Meeting Title: BF Interview: Amber <> Venkata Date: 2025-12-09 Meeting participants: Venkata Prasad Krupananda, Amber Lin
WEBVTT
1 00:04:10.250 ⇒ 00:04:11.399 Amber Lin: Hi there!
2 00:04:11.650 ⇒ 00:04:14.290 Venkata Prasad Krupananda: Hi, I’m worried, yes, I can hear you. Can you hear me well?
3 00:04:14.430 ⇒ 00:04:16.579 Amber Lin: Yeah, I can hear you. How are you doing?
4 00:04:16.870 ⇒ 00:04:18.139 Venkata Prasad Krupananda: I’m doing good, how are you?
5 00:04:18.570 ⇒ 00:04:24.159 Amber Lin: I’m good, I know you already talked to Utam, how did that go?
6 00:04:24.460 ⇒ 00:04:42.349 Venkata Prasad Krupananda: Oh yeah, that went pretty well. He explained me about the company, and what my role is gonna be, and what you guys are working on. So it went very well in depth, so I got an understanding of what is happening out there. And also, I spoke to Obesh Kumar yesterday.
7 00:04:42.350 ⇒ 00:04:43.830 Amber Lin: Oh, okay.
8 00:04:43.830 ⇒ 00:04:44.770 Venkata Prasad Krupananda: And that went wrong.
9 00:04:44.770 ⇒ 00:04:45.890 Amber Lin: guys talk about?
10 00:04:46.370 ⇒ 00:04:59.520 Venkata Prasad Krupananda: We spoke about a lot of data engineering aspect of what you guys are doing, talking about clients, communication, how do you maintain communication, coordination. Basically, he said you’re looking for someone who
11 00:04:59.680 ⇒ 00:05:01.279 Venkata Prasad Krupananda: is,
12 00:05:01.540 ⇒ 00:05:07.810 Venkata Prasad Krupananda: ready to be there and collaborate with the clients, mostly. So yeah, that was the main topic yesterday.
13 00:05:08.000 ⇒ 00:05:09.759 Venkata Prasad Krupananda: So yeah, that went well as well.
14 00:05:10.700 ⇒ 00:05:21.329 Amber Lin: Cool, okay. I don’t want to talk about the exact same thing that you already told Utam, I don’t want you to have to repeat yourself. So today, mostly,
15 00:05:21.590 ⇒ 00:05:35.760 Amber Lin: We’ll start with a quick introduction. Of course, I’ve read your LinkedIn, so I already… I’ll save you, like, the deep dive there. I mostly want to understand the range of issues you worked on.
16 00:05:36.100 ⇒ 00:05:44.439 Amber Lin: I’m also doing more on the marketing analytics side, so I have some experience or knowledge of what
17 00:05:44.610 ⇒ 00:05:47.869 Amber Lin: The possible analysis is, especially
18 00:05:48.000 ⇒ 00:06:05.540 Amber Lin: For the clients we serve, so I want to understand what your ranges are, and then we’ll deep dive into some of the projects to see how it is. And I know you’re also going to meet with Robert, so he will… he will have more specific questions to see how you
19 00:06:06.580 ⇒ 00:06:12.209 Amber Lin: Like, he’ll maybe have a case for you to solve. That’s what… that’s what he mentioned he will do.
20 00:06:12.600 ⇒ 00:06:13.580 Venkata Prasad Krupananda: Okay, okay.
21 00:06:14.400 ⇒ 00:06:17.030 Amber Lin: Alright, so…
22 00:06:18.090 ⇒ 00:06:33.010 Amber Lin: Super quick introduction. I started with Brainforge about this year, around March. So, I started as a project manager and then moved towards doing data analysis for our different clients.
23 00:06:33.010 ⇒ 00:06:45.559 Amber Lin: And mostly right now, I’m in the marketing analytics, I touched some of the operations, I touched some of the product analytics, but mostly in marketing, and mostly for the CBG companies.
24 00:06:45.690 ⇒ 00:06:50.750 Amber Lin: So… So we get started on…
25 00:06:50.980 ⇒ 00:06:55.029 Amber Lin: What type of analysis or what type of work you’ve done before?
26 00:06:56.200 ⇒ 00:07:09.399 Venkata Prasad Krupananda: Yeah, sure. So, I started off as a, actually as a marketing analyst back in India, and, right now in the US, I’ve been working on, I’ve been around product and marketing analytics, kind of setting.
27 00:07:09.400 ⇒ 00:07:14.109 Amber Lin: the interaction… the intersection of data and business strategy, I can say.
