- Importance of Data-Driven Decision Making
- Role of Market Mix Modelling (MMM)
- Core Components and Benefits
- Optimizing Marketing Spend
- Data Collection & Processing
- Predictive Insights & Attribution
- Regression Analysis
- Time Series Forecasting
- Attribution Modelling
- Real-World Application
- Impact on Sales & Marketing
Data-driven decision-making has become an utmost important part of every business. Compared to the early days, when decisions were based on instincts, today businesses focus more on the analytical side to make right decisions.
In sales, data-driven decision making is crucial for optimizing strategies, improving customer engagement, and driving revenue growth. One powerful way to approach this is through market mix modelling (MMM), businesses can analyze historical data to assess the impact of different marketing channels such as digital ads, TV, promotions, and banner ads etc. MMM helps sales business to rightfully allocate resources, measure ROI, and refine sales strategies.
Data science and analytics enhance market mix modelling (MMM) by incorporating ML algorithms to manage substantial number of datasets and enable real-time optimization.
In this blog we will understand how the market mix model functions, the major role of data science and analytics, and how MMM helps in improving the overall sales strategy.
Understanding Market Mix Modelling
Market mix modelling is a statistical analysis technique that helps marketers measure the impact of various marketing techniques on sales business. It can help marketers improve media performance, optimize tactics, and make better campaign decisions. It uses historical data to analyze different marketing elements impact revenue. The core components of MMM are pricing, promotions, advertisements, and distribution.
MMM helps businesses to make smarter investing decisions as, it helps avoiding unnecessary spending in ineffective campaigns. It helps allocate marketing budgets effectively thus maximizing ROI. For instance, a retail company finds out 50% of sales is happening through digital ads, compared to television ads which is underperforming. The business can reallocate the budget accordingly, to optimize marketing spend and boost overall revenue.
Role of data science and analytics in MMM
Data science and analytics play a key role in market mix modelling, and they are,
Collecting and processing structured and unstructured data – MMM requires data from various sources, some of them would be structured and some unstructured data. Structured data includes, sales numbers and ad spending, and unstructured data like, social media posts and customer reviews. Data science helps in collecting this data, refining, and organizing it with the ETL process, i.e. (extraction, transformation, loading) so that it can be analyzed.
Predictive Insights – Machine learning helps in predicting future sales trends based on hidden patterns in data, which was lacking in traditional statistical methods. These predictions help businesses make better decisions and improve marketing strategies. Additionally, with advanced analytics and ML algorithms businesses can automate the analysis of marketing data quickly.
Enhance granular attribution – Data science and ML algorithms go beyond aggregate level insights to granular level by incorporating granular data insights at the channel, campaign, and customer level. For each of these marketing activities, advanced attribution models powered by AI is used.
Key Analytical techniques in market mix modelling
Market mix modelling involves key analytical techniques that help analyze the spends, marketing promotions, future trends etc. Let us look at them
Regression analysis for measuring impact– Regression analysis, data science and analytics method help in identifying the relation between marketing inputs like ads, spend amount and the business outcome like increase or decrease in sales, revenue etc. It uses multiple regression models at various channels. This helps business to understand how much each marketing efforts contributes to revenue generation.
For example, a business analyzes TV ads, digital ads, and in-store promotions to see which drives the most sales. This allows them to optimize their marketing budget for maximum impact.
Time series forecast for sales trends- Time series forecasting is the process of analyzing the past sales data to identify the future sales trends. It helps businesses plan by estimating the future demand. It uses mathematical models like ARIMA (which finds patterns in past sales) or exponential smoothing (which gives more importance to contemporary trends.
For example, a clothing store sees a peak in sales during November and December especially for winter clothes. Using time-series forecasting they can analyze past sales and predict how much sales will increase this year, by considering factors like year-over-year growth trends, seasonal patterns etc.
Attribution modelling for improving Campaign Effectiveness- Marketing attribution modelling is a data analytics method, which helps businesses to understand which marketing effort contributed most towards customer conversion such as sales or sign-ups. This process relies on various modelling methods like rule-based, data-driven model, and machine learning models.
It depends on customer interactions with various channels before making a purchase. Additionally, it uses models (like Shapley Value or Markov Chains) to determine how much each channel contributed to the final decision.
Real-world Example
An e-commerce company in East Africa, faced the challenge of identifying the impact of different marketing channels and optimizing ad spend while eliminating non-performing ones. The team at Prescience Decision Solution built a solution, Market Mix Modelling (MMM), Marketing Spend Management, and Optimization to analyze sales, marketing spends, channel effectiveness, and external factors like seasonality and events.
The model techniques used here, ensemble models (GLMs & Decision Trees) and ad-stock calculations. This improved the delivery time from days to hours. It eliminated the ineffective channels like Wall Branding and Agent promotions (except in urban areas). Additionally, established ROI for other channels and used simulations to determine optimal budget allocation across channels and time periods.
Conclusion
Market Mix Modelling helps businesses to make data-driven decisions by optimizing their marketing strategies which directly impacts sales. Moreover, data science and analytics plays a significant role by enabling predictive insights, granular attribution, and advanced techniques like regression analysis and time-series forecasting for understanding enhance customer engagement, and drive revenue growth.
At Prescience Decision Solutions, we provide AI and ML solutions across various industries, helping businesses stay ahead in the tech space. Our expertise spans analytics, business intelligence, and data engineering.
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Prescience Team
