by Anantha Harshith Yechuri & Ritika Sharma
Introduction
In the dynamic realm of marketing, staying ahead of the competition requires savvy strategies and data-driven decision-making. Imagine having the power to optimize your marketing metrics, ensuring every marketing dollar is efficiently allocated and driving the most ROI for your organization or for your customers. However, with multiple channels and strategies at play, determining the most effective mix can be a complex challenge. This is where Marketing Mix Modeling (MMM) comes into play – a powerful analytical tool that helps businesses optimize their marketing strategies by understanding the attribution of various factors on sales and other ROAS (return on ad spend) related key performance indicators (KPIs).
Understanding Marketing Mix Modeling
Marketing Mix Modeling is a statistical analysis technique that quantifies the impact of different marketing activities on sales outcomes. By analyzing historical data, MMM helps businesses determine which elements of their marketing mix, such as advertising, pricing, promotions and distribution – are contributing the most to sales. Marketing Mix Modeling (MMM) is the foundation of this optimization journey. This approach leverages Google’s Lightweight MMM model , which simplifies the modeling process while maintaining high accuracy. It enables us to make precise predictions and optimize marketing budget allocation and improve sales lift.
The model considers various aspects, including:
- Adstock Models: The choice of adstock models is vital, affecting how media spend impacts sales.
- Degrees of Seasonality: Considers different degrees of seasonality, fine-tuning the model for optimal predictions.
Key Components of MMM
- Advertising: Evaluating the effectiveness of different media channels (TV, digital, print, etc.).
- Pricing: Understanding how changes in price impact consumer behavior and sales.
- Distribution: Assessing the effectiveness of distribution channels and their role in driving sales.
The Power of Snowpark
Before diving into how the App works, it’s crucial to understand the importance of Snowpark. This versatile Snowflake offering streamlines the importation and management of python packages, making complex data operations and analysis a breeze. It lays the foundation for powerful data manipulation, allowing us to optimize marketing metrics effectively.
Calculating Return on Investment (ROI)
At the heart of it all lies the calculation of Return on Investment (ROI) for each marketing channel. ROI is the compass that guides your marketing strategies, helping you identify the most effective channels. The process is meticulous and comprehensive, ensuring every aspect is considered:
- Data Loading and Exclusion: We start with loading marketing data and excluding specific values that might distort the analysis.
- Data Cleansing and Transformation: It then proceeds to clean and transform the data, ensuring that it’s ready for analysis. Any missing or infinite values are handled, and the data is grouped by the advertising channel.
Image 1: EDA Performed on the clean data
Image 2: Cleaned up data
- Time Series Data Handling: As we are dealing with Time series data, aggregating data on a daily basis provides a comprehensive overview of marketing performance.
- Scaling for Modeling: Proper scaling of data is crucial for modeling. The code utilizes a custom scaler for media, organic data, and target data, ensuring they are all ready for the subsequent steps.
Results
The process tries to find the optimal allocation of budget using a naive Bayesian approach that in turn helps increase the sales. Some understanding of key performance results that can be derived from the code are shown below.
Image 3: A comparison between prediction and real KPI(sales) data
Image 4: Pre budget allocation and post budget allocation sales value
Optimization and Post-Optimization Analysis
The optimization process is where the magic happens. It involves:
- Budget Allocation: The code calculates the optimal budget allocation for media channels based on MMM predictions. This ensures that marketing dollars are directed where they will have the most significant impact.
- Post-Optimization Metrics: After optimization, we calculate and present key metrics that provide a holistic view of marketing performance. This includes ROI values before and after optimization, media channel effectiveness, and cost allocations.
Image 5: Final Dashboard after applying MMM
- Data Storage: To facilitate further analysis and reporting, the code stores the marketing metrics data in a Snowflake table.
Image 6: Stored result for further analysis
Understanding What-if Analysis
‘What-If’ analysis in Marketing Mix Modelling (MMM) is a powerful tool that allows marketers to simulate various scenarios by adjusting different input variables, such as budget allocation across marketing channels. This analysis helps businesses evaluate how potential changes in marketing strategies could impact key metrics like sales, ROI, or customer engagement. By exploring different combinations of spend across channels—such as TV, digital, or social media—marketers can gain valuable insights into the likely outcomes of these decisions before making real-world changes.
With ‘What-If’ analysis, marketers can test the effectiveness of different tactics, identify the most impactful channels, and reduce the risk of underperforming campaigns. Ultimately, this helps organizations make more informed choices, maximizing the return on their marketing investments while remaining agile in an ever-changing market.
We are achieving the same through the app to some extent.
Key Takeaways
- Snowflake’s Snowpark in combination with Google Lightweight MMM, is a formidable tool for optimizing marketing metrics.
- ROI calculations and data preparation are the foundation of data-driven marketing decisions.
- Optimization ensures that marketing budgets are allocated efficiently, enhancing the effectiveness of campaigns.
- MMM approach allows businesses to fine-tune their strategies by identifying the types of messages, offers or promotions that resonate best with the target audience.
- One of the most powerful aspects of MMM is its ability to simulate different scenarios. Businesses can model the potential impact of various marketing strategies before implementation, allowing them to choose the most effective approach.
- Metrics are diligently tracked and stored, offering a comprehensive view of marketing performance.
In today’s hyper-competitive business landscape, data-driven marketing is essential. With MMM working in your Snowflake account, you have the power to access & optimize your marketing metrics and make informed decisions that propel sales. Your marketing dollars are supercharged, driving your business forward with precision and efficiency.
About kipi.ai
Kipi.ai, a WNS company, is a leading analytics and AI services provider, specializing in transforming data into actionable insights through advanced analytics, AI, and machine learning. As an Elite Snowflake Partner, we are committed to helping organizations optimize their data strategies, migrate to the cloud, and unlock the full potential of their data. Our deep expertise in the Snowflake AI Data Cloud enables us to drive seamless data migration, enhanced data governance, and scalable analytics solutions tailored to your business needs. At kipi.ai, we empower clients across industries to accelerate their data-driven transformation and achieve unprecedented business outcomes.
For more information, visit www.kipi.ai and www.wns.com.
