Could marketers learn a trick or two from archaeologists?

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 historic 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.

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.

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.

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.

Are you grasping the moment?

One thing is certain; we won’t forget what we did in these very special Corona Virus quarantine days.

And although none of us know how long our confinement is going to last, we do know that it is going to end.

We are experiencing  an unprecedented once in a lifetime chance to do those things we don’t normally do, and to get marketing ship shape for when we are eventually let out, and the wheels start turning full speed again.

Freed from the daily commute and the office banter, we can make real progress with the infrastructure and the customer knowledge that should drive marketing, and which is so often ignored when times are busy.

By now you must have started to build a ‘must do when away from the office’ list; and we have a few suggestions of things that you might consider including:

– finding out which of all your marketing initiatives in the last year actually made you money
– finishing the unmentionable GDPR project
– planning future recruitment to avoid black hole areas
– getting customer data into a single customer view
– asking customers what they like and don’t like about what you do
– making sure you have the dashboards you need to steer the ship
– getting the team to use some of the incredible array of free online training resources
– achieving consensus on the five most important areas to focus once you are free
– and so on, and on. The list can get very long very quickly

We are here to help; as well as our data science and technology arms, we have a marketing consultancy.  Its aim is simply to ensure that you have the tools and the customer knowledge to unlock the most customer value at the least marketing cost.

If you would like us to help you write or deliver on your shortlist, we are here to provide expert support.

For a quick dip into what our consultancy normally covers, then please click here to view a short PowerPoint.

Hoping that we can help you make the best use of the quarantine.

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.

Keep it clean!

This somewhat short title reflects the overall approach to data cleansing suggested by our partners the REaD group in their latest report.

In other words, it’s no longer just one trip to the bathroom a year, but rather daily hygiene backed up by a culture that won’t accept any excuses for dirty data.

We partner with the REaD Group for a number of data cleansing services including PAF formatting to get address structures right, and suppression of gone aways and deceaseds.

The amount of dirty data residing in customer databases that they remove never fails to surprise us, or our clients.

If you would like to know how much you have, we can arrange a free audit; this will tell you just how many address duplicates there are, the amount of badly formatted addresses, and the number that should be suppressed.

If you want their report, then please read it here REaD Group Report, and if you would like to arrange an audit then please email us and we will call you to discuss.

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.

If you can lend us your customers’ data, we can tell you the truth about them!

Increasing Customer Knowledge

Estimates differ, but apart from the largest companies who all have their own data scientists, and the smaller SMEs who are not into customer analysis, around 60% of those in the middle do not have the customer knowledge they would like to have to run their businesses.

This does not mean that you don’t have answers to some of your queries, but that you probably don’t always have the ability to ask detailed questions about your customers’ behaviour and get correct answers back.

Every organisation has their own very focused areas where they would like to know more about their customers, and we would like to hear what yours are.

To give you a flavour of some of the questions our data scientists have been asked recently to investigate:

  • how do the different channels, and means, by which we recruit customer impact their longer-term customer value?
  • once customers have made their first purchase what is their next most likely purchase, and what is the likely interval between first and second purchase?
  • what proportion of my customers am I retaining year on year, and what appears to be the factors causing better and worse retention?
  • can I score up my customers with their likelihood to respond to my next campaign?
  • what is the relationship between people browsing my website on multiple occasions, and purchasing?

In reality we never know exactly what we are going to be asked to look into next!

But we want to remove the barriers between your needs for customer knowledge, and our capability to deliver it.

The simplest way is for us to meet up. We can discuss a brief together, take it away, and come back to you with a fixed cost proposal. If it’s too high you can say no, and all you have lost is the time for the briefing meeting. If it’s ok you will get the customer knowledge you need at a reasonable price, delivered on time.

Members of our team have been delivering customer knowledge to clients for over 20 years, so we have built up some experience in the area.

And by using us you avoid the problems and costs of recruitment, when you may not need someone full-time to do the work for you.

Interested? Please email our Head of Data Science Anthony Antoniou to arrange an appointment.


A data scientist’s point of view

Big data and analytics can transform how businesses operate. So what exactly is the reality of having a data scientist on board, or even outsourcing the work?

We interviewed Anthony Antoniou, our Head of Data Science, for his point of view on the day to day reality of what a data scientist is expected to deliver, and what actually interests him most.

Q: Why in general do clients come to you for outsourced data science services?
A: Clients come to us when they have a certain level of knowledge, but don’t have the skills or resources to undertake the analysis. Some are very good at phrasing the question they want to have answered, where others give us a much more open brief.

