Decrease in churn of high LTV customers
Increase of LTV on average for a segment of high LTV customers
Projected savings from this campaign for the full year
Real LTV prediction accuracy
CTO, Wild Earth
WildEarth, a profitable subscription business relies on long-term customers; when too many customers churn monthly, WildEarth faces higher acquisition costs and profit forecasts that vary month to month. The best way to retain customers is to motivate customers with a high potential lifetime value (LTV) to stay because they’re the ones that will be the most profitable.
Wild Earth did a lot of proactive subscriber outreach to try to reduce churn and build rapport with their new customers.
But because Wild Earth did not know who their high-value customers were, they were unable to target those customers individually. This resulted in the marketing team sending out identical promotions to both high-value and low-value customers, which is a strategy that can sometimes push high-value customers away while wasting precious resources.
In a bid to increase retention, Wild Earth invested in a churn tool, but it predicted churn at the purchase level, not the individual level. Wild Earth knew they needed to refine their targeting to customers with the highest LTV, so they segmented their customers based on the purchase value and purchase frequency, but that simply wasn’t enough data to accurately predict who was at a greater risk of churning.
Wild Earth wanted to send out handwritten letters to customers in an attempt to reduce churn, but those efforts are not easily scaled. To make it more effective and scalable, they wished they could identify customers with the right personality for such a personal touch. They simply did not have the data to know who to target, when to target, and why to target.
Wild Earth set a goal to understand which customer segments were most profitable, identify VIP customers independent of purchase history, and accurately identify profitable customers at risk for churn that can be retained with intervention.
It is a truth, universally acknowledged, that some customers will churn no matter the offers a brand sends to them, and many of these reasons may be out of Wild Earth’s control. For instance, some customers will churn because their pet doesn’t like the taste of Wild Earth’s food and no matter what promo you give them, they will not come back. Ocurate identified customers most ripe for intervention based on predicted LTV and other behaviors Ocurate maps in its database of 260 million Americans. This would maximize profitability without wasting resources on customers who would churn no matter what, or on customers who would stay with Wild Earth in the absence of intervention.
After six months deployment, Ocurate has proven to provide highly accurate predicted analytics. Ocurate achieved:
In their first retention campaign, Wild Earth ran an A/B test to understand what form of outreach would encourage customers to purchase a second time or maintain their current subscription. They split an audience of purchasers Ocurate identified as high LTV and at risk of churn and targeted Group A with special promotion – in this case, a free sample of dog treats and a tote bag with their next purchase– while Group B was treated normally and went through the cadence already in existence. When compared, the customers in Group A who received the promotion saw their churn reduced by 16%, and the individual LTV of one segment in the group was increased by $32. The losses in revenue incurred by the treatment group decreased by 28% due to prevented churn. This amount does not account for the additional savings in promotions not sent to customers who exhibited no churn risk. Projected for the full year, Wild Earth could see savings of at least $221,705.60 from this campaign.
Ocurate leveraged its deep learning framework and unique data on 260 million adult Americans and applied it to Wild Earth’s customer base to create a profile of Wild Earth’s highest value customers. The profile included insights tied to foundational personality traits (such as neurotic or compassionate), behaviors (such as geographic distribution), and attitudes (such as political preferences).
This is a unique differentiator Ocurate provides to brands. By combining Wild Earth’s customer data with a robust dataset of in-depth behavioral and attitudinal profiles for 260 million American adults, Ocurate can create nuanced, well-rounded profiles of a brand’s ideal, profitable customers.
Taking action on the correct segment of high LTV customers is incredibly important because individuals react differently to the same promo depending on insights that most brands simply don’t have from their customer data alone.
Ocurate generates such detailed insights and identifies more than who’s at risk of churn; they work with brands to identify specific actions to take with each customer segment. Leading brands know LTV is a powerful organizing principle, but its real impact comes from taking action. And that’s Ocurate’s advantage: actionable LTV to accelerate profitable growth.
Our team would love to hear from you!