Why marketeers must not lose sight of value-based marketing

Among the objectives marketers often set themselves, ROMI (return on marketing investment) is normally near the top. The problem is that, although it is comparatively easy to measure the overall investment, it is much harder to measure the value. Value and cost per sale have a habit of varying hugely – hence their importance to marketers.

For example:

  • The value of an insurance sale will depend on the annual premium income, the probability of lapse or renewal, and the likelihood of a claim
  • The value of most retail sales depends on what the customer does next. Do they repeat, buy something different, or never use the retailer again?
  • The value of a media subscription relies mainly on their longevity, but can include some cross sales
  • The value from recruiting charity donors is based on the expectation that they will continue to act with generosity.

It’s quite a challenge however to think of a category of sales where there is just a single fixed value. A few come to mind, including:

  • Estate agents selling houses are unlikely to factor in an individual’s next purchase
  • Repeat funeral plan sales are rare, unless the purchaser is buying additional plans for their loved ones
  • One trip on a Virgin Galactic VSS space plane should be sufficient for most people!

Investing where the value is

But understanding the longer-term value of each sale can secure a company’s future because its marketers can place their marketing investments where the value is. Here are a couple of examples:

  • One of our retail clients is selling a product for which there is no necessity for customers to repeat purchase. Nevertheless, it has enormously divergent examples of customer behaviour when it comes to doing so. The bottom 50% of customers recruited have an average life-time value of £50, while the top 5% have an average of £2800.
  • When we analysed the value of policies sold by another client, a life insurance distributor, we found that, depending on where and how the customers were recruited, the contribution per policy sold varied from + £404 down to – £277, after taking into account marketing costs and expected clawbacks from lapsing.

So why do marketeers shy away from looking at the value they are really creating, and yet invest time in looking at explanatory metrics, such as cost per click-through, or the number of impressions? We guess it’s because predicting longer-term value goes into the ‘too difficult’ category and gets conveniently ignored.

But for those prepared to take the plunge and align their spend to longer-term value, there are two necessary parts to the analysis – accurate marketing attribution to determine the cost of making each individual sale, and an approach to predicting the longer-term value of each sale once it has been made.

Determining the cost of each sale

Acknowledging the fact that in today’s world most sales come at the end of a customer journey, this requires joining together the steps in each journey and then understanding what they cost to deliver.

As the charts below show, customer journeys may involve multiple channels and continue for some time.

Distribution of Distinct Channels in Customer Journeys

Distribution of Customer Journeys length


The technology required to do this must link both online steps – such as click-throughs to a website from social media – and offline steps, like receiving an item of direct mail. Having joined the steps together, there is the question of how much they each cost. In our opinion, that should be the cost of a campaign, divided by the number of steps where it contributed to journeys that end up with a sale actually being made.

An email campaign may be sent to 10,000 people, but only contribute to 100 sales, so the effective unit step cost of the campaign is just 100th of the overall cost. This calculation can then be further refined by sharing the sale value disproportionately between the steps that led up to it, according to the relative contribution to the sale that they made. Having attributed a cost to each step, these can be summed up to provide a cost per sale.

Predicting longer-term value

Deciding how to do this will be driven by the industry sector and the sale type being made, whether, for example, it is a sale recruiting a new customer, or one to an existing customer. If we take as an example just one type of sale, like an insurance policy or a media subscription, then predicting lapse becomes critical to the value equation. There are many different techniques for doing this, but our preference is to use CHAID* to predict the overall probability of someone lapsing within a given time period.

Chaid Model diagram

This kind of technique will divide policies sold into distinct groups, each with a different expectation of lapse rates, based on the known characteristics of the customer and the policy they have bought.

The next question is: when will they lapse inside that period? This is where we use historical evidence based on different lapse timing for different cohorts of policy purchases. If, however, the sale is a retail one, then we will be looking to forecast for each recruit their expected future value within the next season or year. Every business will have its own unique requirements for predicting longer term sales value.

Providing the best ROMI

All this may, when viewed in the round, look somewhat difficult to achieve and there are many compromises that can be made when aiming to link marketing investments to their future value delivered. Costs per sale may be grouped into costs for a particular product category and value may be averaged over a large cohort of sales.

However, we strongly believe that, given the vastly varying value of individual sales made, and the importance of recruiting customers that provide the best ROMI, it is essential to go down this route.

*CHAID analysis (Chi Squared Automatic Interaction Detection) is a statistical technique is used in market research.


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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. 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. Our ambition is to help our clients stay empowered and ahead in this challenging environment.