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What Can Predictive Analytics Do for Ecommerce

Updated on October 30, 2017
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SwiftERM is the world's only fully automated ecommerce data analytics program.

How predictive analytics can deliver immediate benefits to your ecommerce store

There are many academic studies on predictive analytics, but few offer first hand ecommerce experience on how Predictive Analyticsactually can offer immediate help to the ecommerce retailer in Q4 of 2017.

Here are 3 key things that it will deliver immediately:

1. Sales. Dramatic increase loyalty, cross-sell and up-sell.
2. Overheads, staff cost and missed opportunities.
3. Minimise returns and their inherent overheads.

Sales. Dramatic increase loyalty, cross-sell and up-sell.

You would expect any professional business activity to deliver a return on investment, and the art to excellent sales management is to maximise that capital return; value being the appropriate denominator rather than economy. Consider an ordinary consumer “on an ordinary day, they receive a myriad of impressions, from all sides they come” (Virginia Woolf, The Common Reader 1919). Along with everyone else after your consumers, you try to drive sales and loyalty, using perpetual proximity and sometimes even saturation of your consumer. They are yours after-all, why shouldn’t you? You nurture them and in return profit from that affinity. That which you have so heavily invested in. Lets put aside the nay-sayers here, as an average database of your typical high-street companies delivers in excess of a 20% growth in their rank per annum. Not as the ill-informed would have you believe, that they would all be all gone within a few years. So what is the distinction of Predictive Analytics that drive’s this superior increment over other alternatives? The answer is quite simply personalisation. Personalisation of taste, needs, demands, preferences. Personalisation of expenditure, motivation, and purpose. Consider a elegant lady’s wardrobe, I pick her as her buying power is perhaps perceived to be a little higher than normal and spending power therefore likely to be more prolific. She will have an array of brand-named shoes, dresses, lingerie, tops, jeans, jumpers, coats, jackets and indeed millinery. In her boudoir an array of expensive cosmetics, perfumes, jewellery and other refinements that define her; distinct from her coterie of friends and relatives. All the things that identify her (also to you) as an individual, not a clone. You could have data on these things plus knowledge of when she bought them, her timing, why she bought them, and possibly even who from. If you could have that knowledge at precisely the right moment, I find it impossible you could fail to appreciate you’d be very rich indeed.

Overheads, staff cost and missed opportunities.

It would be fair to a give-ground here and concede that Predictive Analytics in itself cannot claim responsibility for negating the need for staff, their inherent overheads together with the direct costs for sales. However, when all this is automated, as it often is, such as in our own SwiftERM software, (which uniquely takes the data and populates emails for you on a personal basis for each individual consumer), then the saving becomes huge. Let us take a long hard look at how much an average (not exceptional) individual might cost you to employ. Accountancy firm ASFB have conveniently provided a calculator here, that helps appreciate all the elements you need to include. But it isn’t just the cost of the desk, computer, lighting, salary and holidays. It should include the subscription to the marketing software too – often an annual commitment. A sum that can run into hundreds of thousands of pounds at the top end, although admittedly a lot less cost at the other extreme, but much less lucrative returns too. However, where the real cost lies is in the errors and mistakes. Not huge ones but in those which fail to select each individual their appropriate item that would have been bought if only had it been presented, together with knowing the perfect time to show her it. A miss is as good as a mile! These are the real refinements that Predictive Analytics alone can address. This is it’s real power. Minimise your returns and overheads, and indeed your fortunes will be achieved.

Minimise returns and their inherent overheads.

Unless you’re in the business, you’d probably be oblivious to the capital cost of returns. Staff to manage, perpetual capital refunds, inability to fully capitalise on the income because of the turnover of cash, which is a huge commitment. But this is also mirrored in the inevitability of customer relations. While it is appreciated that good customer relations drives loyalty, customer retention, and ultimately perpetual sales, it also drives irritation and annoyance on the consumer’s part, despite your best efforts. By the time their products are delivered, identified as not what they want and sent back, the time taken for that process is often identified by consumers as falling into a hole. An pervasive nervousness to spend again seeps in! Their money is outstanding, they perceive you have it and they wait to get it back before buying anything else! You can account for a significant lost of revenue right here. Predictive Analyticsminimises these errant ways, offering each consumer just the brands they like, just the colours they are most likely to buy next, which inevitably leads to fewer return and lowers cost. No staff overheads unpacking, not duplication of effort in the warehouse returning the goods to stock – the bane of our lives. No absorbing spurious postage and shipping costs, which often commonly comes straight off the bottom line.


It would be fair to say that Predictive Analytics is still, in the main, the exclusive preserve of those bigger retailers able to afford it. That while it quickly delivers a return on the investment, the initial cost remains prohibitive to most. This is the future of ecommerce, it’s just a matter of how long it takes for an understanding and appreciation of it to to be widely known.

David Swift CEO SwiftERM

© 2017 SwiftERM


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