Retail Wifi Analytics

REMODELLING ADVERTISING
PRICING STRATEGY WITH
DATA ANALYTICS FOR THE
FASHION RETAIL CHAIN
The client acknowledged the vast potential of data to make positive impacts on operational decision making. 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.
16%
Increase in Advertising Revenues
12%
Increase in high margin product sales
18%
More advertisers
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.

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.
INDUSTRY:
Retail
LOCATION:
European
BUSINESS:
Multi-brand Fashion and Luxury shops mall
SERVICE
DELIVERIES
  • Enterprise-grade ETL
  • Data Migration
  • Database Design and Development
  • Training
  • Reports
BUSINESS REQUIREMENT
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.
BUSINESS
OBJECTIVES
  • Optimise marketing & promotional product display in the retail store
  • Optimise advertise revenues through dynamic pricing for advertising screens in and around the stores
THE SOLUTION
APPROACH
Implementing an analytical solution to get an insight into retail store visitor footfalls and heatmap was key to find answers to their business challenges. This included improving the sales performance of their high margin products by rearranging their placements and coming up with a better pricing model for the Advertising display assets.
The client requirement called for optimizing ad revenues through dynamic pricing for advertising screens in and around the stores. Data Nectar harnessed the power of data analytics to collect visitor data through device detection using a Wi-Fi router in the store.

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.

Data was then ingested into Data Warehouse (Postgres), and then for building Data Marts - for faster reporting of store dimensions. Reports, Dashboards were achieved through Pentaho BI and visualisation capabilities.

Frequent data fetched every 15 seconds helped create Heatmaps of visitor data points. Heatmap revealed helpful insights to identify areas of the store that were having the highest footfalls. That was an important factor to improve product displays for better conversions.

Also, client stores were enabled to analyse proximity footfalls for Advertising screens. Based on data-driven insights, the client was able to define a pricing model that was based on footfalls density in the screen’s proximity instead of fixed time - adding value to the advertiser. The client could also accommodate more advertisers for a given screen based on advertisement budgets and frequency in a more optimised way.
KPIS TRACKED
  • Visitor Heatmap
  • Store visit analytics
  • - Unique visits
    - Frequency of visits
    - Proximity visits
    - Busiest Day & Hours
    - Recency of visits
    - Visit vs. Visitor
    - Dwell time
    - Make of device
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Things get done only
if the data we gather can inform and inspire
those in a position to make [a] difference.
Data Security & Confidentiality
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Read our Data Security & GDPR Compliance