28 00:07:14.110 ⇒ 00:07:19.259 Venkata Prasad Krupananda: at McKinsey, right now, I’ve worked on digital,
29 00:07:19.610 ⇒ 00:07:22.639 Venkata Prasad Krupananda: the website transformation, and also where I used
30 00:07:22.700 ⇒ 00:07:40.919 Venkata Prasad Krupananda: product analytics to maintain and improve their business decisions, and also improvise their clients’ user behavior, build conversion funnels, and also run a lot of A-B tests, to validate those product changes. And also, that helped identify drop-off
31 00:07:41.010 ⇒ 00:07:49.490 Venkata Prasad Krupananda: points and improve the business decisions. And before that, I was at… I was at a higher education sector, Texas A&M University. I worked.
32 00:07:49.810 ⇒ 00:08:09.759 Venkata Prasad Krupananda: data analyst there, and I focus more on marketing and data analytics, and also working on tools like SQL, clean the data, maintain the data, R programming, Python, and also maintain large data sets, track those campaign performances, and also build those dashboards on Power BI, Tableau, and also.
33 00:08:09.760 ⇒ 00:08:10.160 Amber Lin: and…
34 00:08:10.290 ⇒ 00:08:24.200 Venkata Prasad Krupananda: And visualize those engagement trends. So that was my, tasks, and that was my role there. So yeah, as of now, I can say I am driving my experience, from data analysis.
35 00:08:24.340 ⇒ 00:08:30.120 Venkata Prasad Krupananda: to somewhere around data engineering, so that’s my interest, I can say.
36 00:08:30.690 ⇒ 00:08:34.879 Amber Lin: Oh, I see. So, it sounded like when you first started, that was more…
37 00:08:34.880 ⇒ 00:08:35.250 Venkata Prasad Krupananda: Come on.
38 00:08:35.250 ⇒ 00:08:52.209 Amber Lin: Because data is a full pipeline. Sounds like it’s a little bit closer to analytics engineering and a bit of data visualization and analysis, and when you were at McKinsey, it was a lot more of product analytics, and you said you want to move
39 00:08:52.220 ⇒ 00:08:55.300 Amber Lin: Further down the pipeline, closer to data engineering.
40 00:08:55.300 ⇒ 00:09:13.419 Venkata Prasad Krupananda: Yeah, I want to dive deeper into data analysis first, and get a hold of that, because there are a lot more tools for me to learn, and also, before diving into data engineering roles, I think I need to have that expertise level in all of the tools when it comes to data analysis, like Python.
41 00:09:13.420 ⇒ 00:09:13.800 Amber Lin: Hmm.
42 00:09:13.800 ⇒ 00:09:19.089 Venkata Prasad Krupananda: I’m not an expert in Python, but I want to be. So that’s what I’m working on right now.
43 00:09:19.690 ⇒ 00:09:22.929 Amber Lin: Oh, so you want to be even more in the…
44 00:09:22.930 ⇒ 00:09:23.310 Venkata Prasad Krupananda: technical.
45 00:09:23.310 ⇒ 00:09:24.750 Amber Lin: engineering role.
46 00:09:24.750 ⇒ 00:09:25.290 Venkata Prasad Krupananda: Yes.
47 00:09:25.290 ⇒ 00:09:32.359 Amber Lin: I see, I see. And I… I bet the conversation with the wish was quite interesting.
48 00:09:32.450 ⇒ 00:09:34.530 Venkata Prasad Krupananda: Oh, yeah.
49 00:09:34.530 ⇒ 00:09:38.890 Amber Lin: Yeah, and he sent… he sent good notes about you, so…
50 00:09:38.890 ⇒ 00:09:57.630 Venkata Prasad Krupananda: So he sent me… he asked me a lot of questions, about technical aspect of his job, and that was pretty interesting. He asked me a few questions in amplitude and, Power BI dashboard building, so that was a pretty, deep, interesting conversation that I had yesterday.
51 00:09:57.630 ⇒ 00:09:58.170 Amber Lin: Nope.
52 00:09:58.170 ⇒ 00:09:58.920 Venkata Prasad Krupananda: So, yeah.
53 00:10:00.850 ⇒ 00:10:13.489 Amber Lin: Gotcha, okay. My questions will be more on the analytics side, and I’ll note down that you want to go deeper into engineering, because in this company, there is possibilities to different ways.
54 00:10:14.040 ⇒ 00:10:16.449 Amber Lin: But tell me more about…
55 00:10:16.610 ⇒ 00:10:32.649 Amber Lin: The type of analysis that you did in product analysts or marketing analytics, what you think your strong and weak point is, just in the analytics field of what type of analysis you’ve done and what you are good or not good at.
56 00:10:33.410 ⇒ 00:10:47.970 Venkata Prasad Krupananda: Okay, so to start off with that, I can say, in terms of strengths, I’d say I’m strongest in product analytics and marketing performance analysis. I’ve done a lot of funnel analysis and cohort and retention analysis.
