Customer retention solution that improved marketing ROI by 22%

About client

Our client is an India-based fast-growing eCommerce brand with customers and operations spread across the country. Their goal is to deliver high-quality and luxury clothing, merchandising, and accessories to their customers while maintaining the exclusivity and personalized engagement.

Challenges

A major share of the client’s revenue come from their website, which attracts traffic from various sources—Google ads, social platforms, email newsletters, and more. The challenge arose when they have to tackle customers who visit and leave midway.

No data that explains why customers leave: The client quoted that they felt like playing a never-ending guessing game, figuring out why a customer left after browsing or even adding products to cart.

Difficulties with personalized targeting: As they cannot map customer interests with products beyond a specific point, their clearance campaigns mainly had generalized messaging and irrelevant offers. Similar offers and same messages led to high dropouts and low ROIs.

Data is there, but answers aren’t: Data is scattered across different platforms: email tool, CRM, website, Google analytics, and more. But they are unable to piece together the data and find answers about missed opportunities, faded interests, or engagement issues.

Not knowing when to rely on discounts erodes margins: As they had no means to filter down the high intent customer data, they had to share discounts and coupons to everyone, which questioned profitability and returns. The need for value-driven campaigns on intent-driven audience was all time high.

datakulture solution

They needed a churn handling and customer retention solution. Our data science team got in action mode, building this as per their data, business goals, and limitations.

Here’s what the solution does: Picks customers with high shopping intent based on their interactions and tailor their experiences.

What data does it process?

• Duration of stay on the product category, products, & reviews.
• Actions like adding to cart & wishlisting.
• Scroll depth, exit intent, zooms, mouse movements, scroll depth, and other.

The scoring model being trained on the above web analytics data, has weightage assigned to each action. Based on this, it analyzes the score of every visitor and filters down the ones with the highest score, meaning the highest purchase intent.

How did the solution help?

The solution attempts to track customer browsing behavior and collect customer data with high intent. This intent data is useful for the sales and marketing team in the following ways.

Use the data to power retargeting campaigns: Rather than doing random targeting, they divided high intent customer data into clusters and ran retargeting campaigns, ruling out guesswork and randomness.

Connect users with personalized assistance: By connecting repeat visitors with high purchase intent to they could understand and help better, that led space for sales, personalized engagement, and higher customer lifetime values.

Gain more clicks, purchases, and ROI: They used the high intent customer data to run one of their quarterly clearance campaigns, whose ROI was 22% higher than the rest of the campaigns.

Conclusion

As a fast-growing eCommerce company trying to expand into multiple regions and products, their goal is to win customers through consistent but non-intrusive nurturing. With our customized customer retention tool, their team is able to match visitors with demand and target the same in real time before it churns out.