The document discusses the transformation of retail through smart technologies like AI and IoT to enhance customer experiences and gather insights. It emphasizes how offline stores can leverage advanced technologies, such as camera analytics and improved location tracking, to compete with online retailers by understanding buyer patterns and preferences. Gyrus presents solutions that fuse various data sources to enable offline retailers to capture valuable insights comparable to those of online stores.

1. Smart Retail
Digital Transformation of Retail Business

2. Introduction
Smart Retail refers to the smart technologies that
are developed through Artificial Intelligence (AI), the
Internet of Things (IoT), to give the customer a
better shopping experience. Smart retail solutions
help to build an effective and better understanding
of the customer in-store experience according to the
customer’s taste, need, interest, purchase habits in
real-time which makes the retailers provide
consummately meeting customer expectations.
Retail is going through a massive transformation in
multiple fronts. Online Stores are racing ahead with
several advantages compared to Offline stores. The
data collected by the online stores about the buyers
is becoming a huge advantage to target products
and to come up with merchandise that is likely to
address the general set of buyers visiting the online
store.

3. Digital Transformation in Retail
Why Smart Retail
1 To compete offline stores now are going through digital
transformation addressing multiple segments with
mobile applications, etc.
2 The next phase is to get buyer patterns, similar to the
online store.to give a better shopping experience and
also to increase retail business.
3 Offline stores have an advantage in this respect that
they have real customers but there is a technology gap
in a system solution to address this easily.
4 Gyrus presents a solution that addresses this
requirement by fusing information from Camera, Point
of Sale (POS) and Customer Location (LOC) to enable
Smart Retail Stores.

4. Camera based Analytics
With the advancement of vision algorithms, cameras
and the associated AI hardware provide a lot of insights
about the environment. Even more, Object detection,
face recognition, sentiment analysis, demographic
classification algorithms matured and are being used in
real-life deployments.
Alternatively, Cameras at entrance and Exit can be used
to identify
1. Demographics of the visitor
2. Rewards customer
3. Frequent visitor
4. Cross check POS with checked out items.

5. Features of Smart Retail
1. Camera-Based Analytics – Digital Analytics for Retail
2. Point of Sale (POS) – Software for Smart Retail
Management
3. Smart Retail Heatmaps – In-Store Retail Analytics
Report
4. Customer demographic Metrics – Location tracking
Technology
5. Automatic scanning of products - Smart Check out
6. Anti-theft Management.

6. Few Technologies used
Point of Sale POS / Consumer Location
1 POS systems should be able to provide information
regarding the products purchased, discounts/coupons
applied, loyalty rewards, etc.
2 Wi-Fi technology introduced Fine Time Measurement
(FTM) for getting more accurate location measurements.
Advanced methods are layered on top such as Channel
Estimate based Time of Flight measurements and
Trilateration Technologies to have much-improved location
metrics.
3 today it is conceivable to have decimeter-level (20 cm)
accurate location of the user devices. The previous
generation WiFi-APs used BLE, or antenna arrays for
location, and dense deployment making it very expensive,
while these new methods can be deployed with software
running on standard WiFi AP’s.
Info: As an added advantage, cameras can provide security and
surveillance. Camera software can alert if someone enters (or is walking
outside) the store with a weapon.

7. Fusing the three vectors to generate heatmaps based on location
Taking in data from the three technologies, offline
stores provides very valuable insights at par with
online stores such as heat maps below.
Certainly, based on the time spent by user at each
product location, their demographics, and the
reconciliation with the POS system, one can provide –

8. Heatmap Generation based on Location
• Foot Traffic to Store Traffic rate
•Time Spent by per Aisle
• Time Spent per product section
• Conversion Ratio
• Conversion to Time Spent
• Business Lost Ratio
• Business Lost to Time Spent
• Marketability of the Merchandise
• Product placement effectiveness
• Effectiveness of discounts
•Time spent in Mark-Down section
• Conversion ratio in the Mark-Down section
• Demographic wise statistics of Business gained/lost
• Effectiveness of loyalty program
• VIP Customer Identification
• Effectiveness of Digital Signage
• Customer-ID to online ID mapping
• Targeting advertisements
• Customer Heatmaps
• Queue Monitoring
• Zone Analysis
• People Counting
• Shopper Flow

9. Conclusion
Above all, the online retail companies are threatening offline stores with
respect to inventory level, the margin of business, the requirement for less
labor, etc. The latest battle is with respect to data about buyer profiles and
patterns. Also, online stores develop rich buyer models and personas to
target advertisements and provide custom merchandise to lure them to
buying. Moreover, Offline stores can have similar information and can do
better targeting and with new technologies as presented in this blog.
However, the Same level or better information can be available to the offline
stores to help them compete better. A mesh of Cameras everywhere is
expensive, very intrusive and might thwart some customers. Even more,
Intelligently fusing the Wi-Fi sensing, POS systems with Cameras just at the
entrance and exits, intelligence can be brought to offline stores at par with
the online stores.
Gyrus ML/AI algorithms can be used to fuse the data and also to derive all the insights presented above.