Are your 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.

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 you making 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.

It 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.


Can you escape from Analysis Paralysis?

For many marketeers, a significant part of their day is spent pulling together reports from disparate data sources, and then trying to extract from them the metrics they need to unravel how their marketing is actually working, usually with varying degrees of success.

Well, if you are one of these people, we have a means of escape!

You may recall that our cloud-based customer data platform, called UniFida, neatly joins together all your online and offline customer information and builds a single customer view. It undertakes identity resolution, and links browsing and ordering activity to individual people.

We originally designed this tool to enable you to send very personalised communications to individual customers. But it also allows us to provide you with a complete set of marketing performance metrics. Serendipity happens!

The metrics we produce cover what we expect are your key concerns:

  • Customer metrics to tell you about customer acquisition, retention, and longer-term value
  • Campaign metrics to provide you with the results of all your direct communications with known individuals
  • And media metrics to show you how each of your media channels are contributing to the orders you are receiving (including social, display, PPC, email, mail, and SMS)

We recognise that you may not at this point in time need the whole suite of UniFida functionality, but you may be interested in UniFida Marketing Metrics as a standalone module, particularly when priced accordingly.

We believe that it can give you nearly all the metrics you need to manage your marketing at a very reasonable cost (and with us taking care of all the set up and configuration).

If you want to get a quick understanding of this part of UniFida, then contact us.

And if you would like to us to arrange a teleconference with you, then please email us with a good time to talk.

 


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.


Predicting the future value of your customers

Did you know that by the time you on-board a customer you will be able to accurately predict their future value?


From which it becomes clear that the decisions you make at the customer recruitment stage will determine that key metric of future customer value.

These are typically decisions about the recruitment channel and tactic you use, the types of customer you are targeting, and the nature of the proposition you are making to them.

Now you should also take into consideration the fact that a large number of customers that businesses recruit will yield negative value after the costs of recruitment have been deducted, whilst others will be strongly positive.

So, what as a marketer, should you do about this? First stop focusing on cost per acquisition; it’s the wrong metric to be guided by.

Next, we suggest that you should take a multivariate approach so that you consider all the factors together that define customer groups in order to focus on the ones that will bring you value.

We have recently been helping a substantial life insurance broker do this and the result has been transformational; for instance, they can now balance factors like the risk of attrition against the amount of monthly premium paid. They have found that a substantial number of their marketing tactics are yielding negative customer value and have had to be abandoned.

If you would like to discuss how to target your customer recruitment for longer term value then please contact us.

If you would like to read a case study about how we did it then please read our post:

 


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.


How a customer data platform (CDP) enhances, accelerates and democratises decision making

Using data for decision-making

The maxim ‘knowledge is power’ has held good for many years but that power also can be democratised when knowledge is shared. Shared knowledge, and a single source of the truth, both lead to a common understanding of how a business is working, and from this a more effective, faster and smoother decision-making process. No more meetings starting with that obstructive statement ‘I don’t recognise the numbers’.

So how does a customer data platform help achieve all this?

Several reasons combine to contribute to the result:

1. A CDP will build peoples’ identities using data coming from many different sources. This means that individuals may be matched on their postal address, their email, their mobile, their cookie ID and more. The identifier, we call it the permanent URN or PURN, for an individual will be fixed in the system and any new data that matches to it added to the customer record. From this we achieve a finite, and at any moment in time, fixed number of known individuals in the system. We can no longer disagree over customer volumes or the data that is attached to them.

2. All the data that arrives goes into a shared data schema. This means that the data definitions are constant as well as the values within them. A customer data platform can have only one set of values for gender or marital status for instance.

3. The CDP does not dispose of data unless required to do so under GDPR or because it has no conceivable value. When a CDP is being set up historic customer data is loaded into the system, and from that point on data will be augmented. This data usually includes details of on-line activity, when it can be matched, as well as transactions undertaken, and contacts made through any channel both inwards and outwards.

4. A good CDP will check the data feeds as they arrive for consistency of their layout and content. Feeds that do not conform are rejected before they go into the customer data platform. In some cases, such as when we are dealing with names and addresses, they may need to be improved using matches to external verification files like PAF.

5. A smart CDP will go far beyond just recording the input data. It will start to build what we call engineered data. This is data derived from the raw data that comes in to the CDP. It may be a calculation of lifetime value in the first year since acquisition, or the result of applying a customer segmentation or a propensity model. Engineered data fields are then updated each time new data arrives for an individual. It is often the case that charts and reports are built from the engineered data rather than from the raw data. For instance, to answer a question like what is our customer retention rate we need to have an engineered data field that marks whether a customer who purchased in period A also goes on to purchase in period B, the periods being linked to when the individual first appeared as a customer.

