In the dynamic landscape of the retail industry, understanding customer purchasing patterns is paramount for businesses seeking to optimize their sales strategies. Market Basket Analysis (MBA) stands at the forefront of data mining techniques, delving deep into large datasets of purchase histories to unveil associations between product groupings. By identifying products likely to be purchased together, MBA empowers retailers to enhance customer experiences and refine marketing strategies. Through the utilization of advanced algorithms like the SETM Algorithm, businesses can gain profound insights into customer behavior, thereby driving sales and fostering brand loyalty.
About the Client
The client is a well-known organic foods retailer with its operation base spread across Southern and Western Germany. The client approached Quantzig to leverage its expertise in building a market basket analysis solution that would help them increase the effectiveness of their marketing campaigns and optimize the store layout to increase sales and profitability across their business units.
The Business Challenge
The retail industry as a whole is highly fragmented and complex owing to the rapid pace of technological developments and the ongoing digital shift. To thrive in a complex business landscape retailers require specialized tools at their disposal to cater to the dynamic shifts in customer needs and preferences. One of these tools is market basket analysis or affinity analysis. Market analysis empowers retailers to identify product groups that a customer is more likely to buy, given a previous purchase or a contemplated purchase history.
Surprisingly, only a fraction of the retail industry players and decision makers make decisions based on the information regarding their customers’ purchasing behavior. This turns out to be a major roadblock for an industry whose sole purpose is to fulfill the needs of its customers. By incorporating market basket data in BI tools businesses can develop precise strategies to meet their business goals.
The client- a leading retailer of organic food products found itself struggling to improve sales. When analyzed it was found that they lacked the necessary warehouse architecture to support market analysis. Hence, they approached Quantzig to help them develop a new data model that would support the analysis. Today more and more businesses are discovering new ways of using market basket analysis to gain meaningful insights into hidden customer purchase patterns. As market leaders continue to capitalize on this technique, a new predictive model of market analysis is making in-roads across industries in an effort to identify consecutive purchase patterns.
Top Challenges Faced by the Organic Foods Retailer
The client wanted to address the following business challenges:
Problem Statement 1
The lack of a cross-selling strategy for their products proved to be costly. Due to this, the client witnessed a depreciating profit margin for three consecutive years. Hence they wanted to leverage market basket analysis to develop new cross-selling strategies.
Problem Statement 2
The second major challenge faced by the client revolved around those arising due to inefficient inventory management. Hence they wanted to improve the store’ performance by increasing sales and reducing inventory.
Problem Statement 3
The client’s inability to utilize historical data to identify relationships between products and customers was a major roadblock that prevented them from creating products and pricing models to generate new revenue.
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The solutions offered by our market analysis experts empowered the client to develop a highly scalable, reliable and flexible new system with several benefits. To do so, we adopted a comprehensive approach which involved the following phases:
Phase 1
Phase one of this market basket analysis engagement revolved around the creation of a necessary warehouse architecture to support market basket analysis. This involved the use of transaction-level data to build a foundation for the new data model that supported the analysis.
Phase 2
The second phase of this market basket analysis engagement involved the creation of ETL processes for the initial and incremental data loads along with a scalable BI architecture to support market basket application as well as other methodologies.
Phase 3
The final phase revolved around the creation and implementation of a database design necessary to support the various hierarchies of datasets. It also involved the creation of new dashboards to support market basket analysis at the week level, store level, and product category level.
With major improvements in marketing effectiveness, the client was well positioned to identify profitable marketing campaigns and products with a high affinity. The ability to better judge the overall effectiveness of these campaigns resulted in a 15% decrease in the company’s overall marketing budget. Also, by keeping track of key metrics like- average spend per basket and average profit per basket the client was able to track product affinity for their top-selling product categories. Additionally, market basket analysis empowered the client to effectively plan their store layout resulting in a 50% rise in quarterly sales. Furthermore, the design and deployment of the new warehouse architecture along with several database warehouse optimization techniques allowed the client to outperform their peers.
What is the purpose of market basket analysis?
Market basket analysis is one of the most commonly used retailing techniques to increase sales. By leveraging this tool, retailers can determine product affinity and understand the reason behind products purchased together. Knowledge of the products frequently purchased as a group will enable retailers to efficiently plan their store layout. Retailers can also extract insights from market basket analysis and utilize it to develop new products without compromising on the market trends.
In conclusion, Market Basket Analysis emerges as a cornerstone in the arsenal of modern retailers, particularly in the burgeoning eCommerce sector and bricks-and-mortar stores alike. By leveraging sophisticated algorithms and harnessing the power of data, businesses can optimize sales, enhance the shopping experience, and fortify brand loyalty. Whether through predictive or differential MBA, retailers can strategically place products, offer compelling deals and bundles, and tailor store layouts to align with customer preferences. As the retail landscape continues to evolve, embracing MBA and its associated techniques remains instrumental in navigating the complexities of consumer behavior and achieving sales optimization in the ever-competitive market.
