My guest today is Vibhu Arora. Vibhu’s product career spans both startups and top-tier tech companies. Vibhu is a group product manager focused on AI, M a production at Walmart. Prior to that, he was a product manager at Facebook focused on AR VR e-commerce experience. In this conversation, Vibhu shared with us some of the ways Walmart is using AI and machine learning to improve the customer experience. His advice to product managers who want to get into managing AI and ML products and his thoughts on how to build a business case. 

Links

Vibhu on LinkedIn

Transcript

[00:00:00 ] Vibhu Arora: we created a system where Walmart would actually predict your most likely to use payment vehicle for the given transaction. We make a directed bet that the customer is actually going to use a specific payment method and auto select that, which basically removes one friction point from the journey. So one less friction point. So better conversion, better for customers, better for the business. 

[00:00:28 ] Himakara Pieris: I'm, Himakara Pieris. You're listening to smart products. A show where we, recognize, celebrate and learn from industry leaders who are solving real world problems. Using AI.

[00:00:39 ] Himakara Pieris: My guest today's Vibhu Arora. Vibhu’s product career spans across both startups and top tier tech companies. Vibhu is a group product manager focused on AI, M a production at Walmart. Prior to that he was a product manager at Facebook focused on AR VR e-commerce experience. 

[00:00:52 ] Himakara Pieris: In this conversation. Vibhu shared with us some of the ways Walmart is using AI and machine learning to improve the customer experience. His advice to product managers who want to get into managing AI and ML products and his thoughts on how to build a business case. 

[00:01:07 ]

[00:01:08 ] Himakara Pieris: Vibhu welcome to the smart Fredette show. 

[00:01:10 ] Vibhu Arora: Thank you Hima for having me here. I'm excited to be part of the podcast today. 

[00:01:14 ] Himakara Pieris: We've seen. Walmart investing heavily in AI and ML to transform the customer experience. What are some of the interesting AI use cases you've had the chance to work on at Walmart? 

[00:01:24 ] Vibhu Arora: I started out with a product called extended Warranties. It's a very neat concept especially in, electronics and home business verticals where you're buying like a TV.

[00:01:38 ] Vibhu Arora: The TV comes with one year warranty, but at the time of purchase, you get the option to buy extended warranty. And this option exists only at the time of purchase. You cannot buy it later. So it, it has kind of like this timing component and. It happens to be a winner for both the customer and the business.

[00:02:01 ] Vibhu Arora: The customer wins because it's peace of mind. You have like four kids in your home, you're buying a thousand dollars tv. You want peace of mind, put in 50 bucks more. And even if the TV is broken into pieces, you know, it'll be replaced, the business is working off of statistics. How many TVs get broken and, how the pricing works accordingly.

[00:02:22 ] Vibhu Arora: But ultimately , 90 to 95% of warranties never get claimed. Means like things don't break. So it's a highly profitable business. 

[00:02:34 ] Himakara Pieris: Sounds like it's a good example of how you can essentially use AI enabled service products to increase the cart value and the bottom line. Are there any other common types of finance or service use case examples where you're using machine learning? 

[00:02:50 ] Vibhu Arora: One of the most common like use cases for machine learning is fraud detection. If you generalize it one step further, it could even be called anomaly detection.

[00:03:03 ] Vibhu Arora: One of the use cases, which we used for the credit card application process, was that of anomaly detection or more specifically fraud detection. We actually apply for the credit card. A bunch of inputs are taken in your name and, you know sort of like financial information.

[00:03:25 ] Vibhu Arora: And based on that the machine learning model actually performs a lot of checks and assesses the risk and, and then spits out kind of a binary answer. Yes or no, you know? Yes. Whether Walmart and partner Capital One in this case are willing to take the risk to give credit to you versus no this profile is, is too risky to give credit.

