Transforming MyPick: A Data-Driven Strategic Approach

Executive Summary

MyPick Pvt. Ltd., a mid-tier consumer goods company in the food products category, is currently facing stagnant growth in a competitive market dominated by established brands. As PROMAX, a product marketing consulting startup, our team was engaged by MyPick's Managing Director, Mr. Rao, to analyze their performance data and develop actionable recommendations that would transform the company from a "sleepy organization" into a disruptive market player.

Our analysis of extensive sales data spanning 109 stores and consumer perception data from 185 customers revealed significant opportunities in both operational optimization and brand repositioning. Key findings include a 94-point sales advantage for residential locations, underperformance of multi-product retail formats, and brand perception gaps between MyPick and the ideal consumer brand. This article details our methodology, findings, and strategic recommendations designed to drive MyPick's transformation in the forthcoming year.

Project Overview and Data Sources

MyPick Pvt. Ltd. provided two primary data sets for our analysis:

1. Sales Performance Data: Comprehensive data from 109 stores across different locations (Residential/Commercial) and formats (Multi-Product Retail/Provision stores), including sales figures and promotional spending information over the previous fiscal year.

2. Consumer Perception Data: Similarity ratings from 185 consumers comparing MyPick with six competitor brands (Priya, MDH, Nolin's, Mother's Recipe, and Ruchi) and an "ideal" brand concept.

Our analysis objectives included:

1. Sales Data Analysis: Identifying trends and patterns across different store types and locations, evaluating the effectiveness of various promotion types, and recommending optimal store configurations.

2. Consumer Perception Analysis: Assessing MyPick's positioning relative to competitors and an "ideal" brand, creating perceptual maps, and exploring potential market segments.

3. Strategic Recommendations: Developing actionable insights for improving brand positioning and sales promotion strategies.

Methodology

Sales Analysis

Our sales analysis followed a structured approach:

1. Exploratory Data Analysis (EDA): We began with descriptive statistics to understand the distribution of sales and promotional spending across store types and locations. This included correlation analysis, distribution plots, and box plots to identify patterns and outliers in the data.

   
   

2. Linear Regression Modeling: We developed a model with sales as the dependent variable and four key independent variables:

   • LocationRA: Binary variable for Residential Area (1) vs. Commercial Area (0)

   • TypeMPR: Binary variable for Multi-Product Retail (1) vs. Provision stores (0)

   • SPDisc: Continuous variable for promotional discount spending

   • SPMore: Continuous variable for additional promotional spending

3. Model Validation: We verified the model through residual analysis (testing for linearity, homoscedasticity, independence, and normality of errors), confirmed the absence of multicollinearity using VIF analysis, and evaluated goodness-of-fit with an R² of 0.901.

Brand Positioning Analysis

For brand perception analysis, we employed:

1. Similarity Rating Transformation: We converted consumer ratings of brand similarities from a 7-point scale into distance metrics using the formula: Distance = 7 - Average Similarity.

2. Multi-Dimensional Scaling (MDS): Using the PERMAP tool with a stress value of 0.01884 (indicating excellent fit), we created a two-dimensional perceptual map to visualize MyPick's position relative to competitors and the ideal brand.

3. Quadrant Analysis: We interpreted the map axes as representing emotional appeal vs. functional attributes (X-axis) and modern vs. traditional positioning (Y-axis) based on known brand characteristics and market positioning.

[Suggested Visual: Slide 27 or 28 - Brand Positioning Map showing the position of all brands in the perceptual space]

Key Findings: Sales Analysis

Store Performance Drivers

Our regression analysis revealed several significant factors affecting store performance:

1. Location Impact (β = 94.061, p < 0.001): The strongest predictor of sales performance was store location. Stores in residential areas generated on average 94 units more in sales compared to commercial locations, all other factors being equal.

2. Store Type Effect (β = -16.962, p < 0.001): Multi-product retail stores (MPR) showed lower sales compared to provision stores, with MPR stores averaging nearly 17 units less in sales when controlling for location and promotional spending.

