Industry Challenge/ Challenges
Traditional sources of decision-making, such as sales history and executives’ expertise and intuition, are no longer sufficient in the ultra-competitive retail sector. While most retailers aspire to be more data-driven in their decision-making, there are too many system barriers, data accessibility issues, and even cultural barriers in the way.
On spreadsheets, a large amount of unstructured and unorganized data went unused, never being leveraged to get business insights. Multiple variables, such as sales, walk-ins, customer retention, new client acquisition, and others, added to the complication, rendering any manual treatment ineffective.
Without assessing the vital customer data, key strategic decisions about opening additional stores and expanding were being made. To make these strategic judgments, the client sought to draw on a massive database that had gathered over time.
Furthermore, another challenge arose in the form of strict deadlines. Stringent timelines were to be met, as the client required the project to be completed within a week.
To derive insights from customer data on performance (sales, walk-ins, customer retention, new customer acquisition, etc.) of local stores, retail outlets to enable strategic decision-making, facilitating business expansion.
- We started by screening each and every accessible variable/factor and then applying advanced Excel analysis to clean and format the client’s fragmented and siloed data.
- Data cleaning was done to bring it in a consistent format.
- The final data was compiled using shell scripting for it to be used for generating a range of reports and dashboards, such as a summary of customer activity by catchment with dropping customers heatmap, average sales, and customer sales trends.
- Using Business Intelligence (BI) tools, these reports were compiled into a single customized web dashboard.
- The client gained insights from previous data, which aided in the development of crucial business decisions.
- The analysis assisted the client in deciding the geographical location of new stores to be opened based on the information acquired about competition and customer demand.
- The client came to the conclusion that they needed to improve the customer experience in some stores and create stores in cities where they didn’t have any and where competitors couldn’t stop customer attrition.
Analysis of Legacy Customer Data for Russia’s Largest Retail Chain