[00:03:54 ] Vibhu Arora: The other interesting use case. We, unlocked in payments, in checkout. We built a model where if you actually have a lot of payment vehicles in your account, which means, you know, you have your debit card, you have your credit card, you have your PayPal, you have, you know, whatnot, you have like a lot of payment vehicles in your account. And guess what? Some of, some of these you know, are running low on balance, et cetera.

[00:04:24 ] Vibhu Arora: So there's like different states of each of these payment vehicles. So we created a system where Walmart would actually predict your most likely-to-use payment vehicle for the given transaction. So instead of like, instead of being super neutral about the transaction that, okay you're, you're going through this.

[00:04:50 ] Vibhu Arora: And now you can select from any of these options. We used to make we, we used to make like a directed bet that the customer is actually going to use a specific payment method and auto-select that, which basically removes one friction point from the journey. Like the most likely-to-use payment method is automatically selected.

[00:05:12 ] Vibhu Arora: So one less friction point. So better conversion, better for customers, better for the business. 

s[00:05:17 ] Himakara Pieris: Walmart, I think pioneered trending curries in e-commerce as well. Can you share a bit about that? 

[00:05:24 ] Vibhu Arora: If you start your search journey on the app, you, you start with the auto complete screen. And on the auto complete screen, there is actually a stage when you have not even entered a single letter, which we call like the starting screen or the landing screen.

[00:05:43 ] Vibhu Arora: Which is the blank slate screen. So on this screen, we envisioned, the strategy here was, we, we want people to, to learn and know more about, trending products, [00:06:00 ] which, you know, other people are using. And it had like, again, like most good products, it's a win-win for both customers in Walmart.

[00:06:07 ] Vibhu Arora: So customer gets to learn about the trends and they don't sort of like, they can, they can get to come out of their FOMO because they caught on the trend. and Walmart actually wins as well. Cause, it means like more product sales and, and a happy customer. and the feature is like on this, this, auto complete landing page, below your previous suggestions.

[00:06:32 ] Vibhu Arora: We started showing inquiries and, we weren't actually expecting it to be, as big of a, a win, but, it actually did, take off pretty well. and we, we, we did experiments with it and we actually got like, you know, pretty solid feedback , from the experiments and ended up launching it.

[00:06:57 ] Vibhu Arora: So it's now launched fully, so you can actually like [00:07:00 ] see it on the app as well. and again, this feature also, is built on, on machine learning models. It takes in a bunch of signals from the crowd, and has like a, puts them into, through a definition of trending versus non trending. And depending on whether yes or no, a particular query gets.

[00:07:26 ] Vibhu Arora: Stamped as a trending query or not. So, again, you know, kind of like a powerful, cool, fun, implementation and use of ai, in, in Walmart search ecosystem. 

[00:07:40 ] Himakara Pieris: In couple of those use cases, they seem to be directly tied with improving UX and by doing that, removing friction and by removing friction increasing revenue generation, .

[00:07:52 ] Himakara Pieris: One of the challenges that we hear all the time is difficulty that PM's experiencing in making a business case [00:08:00 ] because they're having trouble quantifying the impact at the start. Especially if you are at an organization that doesn't have lot of machine learning and data science resources, you're trying to venture into this area, 

[00:08:12 ] Himakara Pieris: Given this type of context, what's your advice to a product manager? On how to build a business case. 

[00:08:19 ] Vibhu Arora: What your questions sort of highlights is if you're, if you're not like a mature ai culture or a mature AI organization it can be very challenging to, to sort of like build this or cultivate this sort of mindset. So Absolutely. I think, you know, it all, it all begins with like empathy and it begins with the awareness.

[00:08:48 ] Vibhu Arora: If I'm a senior product manager in a, in a situation where we want to try our first AI product and, and AI sort of understand, never [00:09:00 ] existed before, so I would, I would want to have like an awareness and empathy of my. Organization , because obviously, you know, I would need out of many things I.

[00:09:11 ] Vibhu Arora: Lead alignment and funding from stakeholders peers and senior leadership and you know, how these people are th