Analyse retail customer behaviour using AI

Analyse retail customer behaviour using AI — overview

· The variety of available technology is expanding along with the incorporation of AI into brick-and-mortar establishments.

· Due to the necessity to retain the level of customer service demanded by today’s clients while remaining competitive, formerly existing entry-level hurdles like cost and usability are being reduced.

· Social media sentiment analysis has long been seen as crucial to understanding consumer attitudes toward businesses and goods.

· The Microsoft Dynamics 365 Social Engagement gateway now includes social listening, which uses machine learning and natural language processing to determine the sentiment value.

· Another technological advance has made face sentiment analysis practical in physical stores. As customers engage with the products, hidden cameras positioned at important store fixtures are now able to read their emotions, estimate their age and gender, and, in some cases, recognise their faces.

· The system monitors the seven major emotional expressions of surprise, joy, sadness, anger, fear, disgust, and contempt and can detect general attitudes like positive, negative, and neutral.

· The significance of this information is clear; just a few examples include assessing the efficiency of visual merchandising, comprehending consumer attitudes about products on display, capturing client demographics, and alerting store staff to the presence of a devoted customer.

· If you want to go a step further, you may combine the current CRM system to send recognised clients an offer if they have expressed interest in a product but haven’t made a purchase. Based on A/B sentiment tests and POS data, fixtures can be remerchandised with more profitable product combinations.

· When paired with social sentiment, in-store sentiment analysis can be used to assess the general perception of a brand and to make tactical adjustments that have a direct impact on sales.

· The information gathered can be linked to several additional data points about the user that are collected without their knowledge, raising clear privacy issues regarding this technique. The concern is whether the more convenient and tailored purchasing experience that results will make up for the consumers’ loss of privacy.

Benefits

· Improve the level of your offerings.

· Offer personalized care.

· Enhance your advertising tactics.

· create brand loyalty.

How we helped retail start-up with our retail behaviour analytics solution.

Solution Overview

· OptiSol joined hands with the client in implementing a Computer Vision model using the VIIOP system that uses facial recognition and AI analytics to capture a customer’s emotional traits.

· Our client helps small businesses to analyse all of their live video data into actionable insights for better customer experience to increase sales.

· VIIOP system as an SDK which can be easily deployed and managed across different cloud services. It provides high functioning and highly flexible API’s that helps different developers to easily integrate with their existing code base.

· VIIOP system is built on the basis of micro services architecture, which makes the system more reliable and highly scalable. The system is equipped with kubernetes which makes the deployment easier and makes the system more reliable.

Technology Stack

Market size