Julian Berry
Julian Berry 16 April 2015
Categories Content, Technology

Deciding Who Gets What, When And How

Untangling the many strands implicit in contact optimisation.

This opinion paper from Berry Thompson tackles the question of what is contact optimisation, and how an organisation can set about delivering it.


The benefits of contact optimisation, when correctly undertaken, are huge in terms of improved ROI from customer communications (we have cases of 25%+ uplift), as well as improved customer experience and reduced opt-out.


But why are so few companies fully getting to grips with it when the technology is available, and the data is often there? Is it just that managing BAU leaves little desire for change?


We believe that contact optimisation is often seen as too complicated, and requiring too much resource to introduce, when in reality planning resource is much reduced by using the technology properly.


By following a logical methodology that progresses from deciding what stage in a relationship each customer is at, to selecting from a range of potential communications which one to use for each individual, and then applying rules about contact density, we can easily understand the processes required, and get hold of the benefits.


At Berry Thompson we provide a full contact optimisation service; proof of concept, project management, decision engine technology, and insight; we have been working in this field for over a decade, and would like to share our experience with you.


What are we trying to achieve with contact optimisation? 

Forrester once defined contact optimisation as follows: Contact optimization applications work by processing inputs, including customer data, global business rules, contact policies, predictive model scores, business constraints, and objectives to identify optimal solutions.


As a statement this is helpful but somewhat circular as it fails to explain what we mean by ‘optimal’; it does however do a good job at describing the typical inputs that feed into a contact optimisation decision engine.


We would approach the objectives of contact optimisation in a slightly more customer focussed way;

  • To give each customer the mix of communications, over time, that brings the best mix of brand loyalty and sales to generate longer term customer value

This is a very tall order, but in this paper we will make a start at explaining how we like to go about approaching it.


But before we start, may we make a key distinction between campaign targeting, and contact optimisation. Campaign targeting is solely concerned with selecting those customers or prospects most likely to respond to a particular proposition, delivered through a pre-selected channel.


It ignores questions such as whether the customer might have been more interested in something else if it had been offered, or whether the XX% who don’t respond are disenchanted by receiving the communication, or whether the channel in question is one that the customer likes to use. Being based purely on short term returns, campaign targeting is often very sub-optimal from our customer focussed perspective.


How to make the actual what, when, how decisions?

There are a number of working parts in a contact optimisation decision process, all of which need to be brought together at the point of deciding ‘who gets what, when, how’.


The flowchart on the next page describes this, but at a very conceptual level.


We can describe it as follows:


1. Decide on the state of the overall customer relationship, and how this needs to be addressed: 

  • Are they a new customer needing the benefits of dealing with us to be explained to them?
  • Are they a mature customer whose loyalty needs to be rewarded? 

2. Pick the best proposition to make to a customer 

Within a specific customer relationship category, such as being right for cross sales offers, we next determine which proposition, by which channel, is expected to be most effective.

For cross-sales, and other environments like anti-attrition, we normally use a formula:


where P represents the propensity of an individual to respond to the proposition, V is the longer term value of the response or purchase, and C is the cost of making the communication.

3. Manage the contact density that a customer receives 

Contact density is frequently ignored by direct marketers, and is of the greatest importance. We know how response declines when offers are repeated at too short an interval, and equally ROI depends on getting the overall level of customer contact right for the level of the customer relationship.

Contact density rules can be set as absolutes (e.g. don’t communicate more than x times per month through y channel), and/or they can be managed through adjusting the P or propensity to respond according to the interval since the previous communication. The advantage of the latter approach is that higher propensity customers will naturally get more communications than lower propensity ones.

4. Impose overall business rules and constraints 
In any organisation resources are finite, and hence, after all efforts have been made to answer the ‘who gets what, when, how’ question, we need to enter Darwinian territory.

With limited channel capacities, or communications budgets, we have to get the best return from the resources we have. This will mean ranking at any moment in time, all the potential communications opportunities for all customers, and selecting the most productive.

Dealing with the on-line customer? 

With the correct technology in place we can now interact with an on-line customer in real time, sending them offers and web pages that are personalised to their content interest; software packages like Idio are designed to fulfil this. We can call this content optimisation.

Additionally we can respond, not in real time, and send confirmatory or explanatory emails, or other communications, at a later point in time, when we can identify the browser on our database. A classic example is the dropped basket follow-up.

Both activities fall under the heading of contact optimisation, but there are significant differences between the two.

Deciding which best content to serve a browser, in real time, is normally done by putting in place a rule set based on previous browser history by that IP address; rarely are there propensity models in place to support this, but there may be a browsers’ segmentation that categorizes browsers into groups and allows them to be treated differently.

However dealing not in real time, with browsers who can be identified in our underlying database, and hence for whom there are alternative channels of communication, brings us back into the reach of contact optimisation as so far described in this paper; the additional information we have is the trigger or call to action derived from the browsing activity.


The way we deal with it may be separated from the main flow of arbitrating between potential communications, because the need for response is so great that the communication becomes mandatory, or it may be that the type of response is arbitrated from a range of alternative potential customer needs. On other words responding to the browsing trigger may be less important than making a new and unconnected offer.

Clearly web interactions with customers, and non-customer IP addresses, raises a whole new domain for contact optimisation, but we do not regard it as being outside the overall contact optimisation decision making process, rather it is an interesting extension to the conventional machinery.

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