Ribu M Jacob

Yes. So I began my career in 2010, um, as a presales consultant in Tata Consultancy Services. Um, and I did my MBA, uh, from IIM Calcutta in 2013, I moved out in 2015, and in 2016, I started my career in start ups, uh, where I worked in Hotels for a year. I was a business development manager, uh, handling business, uh, for in terms of adding more, uh, properties, hotels to the network. Um, I did that for another year at, uh, Ojo Rooms till 2018. And in 2018 to mid 2022, I was working in Zomato, uh, where I handled the business. I launched, Zomato in Kerala. Um, I first launched in Cochin and then went on to launch another 11 odd cities. Dovato is essentially a food delivery, uh, aggregator, and I handle the business for so that will include all aspects a of the business such as account management, marketing, uh, local marketing, p and l management, and so on. To, uh, I have a work experience of about 12 years, and that is my background.

Process of tailoring a data presentation to different audience types. Yes. The first thing I would try to understand, obviously, is the kind of audience that I'm presenting to. For example, if I'm presenting to senior management, I would ensure that I keep the amount of data on the presentation to a minimum and make it very to understand, make very high level abstract, uh, but with a few key performance indicators that are relevant to that stakeholder. So, um, so I guess the process would be identify, uh, who is the stakeholder that I'm presenting to, understand what are they looking for from the presentation in terms of business objectives. So business objectives will differ from senior management to middle management Objectives do differ. Um, so I would keep those objectives in mind and then data my presentation according to their objectives. And if additional data is is required during the presentation, I would be I should be able to present to them. I should be able to justify, uh, my presentation to the user data if if asked for. Right? Ultimately, the framework would be would be around Understanding the, uh, audience, what their objectives are, and, uh, tailoring the, uh, presentation according to those objectives. Highlighting those data points that, uh, that show you how things are moving, uh

So, uh, actions based on signal analysis, I would have a definite framework around it. My framework would be, um, around a cost versus impact framework. I would provide recommendations in, decreasing in a descending order of significance where what are the options that would provide maximum impact for a certain cost. Um, so the 1st option that I will provide is the one that has maximum impact, um, with the least cost. But depending on the objectives of the stakeholder that I'm presenting to, Their choice would differ, but my framework would be around the cost benefit, cost impact analysis. Yeah. That's pretty much

How have you used your problem solving abilities in past products related to marketing data analysis? Yes. In, uh, at at my time in Zomato, I I use this a lot. So, for example, in Zomato, initially, we did not have, uh, market segmentation. User segmentation, uh, and and we we developed that framework where, um, we divided the Market into, uh, or rather users into particular segments. We call them, uh, the less affluent users, the mid market users and the up market users. Now the the LA users, the lesser flume users were classified on the basis of Their average order value on the platform. Uh, so, essentially, the the the classifier used for segmentation was the average order value. Um, and on the basis of this average order value and the frequency with which they order, etcetera, Uh, we define these segments, and we tailored campaigns around these market segments. So, Uh, initially, when we started off, we did not have any segmentation, and we were using, uh, you know, we were we were providing campaigns for all the segments in Mongo, which did not, uh, provide the best, uh, return on investment. Uh, our post segmentation, what we were able to understand, through the behaviors of most of these users. But that for for example, the LA users cared more about discounts and more about campaigns. So we kind of tailored more campaigns more volume of campaigns towards those segment of users. While for the upmarket and midmarket users, we we had, Uh, slightly less expensive campaigns. And when we not only did we save a ton of money using the segmentation, and providing tailored campaigns. Uh, we also effectively Bettered our performance. We had more loyal customer loyalty.

What approach do you use to analyze marketing trends and patterns within data? So, Essentially, I I would look at the customer journey, uh, involved. Uh, so, for example, I look I look at a marketing funnel. Right? Uh, so customers come in through various sources, and I I'll think Zomato is an example to to Explain this. Uh, customers come in through various sources. They come on the app app. They open the app. They open the restaurant menu, uh, add their items on the cart, and then they place an order. This is a Marketing funnel that is there. Now, um, I look at this funnel, uh, I look at the progress of customers across this funnel, And I also look at the time periods when this happens. In terms of to analyze the volume through this funnel, I segment it based on time. And, uh, so on a macro level, what I look at is how does my volume look on specific at specific time periods. And within each specific time period, how does the volume of these customers move through the funnel? What are the, uh, funnel matrices? Um, how does my custom what is the conversion rate of my customer across each step in this funnel, across these different time periods? What is the conversion rate of each segment of my customer across this fund, across these time periods. This this is the approach that I would take. Uh, I think one one thing I might missed out to mention is the source of my, uh, customers as well. Um, I can have direct sources, or I could I can have sources from other web sources as

