Cotera's Segmentation Model: A Case Study

Problem

Recently, we had a large food & beverage e-commerce company come to Cotera looking for a strong segmentation model.

The Head of Marketing needed to bring down a high one-and-done and churn rate.

She knew they were facing an abundance of customer churn but that her team wasn’t proactively identifying these at-risk customers until it was too late. And on the other side of the spectrum, AOV for their top customers wasn’t growing.

They tried to use the pre-built RFM model in their ESP, but it was too generic and missed too many churning customers. And this wasn’t changing the behavior of their top customers either.

With a company-wide focus on retention, she knew they needed a dedicated tool that specialized in customer segmentation.

They wanted a model that could:

• Leverage AI for more advanced and customized customer segmentation
• Factor in seasonality, product mix, and discount affinity
• Integrate into their ESP to update segments in real time and deploy emails accordingly
• Require zero time/effort on the retention team’s part to manage and set up campaigns

Impact of Cotera:

On churning customers

• Increased reactivation rate by upwards of 19%
• Decreased churn rate by 2x

On top customers

• Increased upsell rate by over 5%
• Increased AOV by 8%

Prior to Cotera, the brand faced several problems:

1) Existing customer segments were too broad

Here’s what the food & beverage brand would have sent out in a marketing campaign before working with Cotera:

• To all customers: “Get 50% off when you make a purchase within 24 hours!”

The issue with this is, although their at-risk customers might need a promotion to nudge them toward their next purchase, their top customers were likely going to buy from them regardless.

And as a result, emails were underperforming with an increasing opt-out rate, and AOV stayed static.

2) Segmentation and custom emails require too much time and work from the retention team

Manually having to analyze customer data and pull individuals into different segments was more than just time consuming. Work like this often led to simple mistakes and oversight — from mis-categorizing customers to sending out the wrong emails.

3) Customer segments would not be updated in real time

Another downside — customers weren’t automatically moving across segments as their behavior changed. This was due to a combination of segments being too broad along with the use of a generic logic that didn’t apply to the nuances of a food & beverage brand.

Solution

For this brand (and many others!), Cotera set up real-time customer segmentation that measured each customer’s brand affinity using historical order data, website click data, and marketing interaction data.

How it Works

1) Every day, each customer gets labelled with custom segments

For the food & beverage company, we split customers into 3 different segments:

• Top New Customer
• At Risk of Churn
• Highly Loyal Buyer

Let’s say this is some of the data our model automatically took in for a random customer:

• Total purchases made: 10
• Purchase frequency: 1 order every 2 months
• Most recent purchase: 50 days ago
• AOV: $35
• Email open rate: 85%
• Last browsed: 2 days ago

Our model then processes this information and would assign this customer to a segment. In this case, this customer would likely be placed into the “Highly Loyal Buyer” segment.

2) These segments are automatically integrated into our companies’ email and marketing platforms (Klaviyo, Iterable, Braze, etc.)

Instead of one general message, they could now automatically send multiple customized campaigns to the segments we built for them:

To Group A (At-risk Customers):

• “Get 50% off when you make your next purchase within 24 hours!”

To Group B (Top Customers):

• “Here are some new products we think you’ll love!”

And the best part about it all is they didn’t have to change a single thing about their current SMS/email systems to deliver these messages.

Impact

Using our Segmentation Model, Cotera was able to:

1) Increase upsell rate by 5% on average

• This resulted in about $18k of incremental revenue for one of our customers.

2) Increase reactivation rate by anywhere between 14% up to 20% per month

• Personalized messages were key to re-engaging churned customers.

3) Lower churn rates more than 2x

• Not only was there a drastically lower unsubscribe rate, but also a much higher conversion rate.

Interested in bolstering your segmentation and personalization efforts? Book a demo here!