The importance of relating data unification projects to business goals – put simply, a single customer view is only worth what you can use it for. The most popular uses mentioned in the report were customer data analysis, orchestration of customer communications and selection of messaging. For our part, we would like to add customer journey-based marketing attribution.
The biggest CDP deployment problem is the client’s organisation – in effect users need to get cooperation across an organisation and then invest in the team skills required to use the capability properly.
CDP projects that are managed by marketers are more likely to be successful than those managed by IT – the most likely explanation we would suggest is that marketers are going to rely on their CDP to deliver marketing results and this requires having full operational effectiveness. They cannot risk it failing.
Companies are showing increasing concerns about privacy compliance – this is most probably the result of increased publicity being given to data security breaches and personal data privacy issues. Indeed, we find it hard to understand how an organisation can manage customer consents without a unified customer view.
CDPs are no longer in the category of marketing technology that only very large companies can afford – many of the survey respondents engaged with CDPs were working for companies with sales in the $10m to $100m range, which is small by US standards.
The update is fascinating reading for anyone interested in introducing a CDP, or maximising value from an existing CDP. Membership of the Customer Data Platform Institute is free, visit their website for more information.
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.
CDPs – Customer Data Platforms – are often just evaluated from the viewpoint of facilitating timely, relevant and personalised customer communications. Properly configured, a CDP can also hold a complete history of every customer interaction, from online to direct mail, opening up very valuable insights into customer understanding.
Here are the top 8 ways a CDP can help you better manage your customer marketing:
1: Measuring how each and every aspect of your direct marketing is performing.
Having every customer touchpoint in place leading up to each of your sales is a critical
requirement for delivering customer journey-based marketing attribution. This works best
when you have a scoring system in place that can share out the value of each sale across the
touchpoints that lead up to it, according to the contribution they make. In this way you can
not only compute the value of each of your online and offline campaigns, but also look at
how different customer groups, for example new vs. existing customers, are impacted by
your marketing activities. And you can start to look at how each channel performs in
different seasons.
2: Knowing the longer-term value of customers recruited through different channels and via different campaigns.
Longer term customer value varies hugely across the different types of customers you
recruit and different channels will attract different types of customers. So, assuming you
know how a customer was recruited, you can start to understand what their longer-term
value is likely to be. This can be problematical where several channels are involved in a
single sale, but you can look at the longer-term value of all the customers gained via a
particular channel or campaign. You can also drill down further and differentiate customers
by other criteria, such as previous relationship with your brand, geography, or even age
band. Understanding customer longer-term value allows you to set maximum costs for
acquisition and get a better understanding of the returns from marketing investments.
3: Predicting lapse levels and lapse timing for subscription products.
Anyone selling a subscription product, whether it be an insurance policy or a magazine, will
know how vital it is to recruit and retain ‘sticky’ customers. Fortunately, the data residing in
your CDP should allow you to model retention with a great deal of accuracy. This is because
you will be holding not only the payment records of each subscribing customer, but also a
great deal of information about them. There are several statistical methods for doing this,
but we tend to use CHAID as it divides customers up into identifiable groups with different
expected levels of longevity. Your historic data can also be used to show what proportion of
your expected lapses are likely to happen each month, which can be of vital importance for
cash flow planning.
4: Building customer segmentations to help you better understand the needs of different customer types.
Marketers need to simplify the problem of dealing with many different types of customer
requirement. The proven way to achieve this is to build a customer segmentation that gives
you a handful of groups for which you can devise different marketing strategies, or even
different products and propositions.
There are many techniques for building customer segmentations, but we like to use one
that allows you to allocate customers with relative simplicity into their correct segment, and
also to find similar types of people outside of your own customer base. For customers within
the customer base, criteria such as value, previous sales or enquiries, and types of
merchandise they buy, often groups customers meaningfully. For customers outside the
customer base, segments are often defined by, for example, affluence or age band. Once
the segmentation structure has been developed your CDP will allow you to allocate each
customer to a segment and plan your communications strategy accordingly.
5: Managing all your GDPR consents in one place.
Customers deposit their consents in many different places. They may unsubscribe from one
newsletter, opt-in to another, decline cookies on a website but approve receipt of customer
marketing when placing an order. A marketer has to establish order in what is often a very
untidy consents landscape and then define clear communications rules about what can be
sent to whom on which pretext.