What tools and techniques would you use to handle and analyze large volumes of data? Um, I have primarily used Excel Excel and Google Sheets for a lot of the analysis. I get the data through through, uh, there are tools that use um, SQL queries to extract data from our ETL processes. That data is then used, uh, on Excel. It is analyzed using Excel, uh, and and then for representation for, uh, proceedings data for presenting for, uh, for showing stakeholders, uh, various representation of the data, use Google Data Studio or Excel. But, primarily, we have our data, uh, infrastructure set up to capture large volumes of data, uh, which was then analyzed. There are batch processes to put this data into the data structures, and then, uh, SQL queries are used to pull out data, uh, for specific use cases where the queries are, uh, are customized in a way to extract specific use data. Of course, the data that is stored in the database are clean, and the the the quality and the integrity of the data is is automatically ensured. And then we pull this data in using Excel, uh, using SQL queries, use Excel sheets to do analysis on those, uh, on the data, and then presented using Google Data Studio.

Yep. I'm I'm, uh, I'm extremely, uh, extremely proficient with Excel. I use vlookup and all the other tools within Excel to to do analysis on the data. Um, I'm also quite familiar with Tableau, um, for data representation. Um, I'm also familiar with, uh, with using Python for, uh, analysis on extremely large datasets. I'm also familiar with SQL to to extract data from, uh, large databases. But but that is just for extraction. Even for analysis, I analysis, I think I use, uh, um, Excel to another extent, but I'm also familiar with Python and it's, And I've I've used Python for extremely large dataset analysis. But Excel remains my go to tool. Primarily, I've used VLOOKUP. I've also modified data using Uh, we should be sick to to to get data into a certain format, uh, and so on. So I'm I'm very proficient with

Data driven decision making processes. Absolutely. Uh, I'm ready. Uh, so so just to give you an example at my time in Zomato, Uh, I will go back to my previous example where we did not have data segmentation user segmentation earlier, but we we brought in user segmentation a year down the line where we divided the users into less affluent users, uh, upmarket, mid market users based on the average order value, the frequency with which they order and so on. So once this came in, we were able to bringing a lot more insight into our, uh, into our decisions. So earlier, while we were, uh, we we we were analyzing the data as a whole, Uh, once we segmented the data, we were able to get more granular insights on each segment, and we were able to target these 2nd segments better, make their user experience a lot better once we analyze them, uh, in in as individual segments. Fundamentally, my process around, uh, uh, using data is, first, we define what our key objective is. And for for taking decisions to achieve those key objectives, how should I how should I process the data that I have? So if the data is already segmented and it's already sorted out, then there is no problem. But, otherwise, we have to figure out how to get the data and analyze the data that would help us drive to make decisions that would help us make better

So I would I have of it, I've been I've been going to use, uh, GPT, uh, large language models like GPT four to to, uh, to for for data representation because it gives me an easy way to interact with Python, use Python to to provide graphical representations. But but when I started my career, I I used Excel for data representation. Post some time with Excel. I also realized that Google Data Studio and uh, tools like Google Data Studio and Tableau are also extremely useful and way more flexible than than Excel is. Uh, so in the middle of my career, I start using tools like that. But right now, uh, I use, large language models and then GPT 4, which makes it way more easier to to help me produce these visualizations that would uh

New experience, how have you used annotations to highlight key data points or trends in your data visualizations? I'm I'm I have to say, I'm not really sure what What you mean by annotations if if what you mean is how have I highlighted data points In a representation, I I make it very clear using so, for example, if I'm using a bar graph, I make it very clear, Uh, when representing the background that this background represents a b c. It for example, maybe the bug, or maybe it's a pie chart. The this pie chart represents our market share in a given geography, and this is our market share. Uh, so, uh, I express that in percentage terms, and then I also provide a a slight annotation aside to indicate, uh, that the percentage is, uh, out of a population of, you know, 25 lakhs, and so on. So that kind of annotation I do provide to make it very clear to the stakeholder analyzing, uh, the visualization, uh, that That what I'm showing you is is this is the market, um, is the market, uh, share that we have. So I I ensure that I use annotations to help the stakeholder who's viewing my visualization, Understand what exactly I'm representing using the visualization. That has that that, Uh, my visualization would leave no room for doubt to the key stakeholder about what it is exactly