6. The CDP will allow access to all users in a company who want to see that single common view of the customer. This may be by way of dashboards or on-line reports, or because they have the tools to extract data directly from the system. The dashboards may be tailored to each individual user or be common to a department or a whole organisation. But whatever charts or tables they contain they will all be derived from the same common but constantly updated CDP. This means that they cannot disagree because both the data they are drawn from is common, and the definitions that they use are shared.

And what types of understanding can be obtained from the CDP?

Having developed a shared, consistent, and complete customer data source, the CDP can be put to support a multitude of uses. What follows is a description of some of the more common ones, but in reality, there is no limit on the kinds of outputs that can be achieved, particularly when the CDP is aligned with a clever data visualisation tool like Tableau.

1. Supporting AI.
Underneath all the hype about AI is the need for good data to support it. If an AI tool is to learn from a flow of data being fed into it, that flow needs to be unchanging in its composition, and accurate to the point of perfection. A CDP is perfectly designed for this role.

2. Tracking the overall customer picture.
Managers looking after customer acquisition and retention will have an almost unlimited number of questions they need to have answered. Some of the most typical are:

  • What volumes of customers are we recruiting through which channel?
  • Do the different channels and media sources provide customers with different characteristics and different longer-term values?
  • How do the different social media channels contribute to my customer volumes?
  • From which geographies am I recruiting my customers?
  • What is my level of second orders and year on year customer retention?
  • What behavioural segments do my customers fall into, and what do they look like when profiled by demographics and lifestyle?
  • Have the characteristics of my customers altered over time?
  • Are there some customer groups that provide such low value that they are not worth the cost of recruitment?

3. Tracking the product picture.
Every business knows the high-level numbers on product sales but beneath this high-level view are many interesting questions, for example:

  • Are different types of customer changing the mix of products they buy?
  • Do different channels lead to different products being purchased, and to different product values being chosen?
  • What is our best recruitment product?
  • How loyal are customers that are first attracted to different products?
  • Given that we know the first product purchased by a customer, can we predict what a customer is most likely to buy next?
  • If we deduct the cost of marketing and cost of goods, how profitable are our different products?
  • Can we build a time series model to predict overall sales of my products based on a combination of customer information, and marketing history, combined with external factors?

4. Next up, understanding the impact of marketing.
A strong case can be made for the assertion that without a reliable CDP the value of marketing will always be an unknown. For instance, if you cannot attribute sales that come in via the internet to prior marketing communications how can you justify the cost of those communications? Or if the longer-term value of customers cannot be measured how can you justify the cost of recruiting them beyond the value of their initial purchase? Some of the many uses of the CDP for marketing have been detailed above under customer and product understanding, but there is an additional layer which comes from being able to link marketing activity in its many guises to an outcome in terms of customer value. Some examples are:

  • Linking expenditure on social media to actual customers recruited and the value they subsequently generate. Very few companies spending fortunes on social media take the trouble to do this, but with the right digital analytics combined with the CDP this can be quite straightforward.
  • Examining campaign results not just from the overall value obtained, but also investigating what types of customers they attracted, and what different longer term values they brought with them.
  • Providing the data to help understand a complex sales funnel where prospects drop out at different stages; in these cases, there is a need not only to understand the impact of the different processes on drop-out levels, but also what types of prospects are better at surviving the overall funnel process.
  • If there is a customer retention problem, then this can be evaluated in terms of lost revenue, and from that budgets provided for retention activities that can then be tracked, using control groups held in the CDP, to find out which provide the best ROI.
  • When a marketing budget is complex and spread over many different activities then there is a need to untangle the impact of the different marketing activities and prioritise the way budget is allocated to optimise results. This requires building an understanding of the relationship between spend in a channel and the usually diminishing results achieved as spend is increased. This is often called a saturation curve. For this kind of analytical activity the underlying data is found in the CDP, although the subsequent analysis is done outside it with different tool-sets.

To wrap it up.

It would be presumptuous to claim that a CDP can solve all marketing problems, but without doubt it can ensure that knowledge is shared, and the information required to make intelligent marketing decisions made readily available. A CDP can be perhaps viewed as the necessary foundation stone for all successful marketing organisations.

Contact us if you’d like to know how we can help your organisation.


UniFida logo

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

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

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


Do you throw 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 (recency, frequency, monetary value) as their criteria for selecting which customers to engage with are allowing themselves comparable customer wastage.

RFM has been recognized 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.

Propensity matrix diagram

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