DIFFERENT STROKES FOR DIFFERENT FOLKS

The concept of personalised pricing has emerged with renewed vigour in the media over the past few months in the UK. This strategy essentially revolves around charging different prices to different individuals at different times based on the information and knowledge that they hold.

In essence personalised pricing is a derivation of the well-known overall pricing strategy commonly referred to as price discrimination. At the outset we should recognise that the concept is not new and has always been around – particularly in retail. For instance a good salesperson will quickly size up the appearance and credentials of someone who arrives in the store or showroom and design their sales pitch accordingly.

By the tone of voice, appearance and knowledge, they can size up the requirements of the individual and hopefully direct them to an appropriate product or piece of merchandise. I say “hopefully” because there is always the temptation to steer the customer to a more expensive item that may not necessarily be appropriate for their needs.

Many organisations have been accused of exploiting customers, either through shoddy selling techniques or pricing policies that are confusing and non-transparent at best and or downright misleading or dishonest at worst. Typical alleged offenders are financial services and utility companies.

The reason why personalised pricing has appeared on the horizon again is due to the convergence of technology and data: something that I repeatedly visit in the various chapters of my text-book.

For instance as we probably know, “cookies” record what websites shoppers visit and at what times. Retailers can also identify the profile of shoppers from their shopping purchases and patterns. This can lead to a deeper understanding of the shopper’s attitudes and responses to promotional offers for instance. Fortified by this sort of information, the retailer can design customised and more effective promotional campaigns that are more likely to generate positive responses.

In theory this looks good for both retailers and shoppers. In the case of the former they can presumably generate more revenue, reward loyal customers through customised promotions and likewise for shoppers, they can benefit from discounts and advance information on special deals of promotions.

Alarm bells start to ring however when retailers also buy information held on individuals by third parties. This insidious practice, which most of us don’t know about (or don’t want to acknowledge), provides retailers with even more focused and detailed information about our shopping patterns and behaviour.

Is there any evidence to suggest that companies in general and retailers in particular are using such data and technology to exploit customers?

European law requests websites to explain clearly what cookies are all about and that they are expected to get customer’s permission before such data can be used.

In the United Kingdom the Office of Fair Trading (OFT) introduced a “midata programme” which encourage banks and energy companies in particular to release data about a consumer’s consumption patterns if requested by the consumer.

In 2013, an investigation by OFT indicated that businesses were not using information to charger higher prices but instead were more likely to use it instead for targeted discounting. However revealingly not that many companies actually used online data. The investigation indicated that only around twenty-seven per cent of companies followed such an approach. This might be explained by the fact that many companies may not have the specialised personnel to analyse and “make sense” of the data and translate into usable information.

A quick perusal of the internet reveals that personalised pricing engenders much debate (have a quick search!). This combination of technology (smart phones, tablets and social media platforms allied to Apple’s iBeacon and Samsung’s Proximity) and the resulting “big data” affords some opportunities for retailers.

For instance they can more accurately assess the willingness or propensity of the shopper to purchase an item at a certain price. Through studying past purchases, they can also calculate the reservation price of the individual shopper. By reservation price we mean the maximum price a shopper would pay before they started to have reservations.

If used for rewarding loyal customers, then clearly it can be argued that it has much merit for the shopper and the retailer. Customer engagement, retention and relationship management argues that ultimately both parties benefit.

However how would feel if you ordered a high-end smart phone only to discover that a friend of yours had acquired the same product at a price of fifteen per cent less than what you paid for it?

In this case there is a strong danger that in addition to creating a highly annoyed shopper, such a practice can lead to an overall erosion of trust in the retailer and also with respect to shopping online. Shoppers may feel alienated and in the worst case scenario, victimised. In the latter case this may because of the post code that identifies where they live, the newspapers that they read or their socio-economic status – all of which can be gleaned from “big data” captured by the retailer or information which they have purchased from a third party operator.

Of course it can be counter-argued that such practices are not that unusual. The next time you purchase an airline ticket from Ryanair or Easyjet and take your seat on the plane, ask yourself the following question. I wonder what the passengers to my right and left have paid for their respective tickets. More likely than not you would find a great disparity in the prices paid? Although these “low-cost” operators have built up a reputation for low prices, they are arguably one of the most prolific users of sophisticated technology and software that analyses demand patterns and behaviour and allows them to maximise revenue and yield from seat bookings.

Other service operators such as hotels have also followed similar practices over the years.

The worrying and underlying aspect of this of course is that as technology and data continue to converge and become easier and cheaper to use, we will see dubious, dishonest and non-transparent practices increase in prevalence.

This may lead to even more evidence of “different strokes for different folks”. Let’s watch this space!

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