Big Data. What’s all the fuss about? (Part 3)
Needs vs Wants
If you are thirsty you need water, but you want a soft drink.
When you are hungry you need food, but you want to eat at a 5-star hotel!
It is crucial to understand the difference - because virtually all advertised products satisfy wants and not needs!
If that sounds dramatic just take a minute to think. When was the last time you bought clothes merely to protect yourself against the elements, because that was why clothes were originally created? If the answer is 'never', here's another question. Do you know anyone who bought a watch merely because he or she needed a device to show time? If the answer is no, it’s hardly surprising, because your laptop, your mobile, and even your car all show the time. So what do you need a watch for?
By now you would have got the picture. We don't buy our clothes or watches to satisfy our needs. We buy clothes because we WANT to look good. We buy a watch because we WANT to be seen with it. Think about it and you'll realise that almost all your branded purchases are determined by wants rather than needs.
Herein lies the problem with attempting to understand consumers through Big Data. Analytics typically identifies needs - what you are looking for and the cold reason why you are looking for it. So if you checked out a watch costing, say, Rs.3000 at a shopping site, analytics will tell marketers here's a hot prospect. Show him all watches you have in the Rs.3000 - 5000 range. For good measure, show him coolers and sneakers for himself and gifts for his partner in that price range.
At first glance this may seem like good marketing. But on probing you realise that it is mere hit and miss. If you keep showing to a million people your hope is that a thousand may buy and you'll tell yourself that a thousand is better than nothing.
Instead, in insight-based marketing you would first ask why at all someone would 'want' a Rs.3000 watch when he already has several devices showing him the time? You'll realise that the person wants to look sporty, trendy, tough, announce he has got his first job, reveal he is a deep sea diving enthusiast, etc...in short, define an image he wishes to project in wearing his new watch. You will weave all these insights into your ad campaign and show it, without any research spending, to all 21-26 year-olds. Guess what? You are likely to sell a lot more than a thousand because you are showing empathy and satisfying people's wants. And at a cost that's considerably lower, since you didn't spend money on Big Data.
The point is this. While Big Data is eminently suited for some specific purposes it is not a panacea for all marketing requirements. It can identify needs and is indeed most suitable for need-based selling - which is why it is great for online stores, since analytics identifies customers 'while they are searching'. And stores don't care about which brand you buy from them; just that you buy from them!
For brands the story is very different. It is imperative that they have consumers oriented favourably towards them even before they go shopping, so that when a store displays several brands, theirs will be preferred.
Can't Big Data itself involve analytics that goes deeper? It can. That's what Customer Sentiment Analysis and Behavioural Analytics attempt to do. But it is important to remember two things here: (a) this means even more money for analytics, and (b) such analyses are not applicable across the board. The first point is axiomatic. The second may need some explaining.
So it is that the IT engineer who perforce keeps himself updated on the digital world, becomes the marketer's go-to ally. In any 10-minute meeting with any marketer, this engineer can rattle off enough new terms to make the marketer simply give up and say," Do the needful". How the IT engineer himself becomes the creator of ad communication is an incredible example of Digital Marketing Myopia!
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