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:
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.
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.