Are you losing customers to your competition?

Are you leaving the competition an open door through which they can capture your best customers?


Most organisations will protest that they are not, and a sizeable proportion of these will be wrong!

The reason is the gap that often exists between how customers expect that they should be treated, and the reality of what actually happens.

Let us give you some examples:

  • We know of one company that sends its entire customer file an identical email a hundred times a year
  • and a second that takes at least a month between recruiting a new customer and sending them a welcome catalogue
  • a third that gives up on customers who have not been active in the last two years
  • a fourth that can’t distinguish between a currently dormant but previously valuable customer who is browsing on their website, and an unknown punter
  • and a fifth who doesn’t understand the longer-term value provided by customers from different recruitment channels

To start to put things right we usually find that all areas of the organisation that have responsibility for some aspect or another of looking after customers need to put their hands up, and agree that things are not going as well as they might.

At this point, with luck, a consensus will start to emerge that something needs to be done to fix the problem; this will usually involve introducing some kind of technology that takes an holistic view of customers and how they are interacting with you.

If you feel the need to mobilise your organisation in this direction, and make it properly customer centric, then we are here to help.

For a start we can offer you a free copy of our short and readable book ‘The Marketers Customer Data Platform Resource Book’; to be sent this we just need you to email us back with your postal address. Alternatively, simply sign up for our mailing list and we’ll send you the download link.

And we would welcome the opportunity to have a chat; just give us a call on 0203 960 6472 and ask for Julian Berry. He is here to help.


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


Solving the challenge of allocating recruitment budgets

Targeting Longer Term Customer Value

Using data science to target multi-channel marketing recruitment spend towards longer term customer value

This post will explain why targeting your marketing spend towards longer term customer value is not a one-dimensional problem. For instance, a group that may look bad by one criteria like retention may within it have sub groups that are of high value, and others that are the reverse.

Background

This case study has been derived from working with a substantial life insurance broker who has been using multiple channels to recruit customers in the UK.

We were asked to look at how best to target customer value as judged by the commission returned in the first 24 months from recruitment.

To give some insight into the kinds of issues we were presented with, lapsing early (i.e. in the first 24 months since inception) was one of the main causes of loss of customer value.

Lapsing early was found more amongst people recruited via outbound direct marketing channels than through more self-driven channels such as inbound to web. It was also correlated with the sum assured and the monthly premium. However direct recruiting is cheaper and higher premiums bring more commission.

The relationship between lapsing early and age was non-linear, with both younger and older people lapsing more than those in the middle.

As well as different recruitment channels having different costs per acquisition they also attract different age groups and sums assured.

Approach

So with so many factors at work how did we set about finding the best marketing tactics to deliver the highest return on recruitment marketing spend?

  1. We discovered that lapses in the first six months are very reliable predictors of the propensity to lapse within 24 months for every customer group (we got an R² of 0.99 when fitting our predicted curve to the observed data); this meant that we did not have to wait for 24months worth of history before making a prediction of lapse rates.
  2. The customer groups we were using were defined by a combination of age range, sum assured, and channel.
  3. We used a combination of known lapse experience with predicted to build the overall expectation not only of overall lapse rates but also when lapses would occur.
  4. The net customer value metric we used was the commission income up to 24 months after probability of lapse minus the cost of recruitment from the channel used.
  5. Hence for each customer group we could predict the net customer value; we also knew the historic number of policies being sold in each group and hence we could calculate their overall value.
  6. The net customer value by customer group ranged from -£1X to +£5X. This enabled our client to focus their marketing on areas where the net contribution was positive, customer numbers were substantial, and the required age groups could be targeted.

In conclusion

The focus for this client’s marketing turned away from broad brush channels like daytime TV towards direct marketing approaches. This switch made a very substantial difference to returns but with two caveats. First many direct marketing channels have a finite capacity, and second all recruitment channels suffer from saturation; in other words, the greater your spend in them, the worse the return.

However we strongly recommend that when looking at the effectiveness of different marketing tactics that you take a multivariate approach; that is allow the full effect of all factors on net customer value to be taken into account.


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


Do you throw away good customers like households waste good food?

Official estimates for UK household food waste in 2015 come to 7.3m tonnes. Of this the avoidable element, if not wasted, would have saved the average household £480 pa.

Yet we have discovered that marketers who allow themselves to be governed by the use of RFM (recency, frequency, monetary value) as their criteria for selecting which customers to engage with are allowing themselves comparable customer wastage.

RFM has been recognized for many years as a great way to select customers for marketing communications, but it has a significant flaw!

People who still have a relationship with your company get ignored if they are not continually buying from you.

We routinely find customers on the bottom rungs of the RFM ladder that can still be profitably communicated with if you take a different approach.

In a recent case of a home shopping company we found customers who could have provided an extra £1m of net demand being excluded by RFM from receiving catalogues.

To find the valuable individual customers in your older RFM segments we build a multi-variate purchase value propensity model. This takes account of a wider range of variables than RFM, such as product categories, and will combine data from individual transactions in many more complex ways than RFM can do.

We then build a matrix that overlay RFM segments with propensity bands.

Propensity matrix diagram

In the top right hand corner of this matrix we have people who would have been forgotten by RFM but who in fact still have considerable demand value according to the propensity model.