Can customer journey analytics improve conversions?

customer journey analytics

Customer journey analytics can tell us some of what we need to know about the routes customers take, but does it help us to improve conversions?

 

What is customer journey analytics?

Customer journey analytics is the science of analysing customer behaviour data across multiple touchpoints, and over time, to measure the impact of customer journeys on business outcomes.

Companies use customer journey analytics because it is an effective way to improve customer experience, increase customer lifetime value, and improve customer loyalty.

Let’s start then with what we mean by the ‘journey’.

This can be used to describe a myriad of different routes from just travelling around inside a brand’s website, to trekking between websites and mobile applications, to flipping between online and offline channels, or to contacting a call centre.

Indirect channels like TV and press may also play a part, and, least trackable of all, conversations actually take place between people.

So, let’s dispense with one myth, that customer journeys are always trackable, as some of them are not at all, and others only partially. For instance, only a tiny proportion of retailers attempt to collect emails or other contact details from customers in their stores, and, when they do, many refuse. Some retailers use ‘beacon’ technology to understand if the customer is a repeat visitor or new; however, matching this back to the customer has raised some privacy concerns.

The extent to which we can join together the stages in the journey is entirely dependent on the evidence by way of personal identifiers that the visitor leaves behind at each stage, and how they can be linked.

For instance:

Email marketing identifies which customers have opened and/or clicked from each campaign. Direct mail provides the details of who has been sent a catalogue, however not if they received it, or even browsed it.

Call centre tracking identifies the number you are calling from.

Website analytics captures the cookie ID of website visitors, how they got to the site, and whether that was through search, direct visit or referral from another online channel like social or paid digital.

Tracking is one thing, but then linking events together is another. To do so one needs to form associations between personal identifiers. A cookie ID becomes much more valuable when linked to an email or a mobile number. An email is more valuable when it is associated with a postal name and address, and so on.

All customer journey analytics relies on being able to link stages in the journey using identifiers that can be matched up. The most obvious case where external links don’t exist are unidentified browsers, where one browsing visit can be linked to another, but not to anything that is going on in the offline world, or in other websites.

We must accept that customer journey analysis can only deliver a partial truth, but this is not to deny that a great deal of value can still be obtained from it.

 

Visualising customer journeys using analytics

We can depict these customer journeys using what is known as a Sankey diagram, named after Captain Matthew Sankey.

sankey customer journey chart
Sankey customer journey diagram

 

 

Alternatively, with a numerical version of a Sankey diagram, like the one below, we can start to understand what the probability is of customers moving onto another stage in the journey, or making a purchase.

numerical version of a Sankey diagram
Numerical version of a Sankey diagram

To understand this chart, start with a channel on the left and read across to find the probability of moving from that point to the next channel (column), or to making a purchase (described as conversion), or to nothing further being trackable on the customer journey (described as null).

 

Improving conversion rates

So, what can be done with the information in a chart like this?

  1. It brings a sense of reality. If you know that the probability of moving from social media to a sale on your website is five per thousand, you will take a more sober view of media owners’ claims.
  2. It provides a really useful comparative understanding of the impact of different channels. In this example the chances of purchasing after receiving and opening a series of four emails (described as campaigns) is 6%, whilst after four direct entries it’s 5%.
  3. It explains the benefit to be gained from multiple experiences in the same channel. The chances of purchasing after a fourth consecutive search is 2.4 times greater than a conversion following a single search.
  4. It shows where the interactions between channels are more likely to happen. For instance, the chances of moving from receiving a fourth email campaign to undertaking a direct search are 12%, compared to 5% after receiving just one.
  5. It also shows us which are the better channels from which to start the journey. In this case starting with an email campaign opening is best.

These are just examples of some of the uses of visualising a customer journey, and you will want not just one but multiple views. For instance, new customers will have very different journeys to returning ones, and so will people from different countries, or those buying expensive merchandise compared to those buying something cheaper.