Your CDP is the one place where, through identity resolution, consents can be bought
together and organised and rules about who can get which communication established. The
CDP can also provide most of the materials for fulfilling Subject Access Requests, as well as
manage anonymisation of data when the right to be forgotten is exercised.
6: Planning business development based on a customer value model.
Businesses need clarity on the growth and quality of their customer base, not least to
understand how to split the marketing budget between acquisition and retention marketing
to meet business objectives. Your CDP will hold a record of the historic value contributed by
each individual customer and you will know what that amounts to in any historic calendar
year. It will also tell you what percentage of customers recruited in previous years typically
order in the current period. Using this information you can, with reasonable accuracy,
predict what value, for example, your customers recruited this year will contribute during
the next year and how this will be distributed month by month.
You will also know how your new customer recruitment usually lands month by month, and
the value new recruits contribute in the period from when they were recruited to the end of
the year. Pulling all this together you can calculate how many customers you will need to
recruit in a future time period to meet a specific overall sales target. We call this the
customer value model, all made possible by data held in your CDP.
7: Providing data for building response and upsell propensity models.
Predicting response by different channels can save a considerable amount of the marketing
budget, enabling marketers to avoid activity that will not produce a strong return on
investment. To build a predictive model you need a target variable, like propensity to make
a second order, as well as predictor variables, which are facts known about the customers –
in this case, both for those who buy the second product and for those who don’t.
The role of the CDP is to provide this data to the data scientist, or the AI tool, which is going
to build the model. But as well as providing the data that allows the predictive model to be
built, it also provides the data that then allows every customer to be scored up with a
probability of doing whatever is being predicted. Indeed, without a CDP, developing and
using propensity models for marketing is made very much more difficult.
8: Recruiting customers to join research panels.
Many companies like to maintain continuous panels of customers who have agreed to answer market research questions, usually in exchange for some value given back to them.
The CDP can provide randomly selected customers for recruitment to these panels, as well
as managing the exercise of sending them questionnaires and recording their responses.
Customer panels are very much simpler to manage if you start with a CDP already in place.
Overall, a CDP should be seen as an essential component in the complex process of
maximising your customer revenue through improving your customer marketing.
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.
In the fourth video in the series, UniFida’s Client Director Jo Young talks about CDPs (Customer Data Platforms).
She explains what they are, why they are important, how to create one and what benefits we can expect.
In this third video in the series, UniFida’s Client Director Jo Young talks about four key customer data questions you need to ask to drive sales, and discover your customers.
We’ll explain the four questions, why are they important, the best way to answer them quickly and what benefits can we expect.
Play Video
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.
As a mid-sized company, there will be many areas where a Customer Data Platform (CDP) may add value to your business.
Your company will most likely have an existing range of marketing technology solutions, such as an email service provider (ESP), an ecommerce platform linked to your website, Google Analytics, and an offline order processing system which maintains a history of all product sales through different fulfilment channels.
Before deciding on whether a CDP can improve on what you already have, there are some key questions you need to ask.
What actual value will we gain by adding in a CDP to our normal configuration of tools?
A CDP will normally provide additional core functions including:
Ingesting data from your website, email service provider, ecommerce system and order processing
Identity resolution to pull together all known data about every customer or prospect sourced online or offline
Data integration, often via API, with other existing martech tools
Building a single customer view to include a mix of raw data received and derived data variables
Capability to automate trigger or batch marketing campaign selections
Provision of dashboards based on the data received
Campaign results reporting or even multichannel marketing attribution
What is the role of a CDP in supporting our marketing and business planning decisions?
A CDP will be able to answer a wide range of planning questions, including:
What longer term customer value (LTV) can we expect when we recruit customers from various sources, or through different offers?
Invariably different recruitment channels will provide customers with varying LTV and understanding these enables the business to set realistic CPA targets by channel.
What proportion of my existing customers reorder each year and how much is that worth? This key question allows a marketer to plan forward knowing broadly how much demand is to be expected before new customer recruitment is added in. It also enables a company to measure how well their overall customer marketing is doing. If the percentage of existing customers reordering is growing, then customer marketing is doing a good job, but if it’s falling something needs fixing.
What value am I getting back from my marketing investments?
Because the CDP can track customer journeys, both online and offline, it can measure the contribution made by specific campaigns or channels to these, and hence their value provided. No longer should a marketer simply take a list of people contacted by a campaign and find out which of these have ordered in a specific time period, because this will ignore all the other components of the customer journeys that led up to a sale.
