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data friendly

Profiling your customers through Big Data: what you need to know

In this era of Big Data, there are increasing opportunities for collection, processing and analysis of vast and complex data sets, which can reveal insight for organisations into their customers; insight such as demography, behaviour, transaction trends, economics etc. As a result, the ability to mine and utilise insights from this data, is what is likely to create business value and hence is generating significant interest to business leaders globally.

In recent years there has been a rapid shift from generic customer segmentation into more individual approach, enabling businesses to treat each and every customer as an individual and deliver exceptional customer service and experience.

Spurred by customer analytics, this process also assists business understand their customers far more (people or companies) – but this understanding can only be derived through continuous collation and analysis of the customer information as well as environment information, to allow organisations to generate insights into customers’ past, current and future behaviour. Where this information can be used to improve the business’ relationship with their customers – it ultimately supports market share gain and revenue drives.

And, while gaining market share and driving revenue are strong motivations for improved customer engagement, there are a number of other benefits that can be realised from properly designed, implemented and embedded customer analytics. These include, improved customer satisfaction, increased number of customers and/or their profitability and decreased customer erosion.

Additionally, apart from these direct effects there are also other advantages, such as gaining a competitive edge (through customer empathy and behaviour prediction), improving brand reputation, using customer feedback for the development of new offerings, as well as being able to decrease cases of misunderstanding and potential fraud.

Customer analytics can be used in any phase of customer lifecycle – which is the key differentiator of customer needs – where analytics can assist in identifying in which phase of the cycle each customers is and suggest appropriate next-best-action for each customer, for instance through the acquisition phase or throughout the retention phase. There are an endless number of specific benefits each market sector can gain from customer analytics – across for instance retail, corporate or wholesale – where a few may include:

  • Financial services – automated generation of customer lead lists for specific sales agents, where each customer has set needs and reasons for servicing/ cross-sell/ up-sell. Increase in sales revenue and improvements in customer relationship and experience management through personalised product offerings.
  • Energy – forecasting of electricity energy supply and energy demand across different geographical areas for optimisation of distribution and grid balance. Decrease in forecasting errors, resulting in yearly savings and reduction in transmission losses.
  • Retail or consumer markets – knowledge of customer demography vis-à-vis purchase patterns and trends can be utilised for planning and executing more relevant and targeted customer communications and promotions as well as improving supply chain effectiveness.

Of course there is still the notion that you need best-in-class BI (Business Intelligence) and best data quality before embarking on customer analytics. This is not true.

While the data quality and availability affects the precision of the predictive models, the precise data models are not the only ingredient for success, and therefore, customer analytics can be successful even without 100% available and high-quality data. In fact, successful business embedment – the integration of analytical solution outputs into business as usual operations, including education, motivation and follow-up support of business people – is what makes or breaks overall success. Customer analytics should therefore be driven by a deep-seated motivation to address key business challenges through improved customer engagement and, the earlier businesses start the better.

  • Ilya Geller

    So called Big Data mostly consists of personal information, harvested through spying on Internet.
    However, IBM, Oracle and SAP already began to structure unstructured data, they are able to create personal profiles of structured unstructured data and target structured the same way information to these profiles. In other words, structured information can search for people, not people for it: people have 100% privacy, they search only within their computers, within what is distributed to them. (Or they can risk their freedom and continue to use Google, finding their way through spam and viruses.)
    IBM, SAP and Oracle can already put all Internet in their database and make money on the delivering structured information, which all becomes to have the status of paid for advertisements.
    Therefore, the collection of private information, the core idea behind Big Data, loses any commercial sense and should become a fraud.

  • aviiii