Can Multi-Touch Attribution (MTA) tell you which parts of your marketing are not working?

multi-channel attribution in marketing

The origins of multi-touch attribution (MTA) were in the digital space, as a result of advertising spend transitioning away from traditional “offline ads” to digital media and channels which were deemed to be more accountable. Journeys within a client’s website, or between websites, could be stitched together and the resulting orders joined back to customers and their orders.

Now that the removal of third-party cookies is going to remove much of the stitching between websites, unless you are working with a collaborative data solution that allows this to take place, you are left just with customer journeys within your own website in full view.

These journeys may however include affiliates, referrers, digital ad campaigns, PPC, and direct search so you will at least know where the visitors came from if not the ad impressions they had been served to get them there.

However, we believe that there is still a great deal of merit in MTA, but not when it is restricted just to online events. (There is also the huge consideration that some of the large analytics platforms use sampled data and not 100% raw data which means the more you dig, the less you see).

Estimates vary about how much of advertising spend is digital, but the consensus appears to be around 55% currently, and that leaves 45% non-digital which is clearly far too much to ignore, much as Google would like us to. We also suspect that with the removal of some of the programmatic advertising volume, the digital proportion is likely to reduce down, perhaps to around 50%.

There is absolutely no reason why MTA should ignore the non-digital channels; but it means that you require the technology to joint it all together at a customer and order level. This is most effectively achieved using a customer data platform which is specifically designed to join browsing activity with off-line into a single customer view.

The non-digital ‘touches’, we prefer to call them ‘events’, can for instance include emails opened, text messages, call centre contacts, retail visits, and direct mail. These are all direct events, but on top of these are non-direct advertisements such as TV, which we discuss below.

There is a lot of unscientific opinionizing about the best approach to weighting events before an order. We are confident that we have found a reasonably good statistical solution for this. It uses a mix of Markov chains and survival curve statistics to give the weightings to any specific set of events. This approach does not presume anything about first or last touch, but rather looks at the evidence presented when the events have all been joined together in a single customer view, together with your customer and order data.

To deal with the non-direct channels like TV, the more ambitious will also want to build econometric models which reveal the overall effect on demand of all channels, direct and indirect, when working in combination. Econometric models often get a bad press as being unresponsive to short term changes in consumer behaviour, and not being granular enough in their spending recommendations, but they are the best tool we have to give the non-direct media their fair share of the credit for sales made.

Techniques now exist to align econometric models with multi-touch attribution so that, in effect, value initially credited to direct channels can be reattributed back to the indirect channels; this usually has a significant influence on the overall share of demand given to the direct channels.

So, to present what we have been describing diagrammatically, a full attribution process is going to look like this:

full multi-touch attribution process diagram

One of the often overlooked, and we believe very significant benefits of MTA, when it is sitting on a single customer view, is that it can be cut by customer type. The simplest cut is to distinguish between what is bringing new customers versus existing. But the cut can be for any customer segment, like high value versus low value customers, or purchasers of particular types of merchandise.

multi-touch attribution view of new vs existing customers

Multi-touch attribution can tell you a lot about how your marketing works, but only when you look at all of your online and offline channels in combination. And for many advertisers using indirect channels like TV, then it becomes important when possible to align MTA with econometrics.

In so far as we only look at events prior to a sale we will learn nothing about what doesn’t generate a positive outcome; however, if we take a look at all browsing events, we can start to examine the probability of an event leading, or not leading, to a sale.

There are two reasons why this is valuable information, although unfortunately often ignored. First, because knowing the probability of say a Facebook advert leading to a sale brings a sense of realism about advertising there, but also because serving people adverts in which they are not interested does damage to your brand.

Back in the heyday of direct mail, people were so fed up with the quantities that kept on arriving that they called it junk mail, and often had stickers on their post boxes asking for it not to be delivered. (Unfortunately, the postman had no choice but to pop it in their box).

