Multi-touch attribution (MTA) is a game-changer for marketing – but why is it often ignored?

illustration of figures joined in connective web

MTA (multi-touch attribution) is an approach to measuring marketing effectiveness that works by linking all the known steps in a customer journey prior to a sale being made. It then uses machine learning to allocate weights to the value of each step according to the way they each contribute towards a sale.

MTA works well for all direct channels, both online and offline. However, it does not cover indirect channels such as TV, press, or brand development, all of which are best measured using econometrics (or MMM) alongside MTA.

A marketing campaign will contribute to a number of recognisable steps in successful customer journeys, each of which will be allocated a share of the value of the sale. The campaign’s value is then judged to be equal to the sum of the values of each of the steps it creates.

As well as providing accurate, timely campaign measurement where sales are not double-counted (unlike siloed media reports), MTA provides valuable insight into which individuals have been influenced by which campaigns.

So, what are the barriers to adopting MTA? Here are answers to six key questions.

1. Isn’t introducing MTA very costly because of the investment in technology and time required to make it work?

MTA does need technology and time to build the customer journeys from online data, such as click-throughs to your website, or offline steps like receiving a DM communication, and then reporting the results. But the cloud-based technology to do this exists. Depending on scale, the cost of running an MTA programme should be less than 3% of marketing costs for a marketer spending around £1 million pa on marketing, and less than 1% for a marketer spending upwards of £10 million pa.

2. Does too much data from too many sources make MTA practical?

Well, it is true that multiple data feeds are required to build the journeys, but in a typical retail case, for example, you will not need more than a first-party data feed from your website, where 100% of allowable browsing activity is needed, combined with a selected feed from your e-commerce system, your customer database, and any offline contacts, such as DM.

You may also want to introduce call centre contacts if these play a significant role in the journeys. To join all this data together, and build the journeys, you will need something equivalent to a customer data platform (CDP). However, most CDPs come with built in connectors, and there are cheaper alternatives to Google Big Query for collecting the individual browsing data.

3. Are the customer journeys too complex to weight, and can I trust the scores given to the individual steps?

It is true that some customer journeys can be complex, but others are very simple. At Unifida, we find examples where journeys are just one step, and others when they are longer than ten. The average is often between three and four. However, if the machine learning that drives the weighting is set up correctly, then the number of steps in a customer journey does not influence the accuracy of the result.

These charts give an indication of the number of steps and time length of journeys you can expect to find:

Multi-touch Attribution Graph showing distinct channels

 

Multi-touch Attribution Graph showing customer journey distributions

The best MTA technology makes the scores given to individual steps in each customer journey transparent to the user, so that you can see precisely how each step in each customer journey is weighted. This in itself goes a long way towards developing confidence in the results. The machine learning behind the weighting should be trained on each client’s data to understand the average pattern and shape of the journeys.

At UniFida we allocate the weightings according to the position of each step in the journey (i.e. is it at the start, end or middle of the journey?) and the time intervals before and after each step. So, a step will be given more value the less time it takes to get to the next one, and vice versa. We recognise there is no ultimate right or wrong in the way that journey steps are scored, but we know empirically that this approach provides what appear to be very sensible results.

4. I know that different types of customers respond differently to different types of marketing, but can I understand that from MTA reporting?

This is often a key concern given that different customer groups, such as new or existing customers, respond to marketing communications in varying ways. To be able to look at what different customer groups are responding to, you will need to import data from your customer database so that, at the moment when a sale is made, you can identify what segment the customer is in.

You will know whether it’s a sale made to a new recruit, or a high-value existing customer, for instance. With the customer segment for each sale identified, you can then filter the attribution reports down to the segment you are interested in.

5. How can I allocate the marketing costs to each individual campaign?

Marketers should have the ability to input campaign costs into their MTA platforms. However, we find that marketers often have limited time or lack access to the detail to input costs for every single campaign element. Even so, they will know how much they spent in a month on a particular channel, such as Facebook. This is where a good MTA platform can help because it can take the overall spend in a channel for a month, calculate how many customer journey steps that spend created, and then compute the average cost per successful step.

