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 marketers ignore AI and thrive?

Can marketers ignore AI and still thrive in today’s competitive market?

“Artificial intelligence (AI) is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.”

Without wanting in any way to dismiss AI, which can be incredibly useful in many different arenas like driverless cars, we believe that its role for marketers has been exaggerated. Indeed most of what we need is barely within the spectrum of technology normally defined as AI.

For most cases where marketers want to execute one to one personalisation, the area where AI could most appropriately be applied, the conventional propensity model is all that is required.

What is most often meant by personalisation is the means to carry out selections of customers for communications based on their expected response or their particular needs.

Here are some examples where personalisation is often used:

– Targeting apparently dormant customers (e.g. those who in fact have a high probability of being reactivated) with offers to reactivate them
– Making a relevant offer (e.g. based on customer characteristics that imply a higher than average probability of purchasing in a particular product category) of a specific item
– Responding to risk (e.g. predicting which customers are likely to cancel policies or stop ordering) so that they can be presented with good reasons not to abandon their policy or purchase

In each case a conventional predictive model can be built, using an historic set of customer data, where a target customer population can be distinguished from the remainder who have not evidenced reactivation, response, or reduced risk of lapsing.

The key point is that we are not asking for this kind of model to be adaptive to rapidly changing circumstances; instead it relies on past customer behaviour to inform what is likely to happen in the present or near future. And this is because human behaviour in most situations where we are reacting to propositions put to us by marketers tends to remain reasonably constant.

We have even tested propensity models on historic data going back four years and found them to work well.

propensity model on historic data

However, to build and apply these conventional propensity models there are some essential requirements:

– a single customer view to provide the greatest possible depth of customer data
– the ability to update model scores each time new data about an individual arrives
– the availability of data scientists armed with tools like R, SPSS, or SAS

A typical predictive model will take the form of an algorithm which will attribute a probability score to each member of a customer base; we judge the success of these models by the extent to which these scores are differentiated from random in the way they can be used to predict customers’ behaviour.

Looking at a recent model we built for the reactivation of dormant customers, the top customer decile had an index of 330, compared to the bottom decile’s 17.

In another case, a model for product category preference had a top decile index of 601 and bottom decile index of 11.

For most of us marketers these results will be seen as providing a huge improvement on random and quite fit for purpose. However, the methodology used does not in our opinion qualify the models to be correctly described as AI.

If you would like to talk to us about developing propensity models for you, or providing the technology for a single customer view, then please do email us back

UniFida logo

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

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

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

Is your RFM analysis throwing away good customers like households waste good food?

Official estimates for UK household food waste in 2015 come to 7.3m tonnes. Of this the avoidable element, if not wasted, would have saved the average household £480 pa. Yet we have discovered that marketers who allow themselves to be governed by the use of RFM analysis (recency, frequency, monetary value) as their criteria for selecting which customers to engage with are allowing themselves comparable customer wastage.

RFM has been recognised for many years as a great way to select customers for marketing communications, but it has a significant flaw!

People who still have a relationship with your company get ignored if they are not continually buying from you.

We routinely find customers on the bottom rungs of the RFM ladder that can still be profitably communicated with if you take a different approach.

In a recent case of a home shopping company we found customers who could have provided an extra £1m of net demand being excluded by RFM from receiving catalogues.

To find the valuable individual customers in your older RFM segments we build a multi-variate purchase value propensity model. This takes account of a wider range of variables than RFM, such as product categories, and will combine data from individual transactions in many more complex ways than RFM can do.

We then build a matrix that overlay RFM segments with propensity bands.

RFM analysis and propensity matrix diagram

In the top right hand corner of this matrix we have people who would have been forgotten by RFM analysis but who in fact still have considerable demand value according to the propensity model.

Want to read more about RFM? Read our white paper completed for a home shopping company where we compared the difference in net value obtained from using propensity model scoring versus RFM. 

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.

RFM score 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.

In addition, a propensity model can take into account not only RFM based information, but also things like age, gender and other demographic information that might be available on customers.

 

RFM or propensity score white paper

In this example, we are dealing with data from a home shopping company with over 1m customers, and a large number of merchandise categories. We first used cluster analysis to group the merchandise categories into six high level merchandise groups.

The RFM score was then built on customers buying across all six merchandise groups whereas the propensity model was developed for one specific group. We used those who had purchased from the specific merchandise group in the previous three months as the target variable.

In order to compare the two targeting approaches, we selected deciles within the customer base by each method, and then looked at the proportion of actual buyers that we found within each decile.

Download the RFM or propensity score white paper to find out the results.

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.