Client operates multi-brand fashion and luxury product retail store chain in Europe. With 28 stores and 130,000 sq. ft. of retail space, client observes approx. 1 million footfall annually.
In association with Data Nectar, the data analytical solution was developed that involved high-performance data ingestion of Wi-Fi data and visualisation of various KPIs.
Descriptive data analysis unveiled actionable insights towards making efficient decisions on their marketing strategies, resulting in higher profitabilities.
Increase in Advertising Revenues
Increase in high margin product sales
The client acknowledged the vast potential of data to make positive impacts on operational decision making.
Across the stores there are 89 advertising screens those are rented by various local and international brands. Client wanted to put the Wi-Fi data in decision making around how they can leverage the best out of their advertising revenues. Besides that the objective was also to have best possible product placements for higher conversion on their promotional campaigns.
Multi-brand Fashion and Luxury shops mall
- Enterprise-grade ETL
- Data Migration
- Database Design and Development
The challenge was to implement the dynamic pricing strategy in advertising screens across the 28 retail stores, with a total area of 130,000 sq. ft. that had allocated space for 100+ Advertising displays/ screens to feature third-party advertising and promotions
High margin product promotions, as well as getting maximum conversion across 28 stores spread out in the region was a real challenge. Also, the fixed time-based revenue model for their 89 in-store screens advertising was a constraint when it came to accommodating more advertisers. A need for a dynamic and a value-driven pricing model was sought for. It was clear that data was an asset to look at to find the answers.
However, the challenge was to find the right data and bring it to a visualisation. Data Analytics & Business Intelligence technologies could deliver insights to make data-driven decisions around their marketing and pricing strategies.
- Optimise marketing & promotional product display in the retail store
- Optimise advertise revenues through dynamic pricing for advertising screens in and around the stores
A data analytics system was implemented to fetch the wi-fi data every 15 seconds through strategically placed wi-fi sensors placed in all stores. This was to identify the number of store visits across the open hours. Wi-fi router IDs were used for store identification, and visitor’s device IDs reflected the visit counts.
- Visitor Heat-map
- Store visit analytics
- Dwell time
- Frequency of visits
- Proximity visits
- Recency of visits
Increase in advertising revenues due to dynamic pricing based on visitor density.
Increase in sales revenues – implemented in-store display/ location of low selling and high margin products in tune with the highest footfall area derived from heatmap insights.
More advertisers could be accomodated due to dynamic and footfall based pricing model allowing optimised use of screentime.
Covid-19 needs new normals. Visitor movement data will play a vital role in sales performance along with high safety norms.