You know that the client has left the company. 

You know that it's necessary to present a lower price, if you want to win-back that former customers. 

But how low should the price be?

We can answer this question. 

Data XL allows insurers to setup the win-back price on a complete insurance pricing strategy.


  • You can have the first results in 10 weeks’ time (in a PoC)
  • It is not necessary to offer a big discount to win-back the former customer
  • We do not use only internal data. We look for external information (from brokers & from the market) and conduct experiments. The info gathering should not be restrained by convenience questions and by ready to use data
  • Identify the features that customers value the most and more importantly how much are they willing to pay for them
  • We use experimental design, and external data, we should be more precise than any other provider
  • No setup cost
  • Fast results
  • No IT layer
  • We can use your outbound call center to have the costs under control
  • We are more than a software and a manual
  • The strategy will always remain in the insurer or broker
  • We (can) work with a success fee 



In our experience with motor the Return On Marketing Investment is >60% (including Data XL fees). Nevertheless, it will depend on the market & LoB.


We focus on the profitable customer subject to "Willingness to Came Back".

If the lost customer was a difficult one and refused the past insurer renewal offer we should make a counter offer (but profitable) that the client can not refuse.

The idea is simple: 

  • An insurer call center can ask former profitable customers where he is now (in some countries this is public information).
  • With market price (see Insurance pricing room) is possible to know the price practiced by the competitive set and with the "willingness module" it is possible to make an offer the customer will accept.

There is no need to offer a bigger discount - we help insurance companies to offer just a sufficient one! 

How do we measure the "Willingness to Came Back"?

Companies spend huge sums on direct marketing to win-back customers. Yet the return on marketing investment (ROMI) is often poor because the response rates tend to be very low — usually less than 2% and often less than 0.5% — and rates have been declining.

To cope with this, many organizations (still) use traditional A/B testing approaches, that test one sort of offers versus another. 

We take A/B testing to the limit and test several variables/factors at the same time.

Experimental design is an efficient method of in-market testing that can improve the performance of direct marketing.

The power of this statistical approach is that it increases response rates by massively increasing the conditions of win back campaigns and identifying which variables are correlated with the  customer's  behavior.

Can we really understand why did the client left?

Maybe the price was not the true reason for the client's lapse.

In the insurance sector, and in the short run, is very difficult to find why customers left. There are not many touch points in the insurance sector. Claims can be a relevant segment variable, but besides that specific variable we can only use the risk factors and the price of the competitive set.

Data XL has a deep knowledge of customer segmentation towards willingness to come back - we can take the most of the variables that exist to anticipate the client behavior.


But a price discount sometimes is not the most profitable answer. So we ask at least two more probing questions to find out exactly what the insurer could do to improve the offer.  Insurers may not get them back at a very profitable price, but will have information to design new products or procedures that are more customer focused.


Data XL platform - Lazarus - has the following modules:

  • Cost based pricing /actuarial pricing: measure the prospective cost of a claim and help insurance companies to redefine their cost structure. Data XL still prefers that the client provides this information as an input, using their one knowledge and expertise.

  • Market Pricing (optional):  an algorithm that, with experimental design and a brokers network or web crawling, can accurately capture competitor pricing.  This model uses the market information and the cost base pricing info, as an input

  • Willingness to came back: an experimental design model (with some proprietary elements) to predict the customer's willingness to return.

  • Optimal Pricing: using mathematical techniques to design the best price: a price that reduces allows a better margin and that maximizes the share of preferences for a specific win-back campaign.

Our mathematical algorithm is more comprehensive than others on the market. In fact, we are the only ones that combine risk cost, competitors' view and derive a willingness to came back function.

Furthermore, while our competitors opt to sell a software platform not a solution, they do not isolate the price from other marketing mix elements. Therefore, their solution is based only on (lowering) the price.

Check our Case simulator & brochure

Fill in your assumptions and see what will be the foreseen ROMI for the Insurance Win-Back project.

Or ask for a pilot project - drop us a line.

Lazarus - Brochure (pdf)


Lazarus - Case simulator (xlsx)