PODCAST: Making AI Real
Episode 1: AI enabled Revenue Management for CPG powered by BRIDGEi2i Watchtower
Listening time: 12 minutes
AI enabled Revenue Management for CPG powered by BRIDGEi2i Watchtower
One of the crucial asks in enterprises today is the need for agile business surveillance. Executives want real-time business performance monitoring with the ability to get contextual alerts and deep-dive analysis. And this need extends to functions pan-enterprise, be it credit risk, supply chain, marketing or talent. For CPG companies, the landscape keeps evolving very fast, and players need to be agile and flexible to keep themselves one step ahead of the competition. BRIDGEi2i’s AI-led Accelerator like Watchtower can help a lot and, in fact, already transforming Supply Chains—be it keeping an eye over a particular SKU’s sales decline or pricing advantages by a competitor—with its exceptional ability to detect anomalies and point out root-causes that need immediate attention and enable cross-functional collaboration.
Monica: Hi, everyone. You are listening to AI to Impact by BRIDGEi2i, a podcast on Making AI Real. My name is Monica Gupta. I lead the design and development of contextualized AI solutions for our clients. Powered by BRIDGEi2i proprietary accelerators, these solutions help drive both functional and pan-enterprise transformation by making AI real.
As part of my role, I often interact with CXOs and senior leaders across business verticals. I understand their challenges, growth, vision, and participate in the design of digital programs. One of the crucial asks in enterprises today is the need for agile business surveillance. Executives want real-time business performance monitoring with the ability to get contextual alerts and deep-dive analysis. BRIDGEi2i’s proprietary accelerator Watchtower delivers AI-powered business surveillance with automated, unexpected business event detection, anomaly alerts and advanced root cause analysis.
We have deployed the Watchtower in a variety of business environments globally, and businesses have been able to reduce their time to actionability, reduce risk impact on their businesses and drive more timely business decisions. In this podcast series, we will delve into the details of the Watchtower and its applications in various business domains.
Today we have with us Pradeep Ghimirey, BRIDGEi2i CPG practice lead. He has over 12 years of experience in the CPG domain and specializes in developing and scaling AI, ML-based solutions across fortune 500 CPG and FMCG customers. I’m very excited to discuss the potential of Watchtower in CPG and also understand some of the client use cases in this domain.
Hey, Pradeep. Welcome to the first podcast in the Watchtower series. How are you doing today?
Pradeep: Thank you for having me, Monica. I’m doing very well. Thank you.
Monica: Thank you for joining in Pradeep, you have been designing distance solutions for CPG for more than a decade now. Based on the current global situation and your conversations with clients, what are the key challenges and risks CPG companies are grappling with?
And how do you think the Watchtower is helping work on these challenges?
Pradeep: Monica, you know, in today’s scenario, the business decisions have to be made real-time. You know, as the landscape keeps evolving very fast, and you need to be agile and flexible to keep yourself one step ahead of the competition.
Any global company, you know, usually have multiple brands and hundreds of SKUs, you know, is they sell across countries to the last network of distributor and retailers both online and offline. There are data pipelines for capturing signals across the board. The critical challenge is to identify quickly, which signals are important.
Pradeep: This’s where an AI-enabled, you know, cognitive surveillance platform comes to the rescue. Now, like think of this as a bot which constantly monitors signals across all granular dimensions and KPIs and quickly identifies if something is fishy and send an alert to you.
So you may, as you may know, receiving five positive problems that need your attention on a mobile image of the statute.
Monica: Okay. Well, that sounds interesting. So, would it be, if I understand you correctly, you’re talking about a system that tells a CXO about the reasons for business movements, say for a category head of a CPG company, it would provide alerts about sales trends of its brands in different geographies.
Would that be a correct understanding?
Pradeep: Yeah, you’re right. Monica, you know, let me explain this in a little bit more detail? Like for example, let’s say you are responsible for managing sales of a breakfast cereal category across Walmart in the US. Now, what would happen is you will get three alerts about sales declining for a few SKUs in a cluster of stores?
Now a certain SKU would have seen, let’s say a 50% decline in sales, in Walmart, in Cherry Hill last week. Well, if you rely on a traditional BI you need an analyst to inform you about this, and it’s not possible for you to check trends for all SKUs across all retailers for the last week and also the chances are high that they do completely miss out on this? Now with Watchtower solution, we can catch it every time it occurs.
Monica: This is good. So Pradeep, I completely agree with one of the points that you made that, you know, you mentioned some good anomalies, which otherwise becomes very difficult and sometimes impossible to track. Given millions of notes and complex hierarchies in businesses these days also being able to catch signals at the time gives business owners sufficient time to take actions, do you think so?
Pradeep: Yes, absolutely, Monica.
And this is really, it goes one step further and also tells you the possible root causes behind these anomalies. Is it because your sales decline because you know, the consumer sentiments are turning negative because a new flavour you introduced, they’re not liking it? The competition who has started giving massive discounts? Or, are you simply facing stock-outs because your distribution center is not having sufficient stock?
Now, you know, so what is happening behind the scene? Know there’s a comprehensive, you know, set of the anomaly detection algorithm, which is running all the time as in when the new signals come and detect whether it’s an anomaly or not? Then it leverages causal graph networks to quickly, traverse and identify what might be causing the effect. So, there are some causal models built to really understand the relationship, and you traverse through the network to understand which of the node is causing a problem as we speak.
And what we have also done, we have also enhanced on the algorithm’s capability to not only detect certain things like, you know, upwards or downward in a movement, it also looks at a let’s say a trend shift. So a new product which was selling well started selling less, over a longer period of time, or there are certain viral trends which you need to be aware of. Or some of your products are viewed very differently from them.