Most organisations struggle to understand their impact on customer experience, and the value which can be generated from enhancing it, due to several reasons:

  • The number of customer touchpoints and the volume of data produced by multiple channels and devices has exploded in recent years. Having this in one place becomes a challenge.
  • Data silos lead to problems of data mismatches, missing and bad data and, time to transform and aggregate the data.
  • Shortage of skills and resources to analyse and make sense of the data, as it often requires skilled data scientists and analysts who are conversant with programming languages like Python, R or SQL.
  • Inability to attain rapid customer insights, and execute triggered activity across multiple channels, can have negative consequences if you’re not aware of each customer’s experience with your company across channels.

Now, your customers expect every interaction with your organisation to reflect the context of their entire experience regardless of which touchpoint they use next. So how do you meet these expectations?

 

Overcoming these obstacles and making journey-driven decisions for each customer at scale

You are probably now in need of an explanation of how you can start to understand your customer journeys, visualise the data, and make use of it?

We recommend first introducing, if you don’t already have one, a customer data platform into which the data describing different parts of the journey can be ingested, and linked together wherever identifiers can be matched. A typical customer data platform will take in data from your email service provider, your website, your transactional or ecommerce systems, paid digital and possibly your call centre and your retail outlets. Your objective will be to ingest as many parts of the customer journey as possible.

The customer data platform will then undertake what is known as identity resolution to join as much data to individuals as is possible.

(Incidentally the customer data platform will have multiple other uses than customer journey analytics, although this remains a key element of what it can deliver).

With this in place, you have the potential to build a table of all the visible and linkable events that precede each transaction. As long as these are date-stamped, technology can then provide you with the journey charts that you will require to help plan your direct marketing.

Most companies lack the comprehensive, up-to-date journey data needed to optimise each interaction, so they are forced to run experiments on single channels, such as email or website, without understanding their wider impact. By having a single view, real-time data visualisation and the ability to trigger activity based on events, you can see how customers respond to each improvement as your customers experience them.

We have developed this capability inside the UniFida customer data platform, so that for any time period, and any segment of customers, the production of this kind of analysis is automated.

Considering investing in a customer data platform? Contact us to get started with one of our complimentary CDP services.

 


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.


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.


Forrester says that analytics is a top marketing priority!

Forrester has based this claim, that analytics is a top marketing priority, on a survey of 750 analytics decision-makers in larger companies employing more than 500 people (‘The Future of Analytics’, Forrester, July 2020).

To quote from the report: Analytics is a top marketing priority. Of the ten marketing priorities we surveyed, marketers ranked improving their use of data and analytics as a top priority over the next twelve months. More than six in 10 marketers (63%) indicated that analytics was in their top five priorities. In separate research, Forrester found that improved analytics drives business results. In that, connecting customer data across formerly siloed product lines and connecting customer and behavioural data across channels can inform digital improvements that increase sales.

challenges faced when it comes to digital analytics
Challenges organisations face when it comes to digital analytics.

Forrester then went on to ask about what was inhibiting companies from delivering analytics with the following results:

With this in mind we want to explain what we are doing to help marketers using our UniFida technology get the analytics they need.

First, we have removed the problem of siloed data. UniFida’s cloud-based customer data platform technology ingests data from people browsing wherever we can match it to customers, as well as from ecommerce, customer order systems, email service providers, call centres, and where available retail. It then uses all available personal identifiers to build the single customer view. We end up with a ‘single silo’ of all customer data being made available for analysis.

Next, we have integrated with Microsoft Power BI so that data manipulation and visualisation can be undertaken. Power BI allows you to create virtually any report you want and publish it within your organisation. Inside UniFida, Power BI can use all the online and offline data available in the single customer view to tell you how your customers are performing and what they are responding to.

Then to help automate reporting we have built into UniFida a suite of standardised marketing metrics. At the click of a few buttons this will tell you all you need to know about customer acquisition, customer retention, and customer value. It can also tell you how your marketing campaigns are performing, and help you compare test results. All this updated every time UniFida receives new data.