These steps can then be summed up at an individual campaign level. This will not be perfect because different campaigns will have different costs per step, but it makes for a reasonable approximation. For more accuracy on important campaigns, these campaign costs can be input individually for precise calculations.

6. After all this, when I get the actual MTA reports, will they be of any use to me?

It always possible to totally ignore the MTA reports and carry on spending marketing money based on whim and intuition, but this would be throwing away a great value opportunity.

We find that clients tend to use the MTA reports in two very different ways: first to look at the big picture in terms of the return on marketing investment (ROMI) by channel at different times of year; and secondly, by looking at the individual results for each campaign and testing to see how they are performing, and how they interact with other channels and different customer types.

The benefit from the ‘big picture’ analysis is that you can use it to shift marketing budget to where the best ROMI can be found, thus cutting out dead wood, as well as spending more when the ROMI is highest. A word of caution – although the MTA provides a good retrospective view of how marketing has performed, it does not routinely provide forward predictions. Where these are required, we suggest introducing MMM or econometrics alongside MTA to provide an understanding of the return that could be expected from different levels of future investment in different channels, which can take into account the full marketing mix and external factors.

The benefits from the micro-level campaign and test analysis are that you can change campaigns very quickly, see how they are performing, determine test results, and understand how different campaigns are interacting with each other – without double-counting sales. Your MTA platform should be reporting in near real-time to help with these decisions.

The overall benefits from introducing MTA will be a function of the improved ROMI achieved from the better understanding of marketing performance over the cost of the MTA itself. Every company is different, so generalisations about improvement should be treated with caution, but you should not be surprised to find that, once you are acting on the MTA results, you can get a x10 benefit from your investment in it.

If this encourages you to consider introducing MTA, or undertaking an MTA proof of concept, because you are struggling to get a proper understanding of the returns you are getting from your marketing, then please talk to us about it. We have experience of introducing MTA across multiple companies in industries as different as retail, subscription, insurance, news media, cruising and lotteries. At no cost, we can also give you an assessment of whether your company is a suitable case for introducing MTA, and at what level of investment.

Contact us at [email protected], or call 0203 960 6472, or check out our website www.unifida.co.uk

Read more about Marketing Attribution >

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.

Marketing attribution is a critical success factor

If accurate and timely marketing attribution is a critical success factor for all companies selling directly to consumers, why are so few companies achieving it, and what is needed to enable its delivery?

Let’s take a look at the key features we would expect to find in an accurate and timely marketing attribution solution:

  • Flexible application to marketing channels
  • Delivers views on customer segment effectiveness
  • Transparent, easily understood workings
  • Timely, easily interpreted results
  • Representative of the whole customer base
  • Robust data granularity for accurate return on marketing investment (ROMI)
    calculations
  • Supports incremental performance reporting
  • Sourced from an objective, independent provider
  • Compliant with GDPR

Our observation of the tools that marketers use for attribution is that most would not pass muster if assessed by these criteria. Google’s GA and G4 both fail on most of them, and many agency attribution reports are clearly not holistic and independent sources of the truth.

Marketing attribution best practice at a glance

There are two well-known methodologies, both of which can provide valuable and accurate results, though they approach the problem from opposite ends of the spectrum.

Marketing Mix modelling (MMM), or econometrics as it is also called, looks at the macro picture of all marketing channels and combines their effects with that of other influencing factors like pricing, competition, external economic factors, weather and even epidemics in working out how each of these are contributing to, or subtracting from, sales.

MMM requires a long tail of data (up to three years) and some careful statistical analysis, however the results are highly informative. They can account for brand effects alongside the immediate effects of marketing, as well as explaining natural levels of demand when no marketing takes place. Because they are looking at the bigger picture, they don’t report immediately on campaign and test results.

As MMM reports are effectively handmade they are usually run quarterly, or even six monthly, so they don’t pass our timeliness criteria, but they can succeed on the others. The level of granularity across media, customer and product types can vary depending upon the statistical significance of the data splits.

Multi Touch Attribution (MTA), or customer journey-based marketing attribution, takes the micro view and examines all the known touch points between a company and a customer in the period leading up to a sale. As such it is limited to the kinds of direct channels that leave a data trail that can be linked to customers, and excludes indirect channels like TV or press.

