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How Big Data and AI Are Driving Business Innovation

Big data and AI have been integrated into many business applications, including some in MR.

Editor’s Intro: There is no doubt as to the growing importance of AI and big data in many avenues of commerce, including market research. Ralf Llansas gives us a nice overview of some of these trends. While I’ve seen some applications of these in market research, I can’t help but think that market research is lagging behind other fields in their application.

Technological advancements we have already come to accept as the norm, most notably the cost and other benefits of migrating infrastructure to the cloud, have turbo-charged business, and fuelled a migration to Digital Interactions.

The following are some of the ways in which big data and AI have been integrated into business processes to drive innovation.

But before we look at the supporting environment which is generating innovation in the field, here are some specifics about the ways AI is delivering innovation you might not know about.

The ways AI and big data are already driving better digital customer experiences

AI is already a big part of your digital interactions. IBM believes that, by as soon as 2020, more than 85% of your interactions with brands will be between a human and an automated response.

Personalisation of key functions for mass populations: The key way AI provides innovation in Digital is through the provision of individually personalised content to a mass audience. Big Data and AI are what allows Spotify to personalise a playlist for each of their 70 million customers, without any human involvement. Amazon raised sales 35% by showing their customers that ‘people that bought this, also bought that’ – another personalised message built with the same tools.

Better and more relevant advertising: As we speak, Google’s Adwords platform is being adapted with a new layer of Artificial Intelligence, around Google’s core search revenue platform. By analysing not just the many elements of user profiles that they already have, Google is adding consideration of each individual’s likelihood of converting on the site behind each of their paid search results. Google’s algorithm will show users ads of goods that they’re more likely to buy, raising the value of a visitor to the website and therefore what they are prepared to pay Google for the click. Everyone wins.

Innovation in content discovery: AI engines which associate content fragments with users who are likely to engage with it are behind some of the extraordinary suggestions enjoyed by Facebook and Google in recent years. Google is now worth $720bn, Facebook $475bn, even after a recent market drubbing. Facebook now collates and disseminates much of the world’s news automatically, using AI to ensure its own success. Two-thirds of American adults get their news from social media and engagement with news on social platforms actually rises with socioeconomic status and education.

Context awareness and the Internet of Things: The Internet Of Things is one innovation, happening in parallel to the AI revolution. Each will reinforce the work of the other. Networking equipment manufacturer Ericsson, for example, is investing hundreds of jobs in the US in Research and Development centered on both 5G and AI. Critically, the company wants to use context – the location and spatial coordinates of each connected device to inform the data it records and how it responds to queries.

Re-engagement: AI is at the forefront of innovation in media, constantly tweaking the media assets you’re shown and, recently, the time you’re shown them, to maximise the chances of you engaging with the content. OVO Mobile is a next generation phone company in Australia. OVO has secured the rights to digital video content such as the world e-Sports league and Australian gymnastics and delivers it in an app to their users. Customers who take a SIM plan from OVO automatically get access to the content OVO provides. Over time, the app learns each user’s preferences and behavioural patterns. For example, suggesting a video clip someone might be interested in, while they are traveling on the bus to work and therefore have the time to enjoy it.

How AI and big data help market research

There are really two sets of information available to companies, which can be used in the pursuit of customer understanding.

First, there’s the information they have internally, in their systems about their customers. Collected in sufficient volume, this constitutes ‘big data’ and it provides the opportunity to establish a loop of feedback which may stem from sources such as customer usage of a service or product. New reporting and analysis tools now enable organizations to get access to trend data, contained in these data sets that they can use to evaluate the evolving requirements of customers. ‘Big data’ is easy to access and it’s usually available in sufficient volume (whether structured or unstructured) to offer a high likelihood of accuracy.

The second type of information that companies can use to understand their customers is Market Research. In this context, market research might usefully be described as ‘small data’ – extremely small in terms of data size but the insights it provides are important. Market research is what’s done on top of analysing internal information. It studies the whole market and, ideally provides you an actionable insight which allows you to convince some people who don’t yet buy your product or service to become customers. Market Research information tends to take longer to find and generally offers more expensive, less substantial data sets. As likely anyone reading this knows, however, it can offer you nuggets of knowledge about your customers that you’ll get nowhere else.

As Renee Smith has covered, the key to unlocking the value in these two different sorts of data is to establish an intent to link them, long before products are brought to market. Internal data can be used to establish segments – large or small – predisposed to a potentially commercially viable service, product or idea, to establish the characteristics of potential purchasers and to provide insight on the levers which will encourage purchase. This information gives more of a ‘rational’, ‘right brain’ insight into potential prospects, at the time, rather than customers.

Market research data, the source of those nuggets, offers hard data too, but it’s here that you’ll find customer voices explaining their more emotional decision-making criteria.

By gathering insight from both sides and uniting them with the intent to identify, understand and overcome both rational and emotional objectives, analysts and researchers can combine their output and generate real value for product teams, delivering a considered and rounded output to market.

AI and big data are driving innovation in task automation

Wherever AI is discussed, there is talk of automation and the fear of job cuts.

The graph above shows the relentless progress that AI and automation are making in business.

Automation not only reduces the cost of running a business by eliminating unnecessary wages, but it also enables the firm to raise the quality of what they output, to standardise around that higher quality and to get things to their customers in a shorter period of time.

Take hospitality, for example. There, automated analysis of customer data records mean the days when guests had to keep going through the same excruciating checking in the process when trying to book a room in their favourite hotel are over. Now, the organizations in this sector have been able to keep records of past clients to ease the checking in process. The hotel understands visitor preferences even before they ask with information from their previous visits.

The impact of big data and AI is only just beginning…

Big data and artificial intelligence are enormous technological advancements, the benefits of which may take a generation or more to fully filter through the economy. They are evolving products that are still being developed and the truth is that this industry is in its early days. It’s hard to predict where either the costs or benefits of AI in business will end.

So far, big data and AI have been able to provide powerful tools that have allowed corporations to streamline their business processes, get rid of redundant systems, innovate in the products they deliver and understand their customers better.

The future holds more disruption from big data and AI, not less. The Internet of Things is nearly upon us. There will soon be hundreds of connected devices for every person on the planet. Each of them will be generating a data stream and storing it in the cloud. Each of those data stores is a depository of big data which can be analysed by AI tools. It will be interesting to see what more can be done in the future with the aid of these rapidly changing technologies.


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