It’s an exciting time to be working in marketing and analytics…
But what does the future hold? Lucky for us, our Data Solutions Lead – Tom Nichols has shared his predictions for the first year of this new decade below.
“A/B/n & MVT testing will dominate the VR space”
For years now, companies at every stage of digital & data maturity have been utilising split testing to help shape customer experience. These tests range from simple changes such as call to action button colour/wording changes to slightly more complex journey pathing optimisations and full scale machine learning based continuous testing & optimisation strategies. Ultimately, these tests can still only influence the website itself. There’s a plethora of variables that simply cannot be accounted for, and whilst these tests are still a great tool to determine the best direction for your UX strategy – they have a way to go before they provide deeper insights. These tests can usually confirm or deny a hypothesis, but not necessarily provide supporting insights on to why that hypothesis was or wasn’t correct. This can leave you guessing at the real insights that could be carried over to the next test.
In the world of VR, developers can define the entire user experience. Emotional impactors such as the weather or atmospheric noise can be tailored to invoke a preferential state of mind for the experience in question.
With VR, users are less likely to be distracted by an external influence such as a phone notification, other browser tab, or a television in the background mid session. Thus minimising the risk of breaking their focus and pulling their attention away from your carefully curated experience.
With eye tracking in VR, there’s no doubt about what catches a users attention, and how long for. Sort users with banner blindness from users without for a more accurate representation of how your content is interpreted. Learn what sort of stimuli pull attention away from your content, and adjust the environment to reduce that chance.
For the self-professed, anti-advertisement segment – (who balk at awareness campaigns or in-game advertising), even fictional adverts for fictional companies will still provide value when testing colours, fonts, voices, positioning in relation to other artefacts and so on. These are the insights that offer long term value, which can be applied to any campaign moving forward. It is these core influencing factors that will leave a lasting impression on your target audience.
“An increase in Machine First Analytics Configurations over Human first.”
Insight & Analysis tools such as Google Analytics are highly configurable, and every implementation is different to best suit the end users, i.e. the humans who need to make decisions off of the data. Events, goals, and methods of definition and measurement fluctuate even within a single organisation, with individual highly specific interactions captured in ways that make reporting easier. However, this can often occur at the cost of a more complete view of the experience. Knowing what to focus on and why is a key requirement for any analyst, but by leaving what you may consider unimportant interactions untracked, you lose the ability to factor them into the equation altogether. This allows for biases and preconceptions to shape the reports you utilise reactively in decision making, instead of proactively finding new insights – not very data driven.
As data schema & data warehouse architectures advance, visualisation opportunities improve, and machine learning algorithms surface newfound patterns – it will only be a matter of time before basing analytics implementations around data point acquisition takes precedence over usability. Much of this data won’t be formatted in meaningful ways to human eyes, but it doesn’t need to be. Another computational layer sits between your data and the human analysing it. Those wanting to keep up with the bleeding edge of digital maturity will have to accept that their data ingestion strategies will no longer be tailored around their ability to interpret or understand them. Let’s leave that to the machines and reap the benefits at a later stage of the process.
“Healthcare will take centre stage in the privacy debate.”
Data Privacy is getting hotter and hotter a topic year on year. We are generally less willing to give away information about ourselves for free, and consider our identity as more of a commodity than ever before. Our data can be used to help or harm us. It seems that for every one marketer looking to personalise our experience, that there are two marketers looking to use that same data to take decision making out of our hands and into theirs for their own personal gain.
Collaboration is vital to our success in everything we do. In a perfect world, sharing our data benefits everybody and harms nobody as we use it to better understand ourselves, our thought processes, and our world. But the misuse of our data has left a growing majority cold to the idea of participating. If this trend continues, we will lose our ability to learn and grow at the pace required to drive meaningful change. By closing ourselves off, our problems aren’t obvious to those who might be able to help, and our understanding of things can only take into account the limited view of what’s presented to us, not reality as a whole.
Early detection of health issues depends on our data. Modern research is far more able to correlate more factors to increased risk of health problems. By knowing where you live, how you commute to work, what you eat, your biometric information, or what you google – we can build a tapestry of whether you are more likely to be on course for, or in the early stages of a serious health condition.
We’re right to be wary of our digital identity and who has access to what parts of it. Privacy is important. But unless the big players can convince us once again to give up a little more of ourselves soon, they run the risk of losing us altogether. Health and well-being are a priority for almost everyone. With advances in technology continuing to improve, it’s an ideal topic on which to sway public opinion back in the favour of select data collaboration, rather than opting out altogether.