Blogs

Why it is not worth debating if MMM can measure Long-Term Brand Impact “Can Marketing Mix Modeling (MMM) measure long-term brand impact?” In my opinion, this debate is largely misplaced. 📌 No consensus on what “Long Term” actually means There is a joke in statistics – ask 3 statisticians for an opinion and you get 5. Marketing is no different. Every marketer seems to have their own definition of what long term means. Before asking whether MMM measures long-term impact, we should ask a more basic question: What exactly is long term? 3 months? 6 months? 1 year? Even marketing experiments such as Geo-Lift or Brand Lift measure impact only

Dynamic Base MMM Models May Not Be Worth the Effort So what is Dynamic Base? In MMM, the Base is nothing but the intercept and one interprets it as the ‘organic sales (or any KPI) that one could get if there were no marketing / media efforts or extraneous factors’. Marketing theory says that the Base (Brand Equity) of a brand is very sticky and hardly moves. Even in our experience of more than a decade, we have seen a established CPG brands base go up or down only by 2 -3 percentage points. Even for E-commerce brands, Pharma, Banking, sports clubs, Streaming companies we have not seen Base vary

The Effect of Meta’s Click-Based Attribution Recategorization Meta recently informed marketers that they are changing how click-through attribution is defined. Historically, Meta attributed conversions to all types of clicks including likes, shares, saves, comments and link clicks. However, most third-party platforms count only website link clicks. As Meta noted: “This difference in what is attributed as a ‘click’ conversion can lead to inconsistency between what an advertiser sees in Meta Ads Manager compared to third-party reporting tools.” To address this discrepancy, Meta has decided to restrict click-through attribution only to link clicks. At the same time, a new attribution category called “Engaged-Through Attribution” is introduced. This includes signals such as

The Alpha Fold – MMM Parallel I finally managed to catch up on the famous documentary “The Thinking Game”. What a story !! I noticed many parallels between protein folding and Marketing Mix Modeling (MMM). 📌The Hidden Structure AlphaFold In AlphaFold, we observe an amino acid sequence and want the hidden 3D protein structure. MMM We observe marketing/media spends and the corresponding KPI (Sales, net user addition, Awareness). We want to know the causal structure – which variables resulted in the KPI, by how much, and the interaction effects between them (if any). This causal discovery is not as difficult as protein structure but it is not easy either. 📌

Nothing Credible about Credible Intervals and Why this matters when reading ROAS or iROAS from Bayesian MMM Credebile : Able to be believed or capable of persuading Many people think Credible intervals are equivalent to confidence interval. They are not. Frequentist methods focus on the apparatus or the experiments. Every commentary like p-value and confidence interval is actually a commentary about the apparatus and not the phenomenon. The phenomenon is assumed to be ‘there’ – ‘fixed’ and ‘true’. The question then boils down to – is apparatus good enough to capture this phenomenon (the parameter). The question that frequentist confidence Interval answers is that – if the parameter is found,

Marketing Mix Modeling (MMM) is Anti-Fragile Sometimes Bangalore traffic is not bad after all. Especially if one can finish a podcast while in transit 😅. I was hearing a podcast featuring Nassim Taleb (Link in comments). In it he was discussing the idea of Anti-Fragile (also a great book by the same name) I couldn’t but connect this concept to MMM because just few days ago one of our client asked – “why do you say you need variation in the data for MMM to work?” Taleb said in the podcast “Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder and stressors” He calls