Are there areas of my marketing spend that should be cut back or removed?
Most companies hang on to some campaigns that should be modified, but often lack the analytical tools to identify them. A frequently found example is spend on PPC where GA is reporting a much-exaggerated contribution, but without the capability to look at all the other elements in the customer journey, the PPC spend is maintained. Another example is where data is purchased to run cold campaigns. A cold list needs to be integrated into the CDP to support the full evaluation of the campaign just as contacts made with existing customers do.
On a much wider basis companies need dashboards to show them the direction of travel across a whole range of KPIs. These can look at anything from how different customer segments are performing, to the proportion of successful vs. unsuccessful website visits. The data in the single customer view, particularly because it combines both online and offline activities, can provide a very rich feed for dashboard reporting using tools like Power BI or Tableau.
The CDP can be seen as a rich historical data store that holds a persistent record of the activities of each customer. From an analytical perspective this is invaluable as the data can be mined by data-scientists to answer every conceivable question relating to sales, customers, marketing performance and even where profit ultimately comes from.
And finally, the CDP allows for the democratisation of data and information. CDPs are normally cloud based, and hence open to anyone with access rights. This means not only access to dashboards, and existing metrics reports, but also the ability to extract data and undertake further analysis as required.
How can we generate more value from existing customers or prospects?
There are many ways in which the CDP can improve cross-channel communications, and hence improve value received from customers.
The capability to respond to triggers is critical to delivering the right communication at the right time. There is some capability to do this embedded inside many of the ESP platforms, but because they lack the richness of data contained in the SCV, they can on the whole only respond to short-term signals like dropped baskets. A CDP will be able to take the wider view, for instance understanding the longer-term value of the individual customer, whether they are becoming more or less loyal to the brand, whether they have missed their usual anniversary of making a purchase, the kind of merchandise they are likely to buy based on their affluence, the behaviour of other customers in their household, and so on. The list of potential trigger activities based on a CDP is huge and a brainstorm to surface these can be invaluable.
In a related way the CDP can target batch campaigns in a very sophisticated manner. For instance, not only can overall response propensity models be built that are far more powerful than RFM (recency, frequency, monetary value) selections, but also these can be used to determine which categories of product an individual customer is more likely to be more interested in.
We often find companies ignoring large swathes of customers who have not ordered for a while, which, when using RFM selection methodologies, they are not able to target economically. However, once a reactivation propensity model has been built, groups of these individuals can be identified that are very profitable to reactivate.
Introducing customer segmentations to determine the types of propositions to make to individuals. Take a very basic segmentation based on age and affluence and it becomes immediately clear how useful is a tool like this. It can be applied to the SCV and used to differentiate the content being used for different groups so that the affluent older generation can be treated differently to impoverished students.
The CDP can be used to manage customer contact density across all outbound channels and to coordinate the messaging. When they are operating in different silos the ESP can send message A and the direct mail campaign can contradict it with message B. The CDP can remove these kinds of contradictions and also manage the overall level of communications sent to individuals. Send out too much and you are not only wasting money, but also drowning the customer, whilst sending too little means that you will be missing potential demand and leaving the customer curious about what they have not been offered. Our testing has also found that different customer segments, particularly those based on different propensities to buy, will respond better to varying levels of contact density.
Lastly, there is the crucial question of using testing and setting up control groups. A CDP can be used to set up control groups who, for instance, are not exposed to any marketing communications from any channel and contrast these with those that are. Testing also provides the knowledge base from which every kind of improvement in customer communications can be derived. But tests need to be selected from, and recorded in, the SCV to avoid cross-channel confusion and provide the ability to measure the longer-term consequences of the tests, rather than just the short-term response from a particular offer.
How do we reduce the time spent on managing campaigns and pulling reports?
The CDP will create one version of the truth for the business, from which consistent compliant selections and reliable automated reports can be produced.
There is a huge amount of drudgery in managing marketing communications if you don’t have the right tools. With a well-designed CDP, and using drop down menus, setting up a new campaign should be a matter of minutes, compared with what many hours if different data sets need to be combined, manual selections made, seed lists added in, and source codes applied, etc.
If your CDP is linked to a suppression house you can also, with a click, send your selection to have the goneaways and deceased flagged in seconds.
