What would you give to know how to stop recruiting unprofitable customers?

In many of our engagements with clients we discover that a high proportion of customers recruited are unprofitable, and for every one of these the company suffers not only their wasted sales and marketing expense, but also a downstream negative return.

For instance, life insurers may be concerned about claims risk, but often a greater cause of lost revenue is customer attrition. If they can recruit customers who stick, and pay higher premiums, this can transform their longer term profitability.

So why not move from a focus on recruitment volume, to one of recruitment value? It may mean lower customer volumes, but it will ensure much better overall profitability.

We want to share with you our five-step approach for stopping recruitment spend going on unprofitable customers;

1. Identify the factors that drive individual customer profitability.
These are going to vary from industry to industry, but they often boil down to purchase value, product margin, and product tenure. In simple terms if the customer buys from the bottom of your range an item that is expensive to deliver, and never repurchases, they are not going to be a prime recruitment target.

2. Analyse the recruitment process features that have an impact on these profitability drivers.
For instance different channels, and tactics within the channel, are going to drive very different types of customer. For one client we compared the longevity of customers recruited through their TV advertising and website (inbound traffic) with those recruited through outbound activities like telemarketing. The inbound traffic was very much stickier once recruited. But also, the types of customers you recruit will have a very strong impact; we like to look at differences in customer contribution by factors such as age, affluence, gender, geography etc.

3. Combining recruitment process features into a contribution calculation.
Once we understand those aspects of your recruitment process that are both controllable, and which have a significant impact on downstream customer contribution, we can then look at how in combination they can be used to identify and predict the future value of recruits.
For a recent client, we found that we had around 1000 combinations of factors that we could identify, that each had a significant number of recruits, and for which we could compute the average longer-term contribution. For instance a combination of factors could be age-band combined with recruitment channel, and particular product features.
For each combination, we could then analyse the average customer contribution provided. This provides the lens through which we can predict longer term customer contribution across all current recruitment activities.

4. Building a heat-map of where to spend the recruitment budget
By combining these individual customer segments into higher level groupings, we can depict go and no-go areas for targeting the recruitment budget.
This approach to targeting needs to be run in combination with an analysis of the cost to recruit by the different recruitment process features. The ideal is to find combinations of features with a low cost to recruit and a high customer value, but this may often yield very few available options. A pragmatic approach is to look at the ratio between downstream customer value and recruitment cost. Using the lens of recruitment process features this becomes very achievable.

5. Predicting forward value from your more recent recruitment activities
A final use of this analytical approach is to model the predicted downstream value of customer recruits in a particular time-period. To do this we allocate recruits in the period by their recruitment process features to a predicted contribution group.
The good news about this last step is that one can validate it on historic data.

We usually manage to get to a high level of accuracy of prediction of the drivers of customer contribution; our latest example was better than plus or minus 2%!

Do you know how much your customer duplicates are costing you?

Deduplication may not be the stuff of everyone’s dreams, but it could turn out to be more interesting than you expect.

How many duplicates can you expect to find?

Our rule of thumb is that within any single customer system there will normally be between 5% and 25% duplicates.

However the more ways you have of identifying an individual, the higher the level of duplicates normally uncovered. For instance, if names and addresses can be combined with email or mobile number, many records can be brought together that otherwise would have been kept separate.

There are no rules of thumb however about the level of duplication between different customer systems held by the same organisation; but as an example our recent work with a media company selling a range of direct to consumer services revealed that for every 100 customer records held across their systems, there were in fact a net 75 individual people.

So why does this matter?

Perhaps the most obvious reason is that deduplication will stop you sending two communications to a proportion of your customer base.

Just stop to think just how irritating it is to have to open or delete two emails from the same source with the same content.

And then, if you are using paper and post, there is a big cost implication of not getting the deduplication right.

The second reason is GDPR. How will you handle individuals’ requests to be forgotten when there are two versions of these peoples’ records? And how foolish would you feel when sending customers copies of the data you hold on them when clearly it came from two separate sources?

But probably the most interesting aspect of deduplication comes when you pull data together from across different systems and sources.

Mr Smith who buys holidays, and is on a dating site you run, has a distinct profile; so does Mrs Smith who buys wine and cooking equipment, and so probably likes entertaining at home.

So whether you have a single customer file, or customers spread across several systems, the case for deduplication is clear, but just how clear it is can only be quantified when you have matched all those customer records together.

To find out more about Unifida can help your business please contact us.