Used to manage E2E Google play account with both (EMEA and APAC regions), understand economics of marketing mix modelling such as economics of programmatic etc.
Handled $1.3- $2 million budget per quarter for APAC.
Worked upon below tools/softwares extensively:-
* Olive and datalab for placement Creation and anomaly detection
* DSPs (DV360, TTD, Adelphic, verizon)
* Google Ads, CM360
* 3rd party measurement vendors (Neilson In-platform, IAS, DV)
* Power BI (Advance Analysis and Visualization), Lookr Studio
* Predictive Modelling (Supervised learning :- Multivariate Linear Regression, Time series forecasting using ARIMA)
Advance optimizations for Even pacing of APAC markets, daily/weekly depending upon performance of tactics/LIs.
What a month looks like as MAE:-
* Weekly comprehensive Deep-dive commentary for Audience segments/ IOs and tactics/ LIs to interpret seasonality, trend and cyclicity in revenue to RGT/Client.
* Daily pacing and Advance pacing optimizations when Req.
* MoM analysis:- Revenue centric optimization on MoM basis. Analysis is done on MoM basis for audience segments and tactics for revenue centric optimizations and suggest advance optimization changes as per performance.
* Daily BPFL Violation checks
* Weekly Health Checks on thursday.
* Impression reporting BI - Weekly
* Client Calls Twice a week.
What I bring:-
I Bring diverse acumen and a dynamic skillset to the table and my Repertoire has found new horizons.
I am working on enhancing my IQ and EQ, and now I have not only just dipped my toes into the holy waters of TQ, but prepared a dashboard in Power BI for my team for MoM Analysis where I provided a tonne of data solutions that have been provided using DAX and power queries.
I enabled ML and AI to get actionable Intelligence insights. Adding to that, I know Predictive modeling in R and Python, where I predicted the Budget for the next quarter using a linear regression model (replacing manual forecasting that we do) + I prepped the model for time series analysis to interpret seasonality, cyclicity, and trend components in the data which is insurmountable to do by a human being.
Currently working on Neural network, MLP for a marketing use case and automating analysis and recommendations via chat gpt.
All this Sweet creamiest layer of the cake should have a rigid foundation that is of 2 masters as below:-
PGDM IB and Marketing
Data Science Master's program
Used to manage E2E Google play account
Greater India Media Associate, PS
HPGreater India Media Associate
HPGroupM
Google B2B (NA, APAC and EMEA Regions)Ad Ops Associate, Amazon and other brands
Kinesso, IPGAd Ops Associate, Amazon and other brands (NA region)
Kinesso, IPG, MumbaiMedia activation executive, Google B2B
GroupMLeading the business units comes under personal systems such as Gaming - Omen, Non Gaming - Pavilion, AIO, Elitebook, Chromebook.
In 3 months, I learned the extraction part of it and through dataslayer and automated a report for Our Marketing Managers for which I recall receiving a recognition as well.
I worked on diverse tasks such as switching to tags from trackers (greatly helped me having more control over blocking sensitive content) to Handling E2E accounts on DV360, Google ads, Amazon, Facebook and LinkedIn.
I run YT/CTV/PG/Direct deals on DV360 and Amazon(PG/PMP and Product searches/browsing/purchases TG) platforms
As of now in the process of automating monthly accruals for PS and moving to CIVA optimizations on DV 360.
GroupM
Full-time 1 yr 10 mos
Full-time 1 yr 10 mos
Delhi, India Hybrid
Delhi, India Hybrid
Ad Ops Associate role entails:-
#DCM Trafficking
#DSP Mapping
#Extracting Reports
#Trafficking DCO in DCM- Dynamic Creative Optimization
#Generating Tags.
#IAS/DV Wrapping
#Pixel Implementation, in DCM as well as in TTD, DV360, Verizon, Adelphic
#nielson in platform
DSPs I work on:- TTD, DV 360, Verizon.
Accounts I work with :-
Amazon, Gallo, Norton Lifelock, Behr, Zillow, Nintendo, Sony, Levi's, Columbia etc
Aman worked in my team for over 6 months and it was inspiring to witness his dedication towards learning new projects and taking on new skills. He went out of his way to support his peers and expertly handled the client conflicts. His ability to innovate automation to help steer operations efficiently is a special highlight on his skillset. I would definitely recommend him!
