Q & A: Customer Journey-Based Marketing Attribution

Below are some frequently asked questions about customer journey-based marketing attribution – and some insightful answers.

 

What is customer journey-based marketing attribution?

People usually receive or respond to multiple communications on their way to making a purchase. These can be online, such as a Facebook ad, or offline, such as receiving a catalogue. Customer journey-based marketing attribution looks at all the interactions between a customer and an organisation before a sale in order to analyse the role of each interaction, and hence their contribution to the sale.

Which channels are normally included?

We expect to include all ‘direct’ channels, in other words those where there is a one-to-one relationship between the customer and the company, or where there is a direct link through an online user clicking on a digital ad (such as PPC) and visiting the company’s website. An email is direct because it goes to a known recipient, whereas a press ad is not because the recipient is unknown. However indirect channels, such as TV, press, and outdoor, can also be very important. These require a different attribution technique called econometrics.

How do you link the steps in a customer journey?

We start by putting a snippet of code on your website to enable us to download into our attribution platform all of your (first party) website visitors’ browsing activity, including, most importantly, how they arrived at your site. This allows us to distinguish, for instance, between a referral, a branded search or a natural search. We then link the browsing to any offline journey steps by joining them to individuals using identifiers they have provided. To obtain the non-web contact activity we ingest feeds into our Customer Data Platform (CDP) from sources such as your email service provider, direct mail contact history, and your order processing system.

As not all journeys lead to a sale, what do you do with the unsuccessful journeys?

The brutal truth is that for attributing value to marketing activities we ignore them, which is not to say that the unsuccessful journeys are unimportant as they are key when we aredescribing customer journeys overall and understanding the total spend through a particular channel (i.e. we need successful plus unsuccessful spend). But a campaign will only have value attributed to it from the journey steps it created that led to a successful outcome.

How far back in time do you go when looking at customer journeys?

We normally look back 90 days before each sale, although some clients ask us to look at shorter periods, e.g. 30 days. To a large extent it depends on the type of purchase and the channels used. For instance, a catalogue will have a much longer shelf life than an opened email, so we need to give it time to have its effect.

How do you decide on what weight to give to each step in a customer journey?

It is obvious that all journey steps are not of equal importance, so weighting them correctly is crucial to obtaining a successful attribution outcome. There is a great deal of online discussion about this subject, with different approaches being debated, but we have opted for a method which is mainly based on the time intervals before and after each journey step.
If, for example, an event happens just before a sale, we give it a high closing score. In contrast, if there is a long interval after the first event, then we conclude that it could not have had too much of an impact on initiating the sale. We also give credit to events that help keep the customer interested without actually closing the sale. If you would like a more detailed note on how our weightings work, we would be pleased to share this with you.

Are there certain types of event that you ignore?

Yes. Multiple opens of the same email on the same day is one example, as is a visit to PayPal just before closing a sale. We try to eliminate anything that does not contribute to the customer’s decision to purchase.

Can you distinguish the different behaviours of different customer groups when responding
to marketing events?

We can. For example, new and existing customers behave entirely differently in terms of the kinds of journeys they make and what marketing events they respond to. Another way we divide up customers is between those who mainly search and buy online and to those who order through a call centre. But you may also wish to look at the impacts of marketing on different types of customer segment, and our platform can support that.

Do you look at how different channels perform at different times of year?

We do, and we find very significant seasonal differences. To show this, we have a specific report providing month-by-month summaries so that we can, for instance, compare email or any other channel’s performance in one month with another.

Do you always look at sales when calculating marketing attribution, or can you look at other goals, such as lead generation?

We often look at non-sale outcomes, and, for instance, recently we have been working for a charity that they wanted us to look at how they get their users to take up different tools that they provide on their website. When looking at non-sales outcomes we lose the value element that we have in a sale, but otherwise the process works in an identical way.

How up-to-date are your reports?

They are always available online at any time and the data behind them is processed each night. So on any day you will be looking at results up to midnight the day before.

Do you aim to answer questions other than the value obtained from customer journeys?

We are finding that this is an increasingly important area, and to respond to our clients’ requests we are building a whole suite of customer journey reports. These will answer questions about the lengths of journeys, the mix of channels used and the sequence in which they appear in the journeys.

