Big data and how best to utilise it has become a perennial subject, and the endless debates around it rarely come to a satisfactory conclusion. However, it is technology that will unlock the benefits, and narrow AI (artificial intelligence) in particular that will bring together big data and other sources of information to create large and informative data pictures.
Big data is mostly about consumers and marketing, and from this perspective the 1960s to the beginning of the 1990s were a simple golden period. There were a limited number of people who controlled the commercial media, the big newspaper groups, TV and then radio channels. Massive viewing figures around programmes such as Coronation Street and large newspaper circulations meant it was relatively easy to put advertising in front of nearly all consumers quickly and easily.
Consumers now in power
However, things have changed radically. Consumers have taken ownership of the media. TV audiences have dwindled, newspaper readership has plummeted, there are more than twice as many consumer magazine titles as 20 years ago, and TIVO and broadband mean consumers can decide what they watch and listen to, and when. They can also cut out TV advertising. The disintegration of the media means the public is in complete control, and there is nothing the old owners can do about it.
Now consumers are hugely powerful people and they have to be treated as such. This in turn means brands need to understand as much about them as possible in order to communicate with them successfully, because when they do reach out with a message they have to get it right first time. If brands are lucky they may get a second or two of consideration before messages are rejected, or are engaged further. There are very few second chances if they get it wrong.
In this circumstance data, and data modelling is essential if you want to know what consumers are thinking and what they are likely to respond to positively. This also ties in with big data and how to use it to best effect. The answer lies in using narrow AI to track consumer sentiment, and separately extract specific relevant information from big data.
Narrow AI is able to do this because it has the ability to instantly trawl huge amounts of information and then report specific required information contextually in order to create accurate reports. Although information has to be narrowly defined within any search, the ability to carry out multiple related searches at the same time means it can provide accurate modelling.
The best way to track the sentiment of nearly all demographics is to monitor social media. Currently there are a variety of narrow AI-based subscription services that provide the ability to track consumer comments in real time. However, they are expensive and most offer limited flexibility.
Social media monitoring consultants recommend that conclusions should not be immediately drawn from raw numbers gathered through monitoring. They believe that it is important to read between the lines and try to explore patterns in greater detail. Narrow AI can do this, but not necessarily through the current monitoring packages, and it is inevitable that the required DIY narrow AI packages will become available.
In terms of big data, narrow AI is again the answer because it enables the user to create valuable analysis based on extracting layers of contextual information. One of the best ways to illustrate this is to highlight a problem Tesco had for many years relating to its loyalty card data. The retailer had massive amounts of information on what consumers purchased, but what it could not see was what customers were not purchasing from its stores.
For example, Tesco could see that individual consumers were buying wine and French bread on Saturdays, but it could not identify that customers were not buying cheese. It could see people buying toothbrushes, but not see they were not buying toothpaste.
Clearly complementary purchases were being made elsewhere, and narrow AI could have been used to interrogate this scenario and provide the answers. Tesco could then have followed up through coupon based promotions to plug the purchasing gaps.
Depending on data regulation of specific countries, narrow AI can also allow data marketers to append information found on the web to existing consumer files. Even if this is not allowed in the communication with consumers, its use in data modelling still provides brand owners with far greater understanding of consumer behaviour.
Further disintegration of the media means it will become increasingly difficult to monitor consumer sentiment and interest patterns. Again narrow AI is the answer. It may be a very simple technology, but if used correctly it can instantly create insights based on searching through vast amounts of information.
- David Senior is CEO of Lowdownapp Ltd. With nearly 20 years' experience in IT he has worked for leading global corporations, but in the last two years co-founded two companies, Spark33 Ltd to advise CxO's on mobile and mobile apps, and Lowdownapp to focus on the use of narrow AI in the creation of multi-layered contextual information-based mobile apps.