Although Data is not the New Oil, it’s undeniable it is extremely valuable for those who own lots of it and, more importantly, know how to use it.
Some companies are privileged in this respect due to their ability to gather vast data about their users’ behavior. Besides the usual suspects – the big tech players (like Google, Apple, Amazon, Facebook, Microsoft, or Alibaba and Tencent in other parts of the world) there are a lot of other players in this area.
For example, large or small retailers with a significant online presence (besides Amazon or Alibaba which both are mammoth players in the e-commerce space, but at the same time they have significant IT&C business, including but not limited to the capacity to sustain the massive IT infrastructure needed to support all of their enterprises, so beyond any doubt, they are technology companies first and foremost). Another example are the car companies – the IT importance (both hardware and software) is growing, new car models provide various connectivity options (including always-on connection to internet), thus generating an increasing amount of usage and telemetry data.
Or course, there’s not just about usage and technical data; often the data cauldron contains text, voice and video information too, gathered from various sources as social media, consumers review from online stores or review sites, or directly from consumers through Customers Experience feedback channels.
And these companies are sifting through their large data troves to analyze and extract as much and various insights as they can – understanding what works or identifying causes of failure, depending on the case; segmenting customers; creating risk profiles; testing various scenarios; making predictions (or at least trying), etc. – all sort of things which help in decision-making. I suspect “Data-driven decisions” is a familiar term to all of you.
All this wealth of data, combined with the power of Big Data manipulation and analytics tools, made some doubt the need for marketing research in the (not-so-distant) future. It seemed that once the analysis of aforementioned data sources could be automated to a sufficient degree, you would have all the information you need for your business to thrive. Now, that the AI genie is really out of the bottle, this option seems even more likely; it looks like you just have to add some AI magic to the mixture, and you have found the perfect solution.
But is it really the case? Don’t be so sure.
The business-generated data can be exploited to generate unique and invaluable inputs, nobody can deny that. What I’m saying it’s that this is not enough to fit all use cases. Such usage and telemetry data-driven insights can help a lot with decisions about what to do, especially in the short and medium term – such as how to adjust the sales and marketing push (double on the successful products, fall back on the less successful ones).
But you are less likely to help you understand why, for example, your products or services are visibly less successful than those of your competitor, although there doesn’t seem to be that much of a difference between them. Or how to develop new products, or what new features you should add (or not). Internal data are of less use, if any, in such situations.
If you are lucky, you can find the answer in the external part of the Big Data (such as blogs, reviews, and social media). But only if you are really lucky; the reasons might not be obvious from the start, even to consumers themselves, and people might not bother to spontaneously spell out in detail why they prefer one product instead of another, what new features or new products they really hope for. And, of course, let’s not forget that the Internet is already tainted; troll farms, bots, fake accounts, fake postings, fake reviews, and so on. Good luck in trying to quench your thirst for insights from this polluted river.
I think you get the idea – sometimes there’s no substitute for asking people directly what they think and why they are doing what they do.
By the way, some may think that AI, besides helping with Big Data analysis, might also provide the cure for cleaning Internet-sourced data. I have a strong feeling that this view is highly debatable. In fact, it may well be the other way around. In fact, I’m pretty sure that AI will be used to pollute even more.
Right now, it might be possible, in theory, to identify bots and fake content, if you are willing to put in the effort, to develop the right tools (AI included) and to spend the required resources. When I say ‘in theory’, I really mean that this is just a hypothetical outcome - while someone might be able to succeed in such an enterprise, there’s no doubt that this is no small feat, and it’s not cheap either. At the same time, you have to pray that the shady players will stand still and will not develop and deploy increasingly sophisticated AI tools themselves.
Which is precisely what they will do. Thus, it is more likely than not that the bots that spew fake posts, reviews, and other content, will be almost or outright impossible to differentiate from real humans. Oh, the irony...
That’s why, among other reasons, I believe that the true and tested ‘traditional’ research is here to stay. Rather than having research completely replaced by Big Data in the foreseeable future, they will continue to complement each other.
Will there be changes in the interplay between these two insight sources? You bet. How the future will look is anybody's guess, but I dare to say one thing is inevitable: at some point, AI tools will become pervasive in both Big Data analytics and marketing research.