After evaluating several vendors, the company chose Indium Software to implement a cognitive analytics solution leveraging computer vision to achieve its goal. Indium enabled this by using the existing security cameras:

• To generate heat-maps across the store
• Using ImageAI for facial recognition, identify customers and count the number of customers who enter the store at a given time period
• Incorporate a customized functionality to allow the cameras to count the number of people who performed a particular activity – like walk to a particular section of the store

The data collected from these cameras was used to analyze customer behavior at specific zones within the store. This was done by building comprehensive dashboards with real-time data refresh.

Indium built a neural network model by training various classes of the object or person, using 2000+ images for image processing and video analytics with maximum accuracy. By creating and testing annotations using sample videos, it was able to constantly improve the accuracy (over time, accuracy went up to 80%). Within the data points that were gathered, outliers were identified.

Since the visitor statistics was linked to the POS systems, they were able to track the conversion rate, improve product placement and implement cross-selling tactics. The solution had an easy-to-use interface that provided all stakeholders with better insight into customers’ behavior as well as shelf zone analysis.

As a result, the retailer was able to experience a 15% increase in customer satisfaction.

Deriving insights using Cognitive Analytics

According a report published by MarketsAndMarkets, the broader market for computer vision-enabled solutions is expected to grow from USD 15.9 billion in 2021 to USD 51.3 billion by 2026 at a CAGR of 26.3%. Of course, this includes the use of computer vision for factory inspections, audits, quality and safety management, etc.

Computer Vision, simply put, refers to enabling computers to identify and process objects in images and videos the way a human brain does. It helps computers to “see and understand” content in photographs and videos. By integrating AI capabilities to computer vision, we’re now able to conduct analytics on data from images and videos.

Today, it can be used to identify an item with 99% accuracy as against 50 percent a decade ago.

There are use cases for cognitive analytics and computer vision in a variety of industries such as:

1. Energy, Power & Industrials: Computer vision can enhance safety, efficiency, and regulatory compliance of the power and energy industry by monitoring equipment for preventive maintenance and inspect linear assets such as power lines and pipelines for safety. It can also help with regulating danger zones and alerting in case an employee crosses a designated safety threshold.
2. Manufacturing: Computer vision is being used in production lines for audits, inspections and overall quality management. Not only can it be used to detect defects, but data from other cameras and sensors can be cross-referenced to identify the root cause of problems. This can speed up repairs and prevent expensive downtime. It can also be used to prevent mislabeling and shipping errors. Workplace safety is another area where it can be used effectively.
3. Retail: From improving customer satisfaction to inventory management, computer vision can be used in many ways to improve sales and profitability. Managing the store environment, enabling self-check-out, prevent shrinkages, and improve POS accuracy are some of the other ways in which it can be used.
4. Transportation & Logistics: Right from autonomous vehicles to managing transportation and logistics, computer vision can be used to streamline operations and minimize supply chain disruptions. It can help improve the safety and efficiency of transportation fleets by ensuring proper docking, loading, fueling, and tire pressure.
5. Healthcare: Medical diagnostics can benefit tremendously from computer vision risk assessment and early detection of disease. It can be used to ensure diligent conformance to safety practices such as handwashing among medical staff, manage inventory in pharmacies, ensure sufficient stocks in hospitals and clinics. It can also help in lowering administrative costs through automated document processing.
6. Legal Enforcement: It can be very effective in preventing crimes by scanning live footage from public spaces and detecting weapons or identifying suspected behavioral patterns.

Indium’s Comprehensive Solution for Cognitive Analytics

Indium is a technology solutions company with deep expertise in digital, data engineering, data analytics services. With its global presence serving customers ranging from innovative product startups, Fortune 100 and global enterprises, Indium’s key differentiators are its specialization in:

• Ai, Advanced Analytics & Text Analytics CoE
• Big Data, Data Engineering, Stream Processing & Data Virtualization
• Low-code development across platforms

Indium Software has a team of cognitive analytics experts who work on the following areas:

• Image classification and analytics
• Object detection
• Facial recognition
• Video analytics
• Text analytics
• Speech recognition

We use FCN (Fully Convolutional Network), Mask-RCNN (Region-based CNN), and Detectron2 for image and video analytics; and deep neural networks and Optical Character Recognition (OCR) for text analytics and speech recognition.

Our range of solutions includes:

• Room Type Classification: Convolutional Neural Networks (CNN), YOLO, VGG16 Is used to classify room type using a pre-trained model. Based on their characteristics, images are categorized into different room types.
• Room Object Type Classification: Convolutional Neural Networks (CNN), YOLO is used to build pre-trained models to identify images and tag the object type in the room. A set of images is used to improve the accuracy of the model items in the image and room classified with a bounding box drawn around.
• Facial Recognition: Indium uses a sophisticated, scalable face recognition system, Multi-level CNN architecture, to detect, recognize, and analyze human faces in images. The system can also detect different face-related attributes such as head position, facial hair, emotion, gender, and age among others.