57 00:10:47.970 ⇒ 00:10:48.380 Amber Lin: Hmm.
58 00:10:48.380 ⇒ 00:10:57.179 Venkata Prasad Krupananda: So, A-B testing, and using tools like, you know, Amplitude, Mixpanel, and SQL, for example, at McKinsey, I built…
59 00:10:57.180 ⇒ 00:11:10.679 Venkata Prasad Krupananda: conversion funnel, and ran those experiments to identify where users were, dropping off, and also what product changes actually improved engagements, and also, this kind of work
60 00:11:10.680 ⇒ 00:11:17.620 Venkata Prasad Krupananda: Made me… may sharpen my ability to translate those behavioral data, into those actionable insights, which helped the
61 00:11:17.620 ⇒ 00:11:42.329 Venkata Prasad Krupananda: satisfy the client as well. And also, on the marketing side, I can say, I’m very comfortable with data cleaning, when it comes to marketing analytics, data cleaning, segmentation, and also campaign performance tracking, and again, coming to SQL, R, and Power BI to, clean those data and automate those dashboards on Power BI, and to visualize engagement trends, and also optimize those outreach strategies.
62 00:11:42.400 ⇒ 00:11:46.700 Venkata Prasad Krupananda: So that’s something I did heavily, for the last, I think.
63 00:11:46.700 ⇒ 00:12:08.670 Venkata Prasad Krupananda: 3 years ago. So, for the last 3 years, I’ve been in data analysis. So, at Texas A&M, and also the other two companies back in India, I was the marketing data analyst, where I did all the… manage those large data sets, and also build dashboards, and help those marketing teams to, make faster decisions. So, that is what I can think of. And also,
64 00:12:09.130 ⇒ 00:12:10.130 Venkata Prasad Krupananda: if I…
65 00:12:10.130 ⇒ 00:12:34.740 Venkata Prasad Krupananda: had to call out a weaker area, I think I would say it’s more on the advanced Python side, like building complex data pipelines and also automation scripts, because those are the two areas where I really want to dive deeper and improve my skills. And also, I can write queries and do analysis in Python, but I’m still pushing myself to get more
66 00:12:34.740 ⇒ 00:12:44.010 Venkata Prasad Krupananda: with those libraries, like Pandas and Numphy, and for deeper data engineering tasks. So, I think that is what I can think of.
67 00:12:44.600 ⇒ 00:12:46.360 Amber Lin: Very cool.
68 00:12:47.190 ⇒ 00:13:03.290 Amber Lin: then I’ll ask more on the, like, what you mentioned as your strong suit. Could you tell me a, maybe just walk me through a project that you think really represents your strong suit, and so that I can get a more, like.
69 00:13:03.700 ⇒ 00:13:15.220 Amber Lin: in-motion understanding of what you’re strong at, because right now, like, I don’t think I can represent your skills very clearly to my boss, so I would love to hear more.
70 00:13:15.840 ⇒ 00:13:25.049 Venkata Prasad Krupananda: So, okay, so, so one of the, one that, one thing that really stands out is the digital transformation project. I work.
71 00:13:25.050 ⇒ 00:13:25.590 Amber Lin: None.
72 00:13:25.590 ⇒ 00:13:40.290 Venkata Prasad Krupananda: at McKinsey, for a healthcare client at Minnesota. So, the situation was that the client, had launched a new digital platform, but the user engagement was dropping after the first interaction.
73 00:13:40.290 ⇒ 00:13:59.049 Venkata Prasad Krupananda: So my task was to figure out where users were falling off, and also what product changes could improve those retention when it comes to engaging the activity continuously. And what I did is I set up a, event tracking in Amplitude and mix panel, and then defined those
74 00:13:59.050 ⇒ 00:14:04.299 Venkata Prasad Krupananda: Conversion patterns, and also conversion funnels, and ran those
75 00:14:05.150 ⇒ 00:14:08.500 Venkata Prasad Krupananda: A-B tests on different onboarding flows.
76 00:14:08.840 ⇒ 00:14:13.999 Venkata Prasad Krupananda: And I also use SQL to pull all the last datasets from their history.
77 00:14:14.420 ⇒ 00:14:31.890 Venkata Prasad Krupananda: the behavioral data from their warehouse, and also combined it with the data from amplitude cohorts to analyze user paths. And through that, I think I found that nearly… so this was a result. I found that 40% of the users dropped right after the second onboarding step.
78 00:14:31.890 ⇒ 00:14:32.550 Amber Lin: Because…
79 00:14:32.550 ⇒ 00:14:35.510 Venkata Prasad Krupananda: Of the confusion, from the layout.
