image image 17th July 2015

Single Customer View – why?

The concept of a Single Customer View has been around for ages, but if you google the term you get inundated by numerous articles spelling out how painful it can be to get there – but what actually does it mean?

The amount of data that a company has access to and holds is growing, every day. The basic marketing data sets we find existing in companies come from 5 main areas:

1. Customers
2. Transactions
3. Prospecting & campaigning data
4. Product info
5. Profitability & financial data

When you add in the less structured data sets coming from Social Media, Customer services, apps you may have out there on your customer mobiles etc. you can really see how it becomes a struggle to get a clear view of what is happening and glean the valuable insights that might make a real difference to your decision making and marketing.

A single customer view is a database that pulls these sources together and joins them at a customer level. So you can see a clean set of customers (de-duplicated) with all their transactions appended. You can also see what they’ve bought and how much they’ve contributed to your business. You can identify your best and worst customers, your best performing marketing campaigns and eliminate your worst. You get one clean central set of data upon which you can do clear and solid analysis for a variety of purposes: Segmentation, campaign planning, test and learn analysis, product development & performance review & importantly it gives you the depth of information to make your communications more personalised and drive higher response.

In their 1-1 response rate report Caslon found that response rates to relevant marketing campaigns are, on average, over 4 times that of responses to static, same-to-all messages. You can only be relevant when you have a good idea of what your customers want to hear about – what have they bought? Where did they buy it? Which channel do they respond to best? Which offers have they activated? What time of day do they favour? All these things and more can be found from patterns in the data…

In “Big Data: Using Smart Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance” Bernard Marr says that you get the most interesting insight when you combine your peripheral unstructured data with your central structured data – while I agree with this, the complexity involved with getting to grips with this wider data set can be daunting. It’s important to get the basics right and start with your key data sets – significant strides can be made by having a central, clean, robust data set to really interrogate your customer behaviour, marketing and business performance.

While the single customer view is a term often bounded about in marketing circles, it’s actually a key asset to be tapped into across the business – it’s a great starting point to head towards a single view of the truth. Business decisions and business cases can be analysed across a solid foundation of data & insight. Managers across the business can source their facts from one data set so time isn’t lost disputing the facts and like my old Director used to say, everyone is comparing apples with apples. In “Analytics at work – smarter decisions, better results” the authors purport that analytics can transform just about any part of your business and that many organisations start their analytical journey where they make their money – customer relationships. It is certainly is where insight can make the biggest difference to profitability and business performance.

It doesn’t have to be that painful, as long as you can get your data out of the variety of locations it is stashed in, it can be quite simple to pull it all together. The two main things you need (and please excuse the geek speak) are match keys & a thorough matching process. Match keys help you match one data set to another (e.g. Customer ID/Transaction ID/Email addresses) and the matching process is the way you train the algorithm to spot patterns in your data to remove any duplicate entries in your data (and that’s what we do!).

We Love Data

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