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.

Marketing effectiveness – measuring the long-term impact of direct mail and other channels

In our previous blog post on return on marketing investment (ROMI) and seeing the bigger picture in terms of measuring the effectiveness of all marketing channels together, we explained why it is important to not only calculate return, but also track it over time.

The question is: what do you do if a marketing channel shows a declining trend, or if its ROI is less than other channels?

Pulling customers through the sales funnel

The temptation is to pull back tactically on that particular channel in favour of less expensive, and arguably more effective, channels. Moving the marketing budget to maximise effectiveness can happen frequently – however, most marketers know intuitively that channels can and should work together to pull customers through the sales funnel. This is often difficult to prove because media reporting is usually ‘last click’ only and/or is not granular or complete enough across all channels.

Some channels are more effective at influencing and raising awareness, others are better at converting and some work best in combination to keep the customer in the sales mindset. So, how do you prove this to a board of directors who may be looking for marketing budget cuts and who perhaps only see a sizable difference in cost of sale and ROMI between channels? And how do you know the longer-term impact of pulling back on some channels in favour of others that seem to be more effective?

Direct mail resurgence

Direct mail is a good example of a channel that, on the face of it, can be rather expensive and may be at the top of the list in terms of budget cuts. However, direct mail has seen a resurgence during the global pandemic period and, although it is still seen as expensive, it has some very interesting marketing characteristics.

Interestingly, in our recent blog post on measuring the carbon footprint of various marketing activities, Kg CO2 per sale for email was shown to be higher than for printed direct mail.

At UniFida, we have been studying direct mail results using our unique marketing attribution solution, which provides detailed ROMI over time for different channels. It also shows where in the sales funnel each channel is most effective with particular types of customers – i.e. existing customers, or those new to a brand.

Retail example

One of our retail clients is seeing some interesting results. In the graph below, direct mail in the form of catalogues is seen as most likely to impact sales in combination with another media channel and, by contrast, Search Engine is most likely to act on its own.

marketing channels working together graph

As this client expected, the ROMI for direct mail is lower than a number of other channels, but it is having the strongest influence at the start of the sales funnel – meaning that it is creating awareness, leading to new sales through encouraging steps, such as searching online and creating sales that otherwise would not have happened.

This is illustrated in the chart example below where the strength of direct mail activity is at the Initiator stage (the start of the sales funnel) against other channels. By comparison, for this company email has a stronger influence in the sales Closer stage.

Media influence in the sales funnel graphWhen we looked at the impact of media in converting new customers, the % influence of direct mail (catalogues) at the start of the sales funnel was even more pronounced, but email was less of a Closer and its influence on new sales was more evenly split across the sales funnel.

Quite often companies have individual contact details and permissions for direct mail and not for email, as customers find the former less intrusive. ‘Cold’ direct mail is also an option, with quality data providers offering targeted individuals with permissions to mail.

Speak directly to a targeted audience

Direct mail can work well for even the most complex propositions and, with third-party cookies being phased out, it represents an opportunity to speak directly to new, highly targeted audiences. It is also easy to test – however, it’s important to ensure that your measurement looks at the bigger picture in terms of ROMI.

Direct mail may also be adding to the effectiveness of the entire sales process, so you need to evaluate how it is bringing in more valuable customers than would otherwise be difficult to reach.

So, when looking at your marketing results, the challenge is to step back and examine the long-term impact of the marketing mix. For every channel you should consider the balance between individual channel ROMI, the interactions between channels and the role each is playing in funnelling sales.

Proof of concept

UniFida can deliver the required expertise and technology ‘out of the box’ to help you automate ROMI evaluation. We can start with a low-cost proof of concept to demonstrate how ROMI can be calculated for your business.

For more information email [email protected] or call + 44 203 9606472.

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.

Developing use cases for a customer data platform

How can developing use cases for a customer data platform deliver what your business needs? Here we discuss how use cases came about and introduce key stages to successfully implement valuable marketing technology.

jigsaw puzzle

In praise of use cases… or ‘anvandningsfall’ as they were originally termed

Back in 1987 a Swede called Ivar Jacobson presented the first known article on use cases as a means for capturing and specifying requirements for computer systems. He didn’t much like their original long Swedish name and eventually settled on ‘use case’ which has since been universally adopted.

