My career has placed me at the intersection of financial services and fintech – from working with legislators and regulators to create disruption; to working with organizations to cope with disruption; to investing in and taking board and advisory positions with the start-ups I believe will exploit disruption.
During this journey, I have found that disruption is re-defining the characteristics of companies that will be successful in our Open Banking Future – from rigid heritage brands focused on one sector to highly elastic brands capable of delivering new services to new customers across sectors. I have also seen the transformation from push-marketing of big dumb products without consideration for the end customer to the ability to talk to and listen to consumers individually at scale. Most recently, I have seen the transition of product-push strategies to delivering hyper-personalized, predictive and pre-emptive services and the movement from company-owned static data to customer-owned dynamic data.
These transformations have an impact on all elements of consumer delivery and innovation. For instance, we are seeing a redefinition of what we can expect from the research industry. This is important to note because the banking industry is constantly running projects to talk to customers and non-customers alike about service, needs, satisfaction, aspirations, opportunities, product ideas, lifestyles, Brexit, house prices, mortgages, credit cards, and the world in general. Annual research budgets run into the millions.
Forty years ago, most of this research was conducted face-to-face. By the mid-90s, most of it was done by telephone. By the early 21st century, it had begun to shift largely online.
But these are just channels for asking questions and getting answers. It is only recently that there has begun to be significant genuine disruption to this pretty basic call-and-response model.
Financial marketers need to be aware of how digital technology and a much more digital consumer is changing the way research is conducted. Here are 10 of the major ways technology is impacting the field of marketing research.
1. Mobile platforms. Just as with banking itself, many research surveys have moved to a mobile-first approach, and some research providers (both disruptors and traditional players seeking reinvention) have even developed apps to make life easier for the participant. That is a genuine shift in the relationship between the marketing researcher and the participant from transactional to engaged. The research feedback becomes part of the customer relationship itself.
2. Real-time response. With mobile engagement in mind (and mobile devices in hand) data collection can start to approach what really happens in the moment, rather than being about flawed recall after the event. There are a number of new non-traditional research agencies capturing this real-time data, getting people to report service experiences, advertising exposure, and so on.
3. Engaged communities. Research communities have grown in popularity, providing banks and credit unions with a resource for fast and rich feedback on an ongoing, proactive basis, as well as a panel to tap into for specific questions. There are now many community platforms in the market, all offering similar functionality.
4. Passive data feeds. Some of the new tech firms emerging in the world of research track peoples’ actual behavior via mobile. (With their consent, of course!) This could involve geo-location, what they’re watching on TV, which sites they’re surfing online, and so on.
5. Short and sweet engagements. In a low-attention world, it is noticeable that research providers are using shorter lines of questioning, in a less formal, more conversational way. People don’t want to complete long surveys (unless they are very well incented). And technology lends itself to light-touch brevity.
6. Move from structured to unstructured questions. Rather than asking everything on a pre-configured scale (“marks out of 10”) these approaches allow the customer to express themselves in their own words, perhaps through open ended text responses or short videos.
7. Scalability advantages. Simply, you can get far more data, faster, for less money. That is the trend in all things, but it is disrupting the more traditional model of contained survey samples. The downside is you then have to find a way of analyzing it all.
8. Multiple data sources. We’re awash with earned data sources such as social, and they are increasingly used as a reservoir of potential insight. The upside: It’s super cheap, readily available and very large. The downside: It’s extremely noisy, and it’s made up of atypical people behaving atypically. This means strategists often need to triangulate earned feedback with more representative feedback from paid-for research.
9. Automated analytics. AI and machine learning tools can help make sense of all this. Analytics firms, including research agencies, are increasingly deploying these tools to find and make sense of patterns in data. This is relatively easy when data is structured. However, you just need a lot of computing power to crunch through the 1s and 0s. It is less so if it’s unstructured (natural language text and visuals). The latter requires not only highly sophisticated algorithms, but also significant human interpretive expertise – data artistry, in short.
10. Augmented Intelligence. As all this evolves, the likelihood is that the role for humans in the grunt work of collecting data and running basic research analysis will diminish. But at the same time there will be a parallel shift, because there is an increasing need for skilled insight and foresight specialists to make sense of it all. Machines can give you an accelerated reading of what’s there, but we should not confuse that with the ability to actually understand.
Future of Tech-Based Research
Now, with all that in mind, what exactly is the future of tech supported research-based insight and foresight?
You’ll discern a problem here: Humans don’t scale easily. So, we are now seeing early signals of emergence of a new breed of tech start-ups seeking to bridge the gap between human science and big data in a more intuitive way.
These new players do not feel like heritage research firms, or point solutions to particular problems. They seem more like wider analytics platforms that can be deployed at scale across a range of paid, owned, and earned data – including research – in a flexible yet consistent way. My prediction is that data-agnostic approaches allied to metrics that can measure meaning in an intuitive and resonant way – not just pump out impenetrable word counts or reams of statistics – will enable human scientists to scale themselves intelligently and enthusiastically.
And they’ll be able to constantly access answers to the big questions that really matter, at pace and scale, unconstrained by the limitations of slow, expensive old-school techniques: What do we express about our organization, and how does this compare with what people say and feel about us? How is our organization positioned within the competitive context of our category? How are we positioned in the landscape beyond our category? What is the broad cultural atmosphere within which we exist? And what does it mean for our future?
If we can answer these questions by unlocking the richness of the vast unstructured data treasure troves ignored by the data scientists, we can make better decisions, faster, by stepping outside our historically constrained world view and truly connecting with the complex pulse of consumer culture.
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