The Customer Persona is Dead! Long Live the Customer Profile!
The era of customer personas is over.
Trying to fit your audience into pre-defined categories isn’t just rudimentary, it’s ineffective. That’s why persona-based marketing, which is an inside-out approach to understanding audiences, increasingly leads to irrelevant experiences. Brands are so used to segmenting customers as “males aged 18–24” or “females in their early 30s” that they risk missing the big picture. And in the worst case, these unfortunate biases can have an effect upon their relationship with customers.
Context is everything, especially when trying to make sense of people’s activity across so many channels. It’s time to kill persona-based marketing and embrace a more progressive approach to personalisation that reflects this reality. The challenge is that there is no linear way of driving the customer experience today. Customers have so many ways of accessing content and interacting with brands that targeting them based on data from just one of these channels is not enough.
The second challenge is the fragmentation of media today. TV, online, mobile, walled gardens like Facebook or YouTube – each of these channels isn’t just used differently, it also generates different forms of customer data. It has therefore become crucial to collate data from all of these sources onto a unique system and develop a complete view of each customer.
A complete customer profile is the holy grail of personalisation, but it is a complex beast that requires the right mix of technology, process, and culture to get right. Let’s tackle each of these factors one by one.
Be mindful of the ecosystem you’re serving
Different channels require a different approach to campaigns and targeting. It’s not uncommon that a brand’s analytics setup works well for desktop audiences but isn’t suitable for understanding mobile users. Or for an audience to scale well in a web environment but not in an app.
It’s crucial to first define the ecosystem of people and channels you’re serving, and reverse engineer the right tech stack from there. Again, customer behaviour should govern every aspect of your decision-making, even at the technology level. The ideal tech stack connects all these channels together so you can adapt your approach based on the needs of specific campaigns and audiences. This becomes particularly important today, with data management becoming a major priority – without a link between channels, data leakage and loss become higher risk issues.
Get granular with data, but only when you need to
The general mantra around data today is “the more, the merrier”. With more data sources than ever to help them understand audiences, brands are building more complex profiles than ever to help them personalise experiences.
This isn’t a bad approach per se, but it’s not necessarily the right approach every time. How deep you dive into your data ultimately comes down to your campaign objectives. For example, if you’re chasing a specific KPI for a highly specialised audience (i.e. generating leads among CIOs working in your top 25 B2B accounts) it makes sense to get granular, but in the case of an always-on campaign a broader approach might be best, so you can broaden the net and drive awareness.
Getting granular also raises the challenge of working with too much data, which further complicates things when trying to refine your approach for individual campaigns. This isn’t just about working with many data sources, but also about understanding where that data has come from when working with third-party partners and DMPs. Even the difference between probabilistic data (which is collected automatically) and deterministic data (which people submit willingly) will affect how that information is analysed and factored into targeting.
Leading brands tend to opt for flexibility, choosing a tech stack that allows them to draw on the right DMP (or DMPs) for each campaign. They see the value of working with a partner that understands their data needs, is connected to a broad range of DMPs, and most importantly understands the story behind all the data these platforms collect.
Test, learn, and be flexible
The topic of flexibility bring us to the importance of testing. It might sound like I’m going retro and reverting back to how we understood audiences in the 90s, but there is no substitute for taking a step back and testing your hypotheses with real customers to understand what makes them tick.
Early in my career, a client asked me to run a Facebook campaign only targeting pilots and cabin crew, who they saw as their only relevant personas. My instincts told me the brief was too restrictive, so I ran a parallel test to see how their message would resonate with other audiences. The tests revealed that sport lovers in particular were exceptionally receptive to our message, driving 50% more conversions week on week than the expected audience. As a result, our client reduced their spend on pilots and cabin crew and differentiated with investment in more potential audiences.
We live in a digitally collective world where people change their ideas about products and brands almost daily, and in which there are no longer lines between how we consume content in our professional and personal lives. There is no way a brand can accurately define the breadth of people who will care about their message, and this is where they are missing out.
The AI effect
I couldn’t discuss the topic of targeting and audience testing without mentioning Artificial Intelligence. From Norwegian telecoms leader, Telenor, to London’s Heathrow Airport, brands everywhere are using AI to collect, analyse, and act on customer data more quickly and for a broader audience.
The beauty of AI is that even as we continue to refine algorithms manually (up-weighting and down-weighting various signals, improving its approach to targeting, and so on) the technology is also developing on its own. Today’s algorithms are constantly testing and learning about customer behaviour at speed and on an incredibly large scale.
AI is like a new employee you need to train before they take on more responsibility. And it’s learning fast. It will become the marketer’s ultimate assistant, predicting what we’re going to do next and providing us with added insight to ensure we make the best possible decision every time. The technology is by no means perfect yet, but judging from the innovations on display at CES we’re already much closer than we were just a year ago.
Read more about how Adobe Sensei AI is bringing new speed and insight to customer analytics. And click here to learn how Adobe Audience Manager is helping brands build complete, accurate customer profiles.