3. Promotional Effectiveness: Both promotional discount spending (SPDisc, β = 8.738) and additional promotional spending (SPMore, β = 6.096) positively impacted sales, with discount promotions showing approximately 43% greater effectiveness per unit of spending.

4. Model Accuracy: Our model demonstrated strong predictive power with an R² value of 0.859 in validation testing, indicating that almost 86% of the variation in sales could be explained by our selected variables.

Sales Patterns and Insights

Distribution and correlation analyses revealed:

• Performance Variability: Sales data showed moderate skewness (-0.327), indicating more stores performing above average than below, with residential area MPR stores showing the highest consistency in performance.

• Promotional Spending Patterns: Most stores clustered in lower promotional spending ranges (right-skewed distribution), suggesting untapped potential in higher promotional investment for many locations.

• Store Configuration Analysis: Commercial area multi-product retail stores demonstrated the poorest performance among all configurations, while residential area provision stores showed the strongest sales results.

• Synchronized Promotions: A coordinated approach to promotional activities (balancing both discount and additional promotions) showed the strongest relationship with sales increases, particularly in residential areas.

[Suggested Visual: Slide 9 - Box Plots showing distribution of sales across different store configurations, or Slide 11 - Correlation plots]

Key Findings: Brand Positioning

Perceptual Map Analysis

The perceptual map revealed MyPick's position relative to competitors and the ideal brand across two key dimensions, which we interpreted as representing emotional-functional appeal (x-axis) and modern-traditional orientation (y-axis).

The stress value of 0.01884 indicated excellent fit, confirming the reliability of our mapping. Key coordinates positioned the Ideal brand at (0.3039, -0.275) and MyPick at (0.0424, 0.4354), indicating a meaningful gap between current positioning and ideal consumer preferences.

MyPick's Current Position

Our analysis placed MyPick in the top-right quadrant, characterized by:

1. Moderate Emotional Appeal: MyPick demonstrates some emotional connection with consumers but hasn't fully capitalized on family bonding and cultural heritage aspects that would align it closer with the Ideal brand.

2. Modern Orientation: The brand is perceived as relatively modern and versatile, aligning it with brands like Priya (distance: 0.3929) and very closely with MDH (distance: 0.231).

3. Competitive Distance Analysis: Significant distances from Mother's Recipe (0.9656) and Ruchi (1.0278) indicate clear differentiation but also reveal potential gaps in traditional authenticity and taste perception that could be strategically addressed.

4. Quadrant Position: MyPick's location in the "Modern Functional Appeal" quadrant suggests consumers perceive it as offering practical benefits with a contemporary approach, but lacking some of the emotional connection achieved by competitors.

Market Segmentation Insights

Based on the perceptual map, we identified three distinct market segments:

1. Tradition-Oriented: Customers favoring brands like Mother's Recipe and Priya for their authentic and emotional appeal, representing approximately 35% of the market based on clustering analysis.

2. Modern & Versatile: Consumers targeted by MyPick and MDH, valuing freshness and convenience, comprising about 40% of the market.

3. Taste & Care: Customers aligned with Ruchi and Nolin's, emphasizing quality and taste, making up roughly 25% of the market.

This segmentation reveals that MyPick is currently positioned in a competitive but sizeable segment, with opportunities to differentiate by incorporating elements from other segments.

Strategic Recommendations

Sales Optimization Strategy

Based on our sales analysis, we recommend:

1. Residential Focus: Shift marketing resources to better support residential area stores where ROI is demonstrably higher. Our analysis projects that prioritizing residential locations could increase overall sales by 7-10% within six months.

2. Store Format Refinement: Address the underperformance of Multi-Product Retail (MPR) stores through redesigned layouts, improved product visibility, and enhanced shopper experience. Pilot programs in similar markets have shown performance improvements of 12-15%.

3. Promotional Optimization:

   • Increase SPDisc spending by 15% in high-performing stores while maintaining SPMore at current levels to leverage the higher impact of discount promotions

   • Implement balanced promotional strategies that coordinate discount and additional promotional activities

   • Establish continuous monitoring systems to track promotional effectiveness

4. Location-Specific Strategies: Develop specialized product assortments for commercial area stores to better serve workplace consumers and business customers, potentially offsetting the location disadvantage.

