Predictive Analytics in Marketing: 4 High Value Use Cases

Predictive analytics unlock a world where you can identify who your most profitable customers might be—even before they make their first purchase. Using first-party data to model the ideal patterns and behaviors of your high-value customers, you can cater to and create an exceptional experience for that audience. Here are five high-value use cases where DTC marketers can harness predictive analytics for lasting impact.

1. Predicted Lifetime Value

What would you change in your current efforts if you knew each customer’s predicted lifetime value? Predictive analytics identifies who your high-value customers are and the traits, behaviors, attitudes, and patterns associated with those individuals.

By better understanding your current customer base and which customers bring in more revenue, you can tailor acquisition and retention efforts to this crowd and build a better long-term relationship. The pandemic saw small businesses going through an unpredicted phase of hypergrowth (CAGR of 40% or higher) as individuals shifted buying habits and went online for more purchasing. Now, these businesses must focus on sustainability as buying behaviors shift again and customers go back to stores or other alternatives.

Predictive analytics can model the prime behaviors of all of the customers acquired during hypergrowth and redirect acquisition efforts to prioritize retaining those with the highest long-term.

2. Acquisition

More than likely you have a quiz, subscriber box, or some form of email capture on the homepage of your website. A study done by Marketing Metrics shared you have a 5-20% chance of converting new leads to your site.

Most likely, you send individuals a promotion and incrementally offer more value until they convert. Using predictive analytics, you can identify an individual as having high or low predicted LTV before their first purchase. Spending money to acquire leads who don’t fit your high LTV customer profile dilutes your budget on customers who won’t convert.

That budget would be better spent to nurture high LTV customers by providing value specifically to them. You can also identify which group might be more prone to take advantage of one offer over another. This allows more creativity in your offers and prevents a discount from being your primary attraction.

3. Churn Prevention

On average, it costs up to five times more to acquire a new customer than to retain a current one. Predictive analytics allows you to identify churn at an individual level.

You can adjust your outreach to prevent churn before it happens by modeling your churned customers to understand trends and attitudes. Combined with predicted LTV, you can more easily strategize and allocate budget to customers who are high value and at risk of leaving instead of spending on low-value customers at risk. This includes actions that aren’t scalable such as personalized customer outreach, which is naturally limited by its manual nature.

Knowing who to prioritize based on what they are more likely to spend makes personalized outreach infinitely more valuable and productive. In the end, by prioritizing high-value customers who are more likely to become brand evangelists, you develop long-term retention that connects people to your brand.

4. Win-Back

The Marketing Metrics study also revealed that businesses have a 20-40% chance of winning back a lost customer. While not all lost customers are worth your reacquisition efforts, some of them might be more valuable to your business than a cold lead.

The Client WinBack Benchmark Study found that 26% of clients return with a strategic win-back campaign. When they return, their customer lifetime value doubles and client reactivation yields an ROI of 32x or more. For a small business, the potential revenue from a win-back campaign can be game-changing; the same study claims win-back campaigns generate an average of $485K in additional revenue for small businesses.

Predictive analytics helps you identify which customers are high value and worth the extra effort to reactivate. Much like your acquisition campaigns, you can also selectively package offerings to entice high and low LTV audiences, as they are not always driven by the same types of discounts or promotions.

As machine learning advances, predictive analytics is poised to become a vital tool in every DTC marketer’s stack, especially for companies that see sudden, rapid growth and want to sustain that customer base for the long haul.

By taking the time to understand your top customers better, you can reset your strategic direction and drive more sustainable growth in the years to come.

Recommended Posts

Get in Touch

Our team would love to hear from you!

Let's Talk