Cotera's Replenishment Model: A Case Study

Problem

When a leading supplement brand was in need of an easy way to send out emails right when a customer was most likely to have run out of a product, they came to Cotera.

Their retention team had tried exploring different ways to optimize email schedules manually, but estimating the right timing on their own was near impossible.

This meant that there was so much wasted potential for campaigns sent to their top customers — but they knew they couldn’t make the needed predictions on their own.

So, they began looking for a replenishment model that could automatically:

Send emails based on individual customers’ usage patterns
Up-sell users to larger-sized products
Work in tandem with product recommendations to cross-sell users into other high margin products
Include a direct reorder link with the customer’s cart refilled, in any email

Impact of Cotera:

Up to 9% increase in the repeat purchase rate
Up to $18k/mo in additional revenue

Prior to Cotera, the brand had faced several problems:

1) Email campaigns and order reminders were sent out sporadically.

Without the ability to estimate when a customer would next need to re-order, it was impossible to optimize the timing of email campaigns.

Rather than personalizing an email campaign schedule according to the individual purchase data of a customer, they used to follow a generalized one:

Post-Purchase Email - 2 days after delivery of purchase
Marketing Email - 2 weeks after delivery of purchase
Follow-up Marketing Email - 1 month after delivery of purchase

But so much customer data was being collected: time gaps between purchases, quantities purchased, timing of browsing, etc. And not making use of it all meant missing out on huge revenue-growth opportunities.

2) Email campaign content wasn’t catered to individual customers.

Now not only was the timing of emails not catered to customers, but neither was the content. Simply showing customers what was “trending” or “new” was no longer cutting it — it wasn’t engaging nor captivating because it wasn’t relevant to their specific needs and wants.

So, faced with these struggles, they turned to Cotera.

Solution

For this brand (and many others!), Cotera set up an item replenishment model that allowed them to:

Personalize the schedule and content of replenishment emails by customer
Sync with our recommendation engine to suggest products customers were also likely to purchase
Quickly add links to a customer’s last cart in any email

How it Works

1) Uses data to estimate the optimal schedule for email campaigns to individual customers.

Let’s say that the supplement brand had collected data as simple as this for a customer:

Customer A:

8/24/23 - Protein Supplement (x1), Iron Supplement (x1)
9/10/23 - Protein Supplement (x2), Iron Supplement (x2)
10/12/23 - Protein Supplement (x2), Iron Supplement (x1)

Our model would then predict that Customer A runs out of one box of both goods every 16 days on average. Taking quantity into account, it might recognize that the customer is out of their Iron Supplement but not yet out of their Protein Supplement on 10/28.

That day, they could send a message to their customer saying:

“Hope your Protein Supplement order has been satisfactory thus far! We noticed that you may be running low on your Iron Supplement — in the meantime, you can order more here with just one click.”

2) Helps leverage this information to upsell effectively

Now, they could also leverage the timing of replenishment emails to upsell relevant products.

Taking the same data from Customer A, for example, our model might decide that this customer would enjoy the product Fiber Supplement based on what similar customers have purchased. In addition to a personalized refill cart attached to the email, a product recommendation at this time would be effective since the customer already needs to make a purchase anyway.

Impact

Through Cotera’s replenishment model:

Companies saw on average a 4%-9% improvement in repeat purchase rates (depending on their subscription model)

For most companies, this resulted in $12k-$18k/mo in additional revenue.