The Single Customer View: pointless exercise or precious asset?


For anyone engaged in analytical marketing, creating a 360-degree single customer view (SCV) is nudging the top of your ‘to do’ list. Why shouldn’t it be? Knowing a lot about your customers can never be a bad thing, surely? But is a holistic view really necessary?

Before we go there, we should really lock down a definition of a single customer view. That’s a pretty difficult thing to do given that almost every organisation has its own definition of the term. Many organisations see the SCV as being interchangeable with an enterprise data warehouse. This is dangerous ground in my opinion. Yes, while both are collections of data from all areas of the business, the data warehouse is often built to support management and financial reporting or provide insight into operational processes and procedures. They are not built to cater for such questions as ‘what happens if we load this new data and see what we can learn’ often followed by ‘oh, that’s not what I expected let’s delete it’.

The single customer view should be a living organism that expands and alters shape allowing continuous experimentation and learning. However, this approach requires some nurturing if it’s to fulfil its potential in delivering business value.

The #SingleCustomerView should be a living organism that expands and alters shape. #SCV #CustomerIntelligence Click To Tweet

Lay the foundation and start small

Unlike a data warehouse if you approach the SCV in the right way you can get it started and delivering insight in weeks. What’s the right approach? Well, if you begin from the perspective, “We need a single customer view, what data shall we include?”, it won’t work. Businesses who successfully build the SCV and keep it relevant, start by asking: “What information do we need to move forward in our relationship with the customer?”. They have a clear idea about use cases (up front) and they’re also clear about value; cutting the cost of serving customers, improving margin, driving satisfaction etcetera.

However, it’s not as simple as looking at use cases and building up a suite of data to support them. It’s also about how you frame that data, logically aligning it to your relationship with each customer. I recommend a unique three-tiered approach that separates everything we know about each customer into three parts:

  1. The historical view
  2. Your current position
  3. A prediction of customers’ future needs, wants and behaviour

This layered approach gives you a solid, standardised foundation upon which to build your analytical marketing programmes.

There’s good news on costs too. You might imagine that when you tell your board you want to build a single customer view you’ll clock a misty-eyed look at all the money they think you’ll be floating down the river of incomplete big data projects. That doesn’t have to be the case. Look at how much it’s going to cost to set up and maintain, mapped against how it will improve your marketing capabilities. By starting small and expanding incrementally, as new business needs manifest themselves, you’ll contain costs and you won’t keep waiting for that magical moment when the data is ‘good enough’ to launch.

The right kind of data

To answer the question I first asked, a holistic view is extremely powerful, but you don’t need it from the start. You don’t actually have to waste time in the early stages collating every single piece of information you have about every single customer. The kinds of data you include in your single customer view need to be incredibly carefully thought through. Why? Because there’s little point taking the time, trouble and cost of including data types that do not deliver value. Why start pulling in social media feeds unless you need to support a specific analytically-based use case, such as better understanding and targeting communities of influencers, for example?

A holistic view is extremely powerful, but you don’t need it from the start #SCV #CustomerIntelligence Click To Tweet

The most powerful types of SCV repositories include three types of data for fuller context and improved marketing decisioning. These are best classified as: Closed data – data that only you the organisation have access to; shared data – information about customers you may have bought from a third party or obtained through a data collaboration activity; and open data – information that’s freely available at no charge to your organisation. Examples include:

  1. Closed data: credit scores, customer care history, transactions
  2. Shared data: geodemographic codes, social data, geofence data
  3. Open data: national statistics, weather, transport status

Build SCV from existing solution

A single customer view can be delivered as part of a Customer Intelligence solution. However, should you already have a marketing analytics solution in place, you can still build from that an effective SCV that answers real business questions. If you’re passionate about getting it right and making it a truly precious business asset, it can be exactly that rather than a costly and largely pointless exercise.

See how valuable a single customer view is in optimising your marketing effort by reading Maximising Moments of Truth





About Author

Mike Turner

Principal Business Consultant - North EMEA CI Practice at SAS

Mike Turner loves innovation and it has been a key part of his career to date, from being part of the creation of the first personalised magazine in the UK for customers of the Rover Car Company, to connecting organisations in collaborative networks so they can leverage the power of capabilities such as Artificial Intelligence, IoT and cloud solutions. Having gained more than twenty-five years of experience he is now using that knowledge across a spectrum of activities, from supporting a masters program in digital marketing at one of London’s top universities, through supporting startups in the agile delivery of their chosen business solution via a SAS Fintech program, while continuing to advise established organisations across all industry sectors in all aspects of customer intelligence.

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