It looks at the short-term effects of marketing, with its strength lying in its granularity. By examining the role played by each step in the customer journeys that lead up to a sale, it can see precisely where a particular campaign or test has influenced each journey, and hence give value to it based on its attributed fraction of the resultant sale.

These steps can be online, like visiting a website after clicking through from a social ad, or offline like receiving a catalogue or having an interaction with a call centre.

MTA can meet all our criteria, and provide very detailed campaign and test attribution, except for the omission of indirect channels like press and TV.

For companies spending material amounts on both direct and indirect channels there is no question then that they should deploy both MMM and MTA. However, it is also possible to fuse the macro and micro outcomes into one overall view of the value delivered by marketing. The results from the MMM will be used to remove value from the direct channels and reattribute it back to the indirect channels, from known information about the effect and timing of indirect marketing on different customers, products and sales channels.

So, is this what companies are doing?

It seems not.

Given that the best practice techniques are well known, and that there is a widespread acceptance that much of marketing budgets are wasted, it’s perplexing why so few companies go about setting up their own attribution reporting, and instead rely on the very incomplete and often biased results provided by Google.

Estimates vary regarding how much of marketing spend is wasted, but the consensus would appear to be at least a quarter. A Komarketing survey of marketers in 2019 found that 37% acknowledged that much of their marketing budgets are wasted. And eMarketer, in the report below from Jan 2018, found that marketers believe they wasted on average 26%.

marketing budget wasted diagram

In that case, what’s stopping them from looking for a better solution?

The most significant cause of marketers not setting up their own attribution is that Google is largely free and nearly universally available. It has become the common currency because so little cost or expense of effort is required to use it.

In contrast independent marketing attribution doesn’t come free. Let’s put that in context though – for marketers spending upwards of £1m pa, the costs start at less than 5% of that budget. For marketers with a bigger budget like £10m pa, the costs are likely to be little more than 1-2%. Those numbers reduce further if the mix of channels is relatively straightforward and MMM is not required. Compare that to the current scale of budget misallocation, and cost shouldn’t be a concern.

A second possible cause is that marketers just don’t realise that easily implemented alternatives to Google are available. There are challenges naturally; it takes expertise, for example, to set up the right environment and execute certain statistical processes. The good news is these experts are available and keen to undertake the work.

A third and possibly most significant cause is that mis-spending of budgets is often invisible. Marketers, having been led down the garden path by a combination of Google and their agencies, know that no one will challenge them if they sit back and accept the often misleading and inaccurate attribution results that they are used to receiving. When this is both free and effortless it’s all too easy for the impact to go unseen, and therefore unchallenged.

Is marketing attribution best practice really going to make much of a difference?

Absolutely!

To give an example of how Google can misrepresent results, we recently compared our own MTA reporting for a UK retailer with that provided by Google:

Our MTA reporting compared to Google reporting

Catalogue sends and Internal (telephone) orders were not available to GA as it does not ingest personally identifiable information, despite constituting 45% of the drivers of orders.
GA gave Pay-Per-Click 46% more value than our MTA, and Search Engine over four times as much contribution. As GA does not allow users to inspect how their calculations are made, we are not in a position to comment on their methodology, but it does appear to be strongly biased in favour of channels they own.

In conclusion, we suggest that marketers have for a long while been seriously misled by Google, and by their agencies using Google’s tools, over how their marketing is performing. Now that alternative methodologies are well known, and there are suppliers and in-house teams capable of providing attribution services, it is reasonable to expect that the status quo regarding marketing attribution is about to be seriously challenged.

 

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.

Delivering successful marketing attribution projects

You might think that introducing accurate omni-channel marketing attribution is a sensible thing to do and that there would be a limited chance of failure. But think again, as these projects can and do fail.

The reasons are mainly to do with what they are replacing. Prior to the start of such a project there was probably no one person with overall responsibility for marketing attribution, or even someone with it in their job description. Instead, there are likely to have been many local attribution activities, each one designed to prove to the business that team X or Y, or channel Z, was doing a good job. The most typical team silos are digital and direct marketing, yet these teams have the most to benefit from working together on attribution.