So why are we singing their praises?

Developing use cases for a customer data platform does not require technical knowledge, they allow your teams to collaborate on the desired business outcomes and uncover gaps. One of the key things with a use case is it ensures your stakeholders have defined the business need and, how the activity will be measured.

Developing use cases for a customer data platform

An example of a marketing use case is “Use data to deliver relevant, personalised omni-channel campaigns in order to increase revenue and reduce marketing costs”. The use case is pretty straight forward. The brand wants to communicate with their customers across multiple channels in order to generate revenue and potentially reduce wasted marketing spend.

Many businesses fail to develop core use cases to solve a problem or deliver on a strategy. By developing core use cases, which are prioritised based on the business goals and can be measured, it will give you the north star to focus on and deliver against your goals.

We see at least three stages in the process of successfully introducing marketing technology where they are of crucial importance.

Articulate and Document Use Cases

First by going through the discipline of articulating and documenting use cases a business can clarify exactly what they want this nebulous item, a marketing system, to actually do.

It provides a non-techy way for the requirements to be mapped out so that the user community can articulate step by step what both it and the system are expected to do, and what the outputs should look like.

It also allows for consideration of time. When and how quickly should processing happen including volume. Thus, allowing the system providers to get a handle on whether for instance they are dealing with ten thousand or a million customers.

Given that nowadays almost all martech is purchased off-the-shelf rather than being built inhouse, the combined use cases can help start the process of vendor selection. Rather than being told a long list of the glossy features that can be delivered by the martech salesperson, most of which you don’t want in the first place, the company can factually check whether the system being proposed can actually do what you require.

Develop the Business Case

Next, the use cases can feed directly into developing the business case. If for instance you are going to be able to do A that you couldn’t do before, how much customer value are you going to be able to generate compared to where you are now. Or alternatively how much staff time will be saved using the new tool to deliver B more quickly?

We find that business cases for martech generally span across four key areas:
1 The incremental revenue generated by being able to do something that was not possible before.
2 The cost of time saved by using a better tool to deliver something more quickly.
3 Reduction in technical debt by streamlining and unifying data and platforms.
4 Reducing reputational risk by having clear GDPR measures in place

Once past the business casing stage, many organisations will want to start with a live proof of concept or POC. If you select a few areas where the new technology should add value, and where it can be set up and configured quickly (please note I am not writing LHF!) then a POC can be put in place.

There is no better way to finally confirm that everything works from the technology to the customers responding to it. In addition, a live POC that works, gets quick buy in from all levels in an organisation. The POC will also pick up on what is not working and enable you to put it right.

Set Up, Configure and Deliver with the Use Case Specification

And finally, when the full martech needs to be set up and configured, the developers can take the use cases as the specification against which they are going to have to deliver. The company can sign off the configuration as done when the use cases work.

At UniFida we like to help our clients with developing their use cases at the start of the process of introducing a customer data platform. We do this for all the reasons articulated above, and incidentally it helps us understand quickly whether we can in reality deliver what you need.  Having developed several client use cases, we can help stimulate your thinking around what they might provide.

Our offer! We have made a decision not to charge for this kind of consultancy as it helps you understand what you need the technology to do, and for us to understand what we may be called on to deliver.

Please do get in touch if help with developing use cases for a customer data platform is what you are looking for.

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.

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.

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.

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.

Attaining a multi-touch attribution strategy

‘Attaining a multi-touch attribution strategy that works is like looking for the holy grail’. This is one of the conclusions in a report just published by the CallRail Research Unit (click here to download the report).

A key finding from their survey was that ‘36% of marketers say that lack of insights into the effectiveness of tactics, or an effective attribution capability, is the most damaging factor to their marketing efforts; a further 25% ranked it the second most significant factor’.

It so happens that we have recently completed developing a multi-touch attribution capability and it’s now part of UniFida.

Attaining a multi-touch attribution strategy enables you to understand the relative influence, and ROI, of all your online and offline marketing channels and media.

We would like to give you a live demo and show you how it works. If you can spare us 30 minutes, please send us an email and suggest a convenient time for you.

 

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.