80 00:14:35.510 ⇒ 00:14:35.830 Amber Lin: No.
81 00:14:35.830 ⇒ 00:14:53.289 Venkata Prasad Krupananda: tested. We tested a simplified version, and the conversion rate improved by about 18%, and the result was just higher engagement, but also a clear framework of how the client’s product team could use those analytics to guide.
82 00:14:53.290 ⇒ 00:14:54.200 Amber Lin: Gotcha.
83 00:14:54.240 ⇒ 00:14:57.180 Venkata Prasad Krupananda: So, that is one thing I can think of.
84 00:14:57.180 ⇒ 00:15:05.530 Amber Lin: Yeah, awesome. Let’s just dive deeper into that. I’m very, very curious about what you did there. So, when you…
85 00:15:05.750 ⇒ 00:15:10.879 Amber Lin: try… when you… I understand when you set it up and you stitch the historical events to the…
86 00:15:11.000 ⇒ 00:15:26.909 Amber Lin: performant, to people’s behavior. How did you select, say, the cohorts, or how did you select what to look at? Because there is a lot. How did you decide who and what to look at?
87 00:15:27.780 ⇒ 00:15:43.310 Venkata Prasad Krupananda: Yes, yeah, I know, I understand it’s pretty vague when you just start off. It’s pretty vague. So, the way I approached it is, it was pretty structured. So, first, I started by, aligning with the product and user experience.
88 00:15:43.310 ⇒ 00:15:43.700 Amber Lin: meetings.
89 00:15:43.700 ⇒ 00:15:50.840 Venkata Prasad Krupananda: teams to define that success, actually, define what that success they’re looking for actually.
90 00:15:50.840 ⇒ 00:15:51.270 Amber Lin: Bye.
91 00:15:51.550 ⇒ 00:16:04.219 Venkata Prasad Krupananda: for that onboarding flow. So, in this case, it was users completing the setup and reaching their first meaningful action on the platform. So then, I think,
92 00:16:04.220 ⇒ 00:16:09.100 Venkata Prasad Krupananda: Using amplitude, I looked at the event taxonomy and things like
93 00:16:09.100 ⇒ 00:16:31.639 Venkata Prasad Krupananda: accounts created, and forms started, and forms submitted, and also the first transaction, and also I grouped those users into cohorts based on those key milestones and their time of completion. For example, one cohort was users who dropped off after starting the first form.
94 00:16:31.640 ⇒ 00:16:37.739 Venkata Prasad Krupananda: And another was user who completed onboarding, but did not transact within 24 hours. So these are the.
95 00:16:37.740 ⇒ 00:16:38.060 Amber Lin: kinds of…
96 00:16:38.060 ⇒ 00:16:59.319 Venkata Prasad Krupananda: indicators that we look for, and to make sure I wasn’t missing out on any context, what I would do is I’d pull those, historical event data, like I said, from the warehouse, and just for comparison, and using SQL, clean the data, join it with amplitude user IDs, and also layered, in metadata, like.
97 00:16:59.520 ⇒ 00:17:08.429 Venkata Prasad Krupananda: device type to identify those, heavy traffic usage. And also, I think after that,
98 00:17:08.619 ⇒ 00:17:26.549 Venkata Prasad Krupananda: that will help me see the patterns, right? So, once you have these, 5 to 6 KPIs, I think if you work on that, that really showed us the pattern, and the more, like, mobile users or, and old device users had much higher drop-off, because… because
99 00:17:26.960 ⇒ 00:17:32.460 Venkata Prasad Krupananda: old device. So, if people who are using the newest versions of mobiles, they would finish it off.
100 00:17:32.460 ⇒ 00:17:53.320 Venkata Prasad Krupananda: So, these are the things that we noticed, and so yeah, the cohort selection was really driven by business goals first, because that is our priority when it comes to business goals, and then refined those behavioral patterns, and also segmentation data, and also data validation across tools, again, when it comes to
101 00:17:53.320 ⇒ 00:18:03.359 Venkata Prasad Krupananda: SQL, and then connecting it with Amplitude data. So those are the data validation across the tools. And also, this combination gave us a clear picture of,
102 00:18:03.420 ⇒ 00:18:06.789 Venkata Prasad Krupananda: Where the friction existed, and also where.
103 00:18:06.790 ⇒ 00:18:07.150 Amber Lin: Fair enough.
104 00:18:07.150 ⇒ 00:18:25.709 Venkata Prasad Krupananda: the user engagement was, to be prioritized and also, you know, made more efficient. So, I think that is, something that I can talk about when it comes to improving those product and, user experience. Yeah.