Is customer journey-based attribution GDPR compliant?

Yes. It uses only your organisation’s first party data and excludes cookie and analysis opt-outs, for example.

Why not just use Google?

Whether you are using Google Ads or the new or old version of Google Analytics, they have well-documented flaws – namely inaccuracy and incompleteness. Google Ads uses the last Google Ads click, but Google Analytics uses the last click across all channels, so over-reports as it does not take into account other channel’s contributions. Google recognises that this is an issue and has developed Google Analytics 4 (GA4) to
replace the previous version called Universal Analytics – but this will not solve the core flaws. GA4 will use black-box algorithms and again will not take into account all marketing activity. Google is sampled and segments cannot be applied retrospectively for analysis.
Google does not use first-party data or individual identifiers, so it cannot be joined to other data sources of marketing activity.

 

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.

Cutting marketing budgets

Wielding the axe on marketing budgets – how can you cut creatively?

In today’s challenging times, many organisations are looking to reduce their marketing budgets while minimising the overall impact on sales.

Of the £20 billion+ spent per annum on marketing in the UK, the challenge is to identify which parts of a marketing budget are actually wasted. If you Google ‘How to cut my marketing budget’ you will find yourself flooded with generic advice, but no specific guidance on how or where to wield the axe.

So, you need to be creative. Here are some specific questions you need to ask when assessing your marketing budget allocation:

1. Which customer segments should we focus our marketing budget on?

This question should always be asked at the start of a budget reduction exercise – and the answer may not be what you expected. For instance, at UniFida we recently discovered that for an insurance client of ours their least affluent customers were providing the highest longer-term value per sale.

As well as investigating specific market segments, you should look at splitting your investigation between the impact of marketing to recruit new customers compared with spend on existing customers. Both will respond in very different ways to similar campaigns.

2. Should you start your investigations at a channel or a campaign level first?

The problem with starting at a campaign level is that you are very quickly swimming in the weeds. You may find that there are literally hundreds of campaigns when you look across all your channels and by axing individual ones you are ignoring what their cumulative effect is, as well a potentially stopping, say, the worst ten emails, when in fact they perform better that some of your AdWords campaigns.

We suggest you start at the channel level and aim to get to the same marginal ROMI (Return on Marketing Investment) for each one. If you can achieve this then you will have a perfect channel level budget distribution. The marginal ROMI can be described as the return from making a small increase or decrease in the spend for any channel.

To measure marginal channel level ROMI you will need some quite specific tools, a description of which comes later.

3. Having fixed your channel level budget allocation, how do you progress with campaign pruning?

An initial risk is that some campaigns do not need to be axed – they may just need better targeting, or revised content. Clearly these problems need to be dealt with before any cuts are undertaken.

Then, turning to the individual campaigns, you must regard each one as part of an overall marketing ecosystem that, working in combination, encourages customers to undertake customer journeys that may or may not end in a sale.

steps in customer journey diagram

So, you will need to judge the effectiveness of the campaign in terms of how much it contributes overall towards the journeys that lead to a sale, but you may also be interested in the role it plays in initiating, holding, or closing sales.

To achieve both of these you need to know where events created by the campaign crop up in your customers’ journeys and what impact they have. As an example, a social media campaign may be very good at getting new customers to visit your website, but it may need some PPC support to get them to actually purchase.

What specialist tools do you need to achieve all this?

All this will only become achievable when you start examining your marketing effectiveness at the granular level, i.e. each step in a customer journey. A step may be receiving a catalogue, opening an email or a visit to your website from a referrer.

In combination, and ignoring indirect channels like press or TV for a moment, these steps in your customers’ journeys are what marketing delivers. On average there are around three steps preceding each sale, but some journeys will consist of one step, or others twenty.

 

The right tools for the job

With this in mind, you need a tool that gives a value to each step based on its contribution towards a sale and allows you to aggregate these steps up to all those created by a campaign, and then up again to all campaigns that take place in a channel. (Interestingly there are many questions around timing in this as well because campaigns can have long tails).

This requires technology to link online and offline customer journey steps and then give to each step a weighting based on the contribution it makes to the overall journey.