Finally, we have just released our innovative solution to marketing mix modelling. We call it ADEE or algorithmic direct event evaluation. By looking at all the online and offline events that occur in the 90 days before a sale, and using our proprietary AI to weight them, we can tell you what are the drivers behind every order. When summed up this tells you precisely the contribution made by each direct marketing channel you are using from Google PPC to catalogues or email.

We are not trying to say the UniFida has an automated answer to every analytics question you can throw at us, but we expect that it will definitely cover the majority of them. And for those that it cannot solve we have our in-house data science team who can for instance build you a customer segmentation, or a propensity model to predict which of your dormant customers are most likely to be reactivated.

Forrester says that analytics is a top marketing priority. Many of the team who developed the UniFida technology have come from a marketing data science background, so every decision we have made when designing the tool has incorporated the need for marketing analytics.

So, let us know if you would like a quick demo of what UniFida can deliver, as we would be delighted to show you it in action.

 


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.


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

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

We are thinking of questions like:

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

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

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

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

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

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

So, what can we conclude so far?

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

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

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

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

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


UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.


Could marketers view historical customer data like an archaeologist?

Could marketers view historical customer data the same way archaeologists view excavating a historic site? It may sound daft but we believe that the answer is a decided yes!

Archaeologists view a site that they are excavating as a series of layers. With each layer representing a distinct historic period –  this dating approach is known as stratigraphy.  They use this to associate different items of evidence with each other and can, for instance, differentiate Bronze Age pottery from Iron Age by how deep they find it in the ground at a particular site.

But the preservation of remains and artefacts within a layer tells much more. For instance, petrospheres are now known to have been used for smashing large bones to extricate the marrow. This is because these spherical stones and the broken bones have been found together in the same layer of Palaeolithic sites in the Middle East.

So, we marketers can look at historical customer data in a similar way. We can see what customer behaviour has taken place in each time period, in response to what stimuli, and learn vast amounts from that.

For this to work we need to make sure that our ‘stratigraphic’ customer data has been carefully collected and maintained. Clients need to ensure that all transactions, contacts and customer attributes [such as their source of recruitment and demographics] have not been discarded along the way.

What will this customer data tell us? What Tutankhamen can we expect to uncover?

If we take a group of customers recruited in a specific time period, we can look at the order value they on average provided in their first, second and third year from recruitment.  This will the help guide us to understand how much we can spend on recruiting them.
historical customer data acquisition chart

Now some of these customers will have only purchased once, and others will have purchased more often. Having uncovered the different groups we can start asking what differentiates them.

historical customer data retention chart

Often the source or channel of recruitment is the biggest factor in determining what their future value will be. Will a Facebook derived customer be worth more or less than one that comes from Google PPC? Their age at time of recruitment and their geodemographic can be of great significance.

Looking at the different customer layers we can start to ask questions about how the external environment has impacted their behaviour. Customers recruited in 2008 and 2020 cannot be expected to behave like customers recruited in more normal years. And when the economy shrinks, we can look to see whether demand has just been postponed or lost forever.

Could marketers learn a trick or two from archaeologists? Historic customer behaviour data sets are a gold mine if used properly.  To extract the value you will need both the customer data store, and the data archaeologists who can uncover the buried secrets.

In marketing we call these archaeologists data scientists.

We have developed our company UniFida along the lines of an archaeological dig; we collect and store our clients’ customer data (protected by UKFast, UK-based data centres ISO certified, PCI DSS compliant and secured to UK government IL4 standards) in our cloud-based technology, and we then deploy data scientists to extract meaning and learnings from that.

Please don’t hesitate to get in touch if you are sitting on a customer data site that needs careful ‘excavation’.


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.


The identity resolution process: are customers turning into chameleons?

It may be noticeable that, like chameleons, they are becoming harder and harder to identify. And there is a reason for this. They are constantly changing their personal identifiers, like email, mobile numbers, or cookie IDs. The process to properly identify individuals we call “identity resolution”, and failures in identity resolution may sometimes have quite negative consequences.