105 00:18:25.710 ⇒ 00:18:29.409 Amber Lin: Awesome. Seems like you have a good framework to approach, like, what to…
106 00:18:29.650 ⇒ 00:18:30.100 Venkata Prasad Krupananda: to priorit.
107 00:18:30.100 ⇒ 00:18:34.539 Amber Lin: prioritize what… where to find things when it’s very ambiguous.
108 00:18:34.540 ⇒ 00:18:35.940 Venkata Prasad Krupananda: That’s what I knew. Huh.
109 00:18:35.940 ⇒ 00:18:50.560 Amber Lin: Yeah, I’m also very curious about how you designed the A-B test. So, you found the pattern, you know what’s wrong, how do you decide what type of test to perform, and how do you,
110 00:18:50.840 ⇒ 00:18:54.639 Amber Lin: Analyze the results and make recommendations to the client.
111 00:18:55.320 ⇒ 00:18:59.250 Venkata Prasad Krupananda: Yeah, perfect. So, when it comes to A-B tests, I think,
112 00:19:00.450 ⇒ 00:19:06.079 Venkata Prasad Krupananda: what I can think of is once we, identify the friction prints, the next step was to…
113 00:19:06.080 ⇒ 00:19:29.220 Venkata Prasad Krupananda: The next step is always to translate those into testable hypotheses. For example, in that onboarding process, the particular case that I was working on, we saw a big drop-off on the second form, second step, I can say. So, for our hypothesis, that was, you know, simplifying the layout, and also reducing the number of
114 00:19:29.700 ⇒ 00:19:32.420 Venkata Prasad Krupananda: Required fields, and
115 00:19:32.420 ⇒ 00:19:56.940 Venkata Prasad Krupananda: which would, you know, eventually increase the completion rates, was our belief. So to design the A-B test, I worked with the various other product teams and also engineering teams to define the control and also the variant, and we made sure both of the versions of A-B testing were tracked consistently on amplitude, and also the same event schema.
116 00:19:56.940 ⇒ 00:19:58.160 Venkata Prasad Krupananda: So we could…
117 00:19:58.160 ⇒ 00:20:21.320 Venkata Prasad Krupananda: measure those metrics using, the completion rates, and also time of the complete, completion, hourly, or whatever, and sub… and the subsequent engagement as well. And for the analysis part, I think I exported the event data, and used SQL and Python to, run some statistical analysis. And also, and also I remember using SPSS,
118 00:20:21.320 ⇒ 00:20:28.320 Venkata Prasad Krupananda: To run those hypothesis testing and statistical analysis, and also mainly t-tests and,
119 00:20:28.870 ⇒ 00:20:45.680 Venkata Prasad Krupananda: Yeah, I remember t-tests, depending on that a lot, and for the metric type, and also I checked, for sample size, sufficiency and confidence, intervals to make sure the results were reliable, because, you know.
120 00:20:45.680 ⇒ 00:20:58.879 Venkata Prasad Krupananda: results is what we are looking for when you’re going… when you’re doing the A-B testing. And once we confirm the variant performed significantly, and about an… I think for that particular case, I had about 18% left in conversion, and I summarized
121 00:20:58.880 ⇒ 00:20:59.200 Venkata Prasad Krupananda: Excellent.
122 00:20:59.200 ⇒ 00:21:01.730 Venkata Prasad Krupananda: findings in Power BI Dashboard, which is the…
123 00:21:01.730 ⇒ 00:21:19.299 Venkata Prasad Krupananda: last part of any, business process, making… creating a dashboard and, explaining it to the senior management or your teammates. So making sure, the recommendations is focused not just on rolling out the, winning variant, but also
124 00:21:19.630 ⇒ 00:21:38.859 Venkata Prasad Krupananda: on applying the same simplification logic to other parts of, onboarding journey. So that is what, that is what we followed for every project, when it comes to, you know, running those A-B tests and deciding on what to, choose and who to target.
125 00:21:39.540 ⇒ 00:21:42.049 Amber Lin: I see, very, very cool.
126 00:21:42.520 ⇒ 00:21:47.859 Amber Lin: Like, I really like your approach, and I was just talking,
127 00:21:48.190 ⇒ 00:22:03.449 Amber Lin: to Robert, who you’ll talk to soon about the different types of analysis and the different types of approach… approaches needed. I know we have, like, 10 minutes left. I want to make sure that you have time to ask me questions.
128 00:22:05.200 ⇒ 00:22:14.699 Amber Lin: Why don’t we start there, and if we still have some time left, I still have a lot of questions I would love to ask you, but I want to make sure I answer your questions first.
