The UK’s first official Black Friday certainly created a stir, not all of which was positive. The backlash is still being felt as retailers assess the benefits versus the challenges of creating a successful Black Friday campaign while keeping their brand reputation intact.
A frenzy of consumers fueled by impulse-purchasing fever may sound like a marketers’ dream come true, but the harsh reality is that once the chaos died away, what was left was more a sense of missed opportunities. The opportunity to better engage with consumers, or to deepen the brand relationship, all the way through to the opportunity to increase the average order value.
Much has been written about consumers’ frustration as servers crashed, deliveries failed and how, finally, a surge of returns not only affected logistics but further depleted margins.
However, with the benefit of hindsight, last November’s Black Friday could be turned into an ideal test bed to demonstrate the power of using consumer data taken from that period to better deploy programmatic technology to build and refine audience reach.
Beyond Simple Retargeting
As marketers are well aware, insights derived from data technology and cookies offer a raft of opportunities beyond simple site retargeting and look-alike prospecting.
Brands can mine post-purchasing data to focus on identifying those customers who displayed good post-purchase habits (such as low return rates). They can filtering then those customers by their interests, actions and demographics, with the aim of re-targeting them using hyper-personalised programmatic marketing. This would change the emphasis from merely re-targeting, to re-targeting a select group of customers, all based on leveraging the intelligence of the DSP algorithms and their millions of data points.
So far, deploying a mix of ad tech and marketing data platforms has enabled marketers to reach new levels of precision in their targeted advertising. This includes 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.
Matching Data And Behaviour
Now that these platforms are becoming more widely accepted, it is time to intrinsically align them with a clearer definition of the types of customers brands are most likely to value.
If marketers refine exactly which customer behaviours they rate as optimum, they could cut through more rapidly to those customers they most want to focus on. From there they could develop contextual marketing messages and experiences which would be tailored to suit this specific customer base.
Amazon has gone some way towards doing this by building a model around highly relevant communications and recommendations. However its approach to date has been a blanket one, targeting customers with the aim of just selling more, rather than defining and then specifically targeting those customers most likely to add value.
Overlaying post-purchase retail data onto a targeted programmatic advertising campaign would not just enable marketers to engage more meaningfully with their ‘best-fit’ customers. It would also increase their positive sales percentage by ensuring these particular customers’ shopping patterns were their ideal match. Furthermore it would enable them to identify new customers who mirror this profile and who may offer value beyond sales, such as potential influencers.
Integrating smart programmatic modeling and then filtering the customer base into a hierarchical structure of behavioural-demographic segments would ensure the targeting is kept dynamic and the messaging more personalised and relevant.
If a retailer 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 fewest returns; those who make the most repeat purchases; or those whose data show little to no activity, marketers would be able to take very specific actions accordingly, either to re-activate them, steer them towards more relevant products or let them go. This could be taken a step further by rewarding customers that show ‘good’ habits, thus building advocacy and brand value.
Moving Away From Guerilla Marketing
This approach would mean shifting away from the guerilla-style marketing which centers around fueling a pricing frenzy. This was the approach which brands from Amazon to Asda favoured last November. Instead brands could leverage the rich data available to them to better engage with their customers and reduce brand damage, not to mention cutting returns and complaints.
The ability to analyse historical purchasing data allows marketers to determine the value of each customer, by indicating how a customer may act in the future. But it also enables them to be increasingly predictive in anticipating their future needs.
In the UK, with our first Black Friday firmly behind us, it is time to learn and optimise accordingly. Marketers need to ensure they have developed a well-tuned platform by next November which can rapidly and effectively hone in on those ‘best fit’ customers. After that, overlaying this on any regular event will seem like child’s play, from targeting recreational gamblers on the eve of The Grand National, to lovers on the 14th of February.