Customers actively dislike not being recognised, for instance being treated as a new recruit when in fact they have been buying from you for years, or being sent the same message twice, and in addition to that there is a cost for the organisation with added communications costs.

Lack of good identity resolution processes also makes a nonsense of trying to calculate customer lifetime value or undertaking forward business planning based around your expected rates of recruitment and attrition.

 

So what does a good identity resolution process consist of?

We see it as matching all available personal identifiers, from every one of your customers, to get the best possible chance of joining your customer data inputs from multiple sources into actual customer records.

This used to be a relatively straightforward task when the main personal identifier was the postal name and address, although that in itself posed some considerable challenges.

With the usual mix of badly typed addresses, varying address structures, and incorrect postcodes we often find there is a problem just within name and address matching. In a recent case we found 25% name and address duplicates.

But the postal address is just one of multiple personal identifiers, each of which can change at any time.

We have all become identity chameleons, changing our mobile numbers, emails, cookie IDs etc with great regularity.

There is however a relatively simple solution – just keep hold of all the personal identifiers you have been able to link to each individual since you first recognised them, so that you have the best possible chance of identifying them when the reappear.

This is exactly what our cloud-based customer data platform does with the data it ingests; as each individual item of customer data is taken in, its identifiers are matched across the entire customer base.
an example of cdp identity resolution

If you think that you may have an identity resolution problem with your customer data, we can offer you a very low-cost solution; we can trial match all your customer data sources together in UniFida, and report on the amount of duplication that exists between them.

This will tell you how many customers you actually have, and how many duplicates you are carrying.

 


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 customer value metrics the backbone of your marketing?

It’s intuitively obvious that they should be, but what may not be so clear are which actual metrics you need, and how to connect them to different areas of your business decision making processes. Let’s take four key ways in which you can take advantage of customer value metrics.

 

1. High-level business planning

Your turnover is equal to the sum of the customer value provided in any period. So, to look forward to how your customer value is going to be provided in the future you need to be able to project from your current customer base, remove those that are going to attrite, and add those that you are going to recruit.

The metric to support this is the average value per customer in each year since they were recruited. So how much value in their first, second third year etc. This allows you to very easily roll customer value forward for planning purposes.

When you start from your planned turnover in say next year, you can then tell how much of that is going to be provided by the exiting customer base, and how much will need to be provided by how many new recruits.

You will also want to apply some assumptions about how value is going to be altered by improvements to the way you look after your customers, and then you will have the basics of a customer-based business plan.

 

2. Understanding which customer groups provide what level of value

You will be very aware that not all customers are equal when it comes to their level of spend with you.

So, you will need to dissect your average customer value by the type of customer they are. Factors such as age, gender, and product categories purchased can all be used to profile the value of your customers.

The benefit then is that you will know what groups to target your recruitment efforts at.

 

3. Examining the customer value provided by different channels and media

This type of analysis leads you directly to understanding the ROI provided by different channels and media.

Indeed, we like to use a metric which is the amount of longer-term customer value derived from every £1000 spent in a particular recruitment mode.

You can undertake this at a very micro level, such as individual media, or more macro level, such as a channel.

There is though a caveat; many customers are now recruited as a result of contacts from multiple channels. However, this does not prevent you from looking at the customer value obtained from each recruit for whom the channel has played a part.

 

4. Where to focus retention?

This is a harder question to answer as your higher value customers will often be the most loyal.

What you need to know is which of your higher value customers are more at risk than others.

For this you will need an individual level predictive model for risk of attrition with which to score customers, and find the higher value, higher risk, group.

 

Some conclusions

  • Understanding all aspects of longer-term customer value is critical for every successful marketeer.
  • To achieve this, you need a single customer view that can track customer behaviour through time.
  • You will then need to be able to obtain the metrics.
  • It won’t come as a surprise to regular readers of our newsletters that our customer data platform UniFida has been designed to provide most of the metrics we have been describing on demand.

In some cases further analysis will be required, and our data scientists are happy to help with this.

 

If you would like to talk to us about how to get the customer metrics you need, then please email to say when and how you would like to be contacted.


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.