But again, if you don't know how to use the tool, you could wind up in a lot of trouble, because there are differences between correlations and causation.
Three takeaways worth carrying forward.
- 01
The real work, at least from a marketing perspective, starts in understanding how that lake was created and how to utilize that lake going forward.
- 02
Now AI assumes that you don't have a hypothesis and finds trends within the data which may not be women and men at all.
- 03
You know, people have an umbrella because it's raining, or is it raining because people have an umbrella?
Connected to the DMAasia operating frame.
Proof
This episode connects AI in marketing to credible evidence, leadership archives and field-tested marketing practice.
Access
This episode connects AI in marketing to routes into communities, councils, juries and senior conversations.
Ascent
This episode connects AI in marketing to learning, recognition and the next generation of marketing leadership.
AdChoices / DAA
This episode connects AI in marketing to privacy-first marketing, consumer trust and responsible data use.
Unfinished Business
This episode connects AI in marketing to open industry problems that still need senior participation.
Durable lessons from the record.
- Start with the consumer problem, not the channel.
- Treat digital transformation as a behaviour change, not a software purchase.
- Measure effectiveness without losing the brand story.
If AI in marketing is now table stakes, what will make the brand genuinely harder to ignore?
Take it into Unfinished Business →READABLE TRANSCRIPT1,776 words
Let's go back to what makes people loyal in the first place. Why are you loyal to a product? Let's just ask you the question. Let's turn it around a bit. So how's the big data and artificial intelligence shaping up the marketing landscape in India? I think big data, a lot of it is noise. So your ability to get rid of the noise and find the nuggets of truth in between that is the first capability which you need. First of all, you can be able to collect it. Then once you collect it, you need to find value in it. Then you
need to know how to deploy that value. I think it's helpful when we're thinking about big data to realize that it's not just a question of creating a data lake and saying, okay guys, we're done. The real work, at least from a marketing perspective, starts in understanding how that lake was created and how to utilize that lake going forward. So to that extent, I think big data is changing substantially, but only if we know how to make a change things substantially.
AI, on the other hand, see we always had analytics. We used to look at data and we'd say, oh, this column is greater than this column, so therefore we're selling more in this city than that city. We'd look at a person and we were analyzing data all along, so nothing has changed much. So what has really happened to analytics now is that previously you had an H0, a hypothesis, and you'd say, hey, I've got a hypothesis. I think women buy this product more than men, and you test it.
Now AI assumes that you don't have a hypothesis and finds trends within the data which may not be women and men at all. It may be something else altogether. So it gets a lot more exciting. But again, if you don't know how to use the tool, you could wind up in a lot of trouble, because there are differences between correlations and causation. And it's not always clear which one is which. You know, people have an umbrella because it's raining, or is it raining because people have an umbrella? Right. We don't really know which causes which.
Chicken and egg situation. Well, chicken and egg, yeah, that too, but also more that, you know, the difference between correlation and causation. And if I don't know which one is which, I may be trying to find something which has no meaning. Right. So as I said, everything changes and nothing changes. Right. So what role does the new technology, big data, artificial intelligence, play in acquiring customers? So I think the way to think about technology is, think of it like plumbing. Right. The water which flows
through the plumbing system is basically data. Right. So I put and say, place some really nice plumbing, but I don't have water, it's of no use. I put in place really nice plumbing, and I've got dirty water, it's of no use if I wanted to wash my hands or to drink. Right. So I need to have the plumbing in place, and I also need to have the water flowing through at a reasonably clean level for it to be useful.
Now, technology is the enabler for both the collection of data, the management of data, and the deployment of the data. Right. So I think the two work together. Now, the third layer of this is communication. Which is I've got the right data, I've got it in the right place in the right way, etc., etc., but if I don't communicate effectively, it's all to waste. Right. So I think what technology enables you to do is get the right communication in front of the right person at the right time.
And human intelligence really is still needed at some level to figure out what all the rights are. Right. Sure, you're supported by big data, you're supported by AI, but finally, and technology is the enabler. But finally, there is a role for the marketeer who still needs to figure out what it's all about. Right. And make it all work. Understand the human behavior. Understand the human behavior and I have the insight which is that spark or genius which allows you to effect that behavior. Right. Right.
You know, I mean, if you can't effect it. Right. It's just, you know, getting a nice warm feeling and nothing much is happening. Right. So your expertise lies in customer loyalty, one of the things that you've been doing all these years. So how do you ensure that millennials are kind of loyal to a product?
Oh, now there you have me. How we make millennials loyal to anything is always a big question. But they are loyal in streams in different ways. Right. So I think, let's go back to what makes people loyal in the first place. Why are you loyal to a product? Let's just ask you the question. Let's turn it around a bit. So why are you loyal? What makes you loyal? I'm not. I keep changing products. You keep changing products all the time. There's no product to which you're loyal? Yeah. No. Okay. So what makes you unloyal to a product?
In today's time, it has mostly discounts. So discounts are something which make you change your mind. Maybe something looks good and so you change your mind. Maybe it's new and so you change your mind. There are many reasons why people are disloyal, right? Right. Now, if I know why you're disloyal, I now know how I should play with getting you to be loyal. Right. Right? Because, you know, if I take that away in some manner or if I can replace that in some manner, then I can perhaps make you loyal. Right?
So I think loyalty programs basically give you four things, five things really. Four key and one external. The first one gives you data to play with. Right? And you have transaction data, you have profile data, you have interaction data, all kinds of data. And I know it about you as an individual.
Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right? And you have to pay for it. Right
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get into that queue. And I say, well, give me 10,000 miles. Now, you may say yes. You may say no. Right? Let's say you say yes. Now, I know the value of your getting into the short queue is 10,000 miles to you. Right? Let's do it the other way around. You're in the short queue. You're a very established member. And you've got platinum status. You're standing in the short queue. Now, I come and say, join the long snaking queue. Right? And I say, I'll give you 10,000 miles. What's your reaction then? Do you want it? Do you not want it? Right? Chance, sir, you'd pay for it.
You'd probably say, no, I don't want it. Because 10,000 miles to you is a small amount of miles relative to the inconvenience standing in the queue. Right? So, as you think about the four facets of loyalty, which are, you know, the communication channel, the data, the recognition, the reward, and you use them all together with the fifth facet, which is partnerships, which you do with other people in order to recognize your members better, you have a very powerful tool to take on the tendency to go for a discount.
Data crunching is a very difficult job. So, what do you do when not data crunching? How do you unwind after work? Well, I've got several hobbies. So, I sail a lot. So, every weekend, I'm on the water. And every weekday, if I can fiddle it. I also play classical guitar, which is a bit of a boring thing to do. It's a demanding hobby in a very solo field, but, you know, it's something which I enjoy a good deal. And I read. And I've got a library of about 22,000 miles to do. So, that means I've done a lot of reading over the years.