PPC Protect estimates that in 2021 the average person (we assume in the US) will see up to 10,000 ads per day, whereas in 2007 estimates were only at 5,000 ads per day.

Common sense suggests that this must be way over the top of what is either necessary or enjoyable, and people will increasingly assert their objections to it.

Clearly brands that focus on the relevance of their advertisements will create a much more favourable impression than those that just focus on volume.

We have started to investigate browsing behaviour in terms of its likelihood to lead to a sale, with the following result:

Probability of browser moving to and from events and a sale
Probability of browser moving to and from events and a sale

To explain how this table works (and it was built using actual online and offline event data) it shows the probability of a person moving either from one event channel to another, or to a sale. So, if you start with picking a channel on the Y or vertical axis, you can then move along the row to view the probability of a customer moving to the next browsing state. For instance, someone coming to your website from a social network has a 96% probability of doing nothing further, and a 0.55% probability of being converted to a sale without engaging with additional channels. They also have a 0.67% chance of moving next to a search engine, whence they will have a 3.3% probability of making a purchase. However, someone receiving a campaign has a 6.5% probability of conversion without using other channels, and a 6.7% probability of moving next to a search engine.

So, in conclusion, we suggest that there is a strong role for multi-touch attribution, post third party cookies, with or without econometrics, and another new role for data science in investigating what we might call dark advertising, the stuff you see, but which makes little or a negative impression.

Read more about how Unifida’s marketing attribution works and what it can deliver.


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.


Are you in the dark about your omnichannel performance?

attribution share to measure omnichannel performance
Chart showing the attribution share in an omnichannel environment

Marketing mix attribution is often one of the biggest problems a marketer can face when trying to measure omnichannel performance. How to fathom out in an omnichannel environment how much each channel is really contributing?

And how much for instance are they contributing to new customer recruitment v. existing customer sales?

Google has a solution for attributing what goes on in the digital space, but this leaves out important areas like emails opened, catalogues received, SMS messages, outbound calling, even retail visits.

So, we set about developing ADEE, or Algorithmic Direct Event Attribution.

For us it’s the culmination of a journey which we began by solving the problem of attributing orders to events, where clients were using both online and offline channels.

Curiously, nobody else appeared to be doing this.

We needed to create a result that made sense of the relative contributions of all the online and offline events that took place before each order is placed. (By the way the average is around five per order).

We needed to apply a fair weighting to these events that described the influence they had on each eventual order.

Then we had to add up all the events to the channels in which they took place to understand the value contributed by each channel.

Finally, we needed to let our clients decide whether they wanted to look at all customer orders, or for instance just new customers, or customers buying a particular product category.

I am delighted to say that we ended up creating ADEE!

If you would like me to send you our white paper on ADEE then please email us on [email protected].

It could transform your understanding of the true contribution that each of your online and offline channels are making.


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.


Where do you go to get answers to your most pressing marketing questions?

Being tasked with finding answers to marketing questions to support your marketing decisions and advance your campaigns is no easy feat.

We are thinking of questions like:

– where are my most valuable customers coming from?
– what’s the best next offer I can make to each of them?
– how can I identify those dormant customers that are most likely to be reactivated?
– how much should I budget to spend in each of my online and offline channels?

In days of old you would most probably have fired questions like these at your advertising agency, and they would have responded using a smattering of science combined with a lot of judgement.

In today’s evidence-based world there are few one-stop solutions that can properly answer questions like these because to do so requires the right combination of marketing savvy, data, and data science.

However, there is something without which none of these questions can be answered, and that is the single customer view, where all data about your interactions with your customers are held.

For example, just taking the four questions we started with, you will at least need to know:

– how each customer was recruited?
– what their propensities are to buy from each of your main product categories?
– what sorts of customers are self-reactivating?
– all the online and offline events that preceded each of your customer orders?

So, what can we conclude so far?

That your single customer view needs to be skilfully designed to hold both the ‘raw’ facts such as details of a transaction, or a website visit, and also the ‘derived’ facts like a propensity to behave in a certain way.

