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May 2018

How to realise the potential of big data

As we've said on a couple of occasions recently big data (aka data analytics) has enormous potential application in many areas of sales and marketing. Data analysis can be applied to trade promotion activities to refine and better target them; to salespeople to improve their performance.

Implementation and adoption, however, can be challenging. Writing recently in Forbes Magazine, two McKinsey&Co consultants, Matt Ariker and Nimal Manue, said: "The type of question we hear most is: How can I get the big data train moving? After all, there’s no shortage of rational reasons to get big data programs rolling: companies that use customer analytics extensively are more than twice as likely to generate above-average profits as those that don’t; an integrated analytic approach can free up to 20 percent of marketing spending; and injecting big data and analytics into operations can help companies outperform their peers by five percent in terms of productivity and six percent in profitability. So what’s the problem?"

The listed three obstacles: “It’s too hard and not worth the effort," “I know better" and " “I don’t trust you.” The latter, they said: "is probably the toughest issue to overcome: the psychological concern that machines are replacing humans."

Also, it might not be immediately obvious just what data should be analysed, and how. Ed Farquhar, marketing director at PROS - a UK based company that "helps you use your big data to sell better" - says: "It’s not always easy to cut through the hyperbole to understand exactly how big data can be applied to solve specific business challenges.

Helpfully he lists several best practices, which "have emerged that can help guide your efforts."

The first of these is to examine current information systems and evaluate what kind of customer and transaction data you possess. Then "Once you have a grasp on your existing data, you can concentrate on making it available to your sales force in a timely manner to tie in with setting sales goals and helping to negotiate deals in the field."

He points out that, thanks to cloud computing, organisations do not have to invest upfront in big data analytics tools and the hardware to run them on - they can demand significant processing resources when data sets become large. "Organisations of all sizes are able to utilise data science and analytics in the form of software solutions available as a service through the cloud.

"These solutions allow sales organisations to segment and analyse their data to identify common buying preferences, and prescribe new pricing and selling strategies unique to each customer segment. They’re also able to identify additional products and services similar companies are buying, which provides additional opportunities for growing their sales opportunities."

The upside of all this is that: "Sales teams will know precisely where to focus their efforts, with the confidence of knowing the deals most likely to close. Through advanced solutions, sales organisations can harness the hard-earned experience of the whole sales force, and apply the collective wisdom and memory of an entire organisation where it counts most, i.e., at the point of sale or negotiation in the field."

Barriers there might well be, but with results like that it is well worth striving to overcome them.