00:05 | Debjani:
Hi Ashish, welcome to the program. How are you?
00:08 | Ashish:
Good, Debjani. Glad to see you.
How can we unlock the value of enterprise data?
00:12 | Debjani:
You’ve had lead roles heading up digital at Wynn and Kohl’s, and now at Gap. So I’m very glad that we can talk about what you’ve seen evolve over the past five, six years and where the industry, in general, is going. Talk to me a little bit about how you see data being used by marketers, what the trends are, and how you see it evolving over time.
01:24 | Ashish:
Kohl’s was the first time when I got involved; I think it was 2016. We started with the collection of data, and that was a big challenge. At Kohl’s, there were a lot of data sources, structured and unstructured, all kinds of data. Bringing them together and mining that was the challenge.
And then the second stage, mostly at Wynn Resort, is where we were able to create a meaningful stitching of customer data and transaction data. You can imagine in a resort there are so many businesses that are coming together, so how to bring all of that data, which is really in a silo kind of form, and stitch for a customer and create a customer 360 view. It is not easy to just bring a lot of data together. Not only to obtain it and make a unique view of the customer but also to keep maintaining it, that’s something where there were a lot of complexities.
In the same kind of stage, we also did a couple of experimentation with voice and Alexa as something that was used for implementing some of the experiential aspects using NLP; where if the customer is in the room, how to act as a self assistant in terms of guiding what is available in the resort and how they can navigate it. How they can use the automation that we were using in the room. AI and ML was at the forefront, and using NLP as the back prop was something that we experimented with. We did see that 60-70% of the customers were interacting with Alexa, either just for fun or they were finding it useful, but it was a good use of that technology as well.
What is the role of data and access?
02:44 | Debjani:
Let me ask you a bit more about the data. I think there are multiple schools of thought on this. CDPs have seen a rise, and enterprises are thinking about adopting CDPs and putting all that silo data together into one form. And I think that is still being debated within the enterprise. Some are going with a full CDP approach, others are saying, you know, I can do an API layer, and the data can reside in different places. What’s your viewpoint, and what have you seen in the three or four, you know, sort of journey points you’ve had through this?
03:29 | Ashish:
Bringing data together is a journey that for enterprises it is going to take some time. I have not seen 100% of the data that is coming together in one place. So that is going to be a journey in and of itself. But bringing them incrementally, whatever is more meaningful, is something that is going to be a journey for an enterprise.
The second thing is, you can bring historical data, which is important, and if you are able to do any % or 70%, that itself is an asset that should be democratized in a way that not only people can query and get a meaningful outcome of it, but also, there are ways for systems to get it by API and things like that. That is going to be a journey that will continue.
On the other hand, if you think about a customer journey that’s happening in-the-moment, and if a customer is landing on your page, on the website, or they are in the store, or even if they are in the resort; there is a lot happening around them. So that is one thing where you need something real-time where you can react in that moment. And the second thing is the customers’ sentiments or their mindset between two journeys also can change. For example, in our resort business, we used to have a customer coming back in 13 months. In a typical retail business also it’s like once a month, or sometimes customers only come on or during Black Friday. It’s a long interval that happens in between and while that happens, and no matter how much data you have historically, the customers in-the-moment, their thinking, has a great influence on what they are going to do.
Although you have a CDP, which is collecting data, which keeps enhancing, you need to react to things that are happening on the edge. That context is very important for you to mix it with what has happened in the past to create a meaningful outcome for the customer. And I think that a good experience will mean that you have both.
How do you meet the high bar set for customer expectations?
06:08 | Debjani:
I like the idea that you talked about that said incremental view is never complete, you’re getting it incrementally, and I think you’re also talking about short-term user behavior, right? In an 80-20 rule, how do you add value to the consumer in-the-moment? Perhaps, what they’re doing in-the-moment, the context around them in this moment, is proportionally more important at this time than what they did, you know, a month ago or 6 months ago and so on. I think the players like Uber and others have sort of spoiled us, that says that you need to know which pavement I’m in, in regards to location, and what color is my car which is just coming in. So if that’s the expectation of the consumer, how do you think the enterprises today, the ones that you work for and continue to work for, are placed to meet that expectation that the likes of Uber and others have set? Is retail ready? Is Hospitality ready to do that?
07:24 | Ashish:
I think that retail and hospitality are no different and that we need to start preparing to meet that expectation.
If you take that to an enterprise retailer, they have at least 5-6 different touchpoints and they are still disjointed, where not knowing what the customer has just done in one channel or in another channel, it has been a challenge. I think that is an area where a lot of, I would say, thinking is going on, and we are working in terms of not just serving one channel at a time, but strategizing how to solve for a multichannel use case.
Can we predict consumer behavior in-the-moment?
08:12 | Debjani:
Where is the industry, you think, in regards to being able to predict behavior? You know, enabling that kind of value for the consumer where you truly understand me in-the-moment, where I am shopping for a sofa, and I think cushions of an X color will be interesting to me.
08:38 | Ashish:
Yeah, today, I think, that’s where most of the optimization has happened on a very linear journey of the customer. Where, if the customer has gone to, for example, a product page, do we recommend that Hey, these are other things that you can think of. But it is for a group of customers.
There are a lot of good examples of optimization. But as you said, 1:1 personalization where truly understanding the customer at that moment, and that moment means a lot of things, it is not only what she has done in the past, or what a similar segment of users has done. But also what is something that she is motivated and why she is clicking certain things in-the-moment. So understanding that is where we can use technology to predict and be smarter than what a human can think of.
We can think of multiple templates of the product page, but still, you can not create thousands of other versions of the product page or attributes of the product. So that is one area where machines can help in terms of not only looking at what has worked for a large number of customers but also at that moment if some tactics need to be thrown in front of the user, it can be done. Definitely, there is an appetite, and these days that is what will make an experience win or lose, based on how closely you are able to engage a customer.