5 key ingredients to success with customer Big Data in Retail

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5 key ingredients to success with customer Big Data in Retail

Every so often in commercial history we confront an opportunity such as Big Data; an approach that if conquered has the potential to transform the profitability of a business. But while its value in retail is widely acknowledged, harnessing insights from the vast mountain of data available is a herculean challenge for most retailers.

Every so often in commercial history we confront an opportunity such as Big Data; an approach that if conquered has the potential to transform the profitability of a business. But while its value in retail is widely acknowledged, harnessing insights from the vast mountain of data available is a herculean challenge for most retailers.

For those embracing a future that will be decidedly data-driven, it’s a challenge that needs to be met. For example, the global management consultancy firm McKinsey & Company predicts that many retailers could be increasing their operating margins by as much as 60% with Big Data. Their report indicates that between 30-40% of this margin increases can be gained in both Marketing and Merchandising alone. Marketing benefits were predicted in areas such as improved cross-selling, in-store behavioural analysis and multi-channel customer experience. Within merchandising customer data can drive improvements in optimising product ranges, pricing and product placement.  
What’s more, it’s clear that much of this benefit can be realised simply by making better use of the customer data that is already available and easy to harness. This includes more ‘traditional data’ from in-store EPOS systems, online and mobile purchases, loyalty scheme information and purchased 3rd party context data in contrast to ‘new data’ sources such as social media.
With this in mind here is our guide to the 5 key ingredients required in generating value from customer “Big Data”:

1.    Think strategically – Consider where revenue increases or cost reductions can most easily be achieved. This will be determined by a retailer’s business model and priorities. E.g. to increase growth in convenience products a store might look to focus on assortment optimization whereas, another retailer seeking to raise the level of online sales might target the effectiveness of direct marketing

2.    Look at traditional data first – Data insight from sources such as sales transactions in store and online is often a more straightforward route to creating incremental growth. Don’t try to initiate a large scale Big Data programme all at once through integrating traditional and new data

3.    Measure and learn – Measuring the results from specific actions is invaluable. It helps to demonstrate the value of the project to senior managers and ensure its future. It also provides additional insight that can enhance the ability of the organisation to make better decisions

4.    Commitment – to fully benefit from the power that big data and insight can bring organisations need to build strong teams of data managers and analysts. Sadly recruiting data analysts together with some expensive analytical tools will never reap the rewards required to sustain the programme

5.    Business process change – if Big Data is to live up to its promise then some of the existing business and decision making processes may need to change to be more aligned around opportunities identified in the insight.  For example, if purchasing decisions continue to be swayed by manufacturer discounts rather than the need to acquire those products that the insight indicates will lift overall sales value, then the full benefit will not be realised

Much of the discussion on Big Data often suggests that the integration of all available data will ultimately create crystal ball style visions and silver bullets for increasing revenues. However, whilst this may be possible in the future, there is a huge opportunity right now to create value from traditional data that is often being overlooked.

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