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Customer analytics start with data. To get better customer insight, most companies begin by analysing their structured transactional data, which typically includes information such as demographics, purchase history, complaints and retention information. Statistical algorithms can help companies to create meaningful segments and gain insight into buying patterns. These insights and tendencies are then encapsulated in models which are used as a basis for future predictions; basically, an extrapolation of past history. Is this enough in today’s markets? Probably not!
In recent years every one of us has become a powerful ‘walking data generator’, delivering personal information (that reflects daily changes in our habits) through many different channels. Information sources include call centre records, email communications and transactional data as well as usage patterns on company websites. Very few enterprises, however, are in a position to probe this ‘gold mine’ of information.
In their quest to make these models more accurate, companies are starting to embrace new sources of data; but most of this data is unstructured and it is quite expensive to have it integrated into traditional data warehouse and data-mart infrastructures, both in terms of cost and time. Moreover, analytical algorithms are continuing to evolve to deal with the changing landscape brought about by new trends (such as mobility, social media and eCommerce), while the need for a very fast computational time is increasingly becoming a necessity to help companies to segment their customer base more effectively, attract more profitable customers, improve campaign handling or reduce customer churn. Propensity models are also becoming more dynamic to deal with the geo-spatial and temporal dimensions, acknowledging the fact that location and time events impact people’s propensity to react to external stimulation; in this case, the ability to react in real-time or near real-time becomes a ‘must have’ feature.
The more data and information to be analysed, the longer the process required (days); while Big Data solutions allow retail companies to analyse huge volumes of data, with more granularity, in a shorter period (hours vs. days). Retailers can now get insight into customers’ seasonal trends and use it to improve the management of stock or create tailored pricing and promotions.
While embracing this new customer approach companies must be aware there is a very fine line between using customer analytics to create value by serving customers with customised precision, and destroying value by surprising customers with actions that erode trust. Privacy policies and a consistent execution across the enterprise are essential and must be properly performed to understand the ever-narrower segmentation of customers and so deliver much more precisely tailored products or services. It is worth it, however, and the reward will surely overcome best expectations.