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. 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 led 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.
Back to Black Friday. Using data technology to evaluate and define customer’s purchasing patterns that occurred during and after last November’s Black Friday would highlight those customers which marketers could define as ‘best fit’. These are the customers they would want to establish a relationship with and retarget.
Taking this kind of granular data would not just enable them to engage more meaningfully with these customers, it would increase their positive sales percentage by ensuring these particular customers’ shopping behaviours were their ideal match. Furthermore it would enable them to identify new customers who fit this same profile and who may offer value beyond sales, such as potential influencers.
Integrating this kind of smart programmatic modeling and then filtering the customer base into a hierarchical structure of behavioural-demographic segments would ensure the targeting is kept dynamic as well as relevant. In effect this could be applied to any regular event from targeting habitual football gamblers on the eve of every big game, to Christmas shoppers and lovers on the 14th of February.
If, say, John Lewis were to apply this strategy by gathering and studying their customer’s post-purchase behavioural data and filtering it to segment their customers accordingly, the messaging would also become more personalised. From the customers’ perspective, they would feel the greater relevance of the brand’s communication with them, resulting in increased satisfaction, brand loyalty and word-of-mouth referrals.
Furthermore, by defining specific trends such as those customers who make the least returns, those who make the most repeat purchases through to those whose data show little to no activity, marketers would be able to take very specific marketing actions accordingly, either to re-activate them, steer them towards more relevant products or let them go.
This approach would mean shifting away from guerilla-style marketing which centers around fueling a pricing frenzy, and leveraging instead the rich data available to better engage with the customer and reduce brand damage, not to mention reduce returns and complaints.
Focusing on the historical data which will indicate how a customer may act in the future will also enable a brand to be increasingly predictive in anticipating future customer needs.
Although it may seem like flipping the application on its head, there is a real case for applying a model which filters customers to ensure their tailored behavioural and demographic profiling actually fits the brand’s requirements.
As it turns out, despite all the bad media, Black Friday could in effect be an ideal platform to demonstrate the power of using effective customer modeling to increase the impact of targeted programmatic advertising.
Those days when a consumer looked at a pair of sunglasses online only to be followed across the internet with ads for varying sunglasses are now deemed quite normal. We should now be looking at new ways of applying this clever technology to ensure brands are engaging more meaningfully and not stabbing away in the dark.
By Nick Wild of PRGRMTK.
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