to Find Account-Based Marketing Tools

Modern customers no longer respond to cold, general forms of advertising. They want personalized messages that speak to their needs and values.

This is why account-based marketing is so essential. It allows business to customize their marketing efforts to reach individuals and prioritize these prospects.

Put It Forward can handle all of your data management functions. Get a demo of our ABM solution today.