Alternatively, if you are looking at indirect channels like press and TV, then you need to introduce an entirely different technique, ’econometrics’, which will also determine the value they contribute towards your overall sales. Econometrics works in a very different way by examining the impact of changes in the spend in any channel over time on the overall level of sales.

How UniFida can help

UniFida provides a one-stop shop for delivering the tools and services for both of these approaches – customer journey attribution for direct channels’ ROMI and econometrics for indirect channels. We can also help with a proof of concept to demonstrate just how effective these approaches are.

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.

Gain valuable customer insights from fishing in the CDP data ‘reservoir’

In an increasingly customer-centric world, the ability to access and gain valuable customer insights to shape products, solutions and the customer purchasing experience as a whole is critically important. For that reason alone, customer data must be seen as strategic.

For example, by pulling together rich customer profiles and rigorously tracking response rates, marketers can know precisely what types of content and over what channel are likely to have the greatest impact on the bottom line.

To achieve that, the Customer Data Platform (CDP) can not only be a key enabler, but also a marketer’s central knowledge store.

Historic view of customer value

As a CDP builds a single customer view, it also accumulates an historic view of all the visible customer interactions with the company, both online and offline. Over time these accumulate into an invaluable and extraordinarily rich data ‘reservoir’.

The ability to ‘fish’ in this reservoir via a CDP enables marketers to address some fundamentals, including:

  • how much revenue is coming from existing customers, as opposed to new ones
  • how much value last year’s recruits provide, compared with those from earlier periods
  • dividing customers into cohorts defined by time periods or specific recruitment campaigns
  • establishing the longer term value they bring to the company

In terms of sales, the data reservoir enables you to track the overall monthly trendline from year to year, and dig deeper into the areas that are showing the most potential. You can see if individual customers are spending more or less overall, or spending on particular product categories and, by using history to establish what the seasonal effects are, you can examine the underlying growth trends.

Impact of marketing

The CDP is also a valuable asset when it comes to looking at the true impact of your marketing campaigns on customers. It can help you answer key questions, including:

  • what is the ROI for each channel for each time period?
  • how are different groups of customers responding to individual campaigns and which ones are keeping their appeal?
  • is the pattern of customer journeys changing?
  • are customers putting more steps in the pathway and spending more time considering their purchase?
  • are customers increasingly taking their own route to purchase and being less influenced by the campaigns you are sending them?
  • are customers browsing for longer periods, or dropping more baskets
  • is this the same across all customer groups?

Segmentation and propensity models

You can also use the data reservoir to build customer segmentations and propensity models – for example, the experience of some customers considered dormant to reactivate will provide the target variable for a reactivation model. Similarly, customer attrition can provide the target variable for an attrition risk model.

The algorithms derived from these models can then be reapplied within the CDP to score individual customers for retention or reactivation campaigns, or to predict next best actions.

So how best to access the reservoir and gain valuable customer insights?

How easy is it to access all this knowledge? Well, there are a number of different approaches that can be taken./ You can:

  • use data visualisation tools like Microsoft Power BI or Tableau to provide a continuous dashboard of customer performance, with tables bespoked to your specific KPIs
  • take a copy of the entire data reservoir and use data science tools like R or Python to answer specific questions and to develop predictive models and segmentations
  • use out of the box capabilities for reporting metrics that have been developed inside your CDP.

Packaged solutions

Here at UniFida we have pre-packaged a large number of customer and marketing metrics within our CDP. For example, we provide multi-channel order attribution that allocates the value of each order back across the steps in the customer journey that led up to it. This means that we can report on the precise value contributed by each channel and each campaign, for any time period and across any segment of customers.

In summary, a key role of a CDP is to build a data reservoir over time to provide an invaluable and irreplaceable source of information about customer behaviour and marketing effectiveness. The reservoir should fill up naturally, and the marketer’s role is to ask the right questions and have the tools either built into the CDP, or applied externally, to obtain the answers.

 

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.

Do you always have confidence and trust in your data to make important marketing decisions?

building trust in data to make marketing decisions

We are asking the question because we expect the key currency of the new post-COVID economy will be trust, and trust in data. 

