We all know personalisation is the key to engaging digital customers. We also know that achieving personalisation at scale is no small feat. Even for Coca-Cola, one of the world’s most recognisable brands, the move to digital required a change in tactics. The company has no issues with awareness – its beverages are consumed by 18% of the world’s population each week! – but awareness and reach alone are no longer enough to drive sales.
That is why Coca-Cola is “entering a new chapter”. In the words of chief digital officer David Godsman, “it’s a chapter of digital transformation. It requires us to deepen our relationships with our consumers, to perfect the idea of mass personalisation, which is not easy to do.”
To achieve personalisation at scale, brands need to develop a better understanding of each customer they interact with, and that’s all about combining all the data they collect from every available source into a single cohesive customer view. There is no way they can handle this task or manage all this data manually, which is why Data Management Platforms (DMPs) rose to prominence.
Today, companies are looking to build on this single customer view with a real-time understanding of their audience across every digital channel, marking the next phase on the maturity ramp for DMPs.
Data Management Platforms are maturing
According to Gartner, 50% of brands already use a DMP in some capacity, either buying their own or relying on an external agency. The issue is that most of these businesses haven’t looked beyond the marketing use case for their platform, which greatly limits its potential.
The real value of a DMP comes when you can apply the same level of audience insight to every interaction with every customer across every touch point. With that, brands can bridge their digital experience between channels and engage customers in a way that feels consistent, relevant and tailored to their unique persona.
Thomas Vaarten, digital director, A.S.Adventure agrees. In a recent Adobe report, he argues that, at this moment, the DMP is just viewed as a tool. In his eyes, it needs to evolve to be the centrepiece and starting point for all customer communications across all departments – from performance marketing to CRM to customer service.
His comments come following A.S.Adventure’s own transformation. The company was previously using a content management system, an email platform, a system for personalisation and an analytics package, but realised it needed a DMP to develop a single customer view. Additionally, A.S.Adventure can now segment its audience more precisely and use these segments as the foundation for all of its digital marketing.
Use data to engage new prospects
Another major appeal of DMPs is that they help brands entice people who are not already customers (as opposed to Customer Data Platforms, which are limited to managing existing customer data). The digital ecosystem is not just more competitive, it’s also much harder to get cut through with so much information at your audience’s fingertips. The ability to identify and convert unknown prospects is a valuable differentiator in this environment, which is why challenger brands taking a data-driven approach have done so well.
Sometimes even the simplest wins can have a big impact. Sandy Ghuman, campaign planning and delivery coordinator at Sky Insight and Decision Science explains how the company’s DMP helped it optimise media spend: “The biggest user case was the media efficiency gain. We’re not wasting paid impressions to sell Sky to customers who already have Sky. Just the ability to… identify those people and exclude them from any prospect activity was a massive efficiency.”
There is an important point to make about the quality of data being fed into your Data Management Platform. As the old IT industry phrase goes, ‘Garbage in, garbage out’. A DMP is a powerful tool for turning raw information into insight the business can use, but the quality of that insight will only be as good as the quality of information on which it’s based. You can’t just dump any and all data into a DMP and expect it to spit out golden nuggets of intelligence. It’s important to carefully select the information that goes in DMP based on what you want to achieve.
A word on GDPR and data privacy
No discussion about data management is complete without exploring what this means for privacy, especially in the age of GDPR. One of the key requirements of GDPR is that companies must be able to show their customers which data they hold about them on request. This is extremely difficult to do if all a company’s data is held in disparate systems, not to mention time-consuming. The fundamental function of a DMP is to consolidate a company’s data in one place, which dramatically simplifies and speeds up this process.
DMPs also help companies manage how their data is shared, as well as the rules and permissions around how that data is used both internally and by external partners. For marketers who need to make decisions each day about which customer segments to target with specific campaigns or offers, DMPs can tell them not just who to contact but also whether they have permission to do so.
Adding AI into the mix
Traditionally, the practice of data management and customer analytics has largely been descriptive, helping brands to explain an outcome based on the results of an action, like a campaign. More recently, brands have been using data in a more predictive way, drawing conclusions about their customers’ future behaviour based on historical data so they can deliver a more personalised user journey.
The ultimate aim is prescriptive analytics. Today, Artificial Intelligence (AI) is being baked directly into data management processes so that brands can determine the next best action for each customer at each point of their journey, continuously pushing them one step closer to a purchase.
To make this happen across multiple channels, in real time, and at scale, marketers need the help of intelligent machines. That’s why the integration of AI directly into DMPs promises to have a significant impact, helping users to discover patterns of behaviour in their data to inform better segmentation and smarter lookalike modelling. Crucially, AI systems are constantly learning and adapting, so that audience segments evolve in line with changing customer behaviour.
Of course, the use of AI in data management is still the domain of a select few companies and risk-takers, but change will come quickly as the approach begins to demonstrate how valuable it is to engage customers in a more proactive way.
The digital customer experience has become the new battleground for marketers. And the keys to delivering incredible personalised experiences at scale – creativity, convenience, relevance and timeliness – all hinge on a deeper level of customer understanding. A DMP may just be a piece of software, but it is also an invaluable tool in helping brands get closer to their existing audience while also reaching new customers. In this way, a DMP done right is a driver of organisational change.