Blogs

Your Incrementality Test Is Only As Good As Your Control Market Most Geo Testing fail even before they begin !! 📌 The Reason ? Choosing the wrong control market. When it comes to Geo lift tests, surprisingly, many teams still do this manually: • Eyeballing charts • Picking “similar looking” regions • Or relying on methods that break under real-world marketing noise That is exactly why we built Aryma Market Matcher. After years of experimentation R&D across client projects, we at Aryma Labs have found that Dynamic Time Warping (DTW) based market selection consistently outperforms synthetic market creation and selection done via SCM and similar methods. Now we are opening

AI is coming for MMM jobs We recently interviewed a candidate for MMM role and were shocked to hear about their bloated MMM process. I have some good news and bad news. Good News – AI will cut a lot of fluff around MMM process. Bad News – This will not augur well for analysts working in these bloated systems. For years, some parts of the industry became bloated around processes that frankly should never have required so many layers in the first place. – One team to clean the data. – Another team to “run the model” by pushing a button on a platform, while having little understanding of

How to validate lift from OOH / DOOH campaigns My last post on OOH drew a lot of engagement. One particular question from a commenter was “It is great to know that interaction effects in MMM can accurately measure impact of OOH. But how do we really validate that the OOH / DOOH campaigns worked?” This is a great question and the answer to this is two fold – Geo Lift tests and Causal Geo Lift Tests. 📌 Geo Lift Tests Let’s take an example Suppose a brand launches DOOH only in selected cities/DMAs while keeping other similar geographies untouched. You can now compare ▪️Search lift ▪️Conversion lift ▪️Store traffic

Why OOH is not direct response and how it manifests in the form of interaction effects OOH is rarely a Direct Response Media. One of the biggest mistakes in Marketing Measurement is trying to force OOH into a direct response framework. People often ask: “Why is OOH contribution in the MMM so low despite massive spends?” My answer – Because OOH often does not work in isolation. It works by changing the effectiveness of other channels. 📌 OOH is an amplifier, not just a converter. If we analyze how people consume OOH and act, things become much clearer. Let’s take a simple example. You see a billboard while driving. You

From Excel Add-ins to Gen AI: Aryma Labs Is Building Both Ends of the Future Innovation does not always mean throwing away the past. Sometimes the real innovation is modernizing what the industry already loves using. Anybody who has operated in the MMM space will know that MMM is a small data problem (relatively speaking). Despite all the hype around modern data stacks and dashboards, a huge amount of MMM data still arrives in Excel sheets. Most MMM datasets are monthly, weekly or daily aggregates. They are manageable in size, business facing and most importantly highly collaborative. Excel remains incredibly efficient for such workflows. Most Clients and vendors still live inside

The Organizational Memory Problem Solved – MMM Synapse Yesterday we launched something unique and innovative. We launched our Gen AI vertical Aryma AI catering to marketing measurement. Our goal is simple – Add the Intelligence layer into the Marketing Measurement Stack without compromising on the core statistical / scientific accuracy. Our Gen AI philosophy is hence – Peripheral Agentic AI. If you haven’t checked out our cool , interactive website yet, here is the link. https://lnkd.in/gxRSzPbS I also wanted to talk about one of our products MMM Synapse, that drew a lot of attention yesterday. We got a few DMs asking for information and Demo of it. 📌 The Memory

There is One MMM Truth. But Infinite Budget Optimization Answers. One of our course subscribers asked a very interesting question. “In the course you mention that there is one ground truth MMM model but in Budget optimization there are many ways to get to the same target KPI. How do you reconcile the two?” It is a great question and it is something every MMM analyst at some point grapples with. Marketing Mix Modeling (MMM) and Budget Optimization are often spoken about in the same breath. But they solve fundamentally different problems. 📌 MMM is about truth. Optimization is about possibilities. In the context of Marketing Mix Modeling (MMM), it is believed

Why Average Video Views > Likes & Comments We are currently working with a brand and they invest a good sizeable amount in performance marketing. One of the debates we had was – Should we give more preference to Average Video Views (AVV) or Engagement metrics like Likes and comments in our MMM? 📌 We believe that Average Video Views AVV > Likes and comments Here is our rationale: If we talk about human engagement (not bots), Viewing always precedes engagement. So AVV captures both exposure and basic attention. Engagement is predominantly driven by a small minority. However there is a large majority of viewers who are not as vociferous

Media Mix is Easy. Marketing Mix is Reality. When brands start their journey be it a new consumer brand, a D2C startup, or even an influencer, their world often revolves around media channels. The focus is on which platform (Meta, Google, TikTok, Amazon) drives more clicks, more sales and more followers. This is where Media Mix Marketing comes in to the picture, analyzing how different media platforms contribute to growth. But over time, every serious brand realizes that sales or any other KPI aren’t just shaped by media alone. They are influenced by: ▪️Competition – A rival’s new launch or discount campaign or a sudden media spends increase can shift

Decision History Scoreboard – MMM vs Marketers At the beginning of every year, we at Aryma Labs chalk out interesting product features to build. One idea that we discussed early this year was ‘Decision History Scoreboard’. Why this idea? Well let me ask you the reader the following interesting questions: ▪️How many times has a seasoned marketer vetoed an MMM recommendation and was proven right? or ▪️How many times did MMM go against conventional wisdom, its suggestions get implemented and was proven right? or ▪️How many times both marketers and MMM were on the same page, and were proven correct? or ▪️How many times both marketers and MMM were on