Reshaping retail analytics using Tableau

About client

Raymond Group is an Indian multinational retail company selling its high-end, home-brand suiting, shirting, and other apparel and accessories. 97 years into the business, they have company-owned and operated stores across the country along with their other sister brands including Park Avenue, ColorPlus, Ethnix, etc. Currently, they have 1500+ outlets all over the country, marking their presence in 600+ cities.

A little context into the background

Raymond Group’s major source of data comes from offline point of sales. This data represents their customers’ membership details, bill counts, order values, store’s square footage, types of goods sold, and more. 

Their store formats are quite different - owing to the mix of brands they own, franchises, and company-owned stores they operate. 

Entire POS sales data from all these stores is fed into a database and used for reporting purposes. 

Every store collects feedback from customers to improve their service quality.

When they reached out to us, they had no visual dashboards and a clear interpretation of their data.

Goals 

Our client wanted to build interactive visualizations for their sales leaders and store managers to view, compare, and monitor sales in near real-time. 

Hourly sales data visual reporting is something they hoped to see. This would help their store managers improve agents’ performance and kick the numbers up. 

They are specifically looking to observe overviewed and granular information about sales for different periods - from minute-level to annual sales data for different shops, states, zones, and store formats, along with options where they can play around with different data points, get answers to questions they didn’t expect they should have, and weave them together to see where they lead. 

A tailor-made solution from datakulture

Our visualization experts understood their business requirements, designed, and developed a dashboard that highlights the following insights.

• Zone, state, and store-level sales distribution.

• Similarly, sales distribution on detailed levels like store type, brands, and products. 

• Numbers on total sales and revenue, customer memberships, average bill amount a customer pays, and sales they make per square foot of a showroom - all for the past five concurrent periods.

• Customer satisfaction levels range from low to high.

• Sales trends of different types of goods - apparel, fabric, and accessories.

• Self-service analytics for store managers with their store-level sales information.

What did we do?

After discovery calls, the team at datakulture turned their semi-structured data into a more suitable form for analysis. This data is from their different stores. 

Post transformation, the data has become ideal for analytics and visualization. 

Now, the goal is to reform their analytic ecosystem using Tableau.

We started with applying the best visualization and user experience practices for the fastest insights absorption and adoption.

For instant action, WhatsApp notifications were enabled. Every hour’s store-wide sales report will be sent to the respective sales managers. 

Let’s not forget that they are omnipresent and have to track down sales from multiple levels—zonal to state level to store level. So, we enabled trench-level analytics through interactive components. This helps them ask both basic and in-depth questions about their sales and revenue and find answers instantly. 

• where do these numbers come from

• which store performs well in a state or a region

• sales and revenue from their sister brands

• What products sell together or what brands get picked together 

• what color combinations work,

And more detailed retail analytics that they can't excavate with simple data. 

With visual best practices, our team has established the difference between the sales parameters within the past five hours, weeks, and months. So, just by looking at it, they can conclude that they consistently perform better than in the past sequential periods. 

One thing that we noticed here is that retail sales vary from day to day. You cannot compare sales on Mondays with Sundays. So, the visual elements will try comparing data of five consecutive Sundays from the past, if you are looking at Sunday’s data. 

Similarly, they can also get in-depth information about their customer feedback data. We have customized the view for each feedback option, showing the data breakup based on zone, region, store, and what product the reviews are for. So, they would know where the satisfied and unsatisfied customers come from, and act on that right away.

Visual aids along with the above-par user experience took their decision-making to the next level, aiding their sales experts in all ways possible. 

Conclusion

Being a successful retail company with unstoppable growth and footprints across the country, becoming data-centric was their goal, and collaborating with datakulture made this easy as pie. 

The visualization-based business intelligence was their next step towards simplifying their data and conceptualizing them in the form of interesting stories. 

Besides, being able to view data on an hourly basis has brought tremendous change in their workflow, which is also reflected in their store productivity, customer satisfaction, sales, and revenue. 

With our successful collaboration, they accomplished this within planned timelines and made real-time insights available quickly for everyone from top management to regional heads to store managers.