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Why Black Friday is ideal platform for programmatic (Part 1 of 2)

March 26, 2015

The US has had over a decade to hone Black Friday. From battling extreme weather conditions and managing customers’ expectations, to increasing server capacity, they are still refining their offering. web design history . The UK, on the other hand, despite taking cues from the US experience, have still found the learning curve steeper than anticipated.

Whilst the likes of Asda and Amazon, the originators of Black Friday in the UK, took an aggressive online approach to Black Friday, one of the issues that rapidly became apparent was that marketing and e-com departments across much of the retail sector were working in silos.

Despite all the best intentions there appears to have been inadequate contingency planning for the huge spikes in demand of web traffic as servers crashed and error pages became commonplace. Logistics suffered too, all of which resulted not only in causing havoc but, in some instances, compromised brand perception.

With high profile e-commerce failures in 2014, Argos, Currys and Tescos will be putting this down to lessons learnt and next November should prove to be a smoother ride – certainly in terms of the logistics and infrastructure.

However one key learning which does not yet appear to have emerged from all of this is the potential value of mining customers’ post-purchase behaviour to assess the real success of these sales periods.

Whilst last Black Friday saw marketers relying primarily on fueling consumers’ impulse purchase patterns, early feedback shows that this may have cost them more in return purchases and even brand damage, than it was worth.

The next logical step should be to focus on reducing the waste in time, cost and energy which impulse purchases can cause. One solution would be for marketers to use data to focus on identifying those customers who displayed good post-purchase habits (such as low return rates) and to re-target them using hyper-personalised programmatic marketing.

Insights derived from data technology and cookies offer a raft of opportunities beyond simple site retargeting and look-alike prospecting, which marketers have yet to explore. So far, leveraging a mix of ad tech and marketing data platforms has enabled marketers to reach new levels of precision in their targeted advertising. From site retargeting – to encourage targeted repeat visits, look-alike prospecting – which uses mathematical models to reach a specified target audience, to companies such as MasterCard selling data based on people who have purchased other products, marketers already have a wide range of targeting options to choose from.

However now that these platforms are becoming more deeply and successfully embedded into campaigns, it is time to start intrinsically aligning them with the types of customers brands’ most value.

By refining exactly which customer behaviours marketers rate as optimum they could cut through more rapidly to those customers they most want to focus on. From there they could develop contextual experiences which would be tailored to suit this specific customer base.

Amazon have lead the way in doing just this by building a model around highly relevant communications and recommendations. However their approach to date has been a blanket one, rather than defining and then specifically targeting those customers most likely to add value. In our next blog we will go into this in more depth.


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