Mahesh Narayanan

So why is permissoned data so important? here's my article on Gen Z permissoned data published in MarTech Zone: https://lnkd.in/dmKV_Pvi

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Even the savviest influencer must be aware of the evolving digital battlefield in today's media landscape. A new study reveals how political advertisers circumvent ad targeting restrictions on platforms like Facebook by cleverly using "proxy attributes" to target demographics based on political and racial divides. The rules of the game of online influence are constantly changing, and the only way to stay ahead is to understand the latest tactics. #influence #ethics #digitalmarketing #politicaladvertising https://lnkd.in/gNuQjede

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Sourabh Khandelwal, Group Manager of Marketing Science at Snap Inc., outlines why attention needs to become a more nuanced metric when measuring an ad's effectivness, driven by the resurgence of MMMs and release of innovative new ad formats that stand to be far better than traditional ones at capturing viewers' eyeballs and driving outcomes. For example, a branded AR lens rendered onto your own face is not the same as a vertical video view, which doesn’t equate to the same as a pop-up ad on a website. Recent research Snap conducted with OMD Worldwide and Amplified Intelligence revealed that AR Lenses deliver five times more active attention than industry benchmarks, driven by contextual and personal relevance. https://lnkd.in/eY8hN3E4

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Interesting insight from Santosh Rao on Marketplace by APM My research talks about metrics Tech/SaaS Leaders should measure depending on your scale https://lnkd.in/gGfBWgn5

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What happens when two industry veterans and former analysts meet and have a private chat about the marketing hyperboles and product overpromise of tech vendors? What do they say to each other? Welcome to Reality Distortion Fields (RDFs)! A new long form conversation podcast hosted by Elias Khnaser and Alessandro Perilli 🇺🇦. In this first episode, Elias and Alessandro talk about: - Humane AI Pin and MKBHD - Rabbit R1 - what's the job of a reviewer/critic/analyst - how companies grow reality distortion fields and if there's a way to avoid them - tech vendors' responsibility towards customers and corporate incentives - how they typically deal with failures, and the contrarian perspective of Peter Thiel - if we really need yet another device - what's the vision for an AI assistant compared to what vendors are selling today Watch it here or wherever you get your podcasts: https://lnkd.in/g9Pdr7P5

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This is the age when nobody knows what the next product will look like—even Google and Apple. ===================== Google refines AI Overviews feature to address bizarre and inaccurate responses. Flawed training data and algorithmic errors led to 'AI hallucinations.' Google acknowledges the challenges and commits to improving accuracy and reliability. Restrictions imposed on AI Overviews to limit misinformation and harmful content. Concerns raised about user safety, bias, and transparency. Users have the option to disable the feature and prioritize traditional web search. Gemini AI integration in Gmail needs accuracy improvements for user trust. Google's AI language model, Gemini, generated inaccurate and unhelpful search results, including false claims about Nintendo characters. Google vows to ensure accuracy in the future. #AI #Google #Accuracy #Transparency #UserSafety #Gemini  https://lnkd.in/geazMBJR

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[Voiro Podcast] In a fascinating conversation with Kavita Shenoy, Soumya Gupta (of The Signal) talks about how local or regional print media is gaining traction while English-based print media is seeing a slump. Soumya goes on to say how regional reporting is impacting both audiences and advertisers. Watch or listen to the full conversation: https://lnkd.in/gWC7C6TR Subscribe to Soumya on The Signal: https://lnkd.in/dV5GpSJK #voiropodcast #printmedia #media 

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I am a strong advocate of consumer research-led marketing vs gut-based marketing. Maybe the bias comes from ~5 years in research-first advisory/consulting roles or maybe the other 6 years being on the ground with consumers to drive growth for startups. It just makes it easier to make decisions in marketing. But sometimes in the daily grind, I forget to interact with my own customers - startups. So I am back with another round of helping early stage founders - no strings attached! 30 minutes of solving your most pressing growth challenges, it can be anything from: - Which paid marketing channel to start with? Meta vs Google - Why is my GTM plan not working? - What ROI to expect from brand marketing? - Organic vs Paid marketing - which is better for my startup? - Or just catch up on the basics - what is Growth marketing? If you are a Seed or Series A funded startup, I would love to connect and help out. Like I said no strings attached, just 30-minutes of solving growth. Book your slot here: https://lnkd.in/gkdjMCuX #growth #consulting #digitalmarketing #productmarketfit #gtm #startups #founders