Attribution projects

It is common to find attribution projects limited by media channel, such as including selected digital channels with no account for direct channels, or limited by sales channel, such as just including online sales with no account for call centre or store sales – or both. The attribution work may have been done internally, but more often externally by an advertising agency. And the external agency may be driven by a need to keep funds flowing through their channel, rather than necessarily being focussed on the end contribution of their activities.

Some marketing attribution looks at more superficial measures such as opens and clicks, rather than at the longer-term value generated by a campaign. Again, they often ignore the fact that orders are usually achieved through a combination of customer interactions in more than one channel.

So, a new omni-channel marketing attribution project, centrally and professionally managed, is definitely going to be disruptive for certain vested interests.

Avoiding disruption

To avoid internal alienation and disruption, it is essential that the entire company management team, from finance to marketing, buys into the project from the start and has confidence in the methodology that is being used. They also need agree that they will respect the results produced, even when they may upgrade or downgrade the value contributed by certain existing activities.

However, as one US commentator recently put it, ‘algos make a unified approach possible’. The ‘algos’ (or algorithms) are what make omni-channel marketing attribution possible, and if they are well designed, they will deliver trustworthy attribution results that can be used to guide marketing spend, and optimise budget allocation in the future.

If the business is aware from the start of the consequences – as well as the immense benefits – of introducing accurate omni-channel marketing attribution, then the project can succeed, and the business can optimise the marketing budget and reap the sales and growth rewards.

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.

Marketing Attribution – is there a Knowledge Gap?

Unifida’s Client Services Director Jo Young reflects on the recent Direct Commerce Association (DCA) Summit:

I was fortunate enough to be asked to speak at the recent Direct Commerce Association (DCA) Summit in London. It’s a popular, friendly event, attended by a range of direct and multichannel retail businesses at all stages of growth, serving both niche and mainstream market segments.

The agenda was packed with retailing ‘hot topics’ and sessions sharing knowledge and experience between businesses. I gave a presentation on how omnichannel marketing attribution measures the way that channels work together, and how companies can use this to optimise their budgets. I outlined how one of our clients, Wentworth Wooden Puzzles, introduced attribution, and how the company is changing its focus on different media for different customer types to maximise the marketing budget.

With marketing budgets being heavily scrutinised, there is a constant pressure to prove marketing return and tighten up on waste. Omnichannel marketing attribution measures the way that channels work together (whether they are trackable or non-trackable) and helps optimise precious marketing budgets.

Earlier in the day at a panel session on Marketing Metrics, attendees were asked if they had any form of marketing attribution in place. Only a handful of people from the large audience raised their hands. This was startling and revealed a key marketing attribution knowledge gap – not only in attribution, but probably in the true effectiveness of marketing budgets too. Speaking to delegates afterwards, some said that, when it comes to marketing attribution, they did not know where to start, or thought it would be too expensive.

While many companies recognise the need to have accurate media metrics and spend their budgets wisely, they don’t realise that there are easy, cost-effective marketing attribution solutions out there that make the most of their first-party data, and can help them report accurately in order to optimise their marketing resources.

To see attribution explained simply, you can watch UniFida’s video on attribution basics.

In our Resources section, we also have a variety of articles on how organisations can benefit from attribution, including a blog post on measuring the long term impact of direct mail over other channels.

 

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.

UniFida to present at the DCA Annual Summit

“One marketing budget, many channels – how omnichannel attribution optimises these.”

That’s the subject of an insightful presentation by UniFida and Wentworth Puzzles at the 2022 Direct Commerce Association (DCA) Annual Summit*.

With marketing budgets being heavily scrutinised, there is a constant pressure to prove marketing return and tighten up on waste. In this presentation – by UniFida’s Client Director, Jo Young – visitors to the Summit can learn how omnichannel marketing attribution measures the way that channels work together (whether they are trackable or non-trackable) and helps optimise precious marketing budgets.

“Companies who use a range of marketing channels can find it challenging to understand how those channels interact, and what the optimal mix of marketing is,” said Jo Young. “Measurement can be difficult enough with trackable channels such as paid search, display and email, but adding press, TV or poster advertising to the mix rapidly complicates evaluation. We look at how to tackle this.”

