Experian Decision Analytics offers optimisation solutions that can be applied across the customer life cycle, to achieve both marketing and credit objectives. For organisations to thrive, it is important they make the most of each customer interaction and maximise customer value.
 
John Maile is an independent consultant with a broad range of customer management experience across organisations such as Orange, GE Capital, Saatchi & Saatchi and Great Universal Mail Order.

In this article John, explains how the customer management evolution came about and identifies some simple principles to help you transition your organisation through the marketing evolution phases towards optimisation best practice.

In the Beginning

The marketers among you, especially those who have worked for large direct to consumer organisations, will be familiar with a campaign spreadsheet layout that looks something like this.

Part plan, part results – this campaign report itemises the media/channels used, costs, responses, revenues and ROIs – the whole range of marketing effectiveness metrics.  

And, in addition, a range of additional sub-reports will almost certainly be on hand to provide more detailed analysis on conversions, margins and the outcomes of a whole range of creative, offer and media tests.

It’s quite a lot for the average marketer to take in - and quite a challenge for the marketer to analyse, interpret and then correctly crystallise the learnings into the optimal framework for future campaign activity.

In the Mail Order days of the 80s we did our best to achieve this without the sophisticated predictive analytics and optimisation tools we all now have. But no matter how good the technology, the process thinking that was developed then is still as relevant today. So let’s review the essentials and get to understand the best practice thinking that has become known as ‘alignment for optimisation’.     

The Marketing Challenge

While marketing technology has advanced dramatically over the years, the marketing challenge has remained basically the same as it always was:

To recruit the ‘right’ number of the ‘right’ type of customers - to retain them - and to stimulate the maximum revenue throughout their customer lifetime – at the minimum possible cost.

But what are the ‘right’ numbers? What is the ‘maximum’ customer revenue? And what is the ‘minimum’ budget we will need to spend to achieve them? To paraphrase that famous direct marketer William Shakespeare, “That is the Question”.

Back in the 80s, we were fixated on solving this problem and we spent a lot of time on flipchart workouts trying to envisage the ideal marketing process. The version we finally came up with had the following features:

• Planning - where an optimised plan would be automatically generated from the results of the previous campaign
• Deployment - the plan would then automatically generate the detail selections
• Closed-loop - and subsequent results would, in turn, inform planning for the next campaign - in short, true ‘closed-loop marketing’

Technology was just not up to it then, but it is gratifying that our whiteboard ‘wish list’ diagram has eventually evolved into today’s best practice optimisation model.

But we’re getting ahead of ourselves. Before we can employ campaign optimisation, we must first define the ‘Optimisable Task’ that we are going to set it; or to put it more simply, we need to spend some time to define what we as marketers are actually trying to achieve.

In fact, we already did this when we defined the Marketing Challenge as: “maximising customer value through optimised recruitment, retention and yield management at the lowest possible cost” 

Next, we will need to disaggregate these constituent elements of customer value, and reassemble as an equation known as a ‘Utility Function’. A high level example from the Mobile Telco world might look something like this:

Customer Value = f ((gross revenue per product * margin per product * product mix purchased ) * tenure – (cost to acquire +  cost to retain + cost to serve)) * Credit Risk

At this stage, I need to introduce you to some key principles which will, in turn, introduce you to some important new customer value related terminology:

1. Customer Dimensions. Customer Value is not an entity, it is an outcome, and can only be achieved by influencing its constituent elements, known as customer dimensions. Risk, Responsiveness, Product Propensities, Tenure (Loyalty) and Cross-sell uptake are all elements that can be:

a) Measured – to help define current customer state, and
b) Predictively modelled – to provide data to inform the planning process and then to drive the resultant marketing activity known as ‘Transitioning’.  

2. Transitioning. There may be some customers who will automatically transition themselves to higher value states but if this was the norm, there would be no need for marketers!

It is the marketer’s task to effect Value-Enhancing Transitions to migrate customers through the ‘Customer Value Journey’ - from Prospect to Responder and Repeat Purchaser to High Value status.

It is also the marketer’s task to pre-empt Value-Destroying Transitions (e.g., non-response or non-repeat purchase), deploying remedial promotional activity to get as many customers as possible back onto to the main value journey highway.

3. The Customer Value Journey Map. The Customer Value Journey Map provides a useful tool for Transition Marketing:

• in identifying each customers current ‘state’ (any customer can only be in one map location at any given time)
• in indicating where we want each customer to go next (value-enhancing transition)
• and where they could go instead (value-destroying transitions) is we don’t pay attention
• by showing the quickest route to High Value Status, the journey map helps us to identify the ‘next best action’ for every customer at every customer contact
• and in representing the journey as a continuum it encourages us to think in terms of ‘Longitudinal Customer Strategies’  - a much more effective alternative to One-Off Promotion Marketing 

The value journey map also provides a useful framework for defining and applying the models and metrics output from our predictive analytics efforts. Too often scorecards are developed as one-off standalones to drive one-off standalone promotions and overlaying metrics and probability scores onto the journey map will:

• add structure to our analytics
• add insight to inform our ‘next best action’ decisioning
• and will provide the basis for calculating outcome counts and values to populate the optimisation utility function

4. Customers as a unit of one. We have already stressed that customer value cannot be addressed as an entity in itself but rather as the sum of its parts – similarly, the customer base must be addressed at the lowest atomic level: the individual customer.

Too often, marketing is deployed at a Life-Stage Segment, Media Grouping, Contact Channel or some other high level form of aggregation.  In the past, this was probably the only option we had available due to the sheer complexity involved in mapping (never mind planning) all activity to all customers across all contacts.
With today’s optimisation tools, we can now manage dozens of promotions to millions of prospects and customers across multiple channels and marketing campaigns can be tuned, planned and deployed over time - bringing the Integrated Customer Value Optimisation model we envisaged all those years ago from flip-chart to reality!

Conclusion

Optimisation tools such as Marketswitch can bring us dramatic performance improvements when we apply them to Point Applications such as Campaign Management, Call Centre Optimisation and Churn Prevention programmes – but with a bit of imagination their ability to ‘tame complexity’ can take us to a whole new dimension; offering us the opportunity to move up the evolutionary ladder from ‘Marketing the Averages’ to ‘Marketing the Individual’, and doing so via an integrated contact strategy, over time, throughout the customer value journey.

The journey from undifferentiated propositions to tight-focus, one-to-one, customer transitioning has been a fascinating one so far, but I think you are going to find that, with the new generation of predictive analytics and optimisation tools at our disposal, it's going to be an even more exciting ride in the future. Welcome to the Customer Journey.   

John Maile - Independent Consultant, Customer Dynamics   

Marketswitch Optimization is the best of breed optimisation software tool, with more than 25 blue chip clients - available from Experian Decision Analytics. White papers can be requested below, or alternatively, email us to find out more.

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