To know more about how Indium can help you build your cognitive analytics model integrating it with computer vision, contact us now.

The post Indium’s Computer Vision and Cognitive Analytics Solutions for Better Decision Making appeared first on Indium.

Cognitive analytics solutions entail self-learning systems and algorithms that mimic the human brain’s thought process in order to analyse large amounts of data quickly and accurately that no person could evaluate vast quantities of data and come to the same conclusions. As they are exposed to more data, these algorithms, like humans, become more intelligent.

Cognitive computing is capable of understanding natural language, comprehending images, recognizing patterns, and much more.

Employees, on the other hand, should not be concerned about cognitive computing taking their work. Employees should instead see it as a tool that will help them become more reliable, effective, and competent in their field. Decision-making is aided by cognitive computing in retail.

The integration of self-learning systems that use natural language processing, data mining, and pattern recognition is one of cognitive computing’s core elements. The system’s self-learning capability also ensures that it will learn from its experiences, allowing it to become more knowledgeable and cognitively capable over time.

How Does This Apply to Retail?

Efficiency in operations and cost containment became more important as retailers grew in size and complexity. The explosion of data that came with growth made it difficult to maintain a personal touch. That’s when retailers began to use analytics to learn more about their customers. Customers were divided into groups based on previous purchases and demographics.

As marketers collect more data than ever before, cognitive computing in retail is becoming increasingly important. This information is then analyzed and used to help retailers become more profitable and adaptable. Companies that invest in digital transformation will increase revenue as a result.

The Need For Customer Experience Management

Retailers have long recognized the value of personalizing customer experiences as a source of competitive advantage. Retailers have traditionally provided a unique personal experience for their goods and services – remember going to the neighbourhood grocer/butcher? He would be aware of your meat preferences and would always have the appropriate cut of meat ready for you.

The inference aided in the distribution of targeted offers to a wide range of customers. The next era of online commerce brought with it its own set of difficulties. It paved the way for an omni-channel world in which scenarios such as browsing from home, adding to cart with a mobile device, and picking up the product from the store were made possible.

True personalization has become even more difficult. It required retailers to provide the same experience across multiple touchpoints, and it no longer resided in the brick-and-mortar realm.

How Cognitive Analytics Can Help?

Cognitive analytics can be beneficial to your retail business in the following ways.

Customer engagement

Retail businesses can use cognitive analytics to enhance customer service, personalise customer experiences, increase customer loyalty, and respond faster to consumer demands.

Increased efficiency

Improved productivity and reliability, better decision-making and preparation, improved protection and enforcement, lower costs, and a better learning experience are all benefits of cognitive analytics. Companies may use cognitive analytics to extend their business into new markets and accelerate the development of new products and services.

Price optimization

If sales of a pair of jeans aren’t as strong as anticipated, price optimization will reveal this and suggest a fair price for the product to help clear out the inventory while optimizing profits.

When it comes to pricing, price optimization is often used for competition benchmarking. The aim is to provide insight into how a retailer’s prices relate to those of competitors in their field. Retailers then use the data gathered to make informed decisions about how to best optimize their prices.

Forecasting

Demand forecasting is the science of estimating how many units of a commodity will be sold over a given time span. Forecasting is critical in retail because retailers lose money when they have surplus inventory and are unable to sell it all. Retailers, on the other hand, would lose out on sales if they do not purchase enough inventory.

Demand forecasting is now much more accurate than it was before, when all estimates and observations were performed by humans. This is due to cognitive computing’s ability to analyze significantly more data, reveal correlations through seemingly unrelated data, and provide real-time knowledge adoption rather than relying solely on historical data.

H&M, the fast-fashion chain, uses market forecasting to handle store inventories. Customer receipts are analyzed by the program to assess commodity stock levels. This enables retailers to decide which items need further advertising and which require stock replenishment.

During this method, patterns may also be discovered. H&M, for example, may discover that leather jackets are the most popular on the east coast and change store inventory accordingly.

User design & Experience

Users’ interactions with a website can be analyzed by businesses. Companies can use cognitive computing to gather data and change their interface accordingly, whether it’s investigating the step-by-step route of a customer’s path from the moment they reach the web before they make a purchase, finding ways to enhance customer support, enhancing social media interaction, or deciding which pages experience the most visitors. As a result, the platform can be changed to make it more user-friendly and/or to increase conversions and mobile payments.