And then all of this happens being seen and it comes to you as a number, and you just need to act. Isn’t it awesome?
Monica: Definitely. Definitely. So this is great catching anomalies at the right time is good. But where I completely agree with you and really like is the, it also tells what might have caused those anomalies.
And that is what saves additional time or finding the root causes. You also spoke about some of the new and some of the different kinds of anomaly detection that it’s not just the trend anomalies, but you’re also able to track trend shifts, deviations from trends, peer groups, etc. Now, the next step really becomes, is taking actions on the root cause examples that you gave. And this will require managers from Supply Chain, Marketing, Sales, Pricing, all of them to come together to act. So how do you ensure those right people responsible as identified in the root cause, talk to each other seamlessly?
Pradeep: Yeah, well, Monica, I think that’s a very good question? So, the way it is decided, and it is designed as a collaborative platform that allows you to assign an action to your colleagues, to mitigate any risk and act on. For example, if you notice your competition has been aggressive on pricing, you can share that intel to your revenue management needs to act on.
You know, given the business operates across categories, geographies accounts, the alert can be mapped to owners across hierarchies. Your Walmart account manager will get alert related to sales and market share at an account level. Where is your specific sales manager for the store will get the granular alert only for the store the person manages. So, I know this feature ensures that you know, people only get alerts for the interest or for their area of interest. And also, where they can act. So, and you can also reassign teams in case someone is not able to act. And just to ensure that action is taken.
Monica: That’s really interesting, but I think now we’re talking about closed looping which makes the solution really exciting. So, you talked about some really good use cases and how collaboration across functions makes it easy to correct these anomalies? It looks like businesses spend more time taking actions rather than spending time finding the problem itself. Do you feel this? Do you see this happening across the clients?
Pradeep: Yes, you’re absolutely right. Monica, you know, and now we have brought this point, you know, let me touch upon some other benefit, you know, customers and as you rightly mentioned that the time to act is reduced considerably, and as a solution tells you, what’s wrong with the business and also guides you with the reasons behind it. And the other aspect, which it really helps you with also, I mean, leveraging cross-functional support system. And with that is all of these KPIs are connected and this clarity on what needs to change to solve the problem at hand and who needs to act.
Monica: This is exciting stuff. Businesses find anomalies at the right time with the causes and also are able to collaborate to solve. I’m sure you must’ve seen some great success stories, Pradeep. Can you elaborate on any one of the great success stories for our listeners?
Pradeep: Absolutely, Monica. We have deployed it at, you know, number of Fortune 500 customers across the US. One of the leading home care company in the US leverages it to monitor pricing violations and competitive pricing action, you know, which was impacting their margins and market share. At the end of the program, we are able to prevent revenue leakage, to the tune of 3 per cent, which was amounting to almost a couple of million dollars in a year.
Monica: That’s impressive. It seems like autonomous business surveillance is an emerging area. And given the current global scenario where companies are seeing so many ups and downs and businesses so volatile, do you continue to see traction during this time of COVID also?
Pradeep: Yes, you know, COVID has forced companies to be constantly trying to stay on top of their business and identify potential risks and the pitfalls, especially on the sales side and the distribution side.
So, for example, they need to quickly grasp which products are flying off the shelf, and we just wrote down so that they can manage the productions accordingly. And because the reaction time is limited, a solution like this can point where you need to focus all your energy, to maintain this continuity and growth.
Monica: Well, this was a very interesting topic. We heard about so many important use cases in CPG domain where an accelerator like Watchtower can really help. Thank you, Pradeep for your time today. It has indeed been a great discussion. I’m sure our listeners would find this interesting. Thank you once again, Pradeep.
Pradeep: My pleasure, Monica
Monica: Thank you so much for listening to this episode of the AI to Impact podcast.
That was such an interesting conversation with Pradeep Ghimirey, CPG practice lead at BRIDGEi2i analytics. Leaves me personally, with a lot of thoughts, ideas and actions do subscribe to a channel as we will continue these conversations with experts and thought leaders in our upcoming episodes. Till then, bye. Stay home and Stay Safe.
With new data formats, evolving data ecosystems and AI technologies becoming more mainstream, the intersection of Data Science and AI-powered interaction systems present a whole new gamut of opportunities for the digital enterprise. In our latest AI to Impact podcast series: Making AI Real, we will be interacting with several thought leaders, BRIDGEi2i Business Heads, Domain Experts, and reputed AI and analytics leaders to learn more about endeavours and innovations happening to make AI real for enterprises and gain tangible benefits to stay ahead in the business. Stay tuned to know more!
Monica Gupta, Manager, AI Actions
Monica Gupta is Manager, Data Engineering and AI Accelerators at BRIDGEi2i, and leads the Design and Deployment of contextualized AI solutions for clients at BRIDGEi2i. Bringing in the techno-functional perspective, she handles roadmap, solutions and delivery of AI accelerators as part of her current responsibilities. She has led some sizeable Digital Transformation programs in the areas of Customer Experience, Supply Chain, Sales and Digital Marketing across CPG, Enterprise Tech and Consumer Tech companies. Monica holds a Master’s degree in Finance from Delhi University.
Pradeep Ghimirey, Director – CPG Practice (North America & APAC)
Pradeep Ghimirey is Director – CPG Practice (North America & APAC) for BRIDGEi2i. He has over 12 years of experience in the CPG domain and specializes in developing and scaling AI, ML-based solutions across fortune 500 CPG and FMCG customers. He has vast experience in applying advanced analytics AI solutions to solve business problems in companies like Mu Sigma, Zyoba Labs & TEG. He’s holds a B.Tech in Electronics & Telecom from NIT, Trichy.