At the same time as setting up the campaign you will be able to organise how its results are reported and choose the right metadata to describe it. After that reporting and test results comparisons should be automated.
Ad hoc reporting is normally the elephant in the room when it comes to workload and much of this can be eliminated by having in place the right set of dashboards, plus automated customer and campaign metrics.
Some CDPs will automate many aspects of GDPR requirements, including the coordination of consents and automating the production of Subject Access Requests. Consent coordination is vital for campaign management and again can avoid considerable manual effort.
When looking at the benefits from introducing a CDP, the financial value of marketers’ time saved may seem small compared with the other benefits, but the improvement in accuracy, creativity and job satisfaction released by the removal of boring manual tasks can be very important.
How do we make a decision about whether to introduce a CDP?
There are a large number of areas where a CDP may be able to add value to your business. However, there is no substitute for brainstorming, and then listing, all the potential use cases and working out the value you will expect to get back from each of them. The benefits usually take the form of increased customer value, reduced marketing costs and saving of staff time.
There is also the important question of what impact this will have on your internal IT resources. The answer to this is relatively simple. Assuming that all the data feeds can be automated, once these have been set up to run your internal IT team will have done their job, assuming you are buying a fully serviced CDP.
If you are buying a platform that you will need to configure yourselves, then there is an additional layer of work which may be substantial. It is critical to find out from the vendor exactly how much support you can expect to get.
Research has shown that most companies who introduce a CDP get considerable value from them, but that this augments over time. As the users learn how to get more and more value from the CDP, so the returns grow. So, it is worth lowering expectations for year one, and augmenting them for subsequent years.
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.
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 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.
Now, more than ever, businesses know the importance of looking after existing customers in order to retain them and increase their value.
The key to this is having complete and up-to date customer data that accurately depicts how your customers behave – both offline and online.
Equipped with a clear understanding of their needs and preferences, your marketing department will be more effective at increasing customer loyalty and sales. And just as importantly, your service teams will be able to provide more insightful, helpful and impressive experiences.
Of course, this all depends on how accurately your data is depicting your customers.
Customers in pieces
When it comes to capturing customer data, quantity isn’t the problem. Customers generate huge volumes of it as they engage via websites, apps, email, call centres and retail branches.
The real issue is where it is kept – often in divisional or channel siloes that few businesses can integrate.
This fragmented approach prevents businesses from getting a clear picture of their customers and a true understanding of their preferences and needs.
And it is compounded by the fact that customers are frustratingly random. They might buy online using their office address and mobile number, for example, yet their loyalty membership may be linked to their home address and personal email.
It’s like you don’t know me
A fractured data picture leads to decisions that are based on only a partial understanding of customers. Inevitably, it results in experiences that leave customers feeling unrecognised, unvalued – and yes, unloved.
The sense that ‘the right hand doesn’t know what the left hand is doing’ can be all too evident during personal service encounters. When a customer talks to your service teams, they expect you to know the totality of their relationship, not just the last time you spoke.
A fractured data picture leads to decisions that are based on only a partial understanding of customers. Inevitably, it results in experiences that leave customers feeling unrecognised, unvalued – and yes, unloved.
The fact that your data is siloed is of no interest to them, but if you genuinely want to build customer loyalty and value, it should be critically important to you.
I want a complete picture, and I want it now
The ability to get a single customer view, and in real-time, is what’s preventing many businesses from becoming truly customer focused.
The problem is traditional processes and systems can’t keep pace with the volume of data being generated by today’s customers. This results in large amounts of customer data being overlooked and unused.
Even if data can be accessed, extracting insights takes so long that any commercial value is lost –particularly in dynamic, fast-moving markets. Thankfully hope is on the horizon, in the form of a new breed of technology platform.
With CDP’s everything is clear
The emergence of Customer Data Platforms (CDPs) is giving service organisations the opportunity to see their customers as they really are – often for the first time.
This complete picture is available in dashboard format on the desktop of each user. It puts the awesome power of customer data in the hands of the people who need it most – your marketing and service professionals.
Equipped with true insights, your people can deliver personalised experiences that maximise opportunities and mitigate risks in the context of a customer’s entire relationship with your organisation.
The hottest topic in the world of data-driven marketing, Customer Data Platforms are being widely adopted as much for their ability to improve customer satisfaction as their capacity to boost sales.
