Analysing the customer and using social data

Every day, as we go about our business, we leave an enormous trail of data behind us. It is not only data about our purchases and payments. This data can disclose our preferences, sensitivities, lifestyles and demographics. It can also reveal our relationships with one another. Software tools now exist that can even detect our emotional state.

This avalanche of data has become the raw material that provides intelligence for the enterprise. It tells business what to sell us, through what channel and at what price, what new products to innovate and what bundles of products we would find compelling. The data can predict when we are ready to upgrade or replace the product and which competitor product we might find attractive. It also discloses the factors that attracted us to the business and the things that would make us leave.

In this way, every action a business takes in its relationship with a consumer is determined by data and no two consumers are exactly alike. As the personalisation of customer interactions increases, so too will the diversity of the data that is generated. The spiral of complexity is unending.

It is fair to ask if big data is new – after all, terabyte databases that were used for business intelligence first made an appearance 20 years ago. Back then data was often scarce and realising a “big data” vision was prohibitively expensive and often complex. In addition to technological obstacles, the business benefit was often contested in an era when the “one size fits all” principle of mass marketing dominated strategic thinking.

By 1995 there were fewer than 50 large-scale data warehouses in Europe and not many more in the United States. And those of us who built them were part of a very small elite employed by the more progressive banks, retailers and telephone companies.

Every action a business takes in its relationship with a consumer is determined by data and no two consumers are exactly alike

Today’s landscape is very different. Data volumes have exploded. The cost of managing data has collapsed. The tools available to manage and analyse data have dramatically improved. Markets have fragmented. What was once expensive, optional and experimental has become compulsory, affordable and pervasive. Data analytics has become the new frontier of innovation and productivity.

There are a number of reasons why customer data volumes have exploded during the past decade. One has to do with the availability of customer interaction data that is contained in clickstreams, weblogs and social media. In general, interaction data volumes are a hundred times greater than transaction data volumes.

Another reason has to do with the growing number of sensors that are embedded in items we use every day. For example, many electricity utilities are starting to use smart meters that upload usage data every hour in place of the monthly readings. Data volumes have also increased due to the need to store images and text in addition to conventional transaction data. And these volumes have grown with the number of wired consumers in the world – by 2010 four billion people (60 per cent of the world’s population) had a mobile phone.

But big data is not just about volume, it is also about the speed at which actionable intelligence needs to be available to the business. David Schrader, marketing director of the Active Intelligence Division of Teradata, asks: “How fast is fast enough when you are dealing with sales, complaints, fraud or retention?” Increasingly, businesses want to be able to respond in real time to alerts triggered by the data. The clear distinction that used to exist between operational systems and business intelligence systems has been eroded. In today’s connected universe everything is operational and decision-support systems are a crucial part of the customer-facing business processes of the enterprise.

One company that has pioneered the use of in-car sensors is Volvo. Trouble codes are typically stored in the engine control unit (ECU) until the vehicle requires scheduled maintenance or repair. At the dealership, a service technician connects an analyser to the vehicle and reads out the stored codes from the ECU to guide his troubleshooting and repair efforts. At the dealership, the codes are then uploaded to a central database at Volvo headquarters, where they form a global reference on all mechanical and electrical failures occurring in all Volvo models over time.

Many businesses use social media sites to monitor consumer sentiment about themselves and about their competitors or as a means of gaining feedback on products and services. It is also common to use social data to discover who are the early adapters and influencers within groups of customers. But social media is more than a listening post and can be a viable sales channel that can be used creatively to reach customers. In March 2011, Royal Dutch Airline KLM observed twitter traffic complaining about the absence of a direct flight from Amsterdam to Miami for the Ultra Music Festival and quickly launched a campaign using social media that filled a special flight within five hours.

One of the really interesting innovations in analysing and enriching customer data is the use of social coupons. When a customer obtains a discount by introducing two new customers and those customers in turn introduce further customers, the business is able to observe the web of relationships that exist in much the same way that a social media site, like Facebook or LinkedIn, can observe the degree to which individuals are linked together by common connections or connections at one or two removes. Every business will want to discover the relationships that exist between their customers because they know that small social networks share values, exchange information and act in concert. Telephone companies have known this for some time as they observe that when one influencer within a calling circle defects, so too do all of the other members of that calling circle.

The era of Big Data will give rise to many changes including creating greater transparency by making information more integrated and more accessible and increasing the levels of empowerment and personalisation enjoyed by customers who will be catered to at the level of segment and individual rather than at the level of the market. The optimisation and fine-tuning of many business processes using data will lead to many decisions being automated and the replacement of human and organisational experience with adaptive knowledge systems. A recent survey on the impact of social technologies conducted by McKinsey anticipates a significant impact on organisational culture –self-organising teams working within flatter organisational hierarchies will blur the boundaries between employees, vendors and customers.

One thing never changes. That is the ability to find new innovative ways of using the data and new innovative ways of capturing additional data. While most businesses will build the technological capabilities and copy the innovations of pioneers in the field of customer analytics there will always be a few that will lead the way by discovering new and surprising ways of exploiting the data. This is what will determine competitive advantage in the decades ahead.