- namrta(manager)
I helped the team switch to tags to enable faster ad load and implement brand safety measures to block ads by implementing tagless integration via IAS.
I helped the team create automated report for EOC analysis.
I helped the team work with various vendors such as Hotstar, fandom, twitch, NDTV for RB and YT for SOV, Samsung for CTV and Inshorts for RB. I made sure that Everything is on track in terms of pacing and did budget optimization based on even the 1 day performance of various audience segments in PG deals.
I found a way to retarget the audience we gathered from an instant desal via floodlight audience creation.
I pitched an automated report across Display side of business and take it to real time in Power BI via API called Windsor API Idea to directors and they liked it and see it fit and pitched it to the client.
I along with my team worked and sought any problems that came across for our team for 9 markets we handled which was AU, SG, HK, NZ and some of INSEA markets and made sure campaigns are optimized towards ROAS on Monthly/ biweekly basis for our evergreen app install campaigns.
I handled E2E campaign management, Budget pacing, new tactic launch, new campaign launches, anomaly reports for firebase tests we were running, MOM analysis ETC.
I am quite well versed in below bidding strategies in DV360:-
Maximize performance :-Conversions(requires floodlight), clicks, Installs(app line item only and requires floodlight or firebase)
Maximize brand impact:- viewable impressions, CIVA (Video Line only), Viewable for at least 10 sec (video line).
I also ran YT VAC Gads, VVC in dv360, VAS in DV360, BLS in DV and Instant/PG deals/Roadblocks/SOV
I Know CM360, Trafficking tags, trackers, DCO, Display redirect, I created floodlights in CM360 and QAed in real time environment.
DCM Trafficking
#DSP Mapping
#Extracting Reports
#Trafficking DCO in DCM- Dynamic Creative Optimization
#Generating Tags.
#IAS/DV Wrapping
#Pixel Implementation, in DCM as well as in TTD, DV360, Verizon, Adelphic
#nielson in platform
Understand more about your background experience with paid advertising. Sure. So I I, uh, uh, I was pursuing PGDM when, uh, digital marketing, uh, was a subject of mine, and I piqued my interest because of, uh, because of, uh, uh, it's very, very, uh, attractive, uh, terms that it has, attractive peer advertising that Google Ads or other, uh, Internet companies has to offer for us of voice consumers or or users. So after that, I I joined, uh, IPG as an ad ops distributor where I understood the Scrap and Magic 360 end to end, uh, trafficking placements, uh, ad creation, uh, tracking ads, or tags, display display ads, video WAST tag, video ads, WAST placements, prepaid placements. So, um, I also work with DCOs, uh, native ads. I also started working upon d v 360 later on with Groupon and Google Ads simultaneously. I have co experience in Google Ads and d v 360 across 3 years. So, uh, across 3 years. So, uh, this was my experience with, uh, Groupon, and I also I worked with different KPIs and different, uh, deals on different deals on DSPs, uh, such as programmatic guarantees, uh, uh, PhDs, instant deals. I work with y d two v I s on d v three sixty, uh, v v c campaign, video view campaign on Google Ads, and, uh, video view campaign on, uh, p v 360 as well, video action campaign on Google Ads, uh, to generate more subscribers, uh, every day for, uh, for our YouTube channel. I ran, uh, video ad, uh, video ad sequencing, uh, campaign with, uh, d v three sixty. And, uh, I worked with LinkedIn and non ABM marketing for our lead gen campaigns and video views campaigns with a lead form as well. Uh, this was a scenario, uh, with social and meta as well. I ran meta acts for reach traffic objectives objectives such as, uh, capital objectives such as traffic. Uh, we had views. We had, uh, these were the main, uh, objectives that we usually use to run on Facebook, uh, for our ads. And, also, we we are used to try, uh, different ad types as well, such as, uh, click to WhatsApp campaign, uh, for the lead, uh, lead form, whichever the lead leads that we have gathered. So click to WhatsApp campaign. We used to run conversation ads. We used to run on Reddit. I I ran conversation ads. And, uh, for of also, we used to run roadblocks and, uh, YTSOBs, uh, SOBs
