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Retail Analytics Solutions

Everything about Retail Analytics Solutions


What are Retail Analytics Solutions?

Retail Analytics Solutions assist retail firms to assess shoppers and their purchasing behaviors across a number of geographies and channels. They aid in analyzing the items and transactions that take place via various channels. In addition, they analyze data that is acquired by incorporating several touchpoints like warehouse management systems, POS, ERP, CMS, and so forth.

Retail firms also leverage insights gained from analytics to become more familiar with customers and their requirements, and to meet these, optimize communication so appropriate messages are pushed to them at the most opportune time and place.

In customer insights, analyzing audiences means exploring the interests, demographics, preferences, locality plus other features of a group. The approaches you get from retail data analytics audience investigation rely on the deepness of that investigation. While your audience is an excellent place to begin with spectators’ scrutiny, looking at your rival’s audience and contrasting it to yours can assist you to add insights into how to stay ahead of the game.

Ways of using Retail Analytics Solutions

Appreciating the effect of operational efforts and marketing on in-store sales is complex. Not only have consumer behaviors altered, but the retail setting has also speedily advanced. Mall owners and retailers call for retail analytics solutions with steadfast data to assist them in making well-informed decisions that positively impact their sales.

What are the benefits of Retail Analytics Solutions?

1. Shopper conduct discernment
Retail analytics solutions give retailers a single view of customer behavior. It lets them store data longer and identify phases of the customer lifecycle. Used correctly, analytics can help increase sales, reduce inventory expenses and retain the best customers. From learning the in-store community responses to an item on sale to estimating how a campaign enhanced the store’s exchange prices, retail analytics offer an exceedingly precise picture to vendors of what works and what doesn’t.

2. Proximity Marketing
Using in-store streaming analytics, retail firms can dig out relevant insights from large data sets and optimize for a marketing strategy. It also guides on how best to communicate with their consumers. A typical example of this is the use of streaming analytics for proximity marketing, incorporating beacons and mobile infrastructure to locate customers and analyze their behavior and enhance their experience by providing them with exactly what they need. This allows retail firms to highly customize their offerings based on context, instead of pushing out random product offers via text messages or emails to a mass audience.

3. Optimize store layouts
In-store layout and product placement affect sales and retailers, generally, tend to hire extraneous staff to make up for a sub-optimal layout. Also, because brick-and-mortar stores lack “pre-cash register” data about what in-store shoppers do before they buy, they employ in-store sensors, RFID tags & QR codes to fill that data gap. The intelligence gathered from the analytics data provides information on how to optimize the store layout and how to reduce costs and simultaneously improve customer in-store satisfaction.

4. Boosting Loyalty
By offering essential insights into consumer conduct, retail analytics solutions assist in strengthening the connection between visitors and a store. Retailers that can geo-locate their mobile subscribers can deliver localized and personalized promotions. This requires connections with both historical and real-time streaming data. It permits the retailer to acquire the right information over to the right receiver and make sure a gratifying shopping experience for the consumer. As a result, this improves same-store sales and customer loyalty.

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Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.