Analytic Partners’ Post

Is Bayesian MMM worth the faff? In this post I discuss the benefits and challenges thrown up by Bayesian approaches to Marketing Mix Modelling. Key takeaway: if all you do is swap in a Bayesian model in place of your traditional MMM, you get all of the faff and none of the benefits - you need to use these models differently to get the benefits. Thanks simeon duckworth and Neil Charles for the good discussions on this! #bayesian #mmm #marketingmixmodeling https://lnkd.in/eMsjes9Y

🚀 Just published a comprehensive guide on Click Probability Prediction 👉🏻 https://lnkd.in/gM4xaT3F Dive into how the modern recommendation engines are fuelled. Perfect for students and professional Data Scientists. Check it out on Medium and let me know your thoughts!💡 #DataScience #ClickPrediction #MachineLearning

Exploring the Power of Hierarchical Bayes in Media Mix Modeling 📈 Are you a marketer investing in media mix modeling? Hierarchical Bayes might just be the key to unlocking greater insights and actionable recommendations for your media planning. A few of the benefits include: 1. Granularity at Every Level: Hierarchical Bayes Regression allows you to dissect your data into hierarchies of granularity. Local factors impact store-level effects, while national media influences broader reach. This hierarchy tackles misattribution challenges head-on. 2. Incorporating Domain Knowledge: By incorporating domain knowledge through priors, you fuse external norms, constraints, MTA insights, and A/B test findings into your results. This integration yields consistent, sensible answers over time, driving KPIs that lead to sequential measurements. 3. Self-Norming Strength: In a scenario with multiple products or outlets, Hierarchical Bayes shares advertising impact measurements across similar elements. Outliers are steered towards central tendencies, ensuring robust outcomes and preventing mistakes and phony anecdotes. 4. Adaptable to Complex Media Scenarios: Addressing the complexities of evolving online media, these models are robust when media (or any effect) applies to only a subset of products or distribution channels (e.g., this retail promotion only applies to Target sales). 5. Amplifying Variable Measurement: Flexibility at varying levels of granularity empowers Bayesian models to measure numerous variables more comprehensively than traditional regression methods. 6. Efficiency & Control: Hierarchical Bayes offers enhanced control against data anomalies, freeing up analysts' time from model supervision to result interpretation and actionable insights. Media mix modeling is a sophisticated endeavor, demanding expert tools and partners. To truly comprehend the nuances of media's contribution to your growth goals, look for an analytics partner well-versed in wielding the sharpest tools—like Hierarchical Bayes. Their ability to navigate the evolving media landscape will decode the real impact of each media channel, guiding your optimal growth trajectory. #MediaMixModeling #MarketingAnalytics

I worked on a project that focused on marketing analytics for a medium-sized retail store. The project involved segmenting customers based on their shopping behavior using RFM (Recency, Frequency, Monetary Value) analysis and Unsupervised Machine Learning Algorithm (KMeans Clustering). The purpose is to tailor marketing campaigns for specific customers based on RFM and KMeans results. The following tasks were performed: - Data Cleaning  - Data Transformation and Exploration - Customer Segmentation using RFM analysis - Pre-processing data for KMeans clustering - Selecting the optimal number of clusters using the Elbow Criterion method - Interpreting clusters The following tools and libraries were used: - Python - Pandas - Matplotlib - Seaborn - Scikit-learn (SkLearn) Segmenting customers based on their behavior and using RFM analysis and KMeans clustering can benefit retail stores/organizations in many ways, including: - Targeted marketing. - Customer retention. - Market basket analysis. Here is the link to the document: https://lnkd.in/d7_PdATY

