Como você testa e valida suas hipóteses e suposições de sortimento de varejo usando dados e análises?

To refine and validate our retail assortment strategies, we rely on a robust framework of data analytics. By analyzing past sales data, customer behavior patterns, and market trends, we gain insights into the performance of different product categories. We then use predictive analytics to forecast the potential impact of various assortment changes. A key aspect of our approach is A/B testing, where we introduce new products or adjust assortments in select stores and compare the results to control groups. This iterative process allows us to fine-tune our assortment strategies, ensuring they are aligned with customer preferences and market demands.

The primary KPIs that I use when analyzing assortment are sales and conversion. When considering new assortment strategies I focus on market share in relevant categories, sub-categories and price bands.

In revolutionizing retail assortment, think visionary: Set objectives not just for sales, but for crafting unique experiences. Gather diverse data, from social media buzz to consumer behavior insights. When formulating your assortment strategy, blend creativity with analytics. Think how emerging tech or societal shifts could reshape consumer desires. Use data not just to validate, but to explore and innovate. Continuously evolve your strategy, staying agile to market changes. This approach isn't just strategic, it's magical, turning data into captivating retail experiences that resonate with customers

Should leverage data analytics thru below Sales Data Analysis Examine historical sales data to identify top performing and underperforming products, helping refine assortment strategies Customer Segmentation Use customer data to understand preferences and segment your audience Market Trends Analyze market trends and competitor assortments to ensure your product offerings align with current consumer preferences Customer Feedback Analysis Gather and analyze customer feedback, reviews, and surveys to gain insights into product satisfaction and potential assortment improvements Cross Selling Analysis Explore data on cross selling patterns to identify complementary products and enhance assortment strategies Regular assess above data points

The way I think about KPIs is first think about what you need to know to make a decision. In this area, often you are prioritizing space / sales ratio, margin dollars, units, basket size, etc. If you need to know it to make a decision, prioritize that KPI

Should leverage data analytics thru below Sales Data Analysis Examine historical sales data to identify top performing and underperforming products, helping refine assortment strategies Customer Segmentation Use customer data to understand preferences and segment your audience Market Trends Analyze market trends and competitor assortments to ensure your product offerings align with current consumer preferences Customer Feedback Analysis Gather and analyze customer feedback, reviews, and surveys to gain insights into product satisfaction and potential assortment improvements Cross Selling Analysis Explore data on cross selling patterns to identify complementary products and enhance assortment strategies Regular assess above data points

Amazon Vendor Central reporting is always the starting point. We supplement this data with competitor price data from Rainforest API, and market data from Stackline and Analytic Index.

In a brick-and-mortar retail context you will be limited to the data available but, as above, prioritize what you need to asses your decision criteria. Transaction data is the true bedrock here.

In analyzing data for retail assortment, I've learned the importance of looking beyond transactional data. My approach includes: Cross-Referencing Diverse Data Sets: I combine POS data with customer feedback, online engagement metrics, and social media trends to gain a holistic view of product performance and customer preferences. Finding Insights in the Unspoken: Often, valuable insights lie in what's not immediately apparent. I analyze patterns in customer browsing, abandoned carts, and product views to understand unmet needs or emerging trends. Leveraging External Data: I also look at broader market and economic indicators, understanding how external factors might influence consumer behavior and preferences.

An important part of a hypothesis is your success criteria. "I'm removing X items from my assortment and I anticipate Y% transfer, Z% margin gain, and Q% simpler supply chain processes." Without the success criteria you don't have a hypothesis. "Let's see what happens" is not a hypothesis!

To truly test your hypothesis you need a set of treatment stores that represent the full fleet of stores in sales, demographic, mix, etc. ways. Further, you. need control stores that match the treatment stores' behavior on the tested item. Finally, use that data to run a difference in differences calculation to see the incremental impact of your change

1. Initiating and constantly developing a supplier sheet 2. This supplier sheet will enable us to develop and retain an insight on our unit costs adjustments i.e., in case of any increase or decrease, front and back margins and end year rebate plans if available. 3. Moreover, it will primarily help us plan, prepare, monitor our data and leverage our negotiations skills by adopting more the vise gambit and the nibbling techniques along with each supplier.