Everyone likes to be popular. And the social media world has made that urge to be ‘liked’ even stronger. Now the digital landscape is made up of users chasing the next like, pinning everything on going viral, and refreshing their feeds in perpetuity.

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Marketing isn’t any different. Like consumers, marketers have been romanced by social media metrics. They have become clout chasers, hunting for likes, refreshing the page and searching for the holy grail of authenticity and relevance. 

Social has become such an intrinsic part of the marketing solutions that engagement figures are often front and centre of campaign success measurement. But in the throes of this romance, the tried and tested measures of success – those that are judged by cold, hard market share and sales – have taken a backseat.

Most marketers know the balance isn’t right – the disaffection with likes and such as an efficient measure of success has been eating away at us for a few years. What’s moot is that we’re still fixated on social engagement.

Muddling Metrics

It’s plain to see why: we’re now part of a ‘smart’ world (homes, cars, motorways, and yes, shops too) where online is integral to how we live, work and communicate. So the pressure to score high on social engagement – to go viral – is relentless, spurred by this increasingly online ecosystem. 

Yet the value of getting a thousand Twitter ‘likes’ is disproportionate to the weight we put on it as a marketing metric. While engagement undoubtedly plays a significant role in the success of a brand, I’d argue we’re placing the focus too much on the topline numbers, and not enough on the detail. 

For social metrics to inform sales and market share strategy effectively, we need to look not just at how many, but whom. Different customers engage differently with content, and the real actionable insight can be found in the understanding of their behaviours.

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For example, take two different customers on an e-commerce site. Romaine has very little engagement with campaigns. However, almost like clockwork, twice a year, he will make a “big” purchase, prompted by a particular email. 

Meanwhile, Lucy is big on engagement. She opens every email, like every post on social media, share what she likes (and don’t) with their friends. She buys often, but not at the same financial outlay, as Romaine. 

This is where individual customer’s engagement metrics come into play, developing a single customer view, linking social media engagement to purchase history.

With Romaine, we know that he only engages with campaigns once or twice a year but when he does engage, he normally purchase. You also know that from this single engagement, that their purchase is high in value. Therefore, you can align any campaign to that individual’s customer journey and optimise it for that person, bringing in multichannel personalisation and appealing to past purchase history.

In short, we’re creating an environment where likelihood of purchase is increased.

Stopping the Churn

The traditional business customer churn model is one forerunner to this way of thinking. Some customers leave, others remain loyal. The business churn model looks at the data behind this, showing the number of customers who go during a given period. Comprehensive customer profiles are built as part of this methodology, identifying who leaves and at which stage in the process. 

Again though, this model places the emphasis on a different part of the story. Trying to keep those who leave, by understanding where they’ve left, is at best damage limitation. It’s important, of course – even if you can’t stop that particular customer going, you’ll understand why they’ve left and take appropriate action so others don’t experience the same pain point. 

But, the focus should be on a ‘Likelihood to Purchase’ model. The CRM data variables that are applied in a Likelihood to Purchase model, aligned to a 360 degree view, give you much more insight on why a customer will “stay with you” and subsequently, and crucially, creating actionable insight.

Analysing and enabling the data, and combining it with campaign metrics delivers a deeper understanding of the individual customer, including how and why they engage with particular types of campaigns across multiple channels. This information enables us to see how engagement impacts the customer journey to purchase. It’s this information that enables us to see why not all ‘likes’ are of equal value.

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