But the single customer view is only part of the solution.

Our view is that the go-to resource you need is a combination of a customer data platform (the tool that builds the single customer view), with marketers to specify what it is expected to do, and data scientists to transform its raw data into sophisticated engineered predictions concerning your customers’ behaviour.

This is also the basis on which we have built our company. An understanding that marketeers need that right combination of people, technology, and data science to support their marketing actions and decisions.

If this is what you are looking for, then please email is at [email protected] and we will arrange a Zoom with our founder Julian Berry who will be delighted to discuss how we can 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.


Exactly how much do online and offline channels contribute to sales?

Do you really know exactly how much each of your online and offline channels are contributing to your sales?

The blunt truth is that the great majority of marketers don’t!

Google for instance claims that businesses make an average of $2 for every $1 spent on Google Ads, but this is just a blatant case of Google marking its own homework. Are they saying that $1 Google Ads caused the $2 worth of sales, or just that there is some form of observed correlation? And how do they account for the impact of all the other forms of advertising from TV to outdoor to press to email?

Given the billions that hang on decisions about how to allocate marketing budgets across different online and offline channels, we felt that it was essential to work on giving our clients the tools and the knowledge to properly support these important decisions.

We started with some key assumptions:

– That decisions to purchase are necessarily complex and driven by multiple factors. Many of these factors like brand awareness cannot be recorded in the context of an individual sale, but many can be, and for those that are, we should look at all the known recordable events before a sale, and certainly not just online events, or worse still, just last clicks.

– What we call recordable events are activities like receipt of a catalogue, opening an email, visiting a website from a social media advert, or using Google Ads to find a website. Some of these events are driven by the customer like natural search, and some are driven by the vendor like receiving a catalogue.

– That we would assemble all the recordable online and offline events before each sale and use these as the dataset from which to analyse the true impact of different channels and different time intervals between an order and an event.

– That having overcome the challenge of collecting all the online and offline events together, we would focus our analysis on the central question of how to weigh the different types of events. Intuition tells us that an event 60 days before an order may have played a smaller role in the decision to purchase than one on the same day as the order, but the question we needed to answer was by how much? Also, should we give different weightings to different types of event? Is an opened email more or less important than a website visit happening as a result of a click through from a Google Ad?

– We do not want to suggest that marketers should ignore unrecordable events such as TV viewing or driving past an outdoor lightbox. Rather that their effects need to be analysed using different techniques like time series analysis, and that in so far as credit is given to recordable events it should be shared with the credit due to unrecordable events.

We have used orders and recorded events from two very different retailers to provide the data sets for our analysis. The first and surprising discovery was to find just how many recordable events actually happened. One reason for this is that customers may make multiple visits to a website before purchasing or open an email multiple times. The following table shows the number of recorded events that preceded each order in a 90-day time window:

the number of online and offline events resulting in sales orders

The analysis is ongoing, and we are aiming to publish a white paper on it in August, but there are three important initial findings that we can share:
– different channels should carry different overall weights, and we can analyse what they should be
– that each channel has its own time decay curve. In other words, the impact of events in one channel will wear off more quickly than for another channel.
– that the set of weightings used for new recruits should be different to those used for existing customers

The final results will include quantification of these findings.

If you would like us to share the white paper with you when it is ready please email us. It will be free for our newsletter recipients who order it in advance, but will be sold to others.

And if you would like meanwhile to have a chat to us about how to solve your own marketing mix attribution problem, we would be delighted to discuss, so please get in touch.

 


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.


Post lockdown marketing, did you grasp the moment?

You may recall our post from March where we provided suggestions on what marketers might do during the Covid lockdown. Now, as the lockdown starts to ease and we enter the new normal, we look towards marketing post lockdown.

For our part we have initiated a project concerned with multi-channel marketing mix attribution, working with a small team of graduates from University College London, and Edinburgh University.