129 00:22:15.210 ⇒ 00:22:32.739 Venkata Prasad Krupananda: okay. So, how… when it comes to A-B tests, since we talked about A-B test, can you just tell me how often is A-B test, running from you guys, for your clients? Because it depends on… of course, it depends on the client’s needs, but
130 00:22:32.930 ⇒ 00:22:37.610 Venkata Prasad Krupananda: What I believe is, if you’re running an A-B test, that means there is a problem.
131 00:22:38.550 ⇒ 00:22:44.769 Venkata Prasad Krupananda: That is what I… that is what I’ve seen in the past. So how often do you run these A-B tests for your clients, on an average?
132 00:22:45.340 ⇒ 00:22:47.590 Amber Lin: You mean just for product analytics?
133 00:22:47.590 ⇒ 00:22:49.169 Venkata Prasad Krupananda: Yes, just for part handling, yes.
134 00:22:49.170 ⇒ 00:22:52.920 Amber Lin: I see. Right now, I think there’s…
135 00:22:53.410 ⇒ 00:23:04.790 Amber Lin: we currently are looking for someone to do product analytics other than Robert, or some very junior folks we have. I believe we… among our clients, about…
136 00:23:06.170 ⇒ 00:23:23.060 Amber Lin: 3 or 4 of them are… have product analytics issues. We have just finished our first… we finished our discovery phase for them, and then we’re setting up the experiments to run. So right now, we’re trying to find someone to
137 00:23:23.080 ⇒ 00:23:28.570 Amber Lin: Lead those testings and, I guess, here.
138 00:23:29.360 ⇒ 00:23:39.959 Amber Lin: for the A-B tests, we have to… sometimes we have to convince the clients that we need to run them, because I think different from McKenzie, where the clients
139 00:23:40.130 ⇒ 00:23:46.759 Amber Lin: already know that they have a significant issue and is willing to pay McKenzie to do that. In our case, sometimes the clients
140 00:23:46.910 ⇒ 00:24:02.900 Amber Lin: need us to lead them through discovery to understand, hey, this is… this is important, and then we need to guide them through how can you set up A-B testing, who needs to get involved, and why it’s important to do so. And…
141 00:24:02.990 ⇒ 00:24:12.729 Amber Lin: I can say for… on the marketing side, right now, I am helping, I’m just starting to help run some tests on the…
142 00:24:12.960 ⇒ 00:24:19.800 Amber Lin: marketing more on the campaign side for them, so I know it’s a bit different than what you asked for about product analytics.
143 00:24:19.830 ⇒ 00:24:20.989 Venkata Prasad Krupananda: So…
144 00:24:21.390 ⇒ 00:24:24.190 Amber Lin: I hope that answers your question.
145 00:24:24.190 ⇒ 00:24:29.619 Venkata Prasad Krupananda: Yeah, yeah, I got an idea, yes. That is what I was expecting, because I know how Brainforge works.
146 00:24:29.770 ⇒ 00:24:31.720 Venkata Prasad Krupananda: So what we do is…
147 00:24:31.870 ⇒ 00:24:35.059 Venkata Prasad Krupananda: The client gives us access, and we have the total…
148 00:24:35.060 ⇒ 00:24:54.260 Venkata Prasad Krupananda: 100% rights to do everything, finish the whole process, and then let them know, hey, they’ve done this and this. So, yeah, I know how Brainforge works, and you need permission mostly from most of your clients to run any task, like A-B testing or anything. So, yeah, that is what I wanted to talk about. And, yeah.
149 00:24:55.150 ⇒ 00:25:13.590 Venkata Prasad Krupananda: So I know my tasks will also be involved with a little bit of product analytics, a little bit of data analyst, and also marketing analytics. So when it comes to product analytics, once I start off, let’s say I started off working, and then I have some tasks to run, how do you think my
150 00:25:13.710 ⇒ 00:25:19.170 Venkata Prasad Krupananda: first month would be. Just… just an idea, just to think of.
151 00:25:20.690 ⇒ 00:25:27.979 Amber Lin: Yeah, so usually, I believe… usually for our company, how we operate for new hires is that there is a
152 00:25:28.390 ⇒ 00:25:42.880 Amber Lin: two to three-week test period where, we give some assignments to see if it’s a fit. You can also see if the company working style is a fit, and then we come to a yes or no full-time decision at the two or three weeks mark.
153 00:25:42.880 ⇒ 00:25:59.540 Amber Lin: But if we ignore that and just talk about what the assignments would look like, we integrate people into clients pretty quickly, because we’re a small company, we want to move fast as possible, and there will be someone, maybe the project lead, to ramp you up with a project.
154 00:25:59.700 ⇒ 00:26:07.599 Amber Lin: You’ll get access pretty quickly of all the access we currently have, because we share some of the credentials together. You’ll get introduced to clients, and then you’.
155 00:26:07.600 ⇒ 00:26:07.930 Venkata Prasad Krupananda: take.
