FMCG | Analytics

12 Apr 2017

Arrelic Insights

Predicting Consumer Behaviour

PREDICTING CONSUMER BEHAVIOUR USING ARTIFICIAL INTELLIGENCE

A famous example in corporate world exists where by a company in Minneapolis which mines data to predict pregnancy enabled a retailer to send baby clothes to a teenage girl about which her father complained and cribbed about, only later to learn that his daughter was indeed pregnant!

While the anecdote above shows the power of data mining in consumerism, it is just the tipping point of what data mining along with predictive artificial intelligence can achieve to develop patterns about consumer behaviour and drive sales without even them knowing.

GOOD OLD FASHIONED DEDUCTION

While AI is being developed marketers can use their deductive powers to give the initial thrust for AI to take over. A consumer will be doing different things on Tuesday 10 PM vis-à-vis Friday 10 PM but what he could be doing is where AI comes in.

PREDICTING BUYING BEHAVIOUR: A SPARK BEFORE THE ACTUAL STIR

Using data mining, we can find the point at which the customer will go for specific products. Retailer and FMCG giants can make use of these patterns and impeaching on them without crossing the proverbial line. The art of making it natural lies with Artificial Intelligence. For Example, a customer would go for new appliances when moving to a new city. This can be due to a career change that can be predicted from LinkedIn profiles of customers. Such data open a vast range of possibilities of actually reaching customer without hassles and generating revenue.

SELF- LEARNING ALGORITHMS AND MIMICKING HUMAN BEHAVIOUR

And keeping in mind that these are absolutely keen approaches to utilize information to target purchasers with significant items and administrations, the joining of AI and machine learning implies a radical new ballgame in which advertisers can punt a portion of the hard work.

AI, then again, utilizes complex programming and algorithms to copy how people think, act and react to a circumstance or in a discussion. This, thus, helps brands discover significance in information and make applicable proposals.

Netflix tries to foresee the thing you need to watch next. The more you watch, the more profitable the administration. How would they anticipate what indicates you will like? For their situation, when you've been a part for quite a while, they have bunches of information on your review propensities and can state with 85 to 90 for every penny certainty, you will like this thing and you will like this other thing. It resembles Amazon item proposals, as well. There's a long history of utilizing information mining and machine figuring out how to anticipate your next buy.

The utilization of AI and machine learning is as yet simple, yet , as well, indicates Netflix suggestions that depend on past decisions , need to be constantly refined subsequently by review conduct.

What's more, it's that familiarity with buyer inclinations that tailors accessible choices that we are likewise beginning to see from players like Stitch Fix and Spotify, which take a gander at past buy conduct, way of life and proclivity information to help limit the decisions they offer purchasers. At the end of the day, as opposed to compelling shoppers to pore over an immense scope of choices, Netflix, Amazon, Stitch Fix and Spotify utilize information about buyers to make instructed surmises about what they will most likely like and get rid of what they won't.

Furthermore, that is on account of brands/advertisers can take in specific individuals put in specific requests more than once and are very liable to ask for a normal request, so it's just about building an information profile so that the information gathered can begin settling on choices in the interest of purchasers.

In the hindsight, as this develops, buyers will give less and less information — until their necessities are met as though by magic!!

 

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