Existing customers – why they don’t shop like prospects

It may not come as a complete surprise to be told that your existing customers behave differently from prospects – i.e. people who have never purchased from you before. In fact, the differences are very pronounced, especially when you look at customer journey patterns.

At UniFida we provide technology to help clients capture these customer journeys and then we use AI to analyse them. This enables the true value of different marketing investments to be understood and highlights the journey interactions that are most likely to be successful.

In our study of actual customer journeys, some of the findings are surprising. Possibly the biggest is that existing customers take longer journeys to the point of purchase than prospects. In fact, we found that not only are the journeys longer when measured in days from initial activity to final sale, but so too are the number of sales steps (events) in the journey.

Customer Journeys – New vs. Existing Example

customer type sales info

You can see from our analysis that existing customers take on average 29 days, whereas new customers take three days.

As the chart below shows, many prospects buy within just one or two sales events.

customer journeys graph

Another key finding is that different channels work better for prospects than for existing customers. Search engines and TV can, for instance, be key for prospects, whereas direct channels like email and catalogues work better for customers.

Sales value by media chart

To some extent this is because catalogue and email are “channels of choice” for customer marketing, and hence drive more sales, but also pay per click (PPC) is used more often by prospects.

Different strategies

Given these facts, it’s clear that very different contact strategies are needed for the two different groups. As existing customers seem to prefer the long haul, the challenge is to keep them sufficiently interested during their journeys. This is where emails and texts come in, particularly when the customer has visited your website and not purchased, and also when you want to discourage them from using PPC by offering click-throughs from an email.

By contrast, prospects are more likely to swoop down on a purchase. They may have started their journey as unrecognised browsers, but when they come back to your website conversion techniques, such as website personalisation, become more important. When prospects are on your website there is no time to be wasted.

Example customer journeys

Below are some customer journey examples that illustrate these points. The top two journeys are for existing customers and show the interplay between different channels after a catalogue has been sent to them. We have chosen two particularly long journeys to allow more steps to take place before the purchase.

In the first journey, social media helped close the sale, whilst in the second it was email. Both existing customers used search even though they must have known the name of the company they were looking for, although they may have been looking for alternative suppliers at the same time.

Example: Existing Customer Journey 1

customer graph

Example: Existing Customer Journey 2

customer graph

The next two journeys for prospects (new customers) are much shorter. The top one started with the prospect searching and the sale being closed with an email, so he/she must have provided their email address on the website when visiting. In the second case there were two click-throughs from social media followed by a direct entry a day later.

Example: New Customer Journey 1

Customer type and channel graph

Example: New Customer Journey 2

Customer type and channel graph

What these charts reveal is just some of the complexity, often ignored, surrounding customer journeys and the importance of recording them so that they can be analysed.

By understanding which pathways lead to sales and which don’t, marketers have a far greater chance of making the right moves to convert prospects, and customers, to purchasing.

 


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 Metrics – why do they matter?

When it comes to measuring marketing success, there’s no doubt that marketing metrics are crucial.

Every company will have its own specific marketing metrics requirements. Analysing what these should be, and then having them delivered in a timely and reliable fashion, is vital for the success of every marketing department – and ultimately the company that it is serving.

To show the value of marketing metrics, let’s compare the fortunes of two different companies. Company A had a good set of accurate marketing metrics and used them well, while Company B had some faulty metrics and decided to be guided by them, with unfortunate consequences.

Company A

This company makes its own products and sells them directly to consumers globally. When the Covid pandemic hit they found that there was a massive upsurge in demand. By the end of 2020 they had sold 2.09 times more than the previous year. By the end of 2021 that had risen to 2.24 times.

However, their top priority was to manage supply and demand to keep the time lag between orders and delivery as tight as possible. This meant managing the amount spent on recruiting new customers and on communicating with existing ones.

Fortunately, they had historic marketing metrics that went back five years. These reported month-on-month sales from existing customers compared to new recruits, and which previous years those customers had come from. Once they knew how much the effects of Covid-19 was uplifting existing customer sales compared to previous periods, they could accurately forecast demand from existing customers for the rest of the year. This gave them a clear indication of the level of new recruits required to fill the factory capacity.