She added:

“Every channel has a different role to play in the sales funnel – one seemingly low-performing channel may be necessary to support the role of other channels that can close the sale. More will be revealed in our presentation.”

Jo Young, Client Director, UniFida
Jo Young, Client Director, UniFida will present at the DCA summit

 

*The DCA Annual Summit takes place on Tuesday October 18th, 2022 at the Millennium Hotel, Gloucester Rd, London SW7. More information on the DCA Annual Summit event website >

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.

Marketing Attribution for not-for-profits – it’s not just about sales

Multi-touch Attribution (MTA) for marketing is all about evaluating the effectiveness of marketing investments by examining their role in generating steps in the customer journey. It was initially developed for ecommerce, but not all MTA is about sales.

In the case of not-for-profit organisations (NFPs), it could be about attributing marketing to successful volunteer sign-ups, or donations. Not all customer journey steps are equal – and for accurate results, it is important to ensure that the approach used includes all the relevant channels, and that calculations can include every event in the journeys towards the target outcomes.

The greater the transparency, the better the insight – and the more opportunities there are to optimise marketing to help achieve NFPs’ marketing goals.

UniFida has been working recently for an NFP, an alcohol awareness charity whose objective is to lead people towards areas of their website that offer support. In this case we have been able to plot their customer journeys leading up to the use of one or other of their tools, and from these evaluate the contribution of their different digital marketing campaigns.

This same approach can be used for evaluating marketing in a number of areas, for example, test drives for cars, university courses, or media content. However under the bonnet, the technology and the analytics remain very much the same.

How does MTA work?

1. Collating online and offline customer journeys

This requires a customer data platform (CDP) or equivalent to bring together, for instance, web browsing actions with email opens, and perhaps phone calls and direct mail sends. Each journey will end with the designated result, but the steps to get there need to be assembled in time sequence.

This requires a 100% data feed from the website (from which we collect first-party data), a feed from an email service provider and a contact history file for the direct mail if needed. The average number of steps in a journey are around three, but the average is deceptive as the steps can range from one up to ten or more. Existing customers also usually take more steps than new ones.

 

MTA diagram

In this example, the journey started on the 12 March and ended with a sale on 1 June. The recipient opened two emails and a catalogue, and undertook two entries to the website (one via PPC and the other going directly).

 

2. Weighting each step in the customer journey

All steps are not equal and can play different roles. They can help initiate a journey, maintain a customer’s interest and help reach the end result. We use advanced mathematics to undertake the weighting of the steps and this also looks at the role they play. We split the roles into Initialiser, Holder and Closer.

We train the mathematics algorithm to take account of the particular characteristics of the journeys for each individual client, as they can vary considerably. Higher priced items, or choices with more consequences like university courses, will tend to have longer journeys as more consideration is usually required. The algorithms respond to the time periods before and after each step as well as, for a browsing step, the level of engagement with the website.

MTA diagram

To continue with the same purchase example above, we have now added in the scores for each step in the journey. The IHC score is the combination of the Initialiser, Holder and Closer scores. Each column adds up to a total of 2, so a 0.4978 IHC score will give that step a quarter of the sale vale. The two emails received some credit for Initialiser, but not as much as they would have done if the sale date had been earlier, whereas the Closer rewards went to the catalogue and PPC.

 

3. Aggregating the results

Some marketers are interested in puzzling over individual journeys to understand how channels work together for different customers. Others want accurate answers to questions like ‘How did the email test campaign do?’, or ‘Do I get better returns from using Facebook at certain times of the year?’.

To answer these kinds of questions we have to aggregate up the results. If we take an email campaign, for example, it will have contributed to steps in many different customer journeys, some of which will have led to a successful outcome, and usually a much larger number that have not.

We only look at the opened emails within successful journeys and give a value to each of these steps, depending on the value given to the individual outcome, multiplied by the fraction of the overall journey process that the step contributed. So, mathematically, if the outcome is worth £50, and the step has contributed 30% of the journey, then that step is judged to be worth £15.

In this way every step in a successful journey gets a value and these can be summed up to give a value to the overall campaign. This example will help explain how fundamental the weighting approach is to the evaluation of the campaign results – give the step contribution just 10% of the journey and then it’s only worth £5.