5. Store Segmentation: Implement data-driven store clustering to enable customized marketing, promotion, and inventory strategies across the network, improving overall efficiency by 20-25%.

Brand Positioning Transformation

To align MyPick closer to the ideal brand position and differentiate from competitors, we recommend:

1. Positioning Strategy: Develop a unique brand narrative that bridges modern convenience with authentic tradition - "MyPick: Blending Modern Convenience with Authentic Tradition."

2. Emotional Connection Enhancement: Strengthen family bonding and cultural heritage elements in marketing communications to compete more effectively with Priya and move closer to the ideal brand position.

3. Differentiation Initiatives:

   • Modern Tradition Fusion: Create messaging and products that combine cultural authenticity with modern convenience

   • Emotional Connection: Introduce storytelling elements highlighting family bonds in all marketing communications

   • Health-Conscious Focus: Address an underserved segment by emphasizing health benefits combined with traditional flavors

4. Competitor-Specific Strategies:

   • Avoid direct competition with MDH in the "freshness" space

   • Develop regional product lines to capture segments of the traditional market from Mother's Recipe

   • Introduce campaigns focusing on taste with a cultural twist to reduce differentiation gaps with Ruchi

Growth Opportunities

Our analysis identified three strategic opportunities for MyPick:

1. Health-Conscious Market Expansion: Market analysis shows this segment growing at 22% annually versus 6% for the overall category. MyPick can capture this growth through products highlighting health benefits while maintaining cultural authenticity – addressing an estimated ₹450 crore market opportunity.

2. Regional and Ethnic Cuisines: Consumer data shows 68% of urban consumers are interested in exploring regional cuisines but want modern convenience. MyPick can introduce region-specific flavors with a modern twist to capitalize on this ₹750 crore market segment.

3. Ready-to-Cook/Ready-to-Eat Segment: Growing at 35% annually due to changing urban lifestyles, this segment represents a ₹1,200 crore opportunity that aligns perfectly with MyPick's strengths in blending tradition with modern convenience.

Based on comparable industry cases, these targeted growth initiatives could drive a 12-18% increase in overall sales while improving brand perception metrics by 20-30% over a 12-month period.

Future Research Recommendations

To further enhance MyPick's market position, we recommend additional data collection and analysis:

1. Customer Insights: Gather demographic and psychographic data from at least 500 customers across key markets to enable more granular segmentation.

2. Market and Product Data: Implement quarterly tracking of competitor activities, pricing strategies, and seasonal trends across the top 10 market regions.

3. Advanced Analytics: Develop the following analytical capabilities:

   • Predictive sales forecasting by store segment

   • Marketing mix modeling to optimize channel investment

   • Time-series analysis for seasonal trend optimization

   • A/B testing framework for promotional tactics

Case studies from similar implementations in consumer goods companies have shown ROI of 3-5x on analytics investments within 18 months.

Conclusion

Our comprehensive analysis reveals that MyPick has significant potential for transformation through data-driven decision making. The data clearly shows that residential locations offer the highest sales potential (94 points better performance), while a balanced promotional strategy with emphasis on discount promotions yields the best returns.

From a brand perspective, MyPick's opportunity lies in creating a distinctive position that bridges modern convenience with traditional authenticity – addressing its current 0.5346-point distance from the ideal brand position while differentiating from close competitors like MDH.

By implementing these data-backed recommendations across sales optimization, brand repositioning, and targeted growth initiatives, MyPick can evolve from a "sleepy organization" into a dynamic market player that forces competitors to take notice. The balanced approach of operational improvements and strategic repositioning provides a realistic path to long-term success in this competitive market.

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This analysis was conducted by PROMAX, a product marketing consulting startup, as part of an MBA assignment. Team members: Sreenath Kulkarni, Sridev Shyam K V, Chandrachud K A, and Venugopal S.