Q: So what is an example of an open brief?
A: A client who has a lot of data, and just wants to be shown what they can get out of it. A recent example has been a client with a large number of retail properties, who had several different sources of visitor data. We were asked to pull them together into a single short meaningful report for their board, which showed the trends in terms of the volumes and types of visitor, and where they were actually going to.

Q: And by contrast what would be an example of a very directed brief?
A: Someone who was trying to solve a very specific problem. We have a financial services client who was concerned about lapse rates, and wanted us to examine all the factors that impacted lapse. So we looked at how they were recruited, through which channel, what kind of people were being recruited, what product they were sold etc. Having looked at all the factors individually, we then looked at them multi-variately in order to find the combinations of factors that delivered customers with high and low lapse rates. The results helped the client a great deal in understanding where they needed to recruit.

Q: Overall what kinds of project are given to outsourcing data scientists?
A: Well you can break them down into reporting and modelling. The clients who want reporting tend not to have any internal data manipulation skills, so that they have to rely on external help. However, with more and more self-service tools being made available like Tableau for instance, we expect that the amount of outsourced reporting is going to drop. But having said that, there could always be a role for a data-scientist in setting up a dashboard so that reporting becomes almost continuous. Dashboards can in themselves be quite simple or complex like for instance the customer journeys taken on a website and how they are linked to conversion.

Q: So what about the ones who want a model?
A: Generally clients who want a model tend to be the ones who want to optimise their marketing spend. The focus is on getting better value for money. For instance because emailing out catalogues is so expensive, home shopping companies get very particular about who they send them to. This leads to us building response and value propensity models for them and challenging their traditional recency, frequency and monetary value models, although these can be pretty effective in themselves. To optimise marketing spend one needs to stick to areas where everything is measurable. This often favours direct channels over broadcast like TV, although there are techniques we use for getting to understand the impact of broadcast channels.

Q: So do you get to look at the effectiveness of social media?
A: We are just starting to do this. We have some results in from looking at the connection between buzz on social media and sales for a very large consumer electronics company.

Q: But what about paid for social media?
A: Well the interesting question here is what longer term value it delivers i.e. what sorts of customers, and how they play out over time. Our own customer data platform UniFida is helping us with understanding where web visitors have come from, and what value they deliver over time.

Q: How would you define ‘data scientist’?
A: Well it’s a much over used term but I see it as someone who goes through an end to end process involving all the stages of gathering, manipulating, analysing and interpreting data.

Q: And does that incorporate AI and Machine Learning?
A: Yes it can. The difference with AI (with the application of Machine Learning) is that it should be capable of unsupervised learning, where a model or process is in place that improves itself as more data becomes available. A classic example in the marketing space is where retailers make product recommendations based on changing consumer preferences. The potential for AI in marketing is where there is a continuous ongoing activity like the behaviour of visitors on a website.

Q: What interests you most as a data scientist?
A: Essentially where I can add real value. This can be very visible in certain areas like home shopping. Very often what one finds out has huge financial significance for the client company. I also like a challenge. We were recently asked by a radio music broadcasting company if we could predict which of their stars were rising and which falling so that they know whom to play. Fortunately, they had an app where listeners where asked to record what they thought about specific tracks. We followed the direction of changing approval or disapproval over time in order to get an early indicator of public opinion.

Q: Do you have problems getting the right data to analyse?
A: We often do, although some clients keep their data in a very orderly fashion. Also, we have been developing our own cloud-based customer data platform called Unifida, with the specific objective of pulling together in one place everything we know about an individual, joining on-line and off-line sources. This provides excellent structured data for analysis, allowing me to spend less time preparing data and more time deriving value from it.

Q: What advice would you give a graduate data scientist who was just starting out?
A: The most important thing is to work somewhere where their skills are appreciated, and if possible where they are part of a team so that they can learn from others. And yes, a good variety of work helps a lot too.

How the music broadcasting industry uses predictive analytics to spot rising and falling stars

Our client, a market leading music entertainment business, wanted help to know which tracks to present to its listeners, to ensure the maximum engagement.

With so many tracks to choose from, there are several factors that come into play, such as:

  • How popular the artist is with the audience, and how is that popularity trending?
  • Internal music experts’ judgement on how popular a particular track will be
  • Previous record sales

To aid our data science team we had qualitative and quantitative data made available in the form of surveys, and real listener opinions recorded on an app.

