At the start of the year (and when COVID-19 wasn’t something that had crossed many of our minds), 2020 was going to be a big year for technology in all industries. The vast majority of marketing blogs you read mentioned how much of an impact technology was going to have on our lives, and even mentioned specific technologies that they predicted would have a huge impact in. And with the development of technology, comes an opportunity waiting to be taken.
Whilst the below isn’t by any means a complete list, I’ve considered how some marketing technologies can take consumer insight forwards and what opportunities they can offer to an insight team.
Social media is a vast data source just sat there ready to be used, but on the whole isn’t something that is used to its full potential within market research. As my colleague illustrates in her recent blog, social media analytics can help generate insight across a whole host of topic areas and can offer some real value to insight teams especially when combined with other strategies.
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| There are a few great marketing technologies that have the potential to transform consumer insight when integrated into market research strategies, so why aren't they used more often? |
Social media analytics is a technology that can be easily integrated into any market research strategy, and opens up access to whole new online communities to researchers. Take customer listening as an example; you might be running a project about in-store experiences where you’ve got specific feedback tasks for a pre-screened group of consumers in order to answer your objectives. But there is a whole load of other data, that’s customer-led, that you can tap into via social media. It could be reviews customers are leaving on Facebook or tweets about a poor in-store experience on Twitter that, if analysed, could complement your insight led project. Combine the two and it can help really build the in-store experience picture.
Similarly, word-of-mouth advertising occurs in spades on social media, and is a great source of data for brands to see what they’re also doing right. With social media usage only growing, and the current low rate of integration into research strategies in the insights industry, there is a big, relatively-untapped opportunity that agencies and businesses are missing that allows both consumer-contextual and brand-specific data to work together to supercharge decision-making processes. Given the volume of data generated, having the knowledge and skills in order to be able to uncover the insight you need is key, but is something that if used correctly can really benefit the consumer insight that is being generated.
Artificial Intelligence is something that has been on the market research industry radar for years as something on track to revolutionise our working lives. By allowing integration through automation, advanced analysis via machine learning, AI was sold as being able to speed up data analysis, cutting back project times and leaving researchers more time to weave the story from the data, and bring it to life. I first wrote a blog back in 2017 on the fact that AI wasn’t currently something we could rely on without a sense check from a researcher, but as I was hoping, the technology has improved over the last 3 years (and is something that will continue to improve as the years go on).
As High Beam Global on Medium highlights, going forwards it’s not just a case of needing AI to help do things faster by automating processes, market researchers need it to grow and learn for itself for it to offer the maximum benefit especially when thinking around Natural Language Processing. This then links into a couple of the other areas I discuss in this blog.
As has been the case with AI so far, it’s not going to be a light switch moment where by it suddenly becomes the all singing all dancing technology we need, or be fully accepted by the industry; instead it’s something that will continue to grow and be further refined, as it does I can see it becoming more important in our insight lives.
With huge volumes of data (as we see with social media analytics) and a need for the numbers to jump out of the page has led to the uptake in data visualisation tools. Take Tableau, and Display R, they have become household names within the market research industry.
As a result of the needs from within the market research industry, these have become not only more sophisticated but also include more automated giving you access to your large-scale data sets, in an easier to use way, without any reliance on someone having to input and set the data up each time.
Data visualisation tools, when used right, are a huge win for insight professionals in terms of how quickly analysis can be presented, which is key for the audience the insight professionals are reaching out to. As the end users of research start demanding more data, with more actionable insight, and more quickly, data visualisation tools will be vital for researchers in delivering this. One of the key areas still needing work though however, is around data presentation of qual commentary. This is in part, reliant on the progression of AI and in particular analysis via machine learning, but once it is in place, data visualisation tools stand to transform the insight activation stage of the research process.
We all know about chatbots by now, with more and more of those little boxes that are popping up on websites asking if you need help; they might look small, but can in fact be very mighty when thought about from a data collection point of view.
They offer a real opportunity to collect in the moment customer feedback, for example, at the end of a purchase journey, rather than a customer being sent a satisfaction survey invite a few days or weeks later when the details about the experience is gone from the forefront of their mind.
As with all the other technologies I talk about above, as chatbots evolve and become more sophisticated, for example, offering additional communication options, such as voice as well as written text, the scope of use within market research will increase and in turn, the amount the industry calls upon them will increase.
However, there is a slight drawback to the evolution of chatbots; as they start offering wider communications, their complexity of use within the market research industry will increase. For example, as they start to expand into visual communications, more sophisticated analysis tools will be required.
The evolution of technologies available to insight professionals has enabled insight teams to get an understanding of how their customers behave without having to specifically research out to a single customer.
Having all the information available, and being able to access it with less effort than historically may have been required, means that instead needing of a period of exploratory research, the data generated through these marketing tools, can be used as a starting point for research. I’m by all means not saying skip that exploratory research phase, but assess the information you can access and go from there, obviously in some cases, the exploratory phase is still key.
In any case, insight teams need to be open to incorporating new research opportunities into their toolkit. Otherwise, you might find that as others are generating more insight, better insight and quicker insight, you’re getting left behind.