The volume of customer data is proliferating at an ever-increasing rate. There is data from transactions, from website visits, from social networks and of course and from many other sources. And there is hidden data about customers fundamental needs that drive successful innovation. All of this data needs bringing together intelligently, so that it can be used by customers and companies to co-create value together.
Companies have mostly focused on the mostly low-hanging fruit of transaction data, such as mobile phone call records, items purchased with a credit card, or supermarket items purchased, to-date. In the hands of a customer-intelligent company like Tesco [1], this can yield remarkable results. But for most other not-so-smart companies, the results are nowhere near as impressive. They have spent vast sums of money on marketing automation that struggles to achieve much more than a 10% increase in marketing success. And throwing yet more expensive technology at the data won’t help in the long-term either: the current craze of real-time analytics is just more technology for the same results but quicker.
Customers are getting restless too. Fed up with being spammed by direct marketers abusing the low hanging fruit of transaction data, they are starting to demand control over their own data and to be paid by companies wishing to use it for marketing. This is threat to the old way of doing things but an opportunity for innovative companies. UK mobile telco startup Blyk offers young customers free mobile telephony in return for listening to adverts on their phones. Blyk [2] more than doubled its target of 100,000 customers in the UK in its first year. And customers complained that they got too few adverts, rather than too many. Try asking most bank or telco customers if they want more direct marketing! More and more customers are fed-up of direct marketing spam and opting completely out of marketing altogether. Direct marketers have created their own tragedy of the marketing commons [3].
As the opportunities to use customer data multiply but customers become less accepting of direct marketing, companies need to take a new look at the sources of customer data, at how useful the data is and at how best to use it to co-create mutual value with customers. Just like Kaplan & Norton [4] did in the 1990s for performance management data, companies now need a Balanced Scorecard for Customer Data:
- Transaction Data - Like financial data in Kaplan & Norton’s original Balanced Scorecard, transaction data looks backwards at customers’ previous purchases. Companies assume that what customers did in the past is a good guide to what they will do in the future, which has lead to the widespread use of e.g. RFM models in catalogue retailing. But as companies have found out, the past is a very imperfect guide to the future. Even though most companies still rely on transaction data for the majority of their customer analytics, the results are typically <10% improvements in marketing response rates at best. The average response rate over all direct marketing is still only a miserable 1-2%.
US credit card company Capital One [5] is one of the legends of customer analytics. It’s business analysts continuously analyse customer transaction data to identify opportunities to make highly selective offers to micro-segments of as few as a few hundreds of customers over a very short window of opportunity.
- Contextual Purchasing Data - Like process or customer data in the original scorecard, contextual purchase data looks at things that guide current purchasing decisions. This might include clickstream data about a customers use of a website, critical events in the customer’s lifecycle, or other point of purchase data that helps make sense of the customers individual buying behaviour. Of all the data available to companies, contextual purchase data is some of the most useful, yet few companies have access to this data today. As a result, they don’t really know what drives a customer to purchase a product or to leave it on the shelf. Where companies use contextual purchase data, the results can be up to 30% improvements in marketing response rates.
Toyota Financial Services [6] identified new vehicle customers coming to the end of their finance or lease contracts and analysed their contractual conditions. By making a highly customised vehicle plus leasing offer for each customer coming to the end of their contract, Toyota increased the number of customers repurchasing another new vehicle and taking out a new finance or leasing contract by over 30%.
- Purchase Influencer Data - Like the contextual purchasing data already discussed, purchase influencer data looks at others who influence customers’ current purchasing decisions. This might include on-line social network data, telephone calling community data and even data about who accompanies you when shopping. We are social animals and as been shown many times, are much more influenced by others, their opinions and their behaviour, than we like to think. This applies both to customers’ online and off-line behaviour. In fact, despite the current emphasis on on-line social networks like Facebook, in reality, the vast majority of social influence is still wielded off-line through friends and family.
Finnish social network analysis startup Xtract [7] analysed the customer calling communities of a UK triple-play telco to identify the most influential customers. By targeting these customers with retention offers, the telco reduced customer retention by 26%, compared to 19% from traditional churn analytics, a 37% incremental reduction in customer churn.
- Customer Needs Data - Like innovation & growth data in the original scorecard, customer needs data looks forward at what guides core customer buying behaviour. This is data about customers' aspirations, about the jobs that customer are trying to do and about the outcomes customer desire from doing them. Customer needs are stable over time but the products they hire to do them change, e.g. the job of listening to music at home has not changed, but customers have hired 78s, LPs, 45s, tapes and most recently iPods to do the job. In the same way, understanding what customers really need can be harnessed to drive innovation in products, services and experiences to better meet their needs. Ultimately, this is the only data that will help you define how you can better meet customers' needs better in the future. It is the only data with which to guide the future growth and success of your company.
Outcome-driven Innovation consultancy Strategyn [8] works with companies like Microsoft, Motorola and Bosch to harness customer needs to drive innovation. These companies have seen success rates of up to 80% for new products, services and experiences introduced into the market. That is a huge difference to the 80% failure rate for most new product introductions.
Companies need to start to think about all aspects of customer data, not just the low-hanging fruit of transactional data. That includes contextual purchase data, purchase influencer data and customer needs data. The companies that win through customer analytics will be those that can do this successfully and those that involve customers more fully in the process.
What do you think? Is transaction data enough for your needs. Or do you need to build a Balanced Scorecard of customer data?
Post a comment and get the conversation going.
Tip of the hat to Mike Compton of Optima [9] for the phone discussion that triggered this train of thought.
Graham Hill
Customer-driven Innovator
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Further Reading:
Kaplan & Norton, The Balanced Scorecard – Measures That Drive Performance [4]