Win-Back strategy recaptures lost customers and lost value


Companies of all stripes spend a considerable amount of time and money curating data with the goal of acquiring new customers. Meanwhile, they have mountains of data about former customers languishing in the database. Traditionally, companies do not pay special attention to this group of customers and the in this data is waiting to be tapped.

Why aren’t the data about these lost customers better leveraged in marketing campaigns and sales initiatives? Often, it comes down to mind set. We associate a "lost customer" with a "failure", when they should be an "opportunity." Lost customers already know your brand and your value proposition. Winning their business back may not be as hard as it seems.



Customer win-back is a strategy used to bring back lost customers. This often over looked population has a 4-8x higher likelihood of returning to your service than a new prospect has of starting your service or buying your products. The graphic (left) illustrates the conversion rates for a standard campaign for all potential customers compared to former (lost) customers.
Our approach:


Identify how many customers have been lost and why

• What is the definition of lost customers for your business? What are clear business rules which would help us to identify this group? We look at the historical data (1-2 years) to perform the initial analysis and find the customers who have been lost and are good targets for win-back.

• Perform analysis to understand similarities between the customers in this group. Why did they leave?


Perform segmentation of the lost customers

• Segment customers within the group by two scales:

     • Who will bring more value if they are won back

     • Who can be brought easily

• Identify the subset which will be the primary target group for win back campaigns and serve as a comparison to the “control group” (all potential customers).


Plan the win back campaign

• Analyze first-lifetime service experiences and behaviors of the target group (i.e., complaints, service recoveries, and referrals).

• Personalize a win-back offer to the customer by finding the right channel, referring to the previous customer experience, and making a personal offer.

• Find optimal time for sending the offer to a specific customer for maximum effectiveness.


Execute and assess performance

• Allocate sufficient funding and ensure timely campaign execution.

• Be mindful of the original pan, but flexible enough to adjust campaign variables on the fly as data and insight come in. Know what can be changed and what is a critical component of the approach.

• Continuously re-evaluate the model using machine learning techniques.

• Track ROI of the campaign and compare it with other campaigns.

• Advise how to reduce future lost customers by improving services based on data acquired during the research and execution phase. Machine learning can be used to find the best solutions for customer problems, thereby creating better customer outcomes.


Our technology framework:



Our Key differentiator:


• Logic20/20 has a proven record of building robust, production-grade solutions using various machine learning techniques.

• We leverage your existing technological stack or advise the optimal solution based on the current use case – your solution is designed for you.

• Logic20/20 operates at the intersection of business and technology. Our teams analyze the business problem first, and use technology to solve those challenges.