Saturday, November 28, 2015

Predictive and Real-Time Analytics

I shall be chairing the @UNICOMSeminars Data Analytics conference next week. Exploring the Business Value of Predictive and Real-Time Analytics (London, 2 December 2015)

A lot of the obvious applications of real-time analytics are in fraud detection and predictive maintenance. I shall be talking about some of the things I’ve been doing recently in the retail and consumer sector, using rich consumer data to drive real-time personalized engagement with the consumer across multiple touchpoints. We have been exploring ways to combine real-time analysis of the consumer’s current state (e.g. current location, what products they are currently looking at, readiness to buy, etc.) with a rich understanding of what one might call the consumer’s “purchasing genes” – for example, do they like to spend a long time reviewing alternative products before purchasing, do they like to wait for a special offer or voucher before buying, or on the other hand do they like to be the first in their social network to have a given product. This is a lot more complex than simply putting them into a fixed number of “consumer segments”.

Based on this analysis, it is possible to select an appropriate “next action” – for example, selecting the appropriate banner to display to the consumer when visiting the website, or the right topic of conversation for a human customer services agent.

Thus predictive analytics are helping retail as it moves from omnichannel commerce (which joins up the buying transaction between the online and the physical world) to omnichannel engagement (which joins up all aspect of the relationship with the consumer).

Omnichannel Commerce
(Systems of Record)

joins up the buying transaction between the online and the physical world
Omnichannel Marketing
(Systems of Engagement)

joins up all aspects of the relationship with the consumer

Given the large volumes of data involved, and the reliance on legacy systems to produce and process the data, we are not yet seeing this analysis being completely done in real-time. However, there are some critical factors that have to be done in real-time. For example, as soon as the consumer buys something, our clients want to stop trying to sell it, and move to a post-sales scenario. (In comparison, even the great Google is still showing me advertisements based on what I was browsing three weeks ago. Fail!)

Over the next couple of years, as the technology gets better, the data scientists get even smarter, and the marketing people get more sophisticated, we may expect an increasing proportion of the analysis to be done in real-time, using machine learning as well as more sophisticated analytics tools.

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