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Media mix modeling at the enterprise level can be quite complex but, when done well, has a huge impact across the whole organization. Here are 3 lessons we’ve learned to help make MMM work at the enterprise: 1. At the enterprise level, you need one central point of contact who is in charge of making MMM work for that organization. That person needs to be senior enough that they can get the right people in the room and have enough “political clout” to convince them of the importance of going to these different meetings and taking these different actions. Generally, they also need to have a background in both marketing and data science – either a really technical marketer or really marketing-heavy data science person. 2. All the different teams in the organization need to be educated and onboarded in a way that makes sense for them. The finance team needs to be introduced to the different assumptions that are being made and the forecasting capabilities, and they need to understand the uncertainty that comes out of the model. They don't need to understand the internals of data science and statistics, but they do need to understand how they are going to use this for the budgets that they're building for next year. The marketing team needs to be educated on how to take action based on the model's results. This is a place where sometimes MMM projects can fall short: marketers see a bunch of pretty graphs but they’re unsure what to do with that information. A great onboarding is crucial in helping them make the connections between what they're seeing in the MMM and the actions that they need to take. Finally, the data science team needs to understand what data the MMM is using and then be there to help the rest of the organization interpret and use the results. That triumvirate of stakeholders is what can make a media mix model really impactful and actually useful in an organization. 3. Remember that MMM isn’t relevant to everyone on the marketing team. Media Mix Modeling, in general, is more useful at the director/executive level where people are making decisions about how to allocate budget across different channels, how to evaluate strategy, and how to think about planning, budgeting, and forecasting. For an individual channel manager focusing on managing a single channel, the results coming out of the MMM aren't necessarily going to affect their day-to-day job. They might receive new direction from their boss based on what's coming out of the Media Mix Model, but they don't necessarily need to be using the platform on a day-to-day basis. Keep this in mind when you’re thinking about who to include in MMM training and how you expect them to use a media mix model.

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Nicely done #LinkedInAI - this is spot on 🤫 Learn more about hushh.ai hushh.ai, founded by Manish Sainani, focuses on making user-data universally accessible and valuable with consent for trusted partners. The team is working on deep levels of personalization for consumers and brands, as highlighted during their collaboration with the LVMH team at #vivatech.

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Friends at Factors.ai launching a big product update today. Congrats Srikrishna Swaminathan / Praveen Das and the entire factors team on building this copilot in a very short time. With the LinkedIn ads Co pilot - You can improve reach and campaign performance. - Think frequency capping, account-level retargeting - view-through attribution etc Mainly you can control spends and make it more efficient. Join us at 8.30 to explore the features. RSVP comment section

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It always cracks me up when I see the 'Add AI' option for a column in Coda. In general the idea is to lead with the use case like 'Summarise', 'Elaborate' when showing such options. As a user, my objective is not to 'Add AI', but solve my problem, for which AI could be one of the solutions. This sounds more like an investor pitch, rather than a feature copy. :)

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TRUST TAKES YEARS TO BUILD SECONDS TO BREAK, & FOREVER TO REPAIR One of the biggest risks posed by Gen AI is a further & irreversible erosion of trust. How can technology help counter this risk?