I didn't do anything. Oh, well, it is. Uh, what status do you use to optimize the Google Ads search campaigns for higher CTR. So, uh, basically, I'd I'd opt for more keywords. I I'd make sure, uh, that I enable, uh, I I add over the exclusion lists so that we won't be, uh, we won't be showing our ads over some content such as some contents that we that we wouldn't want to such as, uh, political contents and certain categories and genres. So we have to exclude that manually in Google Ads. There's one more thing we can do. We can have EKG flags, uh, for the same. Uh, we can download a report from verification row verification and, uh, report from Centimeters three sixty. That will allow us to see, uh, of on what apps and URLs our ads are showing upon. Uh, URLs and websites where our ads are showing upon. According to that, uh, the, uh, criticality, uh, as per the criticality state of the of those URLs in the website, we'd, uh, exclude those, uh, websites as per our plan. And, uh, we'll for high CTR, we we also would make sure that, uh, our our bids we are, uh, our bids are, uh, up to the mark. Uh, it it's not, uh, we are not, uh, you know, bidding lower CPA or whatever, uh, if if we are running a maximized bid campaign, uh, for search ad search campaign. So maximize bid strategy for search campaign. Uh, so we'll make sure about that. And what we can also make sure about is we we have enough audience targeting over there that, uh, audience targeting or that are resonating with our ads, our brand. They do have, uh, uh, they are, uh, you know, they are, uh, that we are we have to make sure that we are not, uh, showcasing an ad that a consumer or audience would not, uh, resonate with, such as a person who want that wants to who wants to buy a laptop, uh, and, uh, we won't be pitching a laptop, uh, creative to them, ad to them. We will be pitching the same features. Uh, let's suppose we are running a we're running an ad for Dell, and, uh, we are having recent new launch of a, uh, uh, basically, uh, we have a laptop, uh, let's suppose, So, uh, we are promoting that laptop or through a search campaign. So we won't be showcasing that to the person who already owns 1. We'd rather showcase that to the person who are who are our core audiences in gaming profile, uh, so we would do that. That would increase our higher CTR because person who already has that, uh, won't be, you know, won't be intrigued or or won't be, you know, won't be, uh, won't be, uh, you know, buying that again. So I this is, uh, what we'll do for those audience. So this is, uh, the kind of audience audience play, audience strategy that we would be focusing upon more of work. Because in Google Ads, that is something that we can do to increase higher CTR. Uh, and if let's suppose we ever want to target our we have a first party audiences and, uh, we want to we are basically uh, promoting a new feature in the same laptop, and we have a first party data of whoever owns that laptop. So we, uh, promote that feature and target that, uh, particular, uh, first party audience who has bought who have who are already in our in our customer base and who has already bought our product previously.
He's launched a lead gen campaign for 4 Asian markets. How would you make sure the company is relevant to all 4 markets? So, uh, I have a very good example for this, uh, uh, for the same, uh, for 4 Asian markets. Uh, let's suppose, uh, it's it's our tier 2 markets such as Australia, New Zealand, Singapore. Uh, not Singapore. Uh, so, basically, whatever whatever, are these markets are, so what we'll have to make sure let's suppose, uh, we have a I I used to work with, uh, end to end Google Pay account. So we used to, uh, basically, uh, run ads for new and popular games for us. A new and popular games for the companies that used to pay us for it to run ads upon. So, uh, this is what we used to do. So our audience used to be the same, basically. Uh, there are segments and, uh, whatever we used to target in market audiences for core gaming audiences such as hypo gamers, strategy gamers, action gamers. So we will use those audiences to make sure, uh, we are targeting the same cohort across all markets. So this is what, uh, uh, we ensure that we ensure that we are, uh, are running the same campaigns across all markets. Uh, since the and, uh, our our lead, uh, our conversion was app installed. So we were tracking the app installs, uh, due to the of because we have 5 5 base pixel implemented over there. So this is how we used to count our app installs. And, uh, hence, we used to count our revenue as well through Firebase pixel because we had various pixels implemented on our different
Increase conversation dates. So data driven strategy is something that I I was running a lead gen campaigns. I collected all the leads on, uh, on meta ads. Then, uh, the scenario is what we do after that if you have the leads. So what we did was we ran a click to WhatsApp campaign by uploading the same, uh, by downloading the leads from a campaign and uploading the leads uh, in a in a in a in a same format that it is required by the ads to run a click to WhatsApp campaign. Uh, so, uh, we basically ran a lead gen campaign beforehand and collected, uh, the relevant data that was required for us to run a click to WhatsApp campaign. So this result in an increase increased conversion rate conversion rates for us, uh, in in, uh, when we are running a clip to what Whatsapp campaign. Because these audience are all already was resonating with our ad and, uh, these, uh, audiences were actually made showed an interest on our ad to know about a product such as a
