Predictive analytics is a technique used to forecast future outcomes and performance. Machine learning models find patterns in historical data that data analysts and business leaders can use to generate predictions about the future. Instead of relying on intuition-based and potentially biased forecasts, businesses can now use more accurate predictions to inform their decisions and plans.
Predictive analytics has immense capability to shape your business and help you achieve your most critical KPIs. As a result, you can put your data to work in any industry, no matter how you interact with customers. For example, use your data to reach customers more effectively, provide them with the offers and products they need and want at the right time, and improve your bottom line.
Four commonly known modeling approaches in predictive analytics include:
- Upsell/cross-sell predictive models
- Predictive churn models
- LTV or high-value customer models
- Conversion rate optimization models
Upsell
Predict if a customer will purchase more products
Churn
Predict customers who might leave
High-Value Customers (HVC)
Predict which customers are going to be the most valuable
Conversion
Predict which customers will become paying customers
Today’s Approach to Predictive Analytics
In light of businesses’ need for new growth strategies, today’s data experts have developed sophisticated predictive algorithms. These algorithms can be automated to make machine learning easier for business users. Accordingly, these capabilities and their value are now available to those who don’t have data engineers or data scientists on staff.
Investing in these tools has proven highly worthwhile for companies who adopt a predictive analytics practice, not just an imprecise data-informed approach. In fact, Pecan AI customers have seen impressive results when using our low-code predictive analytics solution:
- E-Commerce Brand: A well-known e-commerce company built and deployed a predictive analytics upsell model within two weeks, doubling their campaign conversion rates.
- CPG Brand: A household CPG brand was able to detect roughly 85% of customer churn and markedly lower global churn by over 10%.
- Mobile Gaming Company: A mobile gaming company was likewise able to identify VIP customers and improve the lifetime value of their users, with a 3.5X uplift in spending per user.
Whether a Fortune 500 brand or an SMB, your business can leverage predictive analytics software for the same reasons. Overall, using a future-driven approach in your everyday operations will boost your outcomes.
If you’re looking to get started in predictive analytics, we created a helpful guide. The guide leads you and other stakeholders through gathering information and making decisions about your predictive analytics strategy.
If you are interested in how Pecan AI can support your predictive analytics initiatives, please contact our sales team today and schedule a demo.