Take customer centricity to the next level
Being customer focused requires more than good intentions. It demands a technology I want a complete picture, solution that lets your organisation recognise each customer individually and enables your and I want it now people to deliver highly personalised customer experiences. That solution – the Customer Data Platform – has arrived.
This guest article was written by Tony Rambaut, Co-founder of our partner UniFida Australia. Originally published in the FOCUS Quarterly March 2021 magazine by CSIA.
Are you considering a customer data platform? Find out how developing use cases can help you understand what your business needs from CDP technology.
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.
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.
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:
Customer data availability and quality
Availability of skilled data analysts equipped with the right analytical tools
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:
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 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.
How exactly does a customer data platform work, and help marketers leverage data to gain a better, more accurate understanding of customer behaviour?
Ingesting and integrating data
The first element in understanding this is ingesting data. CDP’s ingest customer data from multiple sources. Typically, these will include website data, paid digital, transactional, direct mail, retail, email, and call centre. All data received by a CDP will relate in some shape or form to a customer. The data is usually sent to a CDP using an API or via an SFTP site.
Customers have multiple identifiers and these change over time, such as mobile phone number, email address, cookie ID, postal address, customer reference or landline number. This data is collected, and these identifiers are used to generate a single customer view also known as ‘Identify Resolution’. For example: if someone logs into your website with their current email, but with a different cookie ID, then the new cookie ID is added to that particular customer record on the assumption that they are using a new device. Equally, if a new transaction record is received with the same customer reference, but a new address, then a new address is added on the basis they have either moved or added an extra residence.
As new data is ingested, each record goes through what is called the ‘purning’ process. This is the stage at which the record’s personal identifier(s) are matched against all other customer records that are held in the CDP until a match is or is not found. At this point the data may be matched into an existing single customer view or a new one created. Each recognised customer is given a permanent unique record number or ‘purn’.
Identity resolution
Is at the heart of a CDP and is central to all the rest of its functionality. A good CDPs’ functionality is rooted in the knowledge that people have multiple identifiers, and that these identifiers can all change. Over time many or all of these identifiers are likely to change for an individual. The CDP should keep a history for every one of every version of these, although regarding the latest versions as most likely to be current. This collection of identifiers is what it calls on to build the single customer view.
The data in a CDP is held in what is called a schema. This is the way in which the data is organised. Every organisation using a CDP will need their own schema although within an industry, schemas will have a lot of similarities.
Engineering derived data
Engineered data is important for the value it provides for selecting specific customer groups for communications or developing customer insight. It can comprise any variable that can be calculated using an algorithm or other means from the raw data in the customer data platform.
Data engineering can take many forms, from simple examples like banding variables such as age, to more complex ones like keeping a counter on customer’s total historic value. A major use of engineered data is in developing and recording scores derived from algorithms such as propensity models.
An example of an engineered data field is where we want to know what each customer has contributed to a business after the cost of acquiring them. We can then:
Use historic purchase data for each individual in say their first and second year since recruitment
Deduct the cost of acquisition which can be derived the channel they came in from
Deduct the cost of communications sent to them in the same period which is held in the contact history area
Calculate an individual customer contribution
Engineered data is updated at an individual level every time a relevant event happens; so, each new home shopping purchase, eCommerce transaction or physical retail transaction can lead to a changed score in the engineered data section. A great benefit of engineered data is that it allows you to base axis for charts or selections for campaigns on these additional variables.
Analysing customer data
A CDP is essential for gaining a full and accurate understanding of customer behaviour. For instance, without a CDP that combines web browsing history with transactions, it would not be possible to understand the relationship between the two. Again, if individual contact history is not held against a customer record then the effectiveness of campaigns that are sent to the customer, and to which the customer may respond through different channels, cannot be accurately measured.
The CDP builds the single customer view, and it is against this that customer analysis can take place. It provides the dataset that becomes the one authoritative source of information about customer behaviour for an organisation. With this in place decision makers have a firm basis on which to proceed.
There are so many aspects to the analytical tools that can be used to analyse customer data that there is little merit in trying to list them all. Some are built into the CDP and others require data to be first extracted from the CDP and then transferred to them. What matters is that they have the best possible customer data set to analyse.
So, the results from customer analysis form the basis on which key decisions about customer marketing can be made. These include such areas as:
Customer acquisition (targeting and channel choice)
Digital planning
Product development
Customer relationship management
Salesforce management
Pricing
Even corporate mergers and company valuations.
