Why Every Growth Practitioner Needs Predictive Analytics

Author: Ivaylo Bahtchevanov

What is predictive analytics anyway?

Predictive analytics traditionally refers to the process of identifying meaningful patterns in historical data in order to predict future trends and events. Having the ability to forecast potential scenarios can help drive strategic decisions. For example, you might look at trends in customer transactions to identify which products were popular in a given season and then use this information to determine what products to focus on in the following season.

While simply analyzing and identifying historical trends reactively is certainly useful for understanding your customers past behavior, those same patterns are not guaranteed to hold in the future. This is where predictive analytics comes in. Predictive analytics uses mathematical models to understand how different variables influence and interact with one another, and then uses those models to generate predictions about what is likely to occur in the future. 

Why everyone needs predictive analytics

Predicting the future lets you respond proactively to expected customer behavior. It enables the performance of on-time action – the ability to proactively make decisions within an optimal time horizon. This means a business has enough time to either maximize future opportunities, such as anticipating a surge in demand for a given product, or minimize the loss from an undesired outcome such as the loss of a customer, or churn. 

In contrast, using traditional historical analytics, you would respond too late. You would be scrambling to create more of an in-demand product too late, or to win back a lost customer. As anyone who works with customers knows, winning lost customers is incredibly hard and expensive. 

While we’ve touched upon just a few of the use cases for predictive analytics, the potential applications are almost endless. 

See below for a list of the most common ones we see. Watch this space for more on these use cases in the weeks/months ahead.

• Consumer / B2C Use Cases
• Enterprise / B2B Use Cases

Customer retention and churn prevention

• Identify which customers will likely slow down or stop interacting with your business and re-engage them with personalized outreach

Identify high-value customers

• Focus your efforts on the customers who will be most profitable or valuable to your business

Personalization and optimization of organic outreach and GTM campaigns 

• Curate the content of any outreach campaigns (emails, notifications, sales calls) based on what will maximize long-term engagement 

Personalization of Product and Content 

• Curate customer experience across all channels with a unique page of most relevant products or content that user will likely respond positively to

Buyer-seller and user-user matching recommendations

• Connect any given seller with the best possible set of buyers, or match any number of users in a multi-sided marketplace based on the best possible fit 

Ad Campaign optimization

• Personalize ads based on demographics, interests, and observed behavior to maximize engagement