Store product mix, or product assortment, refers to the combination of products and brands that a store carries for sale. It is a crucial factor impacting the store’s overall performance and customer satisfaction. Optimizing the store product mix is a process of strategically selecting and grouping products to ensure that the store meets the needs and preferences of its customers while maximizing profitability. It involves a careful analysis of customer data to identify customer expenditure behavior aligning with the store’s business goals.

Consumer behavior can vary significantly depending on different factors, especially geography. Therefore, it is essential to conduct market research and gather data on the store’s specific customer base to gain insights into their preferences and needs. Market research can involve methods such as surveys, focus groups, and analysis of sales data to identify trends in customer behavior.

According to a Market Research Society of India report, the sector was valued at $2.1 billion in FY21 and is projected to double to $4.2 billion by FY26 as a result of India being the world’s center for analytics.

Also, take ideas from our previous blog on: How Data plays a key role in D2C businesses

Impact of consumer profiling on Store Product Mix 

Consumer profiling plays a critical role in optimizing store product mix. By analyzing consumer behavior, preferences, and needs, businesses can gain a better understanding of their target customers and tailor their product mix accordingly. 

Businesses can use consumer profiling to optimize their store’s product mix by identifying popular and unpopular products, stocking the right products based on the preferences of their target customers, and determining effective price points. By utilizing consumer profiling insights, businesses can enhance their store’s product mix to boost sales and profits.

Market Research and Optimising Store Product Mix

Using actual data profiles of customers through market research to optimize store product mix has several benefits, including:

•       Improved customer satisfaction by offering products that align with consumer behavior insights
•       Increased revenue by stocking products that customers are more likely to buy
•       Reduced costs by focusing on products that have high demand, and reducing the costs associated with carrying low-performing products
•       Effective inventory management with a better understanding of customer preferences, reducing the likelihood of overstocking or understocking products

AI and Consumer Profiling for Market Research

Data analytics plays a crucial role in consumer profiling for market research by providing insights into customer behavior, preferences, and needs. Through data analytics, businesses can collect and analyze large amounts of data from various sources, such as social media, customer surveys, and purchase history, to gain a better understanding of their target audience. 

Firstly, data analytics tools can help in customer segmentation based on different criteria, such as demographics, psychographics, and behavioral factors. These segments can then be used to create data-driven profiles of customers, which can help in understanding their preferences and buying behaviors.

Secondly, data analytics can help in identifying patterns and trends in customer behavior, which can then be used to optimize the store product mix. For example, if the data suggests that customers in a particular segment are more likely to buy organic products, businesses can adjust their product mix to include more organic options.

Thirdly, data analytics can help in predicting future customer behavior based on past data. By using predictive data analytics, businesses can forecast future demand for certain products and make informed decisions about their product mix and inventory management.

With the advancement in technology, AI and big data have significantly impacted consumer profiling through market research. By leveraging machine learning algorithms and processing large volumes of data, companies can gain deeper insights into consumer behavior, preferences, and trends. This allows better consumer classification and optimizes their product mix, marketing campaigns, and overall business strategy.

It also improves the process with: 

•       Increased accuracy: Processing and analysis of vast amounts of data, enables companies to develop more accurate and comprehensive consumer profiles. This, in turn, allows for more targeted marketing and product development strategies.
•       Real-time insights into consumer behavior: Allows companies to quickly adapt to changing trends and preferences.
•       Personalization: Analysis of consumer data helps companies personalize their marketing and product offerings, creating a more personalized experience for their customers.
•       Improved efficiency: Automated processes reduce the time and cost associated with traditional methods.

How Geospatial Consumer Data Can Improve Store Product Mix 

Geospatial consumer data, which enriches market research with consumer profiling based on the geographic location of customers and their purchasing behavior, can be a valuable tool for optimizing store product mix, especially in a diverse country like India. By analyzing customer behavior and preferences at a granular level, retailers can gain insights into the preferences and needs of customers in specific geographic areas and adjust their product offerings accordingly.

Also, take ideas from our previous blog on: How accurate Customer Profiles build profitable businesses

Geospatial data can also help retailers understand the impact of external factors such as competition or changing demographics on their product mix. For instance, if a competitor opens a new store in the area, the retailer can monitor the impact of the new competition on their sales and adjust their product mix to remain competitive. Additionally, if the demographic makeup of the area changes, the retailer can analyze the impact of this change on their customer base and adjust their product mix to better cater to the dynamic consumer behavior.

Geomarketeer by Kentrix can help with optimizing the store product mix based on actual data profiles of customers through market research with geospatial analysis of insights into consumer behavior, customer segmentation, competitor analysis, predictive analytics, and more, helping make data-driven decisions about their store product mix based on actual data profiles of customers through market research, leading to increased sales and customer satisfaction.