Our mission is to find out if we can detect any patterns that hold true for more than one client that help us to understand the effects of timing and sequencing in how events prior to an order combine to contribute to the order actually happening.

So, for instance, is an email a week before an order more of a driver than a catalogue three weeks before, or a social media referral just a day before, and does it matter what order they happen in?

In a multi-channel world these are important questions, and we hope to have some definitive answers for you before too long. If you are interested to discuss this project, and how it might help your marketing post lockdown, please email us.

We have also been pushing full steam ahead with developing our UniFida customer data platform technology including:

  • Redesigning the way you can select audiences for campaigns to make the process much slicker
  • Integrating with Fresh Relevance for website and email personalisation
  • And with Microsoft Power BI for data visualisation

We very much look forward to talking, and even meeting, with you in the post lockdown period as business gets back on its hind legs again!

 


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.


McKinsey has defined ‘Modern Marketing’ for us!

McKinsey’s March article ‘Modern Marketing: what it is, what it isn’t, and how to do it’ takes a bold look at all the enablers and capabilities required for marketing today.

Importantly they define the goal of modern marketing as: ‘to leverage data from all consumer interactions to creatively deliver as much relevant one to one marketing as possible’.

This is interesting, not least for some things it omits, such as developing brand awareness.

The article does however give a broad spectrum of their recommendations as listed in the table below.

modern marketing requirements

As providers of a customer data platform technology and data science, we can’t help being excited by the number of areas where the capabilities of our software and analytical services are required by the modern marketer, as indicated by the blue arrows.

Central to their concept of a customer-centric mindset is the need for: ‘a centralised data platform with a unified view of customers, culled from every possible touchpoint; the continuous generation of insights from customer-journey analytics; the measurement of everything consumers see and engage with; and the hiring and development of talented people who know how to translate insights about customers into experiences that resonate with customers’.

In another section they suggest that it is important to ‘elevate consumer insights and analytics’, and that ‘no marketing activities should be executed without the backing of relevant insights and the ability to measure performance’.

There is a lot we found to be of interest and we strongly recommend reading the full article.


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.


Keep it clean! A Look at REaD Group’s data cleansing report

This somewhat short title reflects the overall approach to data cleansing suggested by our partners, the REaD group, in their latest report; Are you addicted to bad data? Get clean and stay clean.

In other words, it’s no longer just one trip to the bathroom a year, but rather daily hygiene backed up by a culture that won’t accept any excuses for dirty data.

We partner with the REaD Group for a number of data cleansing services including PAF formatting to get address structures right, and suppression of gone aways and deceased’s.

The amount of dirty data residing in customer databases that they remove never fails to surprise us, or our clients.

If you would like to know how much you have, we can arrange a free audit; this will tell you just how many address duplicates there are, the amount of badly formatted addresses, and the number that should be suppressed.

You can read REaD Group’s data cleansing report here, and if you would like to arrange an audit then please email us and we will call you to discuss.


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.


Is our day of reckoning coming soon? Measuring the effect of direct and indirect marketing channels

How do marketers measure the impact direct and indirect marketing channels have on the success of their campaigns?

At a recent conference organised in London by the Institute of Fundraising we asked an audience of around 70 people, all of whom work for charities, whether any had developed a reliable view of how different aspects of their marketing spend impacted their donations. Not a single person present was able to say yes.

In the distant past, before the internet had been invented, and when mail was the only direct channel, it was a whole sight simpler; all you needed to do was to create control groups that you didn’t mail, and then measure how they performed compared to those that you did.

But in today’s multi-channel world, measuring the effect of direct and indirect marketing channels is a problem of great complexity.

Our concern is that, because of the number of different channels and influences that can precede a customer action, like making an order, marketeers may have, to a large extent, given up.

But to do nothing leaves us with a £26bn per annum unanswered question just for the UK alone.

