The Retail Revolution

Transcription

1 The Retail Revolution How new technology is optimising real-time experience data in measuring ROI B V Pradeep, Unilever and Fiona Blades, MESH Experience The Insight Show, London, March 7 th 2018

2 Mall rats desert sinking ship: Inside the abandoned shopping centres that were once a beacon of the American Dream

3 America is facing a retail refugee crisis as thousands of stores shut down and millions of people become the blacksmiths of their era. Business Insider

4 Reach 45% In Store Reach 37% 35% 31% 30% 26% 25% 25% Q Q Q Q Q Q Q Q

5 5 It was announced on BBC News that NatWest will be closing branches across the country due to less customers visiting and more using online banking. There's been large decline in using branches since TV, NatWest, Fairly Negative, No Difference

6 6 Magnum: Pleasure Store goes premium

7 Retail Gaps How to measure displays, sponsorship, pop-ups and events? Who is reached with what engagement? 7 <Source and Base>

8 In research by The Path to Purchase Institute... not one company measured effectiveness via technology 8

9 Existing measures can be costly or suffer from limited data 9 <Source and Base>

10 10 <Source and Base> MESH Experience Real-Time Retail Evaluation

11 11

12 Real-time Retail: the key points Captures people s reactions in the real world in real time. Identifies whether the display has stopping power and engages the viewer. Collects basic demographic data to indicate which groups are engaged. Is tech-enabled without the privacy issues of video. The cameras are unobtrusive. Is scalable and cost efficient. 12

13 MESH Experience Point of View Experience Model MESH believes that for brands to succeed in the new world, marketers need to take an Experience Driven Marketing approach. 13

14 Questions we could answer with Real-time Retail Does my end of aisle display work better than an in shelf feature?

15 Questions we could answer with Real-time Retail What is the cost per Traffic? Reach? Engage? Purchase?* (Available in future) 15

16 Questions we could answer with Real-time Retail Does brand building display work better than price offer display?

17 Questions we could answer with Real-time Retail On which metrics in the purchase funnel Does my seasonal display work better than my occasion-led display?

18 Questions we could answer with Real-time Retail Which stores does the same display work in most successfully?

19 Questions we could answer with Real-time Retail Which days of the week/times of day does my display work most successfully?

20 Working with Unilever Market: Brazil, São Paulo Objective: Assess the impact from a change of store display Brand: Knorr Seasonings Product: New, healthier seasoning range with natural flavourings and 25% less salt POS: Shelf Hanger stopper placed either side of the product range

21 We compared 2 periods to see changes in shopper display interaction. Period 1 no display Fri 3 rd to Thu 16 th Nov 2017 Period 2 display Fri 1 st to Thu 14 th Dec

22 Google s Popular Times feature contextualises Real-time Retail data against store traffic Google Popular Times provides an index for store traffic 0 = no one is in store 100 = busiest hour for this location This is an average based on store visits over time 22

23 Real-time Retail Terminology Viewer Someone who was looking at the display for at least 1 second Noticeability The number of Viewers Attention How long the Viewer(s) looked at the display in seconds Engagement The % of Viewers who were Happy whilst they were looking at the display 23

24 Noticeability increases by 3.5 times with Display No Display 185 Display 647 vs Noticeability x MESH Real-Time Retail: Supermercado Paguemenos, Av. Mirandópolis, Campinas: 3 rd 16 th Nov 2017: 1 st 14 th Dec 2017.

25 Display stands out during peak times Noticeability on Weekdays Noticeability on Weekends 25 Traffic: Google Popular Times data. No Display: MESH RTR data 3 rd to 16 th Nov Display: MESH RTR data 1 st to 14 th Dec 2017.

26 Display seems to impact on men x2.3 x3.1 x3.7 x Off-Peak Peak Off-Peak Peak Women No Display Display Men 26 Traffic: Google Popular Times data. No Display: MESH RTR data 3 rd to 16 th Nov Display: MESH RTR data 1 st to 14 th Dec Peak refers to 16:00 to 20:00. Off-Peak refers to 08:00 to 16:00 and 20:00 to 22:00.

27 Noticeability with display declines from week two Noticeability by Day 27 Traffic: Google Popular Times data. No Display: MESH RTR data 3 rd to 16 th Nov Display: MESH RTR data 1 st to 14 th Dec 2017.

28 On average people pay attention for just 3 seconds 28 <Source and Base>

29 Most peak time views only last 1 second Noticeability by Attention Time peak It s important that your message is easy to take out to 3 4 to 6 7 to Attention Time (seconds) No Display Display 29 No Display: MESH RTR data 3 rd to 16 th Nov Base: Off-Peak 136 Viewers, Peak 49.. Display: MESH RTR data 1 st to 14 th Dec Base: Off-Peak 424, Peak 223.

30 % of Engaged Viewers Shoppers less likely to show positive emotions looking at display 30% 25% 20% 15% 10% 5% Percentage of Viewers who are Engaged 0% 1 2 to 3 4 to 6 7 to 9 Attention Time (seconds) No Display Display 30 No Display: MESH RTR data 3 rd to 16 th Nov Base: 1 second 63, 2s to 3s 60, 4s to 6s 33, 7s to 9s 14, 10s+ 15. Display: MESH RTR data 1 st to 14 th Dec Base: 1s 255, 2s to 3s 228, 4s to 6s 110, 7s to 9s 44, 10s+ 10.

31 Is this due to catching attention of those not buying stock cubes? The display may still be increasing awareness for long term brand building 31 <Source and Base>

32 Number of Engaged Viewers But at an absolute level, the display increases the number of engaged impressions 50 Estimated number of Engaged Viewers by Attention time to 3 4 to 6 7 to 9 Attention Time (seconds) No Display Display 32 No Display: MESH RTR data 3 rd to 16 th Nov Base: 1 second 63, 2 to 3 60, 4 to 6 33, 7 to 9 14, Display: MESH RTR data 1 st to 14 th Dec Base: 1 255, 2 to 3 228, 4 to 6 110, 7 to 9 44,

33 This needs a collaborative approach: 33 <Source and Base> Technical partners (Emotion Research Lab) to create algorithms. Data and analytics companies (MESH) to turn data into metrics that mean something. Academic partners (Cranfield School of Management) to build models. Clients (Unilever) to share the decisions they need to take. Retailers to facilitate running the tech in their stores.

34 Thank you Fiona Blades President and Chief Experience Officer