Marketing Mix Modeling is just linear regression ? or is it ? MMM is something which looks deceptively simple at first but as you keep peeling the onion layer, you realize how complex it really is. All the things that your statistics professor would have warned you that could go wrong with Linear Regression does go wrong with MMM. 😅 And funnily what your stat professor or statistics book forgot to mention could go wrong also goes wrong with MMM. 😂 When you build a MMM model, you are bound to face the following problems: 1) Multicollinearity  2) Endogeneity 3) Autocorrelation 4) Omitted Variable Bias 5) Suppression Effect 6) Regression Dilution 7) P>N problem 8) Negative R squared Value 9) Inflated Zero problem 10) Interaction effects and confounding. And not to mention, you need to also causally prove effect in MMM. To get MMM right you need two things: 1) Deep statistical knowledge and statistical rigor 2) Deep understanding of Marketing Luckily at Aryma Labs, we were lucky to have both of them. We also continuously perform numerous R&D to enhance our MMM models. We deeply emphasize on statistical rigor in our MMM models to clients because MMM model is the nucleus in the MMM project. Saturation curves, ROI estimates, Budget Optimization and scenario planning are all artifacts of the MMM model. If you don't get the model right, none of the downstream outputs are going to accurate. Link to mine and Ridhima's posts on Linear Regressions and MMM are in comments. You can also subscribe to our substack to know everything about MMM or visit the blogs section in our website that has 100+ blogs on MMM. #statistics #datascience #linearregression #marketingmixmodeling #marketingattribution

Demystifying Clustering: Understanding the Basics and its Applications 📢 Exciting News! 🎉 I am thrilled to announce our latest blog post on Demystifying Clustering: Understanding the Basics and its Applications! 📚💡 Learn all about the power of clustering in data analysis and machine learning, and its wide range of applications, including customer segmentation, image recognition, anomaly detection, and document clustering. 🌐🔍 In this article, we will also explore the fascinating world of keyword clustering, particularly valuable for SEO, keyword research, and content planning. Discover different approaches to keyword clustering, such as co-occurrence-based clustering, semantic-based clustering, and topic modeling-based clustering. Ready to gain valuable insights and make informed decisions in data analysis and machine learning? Don't miss out on this informative blog post! Check it out here: [Link to the blog post](https://ift.tt/YNDfG2L) 📲 Stay ahead of the curve with our expert insights! 🚀✨ #Clustering #DataAnalysis #MachineLearning #KeywordClustering #DataInsights https://ift.tt/YNDfG2L

Is Bayesian MMM worth the faff? In this post I discuss the benefits and challenges thrown up by Bayesian approaches to Marketing Mix Modelling. Key takeaway: if all you do is swap in a Bayesian model in place of your traditional MMM, you get all of the faff and none of the benefits - you need to use these models differently to get the benefits. https://lnkd.in/eHBSndni #bayesian #mmm #marketingmixmodeling

Sharing this excellent discussion of some problems with statistical significance from Eric Bradlow et al. From a practitioner perspective, there is another common problem: stakeholders often believe that they understand statistical significance when they don't. That contributes to the kind of binary thinking that Bradlow describes. In the Quant UX book, Kerry Rodden and I wrote, "statistical significance should not be reported. An analyst might or might not consider it, according to the context, but it is not a relevant topic for stakeholders and partners." (quantuxbook.com, chapter 5) For stakeholders, I recommend the "intraocular significance test". If results are visible enough on a chart to hit you between the eyes — whether in terms of difference or LACK of difference — then it is important for stakeholder discussion.

This! As researchers, we may want to consider "statistical significance" when appropriate, but it is not something we should report to stakeholders by default. I also agree that this means that a major responsibility we have is visualizing clearly where differences or the absence of differences are "meaningful" as opposed to just "significant". #quantuxr #quantitativeresearch #statisticalanalysis #freelance #userexperience

Data Science in Marketing: Unveiling Consumer Behavior through Analytics Data Science in Marketing: Unveiling Consumer Behavior through Analytics Introduction: In today’s digital age, data has become the lifeblood of businesses across industries. With the advent of technology, companies now have access to vast amounts of data about their customers, their preferences, and their behavior. However, the challenge lies in making sense of this data […] https://lnkd.in/gyX2Jzvk