Clearly, there are in fact two very different questions to answer:

  1. First how to infer the effect of non-direct media like outdoor advertising and most of TV
  2. And second, how to measure the effect of those channels that are direct like Google PPC, direct mail or Facebook?

Measuring direct and indirect marketing channels

Most practitioners who want to measure non-direct channels use some kind of time series modelling, and this works reasonably well when we are just looking at summarised data, such as the overall sales value of an organisation in January.

But there are big limitations in that it’s relatively expensive to develop the models, they cannot get into the detail of campaign performance rather than looking at aggregated channels, and they rely on the advertiser varying the amount of spend each month in each channel.

When looking at the outcome from a time series model there is also always a large proportion of sales whose cause cannot be explained by the model, and this has to be assumed as being due to the influence of the brand.

However, where marketeers appear to have thrown in the towel unnecessarily, is in respect of measuring the effect of direct media, looking at online and offline channels in combination.

We don’t believe that any organisation selling goods or services to identified individuals, such as home shopping companies or travel or financial services to name but three, has to give up on measuring the impact of their direct channels.

But to do this measurement one needs to work back from each order, rather than forward from the spend in each channels, to unravel what is actually going on in the real world.

We approach this by looking at all the known interactions between an organisation and a customer in a 90-day window before an order is received.

We ignore all clicks, opens, opportunities to view etc. etc. indeed anything that cannot be directly related to an actual order event, and treat these just as noise.

What then comes to the surface is much more complex that any last click proponent would like to admit; we find ourselves looking at a unified view in which emails, PPC, social, natural search, mobile SMS, direct mail, OBTM and any other direct channel employed can each play their part.

This table is a real example of just five individual orders received by a home shopping company, and counts the times each different channel played a role in the 90 days before the order:

Customer type Catalogues received emails received Google PPC Direct entry Phone in Total
Existing 2 3 1 1 7
Existing 2 1 3
Existing 8 2 10
New 3 3
New 3 13 1 17

Even in this relatively simple example the first thing that become apparent is the wide diversity of the routes taken by customers before they actually placed their order.

The good news though is that once you have joined the online and offline data together, and considered the weighting to give to different channels, and to different time intervals prior to an order being received, you have here the solid building blocks for attributing actual value to the channels being deployed.

You can then allocate the value of each order across the channels that influenced it, and end up with an overall value contributed by each channel in a particular time period.

We are not suggesting that this is rocket science, but it does need attention, and technology, to make it happen.

We have built the technology to automate this kind of attribution, and would be interested to discuss it with you if you felt it could help.

Please CLICK here for a short PowerPoint explaining in a little more detail how we do it.

 


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.


Dumping Cost Per Click

Have you ever felt that you wanted to dump cost per click (CPC) as your measure of ROI but time pressures and lack of tools mean that you remain stuck with it?

We have just received an interesting report from LinkedIn Marketing Solutions which explains how a large proportion of digital marketers are still using CPC as their ROI measure for digital marketing.

But we all know that CPC, useful as it is, only describes one part of the customer journey.

Part of the problem is that, as the report explains, marketeers are under constant pressure to make decisions quickly; they have frequent budget allocation discussions and need to base decisions on something.

However, we suspect that a bigger issue is that digital marketers don’t have the tools to measure the true return, based on their overall contribution to sales achieved, from their different online and offline media.

There will often be multiple influences on the journey to a sale, and many of these can be offline, like things sent through the mail, or undetectable on your website, like opened but unclicked emails. It is only when you look at all the online and offline influences in combination that you can start to allocate the value of a sale back to its causes.

This capability is precisely what we have developed in UniFida, so that we can bring together everything that may have influenced each of your customers in the 90-day window before they placed an order.

When you can see for each order both its value, and all the events that led up to it, and then weight them according to how recent they were, you can then do the value attribution job properly.

If you would like to find out more about how we can help you solve this problem, do please send us an email, and we will arrange a call at a time convenient to you.

Read the full report ‘The Long and the Short of ROI’ from LinkedIn Marketing Solutions.


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.