Stepping up the personalisation evolution curve

Katharine Hulls (pictured) says the barriers to personalisation – both cultural and technical – are being dismantled one by one.

First they said customers didn’t want personalisation. Nonsense – not only do customers want it, they expect it.

Then there was no affordable way of storing the depth of granular data required to deliver that personal experience. Enter innovative database technology such as Hadoop and sophisticated analytics; problem solved. Then marketers complained about the lack of detailed customer data – no longer an issue. Right now, the only barrier between brands and a truly intuitive, personal customer experience is culture and skills.

For those companies that are still dabbling at the fringes of personalisation with a few named emails, it is time to wake up. When the competition is able to combine online web data with ERP, CRM and social, the concept of personalisation has changed, dramatically. These companies can ask any question of this deep data source; they can determine a customer’s true value both by revenue versus cost to service and as a social influencer. They can test different aspects of personalisation, focusing on retaining the top tier, or increasing the lifetime value of the next group down, all using real-time offers driven by the combination of complete customer data source with actionable analytics. Essentially, these brands can deliver not only the personalisation that customers desire, but they can make it pay – and prove it.

So, is your company far enough along the personalisation evolution curve?

Debunking myths
One major myth about personalisation has been soundly dismissed over the past year. The truth is that consumers want personalisation; with recent research undertaken on behalf of Celebrus Technologies and Teradata revealing that 63% of consumers across every age group want a real-time, relevant experience. But how far have companies’ personalisation strategies evolved? Today it is fairly straightforward to deliver some aspects of real-time personalisation – for example, when an individual is filling in a buildings insurance quote online, it is simple to present a discount offer to encourage purchase.

But such activity is, in the main, isolated. The offer is often made as a result of that customer’s immediate online browsing behaviour – it is not linked always to his previous conversation with the call centre, prior website browsing sessions and their purchase of car insurance two months ago. The underlying systems and data sources are siloed, resulting in a number of different personalised experiences for customers, depending on what they are doing at a particular time and what devices they are using.

Furthermore, from a brand’s perspective, this siloed approach has made it difficult to determine where to prioritise personalisation activity. Without detailed data from across channels and devices it has been hard for any brand to confidently embark upon more sophisticated personalisation, for fear of failure. No brand wants to risk disengaging high value customers; without a complete view of each individual there is always the risk of making the wrong assumption – and presenting the wrong content or experience. The result has been a low risk, low reward approach to personalisation.

Personalisation with confidence

But this is changing. With lower cost database technologies enabling companies to collect and retain transaction level data from hundreds of thousands, even millions of customers in one location, personalisation is taking another, essential, step forward. By overcoming the constraints of these data silos companies can fundamentally change their depth of customer understanding and, hence, not only deliver personalised experiences but, critically, identify those customers who are truly valuable, and profitable, to the business and need priority nurturing.

Value, of course, in today’s market extends beyond direct revenue. Combine browsing data with online search data plus credit reference and social media information – including the level of social engagement, brand perception and position within an influence network – and a brand has another way of assessing that individual’s overall value to the business.

Proving the value
Creating an effective, relevant personalised experience for customers is a top priority. But not all customers are created equal – and the ability to ask any question and drive specific actions as a result across this depth of information has the power to radically change the way organisations address their entire personalisation strategy. Critically, combining database technology with analytics provides brand with an end to end personalisation model that delivers actionable insight in real time and helps them identify the factors that have the biggest impact on their success; and act up on them. Fast.

For example, take an individual with a strong brand affinity. They’ve looked at a high value product, even added it to their basket, but it is out of stock. A painful lost sale in the making. Your customer data tells you they also have a track record of sharing positive experiences with the brand via social media; a good thing. The solution? Combine supply chain information with customer data and notify that particularly important customer when stock is about to be replenished to ensure they don’t miss out again. Turn that lost sale into a great customer experience and you will be rewarded many times over. The key in this example is that the business not only has the data depth and analytics to gain new levels of customer insight, but the ability to drive actions back in real-time to close the loop.

Of course, when a data analyst can ask any question across a multiplicity of data sources, it can be pretty tough to know where to start. Should the business be focused on building stronger relationships with the top value customers to improve retention – or are these individuals already exceptionally loyal? Should, instead, the focus be on the next group down to increase their average spend? Is a policy of offering discounts to existing customers simply cutting margins unnecessarily? The choices are potentially overwhelming which is why, in addition to some best practice technical guidance and strong working relationships across the business and analytics teams, companies also need to invest seriously in data scientist skills which move them beyond analytics into advanced data discovery to uncover this more sophisticated insight.

According to the 2015 Data Scientist Report from CrowdFlower, nearly 80% of data scientists acknowledged an on-going skills shortage. However, with the data, technology and affordability of personalisation being addressed really quickly – the onus is now on brands to push harder for the right skills and culture to truly exploit this new depth of customer understanding.

Completing the curve
So, where are we today on the evolution curve? The customer demand – even expectation – is there. The simple, one channel, direct, relevant customer contact is a prerequisite. Now cost effective data reservoirs plus intuitive analytics are available to enable brands to gain unprecedented insight into the customer base and prioritise activity accordingly. The only thing missing is the skill base – and attitude.

Two decades since the concept of one to one personalisation was first mooted by the data warehousing experts – today there is simply no excuse for failing to personalise at the individual level. As the race to transform the customer experience heats up, the pressure is on brands to exploit the now proven technologies on offer – and that means overcoming the final hurdle: building a pool of talented data scientists able to take personalisation to the next level and the culture mindset to make those most of those talents.

Katharine Hulls is VP marketing, Celebrus Technologies.