Imagine you are amid your biggest campaign of the year, you are explaining the results to your leadership team, and are faced with questions like “how do you know”, “is the data right” or “why didn’t that campaign reach all of our intended audience”? I’m sure you have been in these types of scenario, which happen every day in the life of a marketer. 

SnapLogic [1] recently published an intriguing research report on how data distrust impacts analytics projects and decision making, which highlighted:

  • 77% of IT decision makers do not completely trust the data in their organisation for accurate, timely, business-critical decision making.
  • 76% of IT decision makers report that revenue opportunities have been missed due to a lack of data insights. 
  • 83% find data is not available at the time it is needed
  • 53% of mid-size companies suffer from too many disconnected data sources.

So, we would like to focus attention on some of the key data and insight issues faced by mid-size B-to-C companies in the UK and make suggestions around how they can be resolved.

Our experience is that these problems often have three separate causes:

  1. Customer data availability and quality
  2. Availability of skilled data analysts equipped with the right analytical tools
  3. A failure by the decision makers to frame the right questions for the analyst

 

Customer data availability and quality

The SnapLogic report reveals that 53% of mid-size companies have too many disconnected data sources, while 40% have poor integration of data sources meaning that data is missing or incomplete.

A typical B-to-C marketing department will often be looking at a distributed data situation with multiple silos like this:

data flow of different silos
Distributed data flow with multiple silos

The problem with this configuration is that there is no place for maintaining the overall customer picture, just pieces of the jigsaw in different places. So, it would be well-nigh impossible to answer questions like:

  • where am I acquiring my higher value customers from?
  • how is my latest email or catalogue campaign performing when most orders are placed without source codes via the website?
  • how do I know which of my dormant customers are worth trying to reactivate?
  • how many of my orders are coming from customers recruited this year, last year, and the years before?
  • how do I understand the ROI I am getting from each acquisition channel?

… and many more.

One solution to the data availability and quality problem is to introduce a customer data platform (CDP) that ingests data from all available online and offline sources and builds a single customer view. Marketers are increasingly focusing on first-party data to drive better customer experiences and marketing outcomes. More than half of marketers surveyed by Winterberry Group say cross-channel audience identification and matching is their highest priority. In fact, investment for identity resolution is projected to reach $2.6B in 2022, according to Forrester Consulting. So, it is no surprise that brands are taking this seriously and most want to create a single customer view.

A major part of what a CDP does is to undertake identity resolution; the process whereby data arriving from different sources is matched together using a range of different personal identifiers such as email, mobile, postal, cookie ID, customer number. The key consideration here is that the CDP needs to maintain for each customer a table of all known personal identifiers so that when a new one is introduced it can where possible be matched in.

The CDP then provides the single central source of truth about customer behaviour from which dashboards can run and analytics can be undertaken; it will also be used for activating multi-channel customer campaigns and for resolving GDPR questions.

 

Availability of skilled data analysts equipped with the right analytical tools

A large organisation like a bank will have upwards of 50 skilled data analysts, but with many smaller organisations it is often the case that they have one or none and rely on external resources to support them.

There are several reasons for this. Cost is a key factor and linked to that, the difficulty of putting a precise number on the value that a good data analyst can bring. Next the demand for analysis normally fluctuates, and a single analyst would always be facing feast or famine. Also, data analysts usually prefer to work in small teams so that they can discuss problems and learn off each other. Being the only data analyst in an organisation is a lonely position, and often they end up just cranking out reports and become dispirited.

A lot of the reporting can be resolved by introducing dashboarding technology like Tableau or Microsoft Power BI, but these tools still need to be configured to produce the right information.

However, dashboards and data visualisation tools can only take you so far. If you need some more complex analysis, or if for instance you want a propensity model to predict the next best offer to make to each of your customers, then a data analyst becomes essential.

To undertake more complex analysis the analyst will need good tools like SAS, SPSS, or R.

For the smaller organisations, the right solution could then be to outsource to an analysis company or to independent contractors, until demand has grown to a scale where the function can be brought inhouse.

 

A failure by the decision makers to frame the right questions for the analyst to answer

This issue is less frequently discussed but, in our opinion, not one to be brushed under the carpet.