For detailing how paid media has impacted overall business for what insights would you provide and which tools would you use to compile this data. Okay. So first of all, uh, let's suppose I'm running campaigns on Amazon, uh, DSP d v 360, Google Ads, PMax lead gen campaign, demand gen campaign, reduction campaign. Uh, I also am running meta ads for reach, uh, reach as an objective. So I'll combine all the data with different different KPIs. And let's suppose for LinkedIn, I'll go for landing page visits. Uh, if we are running a landing page visits, the KPI that LinkedIn already has in the system. Uh, I'll I'll I'll I'll make sure, uh, that I, uh, download clicks, impressions, cost, that it it has spent, uh, a landing page, uh, visits, link clicks, uh, uh, matrices such as such as that. And with meta, uh, the same matrices plus link clicks as well, I I get get it from the meta ads because it is available on there as well. Plus landing page visits as well. Uh, through v v a sixty, I download 2 reviews, uh, for 2 view ads and all, uh, 2 view views, completed views, uh, impressions, clicks, uh, that that that's about it. Cost as well. And then we so that we can calculate CPM, CPCs. These are the system matrices that we can calculate the key KPIs through which we are going to be providing actionable insight to the CEO, such as CTR, CPC, CP CPM, CPV, CPCV, cost per updated view, uh, click to visits as well. So these are the key KPIs that will give us all for performance max campaigns, the number of leads that we have received through performance of p max lead gen campaigns. And, uh, for demand gen as well, uh, the KPIs plus for the action campaign, how many subscribers, uh, we have received from YouTube, that would be the key key KPI, cost per subscribers, and, uh, cost per landing visits, and all these KPIs leading to, uh, apples to app not apples to apples comparison, but basically give give us a data to to so, uh, to or what channels should we be continue continuing in, uh, in the future in the future, in the next quarter as well.
Look alike audience we use for higher quality lead generation. So which, uh, the sources would be first party data that I would use, uh, to create lookalike audience, uh, so that it would why I would have more higher quality lead generation. Because, uh, at that point of time, what I can do is I can do I can create an audience based look like audience based on the customers who has already converted in my campaign and make sure that I create a lookalike audience on on that note so that I'd have a higher higher quality leads instead of MQLs, uh, MQLs, or MQLs. And, uh, that's how I will ensure that I'm reaching to the right customers and customers who are willing to pay, uh, over the counter as well.
So I would use traffic as a campaign objective because it would help me drive landing page visits. And, uh, this is the main KPI I I would go for. Sorry. This is the objective that I would go for to get more landing page visits. And, uh, so previously, I I used to do I used create jump IDs to track our visits as well on our analytics, not just on meta ads so that we can see if there is an anomaly in numbers, but, uh, there wasn't so much
So, basically, CPM is cost per cost per 1,000 impressions. People know this as and, uh, it is calculated by uh, total cost divided by number of impressions multiplied by 1,000.
To resolve a certain drop in conversion rates from paid search campaigns. So, um, I I I will I will look at it. Uh, first of all, I I I do one thing for sure. I download the report. I look for myself, uh, I'd, uh, look for myself, uh, when the drop happened. Uh, I look for myself. I analyze the data. I'll I'll download clicks c clicks, impressions, calls, the basic mattresses, and I'll tackle in CPC, CTRs, and CPMs, uh, for that campaign. And, uh, I would also download conversions, uh, and and see calculate conversion rate, uh, conversions over, uh, the, uh, c calculate conversions, CVR as well, CVR percentage, then cost per conversion conversions as well. So, uh, and, uh, I download an audience report as well to see to see, uh, which is actually available on the Google dash Google Ads dashboard itself, uh, to make sure that uh, our audience, uh, it is that, uh, is it the trend of our audience or is the trend of our campaign? Is the month to month analysis I do for the campaign, uh, to see if this is a trend of a campaign? If this is trend of the audience, then I'll I'll I'll do one thing. I'll I'll I'll add on more audience or create a look alike audience or go for similar audience in Google Ads itself so that I I won't be I won't have any spillovers of our, uh, you know, spillovers over audience that or audience that we, uh, really that I'm not even