MMM is something that always leads you to reminisce about the learning one had or hadn’t during their statistics course. 15 years ago, when I was a student, I attended one statistics seminar. In it the professor told “Always be testing your assumptions”. That statement rings true even now, especially in MMM. In MMM, inference is […]
Why Heteroscedasticity matters in Marketing Mix Modeling (MMM) Read More »
Gentle Reminder : A high t statistic does not indicate strong relationship of IVs with the DV. Just the other day, I saw a post (again) stating that a t statistic indicates strength of relationship of IVs to DVs. So here is a gentle reminder (sharing it again). The high t statistic here does not
When we talk about application of statistics during world war 2, somehow the image of the airplane with red dots (survivorship bias) comes to mind. Don’t worry I am not going delve on that again 😅 There are however other lesser known statistical applications during world war 2 which had a huge impact in the
Are you confused about the meaning of ‘statistic’ in statistics? You’re not alone. Many blogs and posts on the internet use the term loosely. In statistics, a statistic is defined at a sample level, whereas a parameter is defined at a population level. To estimate the parameter, we use a statistic. One of the mental
What does ‘statistic’ in statistics mean? Read More »
A lot of people blame Confidence Intervals and its ‘unintuitive’ nature for switching to Bayesian side of things. But if you are in Marketing Mix modeling domain, frequentist concept of confidence interval makes more sense. Before I elaborate, let me provide a quick recap of what exactly is Confidence Interval. 📌 What is confidence Interval?
Confidence intervals in a way are barometer of your Marketing Mix Models Read More »
One key advice I give to statisticians/data scientists looking to get started on Marketing Mix Modeling (MMM) is – Know your type of Data. A big chunk of the projects in the industry involves dealing with tabular data. This is particularly true of Marketing Measurement and Attribution Industry. However, not many have the knowledge of
Looking to get started on MMM? Know your type of Data. Read More »
Why customers should not expect their Marketing Mix Models (MMM) to have very high R squared value. Somehow over the years, two myth has been propagated : ▪ High R squared value = good ▪ R square is a sign of predictive power of a model I guess we statisticians are partially to blame for
Why you should not expect high R squared value in your MMM models Read More »
1. Spends are more actionable and transactional, as you can measure direct impact on sales. Spends data directly reflects the financial investment in marketing (your CFO would be happy 😅). 2. Spends are more accurate than impressions. 3. Cross-Channel Comparisons – Spend data enables easier comparisons between different marketing channels or campaigns in terms of
MMM pro tip: Use spends instead of Impressions in your model Read More »
Marketers are sometimes given bad advice that they should not go for advanced methods for marketing impact measurements. Instead they are suggested to adopt simple analysis like correlation. Ill advised suggestions like “All you need is only correlation” will do more harm than good. So I will detail out the advice that I came across
Don’t make million dollar marketing decision just based on correlation Read More »
One of the complaints many have with respect to MMM is that it does not provide granular insights. While there are multiple solutions to this problem (will talk about this in future posts), let me unpack the granularity problem. So what is the Granularity problem? Lets take the example of TV. Many brands spends lot
Unpacking the granularity problem in MMM Read More »
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