A single email blast to your entire customer base disables your ability to create a personalized experience.
Actionable Data vs. Numbers
Your customer's buying behavior and the data produced from open rates, clickthroughs, unsubscribes and engagement are essentially meaningless without the ability to detect patterns in the data. Actions that are not specifically revenue-generating transactions contain the information about a customer's intent. Using this predictive intent, you get amplified intelligence.
Customers who put items in their cart and then abandon them indicate a desire, at the very least, for the product that they've taken the time to research and add to their cart. Griffin Technology sends a 3 part Cart Recovery series to customers who abandon their carts, and have seen 6x ROI from their efforts. These three emails also leverage anti-gaming technologyto ensure they don't train their customers to become serial abandoners.
Optimizing your inventory
Within your inventory there may be overstock or maybe a product with a looming expiration date, this inventory can be used with deep discounts to target the non-active portion of the database because you already know what they might be likely to buy. Creating a segmented promotion to pinpoint customers who may benefit from your product, is a great way to move inventory and create up-sell, cross-sell opportunities.
SkinMedix leverages, product, purchase, and customer data to segment their weekly promotions and have seen over $1MM in revenue from this approach. Emails are sent to four key segments: Best Customers, Non-Purchasers, Churning Customers, and All Others. Each segment receives content and discounts geared towards their engagement level.
When customers are at the end to their lifecycle, and they've stopped buying from you, predictive analytics give you the ability to send targeted promotions to reactivate valuable customers. Predictive analytics can help find a discount level where it makes sense to retarget lapsed customers based on their lifetime value or how much they have purchased in the past. The cost of customer acquisition is high so it just makes sense to offer 10% off to a dormant customer who spent $5,000 dollars with you in the past. Scared of getting hit by the "spam button"? Ask the inactive subscriber to update their preferences in exchange for a reward or coupon.
SurfStitch, a leading Australian surf & lifestyle apparel retailer, created a 3-part Win-Back campaign to bring lapsed customers back which has decreased churn by 70%. This 3-part series (shown below) escalates in value and language - the longer a customer is latent the higher the discount. SurfStitch also created a 3-part "At Risk" Win-Back campaign for customers who have not churned, but are exhibiting behaviors that emulate a lapsing customer. Download the SurfStitch case study to learn more.
Personalized Product Recommendations
To effectively retain customers, data must be broken down and refined to create the maximum value. For example, purchase history can be used to predict what that customer might do in the future and make product recommendations based on that knowledge. By using past performances to steer the direction of new email marketing campaigns, you create profitable email lists that contain subscribers who are likely to respond to similar marketing messages.
Using predictive analytics you can analyze all of your email marketing sends to measure engagement and create sophisticated segmentation that targets customers with the messaging they want.
For retailers with replenishable products, predictive analytics are a must. Aruba Aloe leverages our replenishment algorithm to send automated Replenishment Emails when customer are about to run out of their favorite skincare products. Brilliantly, they also include dynamic product recommendations to personalize this replenishment email that much more.
CoffeeForLess.com utiliizes this same data to send replenishment emails just when customers are about to run out of coffee. They pair the replenishment email with a static product recommendation based on particular products they are looking to push, whether seasonal, or for other reasons. Both approaches garner great results with CoffeeForLess seeing open rates of 35%.
High Value Customers
The number of new customers per day is not as a valuable metric as retaining those customers over time. Also, keeping valuable customers is essential to an eCommerce business. therefore, you want a broad view of your customers and the ability to pull data points together, to target customers who are attributed as higher revenue generators.
GolfHQ.com, created a best customer email that pulls in dynamic product recommendations and lets their best customers know they are the best. This email, with the subject line "Congrats! You're GolfHQ's Best Customer!", sees 63% open rates and 8.8% click rates. Download the case study to learn more.
The multivariable statistical approach to identify and classify customers into segmented groups maximizes retention, personalized promotions, and provides revenue boosting cross-sell or up-sell opportunities. Learn more about predictive analytics or request a demo to get a customized walkthrough of our product.