The net result? They were able to fill the factory whilst minimising their marketing spend and achieve a very strong ROMI (return on marketing investment).

Company B

This company also manages their own production, but they had a faulty metric. This was the sales value they were getting from customers to whom they had sent a catalogue. There were two reasons for this:

  • the lack of can accurate single customer view, which led to multiple duplicate records, and
  • an inaccurate match-back of those mailed to people ordering.

The result was that their metrics were reporting a much lower return – in fact, one-third of what it should have been on catalogue campaigns.

This led to a decision being made to abandon catalogues in favour of email communications. However, the company had not been particularly successful at collecting email addresses, and as a result there were only 14% of active customers for whom they had email addresses and who had not opted out.

Despite a very high volume of emails being despatched, the overall order rate in each year, from existing customers began to decline. Over a five-year period, it had sunk to a half of what it had been at the start.

However, because they were successfully recruiting new customers, the actual order value from existing customers kept going up, so nothing was noticed. In other words, instead of using the right metric – i.e. what proportion of the customers you start the year with go on to order in that year – they used the wrong metric, which was simply the absolute number of existing customer orders.

The net result was that Company B lost several million pounds worth of potential sales from existing customers, whilst not even understanding that this opportunity had been missed.

So, the value of accurate marketing metrics is clear. Analysis of those metrics is crucial to the success of your marketing activities – and your bottom line.


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.


Covid-19 “Customer Bump” – What do you do about it?

Many companies selling online to consumers experienced a Covid-19 “customer bump” – i.e. a hike in sales during the pandemic. For most of these companies, these peak sales will have dropped back to a “new normal”.

The question is: how should marketers respond in a post-Covid scenario?

First, below are some examples of customer figures based on UniFida’s clients:

Both clients experienced a massive upswing in new customers recruited in 2020, followed by a reduction in 2021. These indexed customer recruitment figures are calculated by taking 2019 as the base.

However, neither client had an upswing in the % of existing customers reordering:

customer metrics

So, for both clients a slightly smaller proportion of existing customers reordered during the Covid period compared to 2019.

The implication therefore is that the Covid bump came from newly recruited customers rather than existing ones. However, it has to be said that both the level of new and existing customers ordering, are driven by many factors other than Covid, including marketing expenditure.

So, what to do about the Covid era recruits?

First, it’s important to put in place a month-by-month monitoring system for this cohort to track exactly how strongly they are reordering, and whether they are ordering the same merchandise mix as the non-Covid recruits.

If they are reordering less strongly, they need somehow to be re-engaged. And if they are ordering differently, this gives a clue as to what to offer to this group.

Second, consider asking for a demographic profile to compare pre-Covid with in-Covid recruits. We did this for one of our clients and the shift between the 2019 cohort’s demographics and the 2020 was quite substantial:

So, the 2020 new customers are substantially younger than those recruited in 2019, but come from the same income bracket.

It’s important to take a look at how the Covid recruits found you and ordered from you. Their pre-order customer journey may have been different to the pre-Covids – they may have been more likely to find you from social media, or responded to Google Adwords. Whichever route they took will give you a strong clue as to how best to reach them, and others like them, in future.

Armed with information about the Covid bump’s demographics, merchandise choices and channel preferences – and assuming that they are different to the pre-Covid recruits – it will be worth treating them as a separate segment for some time going forward.

Impact on behaviour

Covid had a massive impact on people’s behaviour and, depending on the types of merchandise you are selling, you may wish to try to reincarnate the marketing conditions that drove new customers to you during that period.

This COVID bump will certainly have changed the customer base and marketing metrics. What worked in the past will not necessarily apply to these new cohorts. However, gaining better insights into this group, and testing tailored communications across channels, can turn core segments of this blip from one-off lockdown buys into high-value and loyal customers.

For more information on how we can help with customer insights, 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 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.