Riverflex

How Samsung leveraged the future of retail innovation


Overview

Samsung is a global leader in innovation, known for its cutting-edge electronics, telecommunications, and digital media products, setting industry standards and earning worldwide trust.

The challenge

To drive pre-launch awareness of their flagship mobile product Galaxy S9, Samsung ran an omni-channel marketing campaign designed by their in-house agency Cheil. Central to the campaign was a pop-up physical store – the Samsung Galaxy S9 Experience Studio – located at the Utrecht train station, where 220k passengers commute every day. Due to the short life cycle of a pop-up, it was critical for Samsung to maximise every store experience. This is where they engaged StoreDNA and Riverflex. Riverflex’s digital team used real-time data generated by Samsung’s computer vision technology (traffic, dwell time, age groups, gender) as well as more traditional data sets on e.g. staffing schedules. By performing regular analysis on the combined data sets, a continuous learning cycle was enabled for in-flight optimisation and improvement.

The approach

At the heart of this retail innovation idea is picturing the store visitor’s journey within a conversion funnel. Each moment in the shopping experience is a step towards our ultimate goal of converting the visitor to be a social advocate. The journey begins as a passer-by outside of the retail store and progresses through different zones within the store. In the case of Samsung, they progressed through the welcome zone, product tables and the experience zone. Each zone was treated as a funnel stage and we defined the key metrics tracked.

How Samsung leveraged the future of retail innovation

Key Outcomes


After realising the greatest funnel leakage was conversion to the Experience, efforts were focused on making improvements. This has had the largest impact on overall performance

Opportunities for staffing schedules have been identified and optimised for the next pop-up store. Staff to visitor ratios showed the specific days and shifts that were under-staffed and those that were over-staffed

Passer-by-traffic patterns were defined. The analysis showed clear patterns in traffic and store visitor capture rates. Further analysis could reveal the drivers and enable even more effective messaging