For retailers looking to improve their current business models, cognitive is becoming a lucrative investment. This omnichannel and cognitive consumer journey starts with,

Step-1: Consumer data is collected and turned into useful information.

Step-2: There are many points of customer interaction that have been established.

Step-3: A systematic omnichannel strategy is developed.

Step-4: The omnichannel method is used to intelligently distribute strategies.

Step-5: Personalization is used after analysing customer data.

Wind-Up

In the near future, there will undoubtedly be more opportunities for cognitive insight in the retail industry. The use of cloud-based services and powerful analytic tools with cognitive capabilities enables all of these possibilities.

Retail cognitive computing and cognitive technology have ushered in a slew of new innovations. Cognitive computing has given retailers the tools to become more agile in business through demand forecasting, price optimization, and website design.

The post Is Cognitive Analytics Reinventing A New Landscape For Retail Sector? appeared first on Indium.

Some of the key drivers will be the increasing disposable income that has led to an improving purchasing power of people. The convenience of ordering products online on e-commerce platforms and retail stores will further stimulate market growth.

To meet this growing demand and understand its customers better, e-commerce businesses are increasingly investing in advanced business intelligence and analysis tools. This can provide insights into which products are moving fast, in which markets and how to improve their operations to service the customers better, maximize profits and gain a competitive edge.

3 Focus Areas

E-commerce analytics falls into three main areas:

• Data Visualization and Descriptive Analytics: Dashboards created using historical data of customer behaviour and sales records provide snapshots of all key metrics for improved decision making
• Predictive Analytics: Using churn prediction, market-basket analysis and the like, e-commerce marketplaces can predict the demand for products and design promotions to cross-sell and upsell for improving sales and customer engagement
• Cognitive Analytics: Video and images are analysed for product classification based on predefined parameters to quickly upload new products and avoid errors and time delays associated with manual intervention

Challenges and Benefits

For e-commerce platforms and online stores of retail outlets, an understanding of which products are moving, where their customers are coming from and what their customers are saying are very important.

When a product is performing well, they can boost it further by creating suitable marketing collaterals and also pair it with likely related products to increase the overall sales and growth.

A product which is not performing well will need equal efforts to promote and special offers and discounts to increase its visibility can be designed to improve its sales.

Based on geographies, retail businesses can also plan their campaigns for their stores in those locations and step up promotions for those geographies where they have a presence but not as many footfalls.

Machine learning and artificial intelligence can be used for cross-selling and upselling of related products. For example, when someone is purchasing a mobile, relevant accessories can be displayed to encourage customers to purchase a mobile case or headphones, and so on. When a customer purchases a particular model, they can be tempted with a higher model with better and more features.

Analytics can also be used to understand conversion rates from footfall to sales and the insights used to improve the conversions. Reviews, both positive and negative, are a storehouse of information on what works and what doesn’t.

Negative Review Analytics helps to build the product line with quality to meet customer expectations. Sentiment analysis allows the e-commerce players to build on their strengths, rectify their weaknesses and retain the unsatisfied customer.

For instance, in an e-commerce site, a particular bag was very popular but soon, negative feedback started pouring in. On analysis, it was discovered that the bag was still good but a flap that was added as a design element was made of a different material that did not last long as expected. This is valuable input for the e-commerce marketplace as well as the manufacturers to improve.

Competitor analysis can also be used to devise marketing and, more importantly, pricing strategies to improve the edge over business rivals. Marketplaces and FMCG can especially benefit from this.

Use Cases

Indium used sentiment analysis for a sports retailer where the reviews were analysed to understand customer perception and feedback of the products. Indium’s proprietary data extraction tool, Tex.Ai enabled extracting key phrases to gain insights on customer views. This helped the sports retailer improve on its design and customer service.

For an e-commerce aggregator, Indium used teX.Ai to automate product classification.

Chats with customers, either on chatbot or by a customer executive over the phone can be another rich source of insight into customer satisfaction levels. Using data extraction, the discussion can be analysed for what the customer needs, how it was responded to and if it had been concluded to satisfactorily. This is crucial in building customer loyalty and training the executives and the chatbots to ensure there is a closure.

Analytics can also be used for resource optimisation to reduce the waiting time of customers trying to reach a representative.

Indium Advantage

Indium Software, in its more than two decades of existence, has been providing holistic solutions on cutting edge technologies. It has carefully built a team that is a judicious mix of domain and technology experts.

Our e-commerce team can set up and run a marketplace from the ground up using the latest technologies including in-build analytics. It can also build solutions for analytics on existing platforms using machine learning and artificial intelligence. Strong solution architects, subject matter experts and expertise in analytics make Indium an ideal partner for e-commerce platforms and retail brands seeking to leverage the World Wide Web.

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