Client
Our client, a global leader in manufacturing, commands a dynamic furniture retail portfolio. With 200+ stores spanning the Midwest US, they cater to 300000+ customers. Offering exquisite products for living rooms, bedrooms, dining rooms, home offices, and patios, they redefine home furnishings for new age living.
300000+ customers
Challenge
Business challenges
In an era where data reigns supreme, the retail industry has recognized the imperative shift toward data-first marketing. Without a clearly mapped data strategy, our client faced substantial marketing challenges, including a rapid decline in an extremely critical success parameter—customer experience.
The impact of ad spending across diverse media, including TV, Radio, Google Ads, Print Promotions, Newspaper Ads, Special Day promotions, and digital channels, remained elusive. This lack of clarity translated into ineffective marketing campaigns, directly impacting sales.
Despite vast data generation from online and in-store sales, the information lay dormant in silos, inaccessible for meaningful analysis. Our client needed analytics capabilities to identify opportunities to build brand experiences and run personalized marketing campaigns while addressing preferred channels.
Recognizing the urgency for change, the client sought a solution to transform the way their marketing and finance teams evaluated and responded to comprehensive data. The goal was to predict the effectiveness of each media type on sales, calculate the ROI of initiatives, and provide actionable recommendations on optimal channels for specific regions and seasons.
Key challenges to address
- Limited Marketing ROI Visibility
Despite a significant marketing budget, clear insights into return on investment (ROI) and the overall productivity of marketing initiatives would help strategy.
- Undynamic Annual Budget, Campaigns, and Channels
The client operated within a fixed yearly budget that encompasses nine campaign channels. This fixed budget structure could be mixed up to boost flexibility.
- High Dependence on Traditional Channels
A substantial 60% of the marketing budget was allocated to TV advertising, and their second-highest expenditure was directed towards radio. This limited the client’s ability to explore and leverage the full spectrum of digital marketing and emerging platforms, for an optimal marketing mix.
Solution
The Hexaware solution
Hexaware, through its innovative data science lab, crafted a cutting-edge data-driven marketing mix analytics framework. This comprehensive approach covered:
Exploratory Data Analysis (EDA)
The team conducted an exploratory data analysis on ad-spent data and store sales data for the last three years. This step involved cleaning, organizing, and summarizing the data to gain initial insights.
Marketing Mix Model
The marketing mix is a statistical model used to analyze the impact of various marketing channels (Radio, TV, and Digital ads in this case) on sales. Channels contributing to overall sales were evaluated.
Data Science Lab Library
With advanced statistical modeling in Python and R, our team-built 15+ statistical models using a library created within its data science lab. This library contains pre-built functions, algorithms, and tools tailored for marketing mix analytics, making the modeling process more efficient.
Multiple Linear Regression Model
A multiple linear regression model was conceptualized to identify the relationship between ad-spend on Radio, TV, and Digital ads and sales. This type of model is suitable for understanding how multiple independent variables (ad-spent on different channels) influence a dependent variable (sales).
User-Friendly Dashboard with Power BI
Power BI is a business analytics service by Microsoft allowing your teams to visualize and share insights from data. This step makes the findings more accessible to non-technical stakeholders.
Channel Optimization for Better ROI
The marketing mix model helped identify the most effective channels for better ROI. This involved recommending adjustments to the allocation of ad-spent across different channels for better sales.
Benefits
Improved Marketing Strategy
Understanding the impact of ad spend helped refine marketing strategies for highest returns and adjust campaigns to improve effectiveness.
Accurate Ad Spend Analysis
Our client was able to predict the combined effect of ad spend channels on sales.
Highest Budget Savings
As a result, there was a $40000 in savings in the yearly budget.
Impact Predictions
Predicted media expenditures with the highest and the lowest impact on sales at a high accuracy of 85%.
Data Efficiency
The data model combined data from multiple channels for a holistic overview and maximum reusability.
Customer Segmentation
Insights into which customer segments responded most positively to specific advertising channels, allowing for more targeted and personalized campaigns.
Adapt Better to Trends
Helped understand and adapt swiftly to changes in market trends, consumer behavior, and the competitive landscape.
Long-Term Strategies
The insights help build long-term strategic planning with a deeper understanding of market dynamics.
Summary
Unlocking the potential of data, we’ve empowered our client to forge authentic connections with their customers. Through an advanced data science lab, we acted on the transformative opportunity to elevate marketing efficiency, accelerate speed to market, and enhance customer experiences. The combination of state-of-the-art analytics and strategic insights provides a client a pathway to navigate their industry’s complexities with precision and innovation.