Aggregation can provide answers to a number of different questions. It can sum up to an individual campaign, or to a channel, and that is usually for a time period, such as PPC in July. By looking at different time periods for the same channel we can understand the impact of seasonality on response. But we can also turn the data around and look at how different customer groups respond to different types of marketing.

We have already mentioned the differences in behaviour between new and existing customers, but what about, from the charity example above, whether different campaigns lead people to visit different tools in different parts of the website. A car vendor might, for example, be interested to know which media are better at driving test drives for different models.

channel and share of value diagram

Here is an example of an aggregated report, taking the channel view for a particular time period. Each channel has a share of the overall value based on the steps it contributed that led up to completed sales. It is interesting to see how the sales impacted are for every channel greater than the share of sales. So, if in this case the marketer decided to stop sending out catalogues, then 26,478 sales would have been impacted and many may not have happened.

 

4. Avoiding the ‘black box’ classification

There is a natural fear of basing decision making on results created by unknown algorithms that live inside impenetrable black boxes. Happily, this does not need to apply to MTA, as long as the technology can provide a table that shows the values given to every step in every customer journey. With the individual scores visible, the results can be challenged.

We sometimes set up manual scoring systems that give declining weightings to events that are further away in time to the ‘result’. This is a common-sense approach and useful for comparing the outcome with what the algorithms have come up with. For the more mathematically inclined, it is also important to be able to challenge how the algorithms themselves are programmed and to receive back a full explanation.

 

5. When does MTA not apply?

MTA works when there is a direct link between the person making the customer journey and the steps that take place. So, it is not applicable for TV, or outdoor advertising for instance. For these indirect channels econometrics is the right approach, and the good news is that we can now merge the outputs from the econometrics with the MTA results to provide a truly 360-degree view.

 

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.

Return on marketing investment – the importance of seeing the bigger picture

To use a couple of sporting analogies, every marketer wants to score the equivalent of a bullseye or a hole in one. In other words, achieve a high score with the minimum effort. But just as in sport, it’s not always an easy task – however marketers can hit their targets by relying more on expertise and data, rather than just chance.

It starts with accurate media reports measuring the effectiveness of marketing efforts and investments. A lot of companies make do with media reports that are either not holistic – i.e. they focus on one channel and don’t take into account the existence and impact of other channels – or they are just a ‘snapshot in time’.

In other words, we need to look at the ‘bigger picture’ in terms of measuring the effectiveness of all marketing channels together, as well as any seasonal variations.

Be strategic

This approach should be strategic rather than tactical; companies need to look at marketing metrics in the longer term, rather than take the short-term view. Not looking out beyond the immediate horizon can result in:

  • missing a steady decline, or increase, in a channel’s performance
  • a lack of insight into the performance of each marketing channel at different times of the year, so not understanding seasonal impacts
  • not determining the ability for one channel to boost the performance of another when it is switched on
  • not recognising the impact of budget reductions in one channel on another.

It seems that few companies are stepping back and looking at trends over time, with all the channels measured together. Econometrics studies go some way towards helping to achieve this which, although useful, can be time-consuming and expensive. Also, they don’t provide a clear view of the effects of seasonal marketing activities.

More importantly the outputs don’t have the level of detail – such as campaigns and keywords – typically needed to optimise digital channels and direct marketing.

Optimise channels’ ROMI over time

What is needed is an attribution solution that provides detailed Return on Marketing Investment (ROMI) data over time, measuring digital and direct channels alongside each other, with the ability to drill down forensically into campaign detail.

Such a solution can even indicate at what stage of the sales funnel each channel and campaign are most effective and with which type(s)of customers – i.e. existing customers or those new to the brand. The ability to easily see such trends in marketing performance over the long term reveals a number of key truths, such as:

  • an overall decline or increase in the efficiency of all channels
  • natural variation in ROMI due to seasonality, and
  • the interaction (dependence or cannibalism) across different channels.

An attribution solution with built-in ROMI measurement over time enables marketing teams to step back and take a fresh look at their marketing budget and media mix. It empowers them to make well-informed, multi-channel decisions about how to drive more sales from the right types of customers and deploy the whole marketing budget more effectively.

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.

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.