Should you be investing in marketing measurement?

With UK marketing spend trending above £120bn (the figure for 2021 according to Marketing Week), the question is: should you be investing in marketing measurement? The answer for most marketers is ‘probably’, and for anyone spending serious amounts of marketing money, then it’s a resounding ‘yes’.

But what is often not discussed is just how much you should budget for the measurement part, and exactly what you should expect to get back from it.

Magic formula

When looking at what you should be measuring, we found the formula below on the Harvard Business Review website. Although it is undoubtably correct, it also highlights the measurement challenge faced by most businesses who invest substantial amounts in marketing. Very few companies get near to measuring accurately ‘the incremental financial value gained as a result of marketing investment’.

marketing investment formula diagram

So, let’s be bold and suggest some numbers for what you could spend on marketing measurement, assuming that you want to move beyond Google’s G4 and get something that is transparent and closer to ‘incremental financial value’.

Channel complexity

A big driver of cost is the number and complexity of channels you use and the campaigns you send out. At the simple end of the spectrum are organisations purely using digital, with no call centre and all sales processed by an ecommerce system. Such ecosystems are relatively straightforward to evaluate.

At the complex end are organisations using multiple indirect channels, such as TV combined with direct, and within direct, deploying both offline and online.

To measure just your direct channels, you can almost get away with only using customer journey-based attribution or MTA1, although you will always be at risk of over claiming because you are ignoring both the baseline sales that would happen without any marketing, and the incremental effects on the brand of your marketing.

But when you are using indirect channels, with or without direct, you will need to use an approach like econometrics or MMM2 to understand both the short and the longer-term effects of your marketing.

We find that there are great benefits in terms of granularity and accuracy to be obtained from using both MTA and MMM together. The findings from the MMM provide an umbrella view, and within that the MTA gets into the detail of how effective each direct campaign really is.

Marketing attribution

From our own research into typical industry charges for marketing attribution, both simple and complex, and for different levels of budget, we have compiled the following table. This should only be regarded as a very rough and ready guide, but we felt that some indication of likely costs was more helpful than none.

In this table the percentages relate to the overall marketing spend budget across all channels:

marketing budget table

Given the likely level of improved marketing ROI once accurate measurements are in place, these levels of investment should provide a very healthy ROI in themselves. However, for that to be realised a number of conditions need to be met:

  • There needs to be cross functional support from the outset for the new measurement process being introduced, even though it may show different results from those currently being reported
  • The measurement should be transparent in terms of how it is calculated, and independent in terms of who does it. Media owners such as Google or agencies wishing to protect spending budgets cannot be relied on to do this.
  • The results need to be acted on, particularly across the budgeting process, as it is likely that spend will need to be moved from where it is now.
  • It must be accepted that however much care is taken in the marketing measurement it is not an exact science, and particularly so when extrapolating from how spend is distributed now to how it might be in the future.

However, for anyone with a substantial marketing budget, the benefits from going down the route of marketing measurement should far outweigh the costs.


Footnotes on MTA and MMM

1. Customer journey-based attribution or MTA works by evaluating the contribution of each individual step leading up to a sale, such as clicking through from an email or receiving a catalogue, and then attributing the value of the sale across the steps that led up to it. The value of a campaign is then derived from the value of each of the journey steps it created that preceded a sale.

2. Econometrics or MMM is a time-series based modelling approach that looks at all the possible influences impacting the volume of sales, and includes both marketing activities like TV or press, with external factors like seasonality, economic confidence, weather etc. It is good at estimating both the short-term effects of marketing and the longer term brand impacts.

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. 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.

Top 8 ways a CDP can make you a better customer marketer

CDPs – Customer Data Platforms – are often just evaluated from the viewpoint of facilitating
timely, relevant and personalised customer communications. Properly configured, a CDP can
also hold a complete history of every customer interaction, from online to direct mail,
opening up very valuable insights into customer understanding.

Here are the top 8 ways a CDP can help you better manage your customer marketing:

1: Measuring how each and every aspect of your direct marketing is performing.

Having every customer touchpoint in place leading up to each of your sales is a critical
requirement for delivering customer journey-based marketing attribution. This works best
when you have a scoring system in place that can share out the value of each sale across the
touchpoints that lead up to it, according to the contribution they make. In this way you can
not only compute the value of each of your online and offline campaigns, but also look at
how different customer groups, for example new vs. existing customers, are impacted by
your marketing activities. And you can start to look at how each channel performs in
different seasons.