Given how important these decisions are, it makes good sense when designing a CDP to first start with a list of the kind of results that will be required from customer analysis so that for instance data is held with sufficient granularity to make these possible.
Connectivity to external systems
The CDP can support other systems in their personalisation and management of customer communications. Typical examples are:
Providing customer selections for email marketing systems
Customer segmentations for web personalisation technology
Names and addresses for postal marketing
Target audiences for social media
So just as the CDP ingests data from multiple sources it also provides selected data to external systems. These connections are usually made via an API or via transfer of data to an SFTP site.
Delivering personalised customer experiences
Within the CDP we expect to find functionality for the selection of specific customer groups either on a one-off or on a recurring basis. These groups are usually selected for output to external systems that manage the actual communications. The selections themselves can be simple based on Boolean logic rules, or they may be more complex based on propensity scores applied within the engineered data. They can also be based on triggers, such as a new customer having just been recruited.
The CDP needs to enable these different types of selection, and crucially record what contacts each individual customer has been selected for. Functionality is also required for test and control, and for including source codes with the selection.
Associated with delivering personalised customer experiences needs to be functionality for measuring the results of campaigns. This is often automated within the design of the CDP and should always include the ability to attribute results such as orders back to campaigns, even if they respond through different channels.
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.
As customers we generate massive volumes of data as we engage across multiple channels using different devices which makes it challenging to capture, integrate and activate this data effectively.
Data repositories are often siloed and not integrated with each other or allow easy transfer of data to marketing platforms. Let us now throw in some GDPR and updates from Apple with identifier for advertisers (IDFA) and the deprecation of third-party cookies by Google.
Today’s customers simply assume that your company knows and remembers who they are, what they have done, and what they want, always and across all channels. Their expectations are high, and tolerance is low. So it is not surprising to see that many marketers have made a unified customer experience their highest priority.
What’s the problem with data?
Not having a single customer view creates many challenges including:
Making it more tedious to activate campaigns to the right audience and report on them in a timely manner.
Degrades customer experiences.
Introduces privacy concerns.
Marketers and marketing technologists know that gathering and acting on unified customer information is not easy. In fact, only a small percentage of companies have achieved this and can truly operationalise their first party data. The rest are battling with technology, strategy, budgets, organisations, staff skills, and other obstacles to success.
Traditional methods for collecting that data into unified customer profiles, such as an enterprise data warehouse, have failed to solve the problem. Newer approaches, like “data lakes”, have collected the data but failed to organise it effectively and enable marketers to activate the data into owned and paid marketing channels.
The Customer Data Platform is an alternative approach that has had great success at pioneering companies. The process of collecting and unifying the data is known as identity resolution which is a core building block for enabling better customer experiences and optimised marketing effort. A CDP puts your marketing team in control of the data unification project, helping to ensure it is focused directly on marketing requirements.
CDPs apply specialised technologies and pre-built processes that are tailored precisely to meet marketing data needs. This allows a faster, more efficient solution than general purpose technologies that try to solve many problems at once.
Customer Data Platform Definition
“A Customer Data Platform is packaged software that creates a persistent, unified customer database that is accessible to other systems”.
This definition has three critical elements:
1. “packaged software”: the CDP is a prebuilt system that is configured to meet the needs of each client. Some technical resources will be required to set up and maintain the CDP, but it does not require the level of technical skill of a typical data warehouse project. This reduces the time, cost, and risk and gives business users more control over the system, even though they may still need some technical assistance.
2. “creates a persistent, unified customer database”: the CDP creates a comprehensive view of each customer by:
Capturing data from multiple systems.
Linking information related to the same customer.
Storing the information to track behaviour over time.
The CDP contains personal identifiers used to target marketing messages and track individual-level marketing results.
3. “accessible to other systems”: data stored in the CDP is then made available to other marketing systems for analysis and to manage customer interactions.
What should a customer data platform do?
In essence, a customer data platform combines all your customer data from online/offline sources and unifies this into a single customer view to enable cross-channel activation and personalisation.
A CDP should integrate into existing and future marketing/advertising technology enabling you to decide which channels to communicate with your customers.
It should enable automated reporting of activity on your key marketing metrics. And of course, it should support GDPR enabling you to check customer consent, action subject access requests and the right to be forgotten.
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.
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