Interaction with transaction is the new way forward

Every day the cascade of data generated by transactions discloses consumer preferences, loyalty, value, sensitivities, lifestyles, life-stages and buying patterns, writes Sean Kelly


Only a very few businesses have developed the sophistication to use big data to transform the way they do business. But that is now changing – and changing very quickly.

Reliance on market research as a means of gathering intelligence is rapidly being displaced by the urgent need to study individual customer behaviour and to provide a means of communicating with customers directly.

The conventional business model that is based on selling a narrow range of products to a mass market is being replaced by a business model that sources new products which meet the needs of separate customer segments. Tesco is a good example of a business that diversified into financial services, telecommunications and property on the back of excellent customer profiling in its grocery business.

The era of primitive discounting is now giving way to highly adaptive analytic systems that are capable of deploying highly personalised campaigns and generating marketing optimisation scenarios. The clear and present danger for many traditional businesses is that they will react only when their market share begins to collapse in favour of an information-aware competitor. For many, it will already be too late because of the extensive lead time it takes to truly transform the organisation from a product focus to a customer focus.

The challenge for the average retailer is to distinguish between its mainstream customers, deal-seeking customers, convenience customers, fine foods customers, health-oriented customers, ecologically and ethically sensitive customers, and its most profitable customers, segments, products, locations and categories.

The challenge for marketing organisations is that developing highly personalised and relevant offerings depends on consumers’ willingness to give more, and potentially sensitive, information about themselves. 

The era of primitive discounting is now giving way to highly adaptive analytic systems

As organisations begin to understand the need for the customisation of marketing, in all its forms, it leads, quite naturally, to an immediate requirement for the business to break down its total customer base into distinct segments, each of which warrants its own unique marketing approach.

This is the first step. To achieve this goal requires the mining of customer transaction data in order to achieve rapid innovation in the design of services, rewards and incentives. The outcome of this effort is a continuous stream of offers that are tailor-made to meet the needs of separate and discrete constituencies.

In the early stages of customer segmentation there will be no one segmentation model – there will be many. Each one will describe a different aspect of the customer profile such as the customer’s loyalty, lifestyle and profitability.

In the next stage the business will build its first propensity models that are intended to support targeted campaigns to alter customer behaviour and it will take time to test and refine these models.

In the final stage these different models will be integrated into a pyramid of organisational intelligence that is embedded in all of the business processes of the enterprise.

It is also not enough to think only in terms of transaction data. Increasingly there is a need to combine transaction data with interaction data – where, for example, a consumer self-profiles when browsing a website – in order to determine the priority goals of the business.

Being aware of what goals you are trying to achieve is of paramount importance. Are you trying to deliver a better customer experience? Are you targeting latent demand? Are you cross-selling or up-selling? Are you rewarding loyalty? Associating business goals with the right information strategies is essential.

However difficult the technology challenge of grappling with big data, automated algorithms and artificial intelligence pattern detection proves to be, my personal experience is that the organisational challenge will be much greater.

There are a number of reasons why this is the case including a traditional distrust by marketing of corporate IT, the fact that there is very sparse knowledge transfer between organisations because segmentation models are a closely guarded secret protected by rigorous non-disclosure provisions as well as a complacent reliance on the value of above-the-line branding as the exclusive means of defending the business. But the real obstacle can be located in the total transformation of the business that is required.

Customer centricity is not just another management initiative to be sponsored within the existing organisation – it is the taking apart of the existing organisation and the fashioning of a completely new way of doing business.

The weak economy has contributed to a renewed and an urgent focus on the need to achieve organic growth. This is the principal driver of the current sharp upswing in customer profiling.

Case Study

Sky-high IQ

With its 10.5 million customers, broadcaster Sky has always placed a great deal of emphasis on the importance of data and insight as a way of optimising the experience it delivers its customers – and has invested accordingly.

Data and insight from Sky IQ, its wholly owned customer intelligence division, is integrated across Sky’s business, with the objective that, whenever and wherever a customer engages with Sky, the service is made more personally relevant.

In the future, data sourced by Sky IQ will also increasingly be fed into programme- commissioning decisions.

With a single customer view database as a foundation, Sky IQ supports the gathering of intelligence about Sky’s customers. Customer segmentation and models are created and these provide insights into customer preferences.

Detailed data analysis helps Sky recognise the many preferences of each individual consumer so that offers are tailored to the individual. The company argues that customers benefit from allowing access to their data in the form of more relevant messaging and segmentation, and easier ways to of interacting with Sky.

William Mellis, Sky IQ’s managing director, says: “Customer data means that we are increasingly empowered to make informed decisions, to personalise our services for our existing customers, and identify new customers more easily and to maximise return on marketing investment. It is certainly a challenge to gather and harness this volume of data, but one that is worth the effort.”

Fact file

What is big data?

Every day, we create 2.5 quintillion bytes of data (IBM figures) – so much that 90 per cent of the data in the world today has been created in the last two years alone. This data comes from everywhere: from sensors used to gather climate information, posts to social media sites, digital pictures and videos posted online, transaction records of online purchases, and from cell phone GPS signals, to name a few. This data is big data. The problem is that data sets which grow so large become awkward to work with using on-hand database management tools. Different data management solutions, therefore, need to be considered.