5 Steps To Improve Decision-Making with Prediction Insights
Written on 18.
Prediction insights are based on predictive conversion models instead of human "gut" feeling moving to data-centric insights, measuring the strength of the relationship between your business and your customers.
6 minute read
Table of Contents
Wanting to predict future events is an innate human trait. The human brain takes past experiences, compares them to the present situation, and uses that data to guess what will happen next. Cue in prediction insights and predictive analytics. Predictive analytics is the practice of using historical and current data to estimate future outcomes. It combines sophisticated statistical methods with artificial intelligence so you can best anticipate future results.
Predictive analytics has brought to the fore an understanding of the centrality of data analysis and insights in improving business strategy towards better customer experiences and, ultimately, improved conversion rates for your engagement campaigns. It revolves around how to derive insights from data.
Prediction insights are based on predictive conversion models instead of human "gut" feeling to move from data to insights, measuring the strength of the relationship between your business and your customers. These models are unbiased, can be reproduced, and create actionable insights from data because they use data instead of hunches to make precise predictions. Businesses the world over are embracing predictive analytics at a staggering rate.
According to a ReportLinker research report, the global predictive analytics market will reach $40.3 billion by 2027, growing from just over $12 billion in 2020: a CAGR of 18.4%. The global predictive analytics market is growing because more people are adopting predictive modeling tools and investing in statistics and modeling techniques to extract information from current and historical datasets to predict potential future outcomes and business trends. Leveraging this data helps to make accurate business strategies and decisions to optimize revenue and detect growth opportunities.
What Prediction Insights Can Decision-Makers Use?
Predictive analytics can provide insight prediction into several areas, including:
Customer service insights: Understand how you can cost-effectively manage support tickets and which channels will likely improve response times using predictive analytics’ data-driven insights.
Predictive analytics pushes decision-makers towards creating accurate actionable insights that result in tangible outcomes. To do this, businesses need to take approaches that include the following steps:
Use predictive analytics for customer data insights to predict customer churn and take steps to prevent it. A churn probability depends on the customer's overall profile, their behavior when using your product, and their needs. A customer’s needs may change over the period of using your product, so churn can happen in a week or month. By getting the churn prediction insights you can retain your customers by engaging and educating them on what value do you provide.
Prediction insights help marketing and sales teams understand the engagement campaigns' results before implementing them. It allows us to define the customer segment that will engage better, their purchase probability, and correlate it with the ads budget. See the campaign scenarios:
If I invest $1000 in a one-week Google Ads campaign targeting audience B, I will receive 50 qualified leads and ten deals with an average check size of $500. So my total outcome will be $4000, not including the cost of the campaign itself.
However, if I invest $1000 in a one-month Google Ads campaign targeting audience A, I will only receive ten qualified leads and one deal with an average check size of just $200. In this case, my return on investment would be negative – meaning that I would actually lose money ($800).
Prediction insights help your business get data insights to understand what is most likely to happen in the future and make better decisions. To get the most out of insight data analytics, businesses need to take an approach that defines the objective they want to achieve, collects data related to that objective, and uses an AI data platform to identify trends and patterns. The platform will then generate predictions based on what it has learned and visualize them so you can easily see what is most likely to happen. By taking a data-driven approach to predictive analytics, businesses can achieve tangible results that reduce risks and ultimately lead to success.