Store Heatmap: Retail Insights Through Precision Data

Store heatmap

The electronics retailer, wanted to improve their sales and customer service by analyzing the flow of people into their stores. We created a system using Machine Learning, image detection, and face recognition. The system tracks visitors' movements and the most viewed shelves and products. This information helps the store to focus on selling popular products and to avoid unpopular ones, ultimately improving the sales process.

increase in sales

100

dead zones removed

About the client

One of the largest electronic retailers in Eastern Europe.

Tech stack

Python

Flask

PostqreSQL

MySQL

AWS

The client's needs

Challenges & solutions

Challenge

Measure the impact of promotional displays.

Solution

Built a system based on Machine Learning, image detection and face recognition.

Challenge

Improve customer service in busy areas.

Solution

All cameras work in a centralized form and pass data to a single server that processes the information received.

Challenge

Remove dead zones.

Solution

Solution analyzes the movement of visitors around the store, keeps track of shelves that are viewed most often and products which clients skip.

Challenge

Effectively place products.

Solution

Solution optimizes the process of selling goods, since the information received makes it possible to concentrate on selling goods in which the visitor is most interested, and to identify not popular ones.

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Luxury Retailer AI Demand Forecasting Implementation

Results

DATAFOREST provides meaningful shopper-behavior Insights. They are very responsive and effective, trying to engineer and offer the best fit solution.

Jared D.

CEO Consumer Electronics Retail

The Way We Deal with Your Task and Help Achieve Results

Step 1 of 5

Web development discovery

It's a good time to get info about each other, share values, and discuss your project in detail. We will advise you on a solution and help you understand if we are a perfect match for you.

Step 2 of 5

Discovering And Feasibility Analysis

One of our core values is flexibility. Hence, we work with either one-page high-level requirements or a whole pack of tech docs. In AI demand forecasting case studies, there are numerous models and approaches, so at this stage, we perform a set of interviews to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create a solution architecture, a project plan, and a list of insights or features to achieve.

Step 3 of 5

Solution Development

The work starts with data gathering, data cleaning, and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for further development. Ultimately, we interpret the results. Nevertheless, demand forecasting solution modeling is a process requiring many back-and-forth iterations. We are result-focused, as it's also one of our core values.

Step 4 of 5

Solution Delivery

AI demand forecasting solutions can be a list of insights or models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect the client's participation in the project. While modeling, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high level of communication is also our core value.

Step 5 of 5

Support And Continuous Improvement

We understand how crucial the solutions that we code for our clients are! We aim to build long-term relations, providing guarantees and supporting agreements. Moreover, we are always happy to assist with further developments, and statistics show that 97% of our clients return to us with new projects.

Success stories

Check out a few case studies that show why DATAFOREST will meet your business needs.

All Success Stories

Stock relocation solution

The client was faced with the challenge of creating an optimal assortment list for more than 2,000 drugstores located in 30 different regions. They turned to us for a solution. We used a mathematical model and AI algorithms that considered location, housing density and proximity to key locations to determine an optimal assortment list for each store. By integrating with POS terminals, we were able to improve sales and help the client to streamline its product offerings.

10%

productivity boost

increase in sales

Mark S.

Partner Pharmacy network

View case study

The team reliably achieves what they promise and does so at a competitive price. Another impressive trait is their ability to prioritize features more critical to the core solution.

Client Identification

The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.

customer retention boost

25%

profit growth

Christopher Loss

CEO Dayrize Co, Restaurant chain

View case study

The team has met all requirements. DATAFOREST produces high-quality deliverables on time and at excellent value.

Performance Measurement

The Retail company struggled with controlling sales and monitoring employees' performance. We implemented a software solution that tracks sales, customer service, and employee performance in real-time. The system also provides recommendations for improvements, helping the company increase profits and improve customer service.

17%

increase in sales

25%

Improvement in Employee KPI Achievement Rate

Amir R.

CEO Fashion Retailer

View case study

They easily understand industry-specific data and KPIs, and their efficiency as a team allows them to deliver results quickly.

Emotion Tracker

For a banking institute, we implemented an advanced AI-driven system using machine learning and facial recognition to track customer emotions during interactions with bank managers. Cameras analyze real-time emotions (positive, negative, neutral) and conversation flow, providing insights into customer satisfaction and employee performance. This enables the Client to optimize operations, reduce inefficiencies, and cut costs while improving service quality.

15%

CX improvement