September 2023 | 7-minute read
Tagged: MarketMixModeling | MarketingROI | AWSAnalytics | MachineLearning | DataInsights | MarketingStrategy | CloudInfrastructure | DataAnalysis | MLOps | AWSArchitecture | MarketingOptimization | DataIntegration | BusinessAnalytics | MarketingPerformance | AWSAdvancedPartner
Startups often spend a significant portion of the total available budget on various marketing channels like Television, Digital spending, radio, and paper ads to drive customer visibility and growth. However, often, it is observed that the ROIs for these marketing investments need to be in line with expectations. Additionally, startups need more visibility into data across different marketing channels they invest in and the impact it is creating.
In this blog, I would like to explain how startups can leverage the power of cloud (AWS) and Machine Learning (ML) based “Market Mix Modelling” to understand their marketing spending across various channels and optimize it leading to the appropriate allocation of marketing budget and maximizing its ROI.
Learning on how to perform Market Mix Analysis:
Before I dwell into the details of the solutions, I would like to let readers know what Market Mix analysis means. Market Mix Analysis is the application of econometrics to quantify the effect of marketing and non-marketing factors on sales. It is typically used to
Analyst should start their journey by identifying all the avenues of marketing spending, promotional factors and other available data points (macroeconomic, metadata). Post basic data checks and cleaning, a hypothesis list should be made in sync with the business to perform exploratory data analysis (EDA).
Analysts can perform various types of EDAs to understand the data patterns and based on which one should decide the data treatment for modelling purposes.
Figure 1: Explains various EDAs and decisions analyst can take to transform the data
Above mentioned EDAs will help the analyst understand the various nuances like seasonality, impact of events, other drivers, and their impact on sales. Post this analyst needs to understand 2 important aspects of its marketing spends
Figure 2: Sample results of Ad-stock analysis
Figure 3: Real world example of lag in sales post promotion is carried out
Figure 4: Various analytical models analyst can use to create market mix model
Real world challenges and solution:
Now that we understand how market mix modelling is done, let me highlight some of the real-world challenges analysts often face
To solve the above challenges, companies can leverage AWS Cloud Infrastructure where it can
Sample Technical Architecture that can be used to build a production system on AWS cloud
Figure 5:Representation of AWS Cloud Architecture analyst can follow to set up MMx on cloud
There would be 5 major components in the architecture
3. Data Lake — Single source of truth using Amazon Simple Storage Service (S3) as a storage layer. Migrate raw data from sources to Amazon S3 and then perform data cleaning to store clean data with proper data quality checks.
Figure 6:Representation of how MLOps pipeline can be built on Amazon SageMaker from their website
5. Data Consumption — We can integrate various visualizations platforms to analyze and showcase the model results on a dashboard. Microsoft PowerBI was used to build the dashboard enabling users to monitor and take decisions on marketing spends.
Post production system implementation, Analyst can design marketing spend monitoring decision boards using AWS Data Lake in backend to continuously monitor and take spend decisions.
Figure 7:Sample screenshots of MMx dashboards on Microsoft PowerBI
Companies can increase their ROIs by ~10–15% using the Market Mix modelling technique by adequately applying the marketing budget. Also, by leveraging the AWS cloud infrastructure, it will be able to scale and iterate faster (even outside AWS ecosystem) on the models built and deployed.
If you want to undertake such an activity for your firm and need help, you can reach out to firstname.lastname@example.org.
Ganit is an AWS Advanced Tier Services Partner that provides intelligent solutions at the intersection of hypothesis-based analytics, discovery-driven artificial intelligence (AI), and new-data insights. Over the years, they have successfully deployed various customer targeting and optimisation solutions for its customers.