But Will it Work? The Pressure for Marketers to Drive Pipeline Conversion
Written by: Mark Cowan - CDO, Put It Forward
February 26, 2019
But Will it Work? The Pressure for...
Elsa Petterson
Leadership success manager @ Put It Forward
Table of Contents
- Predictive Analytics for Revenue Generation
- Executive Summary
- Why Use Predictive Sales Analytics for Revenue Generation
- Predictive Analytics Examples
- Benefits of Predictive Analytics
- Predictive Revenue Generation Tools
- Expected Results from Using Predictive Analytics
- Conclusion
- What You Should Do Next
- Key Revenue Intelligence and Leadership Assets
Executive Summary
While concerns about staff and budget shortages caused by the COVID-19 pandemic have held back some businesses' analytics technology expenditures, for others, analytics has become even more essential, allowing organizations to navigate fast-changing client behaviors and supply chain disruptions.
The significance of predictive analytics in business was quite apparent in a 2021 survey conducted by Hanover Research for LogiAnalytics. The survey of over 2,000 knowledge workers found that 90% of respondents considered analytics to be "very or extremely important" when making business decisions. 87% of those surveyed said their firms used analytics "often or very often" to make decisions.
According to the numbers that Markets and Markets published recently, the market for predictive analytics is anticipated to nearly triple in size to $28 billion by 2026. Fast-growing enterprises are leveraging predictive analytics in today's hyper-competitive market to understand their customers better and optimize income, the effectiveness of their marketing spend, and, ultimately, improve their bottom line.
Why Use Predictive Sales Analytics for Revenue Generation
Predictive analytics is an interdisciplinary field of science and business that uses data and statistics to predict future events. We can define it as the process of using new and historical data to foresee your consumer base's results, activity, behavior, and trends.
Predictive analytics models use historical data (i.e., how well certain products sell together; what percentage of people who visit your website end up buying something) and predictive models (factors such as location, season, time of day, type of device used, etc.) to create statistically-supported projections about future behavior patterns or outcomes. But how can predictive analytics be used in business? There are several reasons why you might want to employ predictive sales analytics in your revenue generation strategies. These include:
- To identify new customers, markets, and ways to generate revenue
- To cross-sell new products by identifying combinations that would appeal to current customers.
- To better understand customer behavior, including what motivates them to buy your products or services and their spending patterns and preferences.
- To predict changes in demand and prepare for fluctuations in sales.
- To make marketing and advertising more impactful through audience analysis, including identifying the best possible target audience for a campaign or ad.
- To identify opportunities for pricing optimization, e.g., by the time of day or day of the week, based on historical data that helps you anticipate customer behavior.
- To predict churn risk, enabling you to proactively address customer dissatisfaction before it results in a loss of revenue.
Predictive Analytics Vs. BI for Revenue Growth
Predictive analytics and BI (business intelligence) are two different technologies used to generate more revenue. While both technologies can analyze past data and trends, BI is for diagnosis, while predictive analytics is for probabilistic outcomes. BI will help you understand what is happening in your business, while predictive analytics will help you know what is likely to happen.
Predictive Analytics Pitfalls Your Business Should Avoid
The following are some of the pitfalls to be aware of when using predictive analytics:
- Making wrong assumptions about the predictive analytics training data you want to use.
- Not making enough effort to eliminate bias in your training data.
- Forgetting to validate the results of your predictive model.
- Not carefully weighing the costs and benefits of a predictive analytics solution compared to alternatives such as gut instinct, intuition, and experience.
- Failure to recognize that the accuracy of predictions is only as good as your training data.
- Expecting machines to make better decisions than humans.
- Not basing predictions on a variety of input factors.
Here are some fascinating examples of predictive analytics in action:
- Predicting customers' behavior in retail.
- Predicting the next best outcome or event.
- Predicting legal outcomes in court.
- Predicting the outcome of sporting events.
- Predicting financial risk and creditworthiness for mortgages or other loans.
- Helping computers to recognize images
- Detecting illness and disease outbreaks in healthcare
- Decoding the language of whales
Some of the benefits of predictive analytics for revenue generation are:
- Operational Efficiency: Thanks to predictive revenue analytics, managers can devote resources to new initiatives based on near-perfect forecasts of when ongoing work will be completed. They can integrate predictive analytics into their day-to-day operations to make them more efficient.
- Customer Churn Forecast: You can use predictive models to assess the influence of various variables, such as customer satisfaction, customer service quality, and churn rate, towards discerning what's causing loss and using that information to reverse the process.
- Lead Segmentation: Using data and machine learning, you can create segmented groups to forecast the leads that require greater focus and how much time and resources will be necessary for an ML project. Insights into consumer behavior afforded by predictive analytics allow marketers to do audience segmentation and identify which ones are closest to conversion.
- Campaign Optimization: Leverage predictive analytics project management to choose the best channels for each piece of content, the best language to use when reaching the different target demographics, among other variables that influence user acceptability. You can use predictive analytics to design customized campaign messages as if you’re speaking to an audience of one.
- Customer LTV: The customer lifetime value model refers to the estimated value of a customer to a company over the entire period of their relationship with that company. You can use predictive analytics to do customer lifetime value calculation assessing the customer’s future value to determine how much effort you should put into acquiring and retaining them.
- Business Data: CFOs can use predictive analytics for more precise planning, forecasting, and making decisions based on existing data. Your company can use predictive analytics to anticipate outcomes, expose hidden problems and risks, identify underutilized opportunities, anticipate what's coming, and act fast.
- Risk Management: Analyze a client's credit risk or the potential consequences of that investment to your enterprise and forecast the likelihood of profit or loss.
- Fraud Detection: The increased attention to cybersecurity has made many businesses take extra caution and look for security solutions to resolve vulnerabilities and detect anomalies quickly. Using predictive analytics, you can spot threats and avoid scammers in real-time.
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