156 00:26:07.930 ⇒ 00:26:22.390 Amber Lin: on, a small analytics, project to start off, maybe just to… a simple analytics, not setting up A-B tests yet, but just understanding the data, giving some insights to the client, and…
157 00:26:22.800 ⇒ 00:26:29.050 Amber Lin: I think soon… I’ll dive into the specific needs on the client. So.
158 00:26:29.050 ⇒ 00:26:30.540 Venkata Prasad Krupananda: I think our…
159 00:26:30.720 ⇒ 00:26:34.719 Amber Lin: I guess an example of a new person that we had recently.
160 00:26:34.790 ⇒ 00:26:36.680 Venkata Prasad Krupananda: She… it’s her…
161 00:26:36.680 ⇒ 00:26:40.940 Amber Lin: Second week right now, and then she’s already,
162 00:26:41.190 ⇒ 00:26:57.379 Amber Lin: delivering stuff directly to the client, so she’s taking on the responsibilities there. She’s still getting ramped up, but she’s already doing output. So our training, we don’t have, say, a 3-month training session before you have… before you can talk to the clients.
163 00:26:57.380 ⇒ 00:27:00.659 Venkata Prasad Krupananda: Okay, okay, yeah, okay, perfect, yeah, that answered my question.
164 00:27:01.390 ⇒ 00:27:02.899 Amber Lin: Yeah, thank you for that.
165 00:27:02.900 ⇒ 00:27:09.069 Venkata Prasad Krupananda: But other than that, I think I’m clear about the whole process, and also the
166 00:27:09.100 ⇒ 00:27:28.360 Venkata Prasad Krupananda: responsibilities and tasks, or whatever that comes about the role. So I spoke to them for, like, 45 minutes. That was a good, deep talk. So, I have an idea now. And yesterday’s call was also good, so even today’s call was amazing. It was nice talking to you, but I don’t have any questions as of now, so… Okay.
167 00:27:28.360 ⇒ 00:27:29.060 Amber Lin: I appreciate it.
168 00:27:29.320 ⇒ 00:27:29.840 Venkata Prasad Krupananda: Yeah.
169 00:27:29.840 ⇒ 00:27:48.409 Amber Lin: I think next, you will be talking to Robert, and Robert most likely, because he’s our most… he’s our analytics and strategy partner, he’ll have more questions on maybe a scenario of how you would walk through that scenario, how you might.
170 00:27:48.410 ⇒ 00:27:48.920 Venkata Prasad Krupananda: Okay.
171 00:27:48.920 ⇒ 00:27:50.990 Amber Lin: design projects.
172 00:27:51.360 ⇒ 00:28:04.400 Amber Lin: all that. I guess I’ll end up… end off this call with one question about, the teams that you’ve worked with. Have you led any teams before, or how has the team structure look like?
173 00:28:04.800 ⇒ 00:28:08.880 Venkata Prasad Krupananda: So, we were, mostly we were a team of three.
174 00:28:09.300 ⇒ 00:28:14.980 Venkata Prasad Krupananda: And, I have led my team of three in a couple of projects.
175 00:28:14.980 ⇒ 00:28:15.370 Amber Lin: Alright, so…
176 00:28:15.730 ⇒ 00:28:27.299 Venkata Prasad Krupananda: As a project management lead, but my task was always, data analyst, and also sometimes marketing analyst, when it comes… when the project is…
177 00:28:27.680 ⇒ 00:28:33.060 Venkata Prasad Krupananda: marketing side, or digital marketing side, I would pitch in, because that was my initial experience.
178 00:28:33.060 ⇒ 00:28:51.739 Venkata Prasad Krupananda: If it’s something to do with data analysts, and something very deep, I wouldn’t be the project lead. Of course, I would be the team member, and I would act as a data analyst, but if it’s something with more marketing analytics and marketing analysts activities, I would be the team lead. And yeah, to answer your question, yes, I have led teams.
179 00:28:51.740 ⇒ 00:28:53.960 Venkata Prasad Krupananda: In a couple of projects, and…
180 00:28:53.960 ⇒ 00:28:57.349 Venkata Prasad Krupananda: Yeah, that is, those are my roles previously.
181 00:28:57.350 ⇒ 00:29:02.050 Amber Lin: I see. When you say, a team leap, what does that look like?
182 00:29:05.230 ⇒ 00:29:14.280 Venkata Prasad Krupananda: I mean, it’s a big responsibility, though, but depending on the size of the team, I can say a team of three was good enough for me to handle.