The survey data was rich, and gave a real insight into what people think about a track, but it’s expensive, and often not collected frequently enough to give the information needed to be able to act on, when tastes are changing so quickly.

The quantitative data came in the form of an online music app, where users’ real actions can be measured and, with statistical modelling, be used to predict the outcomes of the survey. The benefits of this are:

  • The ranking can be updated much more frequently to make the most of up-to-date listener behaviour
  • Tracking the ranking over a short period of time, you can see whether a track is becoming more or less popular, thus helping discover rising and falling stars.
  • The deliverable for our music broadcasting client has been an ongoing predictive algorithm, that spots every week trends in terms of which of some 300 tracks are becoming more or less popular.

This is then used by the D.J.s to help guide their selections, bring on new artists, and start to lay off those at the top as soon as the audience interest is starting to flag.

In this fictitious example, the current Taylor Swift track is one of the most popular (ranked number 3) but people are starting to turn it off – it could be time to give Louis Tomlinson a bit more air time?

What would you give to know how to stop recruiting unprofitable customers?

In many of our engagements with clients we discover that a high proportion of customers recruited are unprofitable, and for every one of these the company suffers not only their wasted sales and marketing expense, but also a downstream negative return.

For instance, life insurers may be concerned about claims risk, but often a greater cause of lost revenue is customer attrition. If they can recruit customers who stick, and pay higher premiums, this can transform their longer term profitability.

So why not move from a focus on recruitment volume, to one of recruitment value? It may mean lower customer volumes, but it will ensure much better overall profitability.

We want to share with you our five-step approach for stopping recruitment spend going on unprofitable customers;

1. Identify the factors that drive individual customer profitability.
These are going to vary from industry to industry, but they often boil down to purchase value, product margin, and product tenure. In simple terms if the customer buys from the bottom of your range an item that is expensive to deliver, and never repurchases, they are not going to be a prime recruitment target.

2. Analyse the recruitment process features that have an impact on these profitability drivers.
For instance different channels, and tactics within the channel, are going to drive very different types of customer. For one client we compared the longevity of customers recruited through their TV advertising and website (inbound traffic) with those recruited through outbound activities like telemarketing. The inbound traffic was very much stickier once recruited. But also, the types of customers you recruit will have a very strong impact; we like to look at differences in customer contribution by factors such as age, affluence, gender, geography etc.

3. Combining recruitment process features into a contribution calculation.
Once we understand those aspects of your recruitment process that are both controllable, and which have a significant impact on downstream customer contribution, we can then look at how in combination they can be used to identify and predict the future value of recruits.
For a recent client, we found that we had around 1000 combinations of factors that we could identify, that each had a significant number of recruits, and for which we could compute the average longer-term contribution. For instance a combination of factors could be age-band combined with recruitment channel, and particular product features.
For each combination, we could then analyse the average customer contribution provided. This provides the lens through which we can predict longer term customer contribution across all current recruitment activities.

4. Building a heat-map of where to spend the recruitment budget
By combining these individual customer segments into higher level groupings, we can depict go and no-go areas for targeting the recruitment budget.
This approach to targeting needs to be run in combination with an analysis of the cost to recruit by the different recruitment process features. The ideal is to find combinations of features with a low cost to recruit and a high customer value, but this may often yield very few available options. A pragmatic approach is to look at the ratio between downstream customer value and recruitment cost. Using the lens of recruitment process features this becomes very achievable.

5. Predicting forward value from your more recent recruitment activities
A final use of this analytical approach is to model the predicted downstream value of customer recruits in a particular time-period. To do this we allocate recruits in the period by their recruitment process features to a predicted contribution group.
The good news about this last step is that one can validate it on historic data.

We usually manage to get to a high level of accuracy of prediction of the drivers of customer contribution; our latest example was better than plus or minus 2%!

RFM or propensity score – which wins out when put head to head?

RFM, or ‘recency frequency monetary value’ to give it its full name, has long been the targeting tool of choice for the home shopping industry; so we decided to give it a challenge by building as an alternative a propensity model using exactly the same data set.

An RFM score will describe the overall strength of the relationship between a business and a customer, but the question is whether we can improve on that by building a propensity score targeted at a specific purchase activity or category.

A great advantage of RFM scores is that because they are not proposition specific, they can be used across a wide range of applications; however, if the scale of any actual marketing selection is substantial enough, then the extra resource required to build the propensity score may be justified.

Read the white paper here to get the result…