A considerable amount of the work done by data analysts is wasted because someone does not spend sufficient time thinking about what the real problem is that the analyst should be trying to answer.

Einstein said…

“invention is not the product of logical thought, even though the final product is tied to a logical structure”.

Unravelling this statement in the context of customer marketing, we would suggest that the person who requests the analysis will succeed if they allow their imagination to fire up a range of conjectures that the logical analyst can then set about proving or disproving.

Some analysis is more mundane, but when for instance a business is contemplating several alternative strategic changes then the analyst should look at all the different scenarios that these would potentially deliver, and, as far as possible, provide the business with an understanding of their relative merits.

 

So, in conclusion…

From our experience it is fair to say that a large proportion, probably more than 50%, of medium size organisations involved in B-to-C marketing that we encounter have their customer data disconnected and spread across multiple systems. This is a problem that can be solved, and the costs are not frightening. A CDP will usually cost no more than 0.5% to 0.75% of sales.

However, setting up from scratch an internal insight and analytics department is challenging, and outsourcing will make economic sense until demand has grown. Also, the outsourced provider should have analysts with a very wide range of experience and skills.

And then how to ask the right questions of the analyst? We would recommend giving the analysts scope to try out different approaches, and to look at different angles to a question. Like this they are far more likely to land on an interesting and valuable solution.

[1] Data Distrust Report – the impact of data distrust on analytics projects and decision making published by SnapLogic in 2020, based on interviews with 300 US and 200 UK IT decision makers.

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.

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.

Do you really know whether your company should install a customer data platform?

data from a customer data platform

Customer Data Platforms (CDPs) are taking the marketing world by storm – the Customer Data Platform Institute projects that marketers will spend $1.3 billion on them in 2020.

But the important question for your company is whether you would really benefit from having one?

So, what do CDPs really do? At a very high level they:
– ingest all available sources of online and offline customer data and build a deduplicated single customer view
– provide the capability to profile and segment customers
– enable personalised and consistent communications to take place across all channels by connecting your marketing technology
– support you in visualising customer performance

Do you really know whether your company should install a customer data platform? We have devised a simple 10-point questionnaire to help you understand whether your company could benefit from a CDP. It won’t be telling you whether should definitely should have one, as you will need a business case for that, but it will tell you whether CDPs are worth investigating.

QUESTIONNAIRE

If you want to start to find out more about what a CDP could do for your company then please order our free e-book CLICK

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.

Personalisation in digital marketing: how personal do you want to get?

There is a lot of interest amongst marketers in personalisation in digital marketing. And yes, we all like to be recognised for what we are. But we suspect that people are becoming wary of trivial personalisation or ‘personalisation lite’.

For example, recognising what I last looked at on a website is reminding me of the blindingly obvious.

However, telling me about a local retail event in the near future that is connected to what I was looking at on a website is much more likely to catch my attention.

And, for a full version of personalisation, understanding that I am a longstanding and loyal customer, rewarding me for my loyalty, and telling me about something that is actually of interest to acquire, really does hit the spot.

So, what is required to make this deeper level of personalisation actually work in practice for digital marketers?

Well, the first item needed is an effective personalisation engine. A tool that decides what kind of personal experience I should receive based on the information it knows about me. These engines work by the marketer setting rules or conditions which if satisfied mean that the customer will be sent a specific version of an email, or see a particular image, or offer on a website.

But next you will need to have joined together the online and offline data worlds. That allows the personalisation engine to know both that you are a loyal customer and that you are interested in a particular kind of merchandise or customer experience.

Most personalisation engines deliver ‘personalisation light’ because they ignore the history of your relationship with the company and just focus on what you have been browsing in the last 24 hours or so.

We have teamed up with Fresh Relevance (see www.freshrelevance.com) as our personalisation engine partner to allow you to give your customers full personalisation.

We manage this by sending Fresh Relevance a nightly feed of customers’ characteristics – factors like loyalty, spend, and long-term merchandise category purchases, so that these can be used in combination with their immediate online activities and behavioural triggers.

UniFida and Fresh Relevance can deliver in combination personalisation that really makes a difference.

To find out more, please email us suggesting a time that would be good for you to have a chat.

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.