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Data-driven insights fueled by programmatic advertising have redefined #personalisation and #targeting within the marketing landscape. 💯 Mr Delphin Varghese, Co-founder and Chief Revenue Officer, AdCounty Media, draws attention to the significance of #programmatic in driving engagement through real-time optimisation to yield maximum ROI. ✅ 💰 #targetaudience #hyperpersonalisation #realtimebidding #optimisation #marketingandadvertising #digitalmarketing #ROI #brandawareness #conversion #CTR #engagement #innovation #automation #AdCountyMedia #adtargeting #marketinginsights #campaignperformance #customerjourney #userexperience #socialmedia #crosschannelmarketing #crossdevicetargeting

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Media mix modeling at the enterprise level can be quite complex but, when done well, has a huge impact across the whole organization. Here are 3 lessons we’ve learned to help make MMM work at the enterprise: 1. At the enterprise level, you need one central point of contact who is in charge of making MMM work for that organization. That person needs to be senior enough that they can get the right people in the room and have enough “political clout” to convince them of the importance of going to these different meetings and taking these different actions. Generally, they also need to have a background in both marketing and data science – either a really technical marketer or really marketing-heavy data science person. 2. All the different teams in the organization need to be educated and onboarded in a way that makes sense for them. The finance team needs to be introduced to the different assumptions that are being made and the forecasting capabilities, and they need to understand the uncertainty that comes out of the model. They don't need to understand the internals of data science and statistics, but they do need to understand how they are going to use this for the budgets that they're building for next year. The marketing team needs to be educated on how to take action based on the model's results. This is a place where sometimes MMM projects can fall short: marketers see a bunch of pretty graphs but they’re unsure what to do with that information. A great onboarding is crucial in helping them make the connections between what they're seeing in the MMM and the actions that they need to take. 3. The data science team needs to be involved Finally, the data science team needs to understand what data the MMM is using and then be there to help the rest of the organization interpret and use the results. That triumvirate of stakeholders is what can make a media mix model really impactful and actually useful in an organization.

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AI is changing the way we approach GTM, and we’re excited to bring some amazing minds together to dive into how. On October 18th, join us in Bangalore for an in-person panel featuring: Ankit Jain (ClearFeed), Siddharth Deswal (Enfra), Anand Venkatraman (Happysales AI), Praveen Das (Factors.ai) and Siddharth Sharma (YellowKyte). We will discuss how AI is impacting sales, marketing, and customer success. Hear from experts building AI-driven products in these domains as they discuss how AI is shaping GTM in their respective industries. Get your exclusive invite (link in comments )⬇️ #factorsai #saas #gtm

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The conversations at Saastr last year predicted how the GTM tech market unfolded over the next year. Saastr is a microcosm of people who live in the future and are on the cutting edge. Back then, the excitement was around using AI to automate high volume emails, by taking a page from the pioneers of this approach at Brex, Rippling, and Ramp. It wasn't obvious, but the trend was there if you paid close attention. In retrospect, it took me another 3 to 6 months to realize it, just as the first "AI SDRs" started to become mainstream. This year, I came prepared. I had 50+ conversations with cutting-edge startups and GTM leaders about the future of sales. The takeaway: the market overshot on the promise of AI SDRs. Now, the puck shifted to better targeting using AI, and copilots as the frame of reference for high-quality automation. My sense is we'll see this become mainstream over the next year, as each sales tech vendor launches their interpretation. IMO, I'm not sold on the idea of a copilots long-term. It feels like a crutch for the market to get comfortable with agents taking actions on their behalf. Just like lane assist vs. full self driving, I suspect we'll get back to end-to-end outbound automation. This time, it'll be with better targeting and a deep appreciation of the nuance necessary for thoughtful outbound. Anyone else at Saastr (or otherwise) have thoughts?

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Use your gut more than the data - because the data sucks. Here are real things I saw at a real conference today. A graph with no Y axis units and no X axis at all. “Proof” that a platform targeted ads at A and not B because the researchers had set up different profiles for A and B and documented ads served. How many profiles did they set up? Just two. No attempt to account for geo, day part, etc. No statistical fluctuation. Maybe they had to pay for each profile and they were being prudent. And a LOT of people saying “you can make data say anything you want.” Which isn’t true. But you can tell any story you want with shoddy data, cobbled research and a small data set. It isn’t that you can’t trust the data. It’s just that you have to look a little deeper, and sprinkle - no shovel - more than a grain of salt. And that gut of yours? Give her a second listen.

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