2: Knowing the longer-term value of customers recruited through different channels and via different campaigns.

Longer term customer value varies hugely across the different types of customers you
recruit and different channels will attract different types of customers. So, assuming you
know how a customer was recruited, you can start to understand what their longer-term
value is likely to be. This can be problematical where several channels are involved in a
single sale, but you can look at the longer-term value of all the customers gained via a
particular channel or campaign. You can also drill down further and differentiate customers
by other criteria, such as previous relationship with your brand, geography, or even age
band. Understanding customer longer-term value allows you to set maximum costs for
acquisition and get a better understanding of the returns from marketing investments.

3: Predicting lapse levels and lapse timing for subscription products.

Anyone selling a subscription product, whether it be an insurance policy or a magazine, will
know how vital it is to recruit and retain ‘sticky’ customers. Fortunately, the data residing in
your CDP should allow you to model retention with a great deal of accuracy. This is because
you will be holding not only the payment records of each subscribing customer, but also a
great deal of information about them. There are several statistical methods for doing this,
but we tend to use CHAID as it divides customers up into identifiable groups with different
expected levels of longevity. Your historic data can also be used to show what proportion of
your expected lapses are likely to happen each month, which can be of vital importance for
cash flow planning.

4: Building customer segmentations to help you better understand the needs of different customer types.

Marketers need to simplify the problem of dealing with many different types of customer
requirement. The proven way to achieve this is to build a customer segmentation that gives
you a handful of groups for which you can devise different marketing strategies, or even
different products and propositions.
There are many techniques for building customer segmentations, but we like to use one
that allows you to allocate customers with relative simplicity into their correct segment, and
also to find similar types of people outside of your own customer base. For customers within
the customer base, criteria such as value, previous sales or enquiries, and types of
merchandise they buy, often groups customers meaningfully. For customers outside the
customer base, segments are often defined by, for example, affluence or age band. Once
the segmentation structure has been developed your CDP will allow you to allocate each
customer to a segment and plan your communications strategy accordingly.

5: Managing all your GDPR consents in one place.

Customers deposit their consents in many different places. They may unsubscribe from one
newsletter, opt-in to another, decline cookies on a website but approve receipt of customer
marketing when placing an order. A marketer has to establish order in what is often a very
untidy consents landscape and then define clear communications rules about what can be
sent to whom on which pretext.
Your CDP is the one place where, through identity resolution, consents can be bought
together and organised and rules about who can get which communication established. The
CDP can also provide most of the materials for fulfilling Subject Access Requests, as well as
manage anonymisation of data when the right to be forgotten is exercised.

6: Planning business development based on a customer value model.

Businesses need clarity on the growth and quality of their customer base, not least to
understand how to split the marketing budget between acquisition and retention marketing
to meet business objectives. Your CDP will hold a record of the historic value contributed by
each individual customer and you will know what that amounts to in any historic calendar
year. It will also tell you what percentage of customers recruited in previous years typically
order in the current period. Using this information you can, with reasonable accuracy,
predict what value, for example, your customers recruited this year will contribute during
the next year and how this will be distributed month by month.
You will also know how your new customer recruitment usually lands month by month, and
the value new recruits contribute in the period from when they were recruited to the end of
the year. Pulling all this together you can calculate how many customers you will need to
recruit in a future time period to meet a specific overall sales target. We call this the
customer value model, all made possible by data held in your CDP.

7: Providing data for building response and upsell propensity models.

Predicting response by different channels can save a considerable amount of the marketing
budget, enabling marketers to avoid activity that will not produce a strong return on
investment. To build a predictive model you need a target variable, like propensity to make
a second order, as well as predictor variables, which are facts known about the customers –
in this case, both for those who buy the second product and for those who don’t.
The role of the CDP is to provide this data to the data scientist, or the AI tool, which is going
to build the model. But as well as providing the data that allows the predictive model to be
built, it also provides the data that then allows every customer to be scored up with a
probability of doing whatever is being predicted. Indeed, without a CDP, developing and
using propensity models for marketing is made very much more difficult.

8: Recruiting customers to join research panels.

Many companies like to maintain continuous panels of customers who have agreed to answer market research questions, usually in exchange for some value given back to them.
The CDP can provide randomly selected customers for recruitment to these panels, as well
as managing the exercise of sending them questionnaires and recording their responses.
Customer panels are very much simpler to manage if you start with a CDP already in place.
Overall, a CDP should be seen as an essential component in the complex process of
maximising your customer revenue through improving your customer marketing.


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. 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.