There are many academic studies on predictive analytics, but few offer first-hand ecommerce experience on how AI machine learning Predictive Analytics actually can offer immediate help to the ecommerce retailer.
Here are 3 key things that will be delivered immediately:
1. Sales. A dramatic increase in loyalty, cross-sell and up-sell.
2. Overheads, staff cost and missed opportunities.
3. Minimise returns and their inherent overheads.
Predictive analytics delivers immediate benefits to sales. A dramatic increase in loyalty, cross-sell and up-sell.
You would expect any professional business activity to deliver a return on investment, and the art of excellent sales management is to maximise that capital return; the value being the appropriate denominator rather than the economy.
Consider an ordinary consumer “on an ordinary day”, they receive a myriad of impressions. Along with every competitor after your consumers, you try to drive sales and loyalty, using perpetual proximity and sometimes sadly saturation of your consumers. You nurture them and in return profit from that affinity.
An average database of your average ecommerce retailer grows about 20% 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 drives this excellent growth over other alternatives? The answer is quite simply personalisation.
Personalisation of taste, needs, demands, and preferences. Personalisation of expenditure, motivation, and purpose. Consider an elegant lady’s wardrobe, selected 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.
Personalisation is all the things that identify that person 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 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 wholly autonomous, as it is with hyper-personalisation software.
This populates emails for you of products with the most likelihood of being bought by the specific consumer you are emailing, using the personal buying history and impressions of that specific individual user to most accurately predict imminent purchases.
Let us take a long hard look at how much an average (not exceptional) individual might cost you to employ. It isn’t just the cost of the desk, computer, lighting, salary and holidays. It should include a subscription for 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 who 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 hyper-personalisation alone can cure. This is its 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 drive 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. A 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 loss of revenue right here. Hyper-personalisation solutions, of which there are considerable distinctions between the top 30 vendors, minimise 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 returns and lower costs.
No staff overheads unpacking, not a 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 come straight off the bottom line. Most importantly, most relevant to each consumer, what they want, when they want it, emailed to their inbox.
Conclusion
Hyper-personalisation is no longer the exclusive preserve of bigger retailers able to afford it. Because it quickly delivers a return on the investment, and a free trial to get a return before ending to invest makes it far more appealing to the average ecommerce retailer.
Personalisation is the future of ecommerce, it’s only a matter of time before there is a wide appreciation of it before universally adopted.