183 00:29:14.820 ⇒ 00:29:21.290 Venkata Prasad Krupananda: first experience for me as well. That is… challenging, because…
184 00:29:21.410 ⇒ 00:29:37.519 Venkata Prasad Krupananda: What takes most of the time is the communication between the three of us, first of all. Not even the communication from us to the client. The communication with ourselves takes a lot of effort, a lot of coordination, and that is where we…
185 00:29:37.640 ⇒ 00:29:53.810 Venkata Prasad Krupananda: need to be more, challenging and, you know, get used to that. I think if that is going on well, I think all the other aspects of the team or task would go better. So yeah, that is what I think… I see. That is the basic thing that I can think of.
186 00:29:53.810 ⇒ 00:30:00.060 Amber Lin: Does someone… does the clients assign you tasks to do? Like, do you come up?
187 00:30:00.060 ⇒ 00:30:01.879 Venkata Prasad Krupananda: Is there a partner that comes up?
188 00:30:01.880 ⇒ 00:30:02.970 Amber Lin: The idea on break…
189 00:30:02.970 ⇒ 00:30:27.449 Venkata Prasad Krupananda: It depends on the project. So, if the company decides, if our client decides to let us know periodically what they want, they will keep updating us with what the tasks are, and what they are required from us to do. And if it’s not the company in the picture, we take all authority, and we let them know, hey, we are going to do this from the start of the project to the end.
190 00:30:27.470 ⇒ 00:30:33.850 Venkata Prasad Krupananda: And if you have any problems in the, you know, initial stage or any stage, just
191 00:30:33.910 ⇒ 00:30:37.060 Venkata Prasad Krupananda: We’ll have a meeting, let’s chat about it, let’s talk about it.
192 00:30:37.060 ⇒ 00:30:37.460 Amber Lin: Hmm.
193 00:30:37.460 ⇒ 00:30:39.310 Venkata Prasad Krupananda: It depends on the client as well.
194 00:30:39.510 ⇒ 00:30:39.930 Amber Lin: I see.
195 00:30:39.930 ⇒ 00:30:41.430 Venkata Prasad Krupananda: Their decision as well.
196 00:30:41.610 ⇒ 00:30:54.779 Amber Lin: Oh, so if we were to develop the outline, who was responsible for doing that? Is there, like, a partner who over… because I worked at EY before, and it’s mostly… a partner will give us
197 00:30:55.490 ⇒ 00:31:03.420 Amber Lin: Their discovery outline, or the place they want to go, and then afterwards, we sort of take on the
198 00:31:03.780 ⇒ 00:31:07.720 Amber Lin: pieces of the task and get it done. What was it like.
199 00:31:07.720 ⇒ 00:31:08.750 Venkata Prasad Krupananda: Micro-similar, yes.
200 00:31:08.750 ⇒ 00:31:09.580 Amber Lin: Oh, okay.
201 00:31:09.580 ⇒ 00:31:18.520 Venkata Prasad Krupananda: Yeah, that was similar. That part of what you said, the partner will lead us to that particular point where we are all on the same page about the project, and then…
202 00:31:18.890 ⇒ 00:31:21.810 Venkata Prasad Krupananda: And then the partner is gone. We take over from there.
203 00:31:21.990 ⇒ 00:31:23.480 Amber Lin: Gotcha, okay.
204 00:31:23.480 ⇒ 00:31:26.009 Venkata Prasad Krupananda: Yes, that was… what you said is exactly the same.
205 00:31:26.170 ⇒ 00:31:29.130 Amber Lin: Yeah, sounds like everybody does things the same way.
206 00:31:29.130 ⇒ 00:31:33.500 Venkata Prasad Krupananda: Yeah, mostly, yes. Consulting companies, yeah, mostly they do the same.
207 00:31:34.160 ⇒ 00:31:34.520 Venkata Prasad Krupananda: again.
208 00:31:34.520 ⇒ 00:31:41.360 Amber Lin: Okay, awesome! I really appreciate you taking time to call. I bet Rico has already reached out for the next call, so…
209 00:31:41.360 ⇒ 00:31:48.229 Venkata Prasad Krupananda: Yeah, he’s restart, I have replied, he’s yet to schedule it, so that’s going on. Okay, sounds good.
210 00:31:48.230 ⇒ 00:31:52.219 Amber Lin: You have our email, so feel free to shoot over any questions.
211 00:31:52.390 ⇒ 00:32:01.609 Venkata Prasad Krupananda: Yeah, definitely. Yeah, definitely I’ll do that if I have anything. So yeah, other than that, it was a… it was great talking to you, good piece of conversation, and yeah, have a lot.
212 00:32:01.610 ⇒ 00:32:03.720 Amber Lin: Thank you. Yeah, have a great one.
213 00:32:03.720 ⇒ 00:32:05.320 Venkata Prasad Krupananda: Bye. Bye-bye.