AI Vision to Sky-rocket your Marketing Engagement in Retail Stores

With the increasing adoption of digital devices and the internet, the number of digital buyers has been increasing significantly each year. In 2020, more than 2Bn people purchased goods or services online and e-retail sales surpassed $4.2 Tn worldwide. With the lockdowns in place, retail e-commerce sales grew more than 25% globally.

Looking at these statistics, one may argue that online shopping has made customers way too comfortable skimming and buying products from the comfort of their couch – leaving them uninterested in going back to brick-and-mortar stores.

However, the reality isn’t so. Shoppers like seeing the product with their eyes, holding it, and experiencing it before buying it. So, retailers to improve their in-store marketing engagement in order to keep them coming back.

Through innovative AI insights, marketers and store planners can showcase products in places where the consumer is more likely to be compelled to buy it. Wondering how that is done? Read on to find out.

Win hearts with demographic intelligence

Imagine directing first-time visitors to exactly where they want to go without any assistance, to be able to know precisely what part of the store they’ll enjoy visiting the most. With AI Vision, you can gain demographic insights and intelligence to direct customers to the specific parts of the store that their peer group enjoys the most.

 With your cameras, identifying and grouping similar people and behaviors, it becomes extremely easy to predict behavior and leverage it to provide a more nuanced and personalized experience to the shoppers, making them more likely to come back.

Experience ROI with hyperlocal campaigns 

Another advantage is that by identifying the shopper demography and behavior, it is easier to optimize your hyperlocal marketing campaigns store by store in real-time.  This is helpful because ads that may work in a specific area may not be as effective in another. This will ensure hyperlocal messaging specific to your target audience. Identify what else your retail location could offer to better suit a customer segment’s needs with an AI-based analytical approach that leverages person-level metrics. This will allow your business to track profitable customers and their preferences. By determining what these customers prefer and how they behave, your organization will be able to improve its messaging to this segment. As a result, conversions from high-value customers will increase. 

Watch your buyer’s steps

Facial recognition, combined with demographic intelligence, can help you customize in-store advertising based on the audience while also providing valuable insights about what works and what doesn’t.

Similarly, AI Vision can enable footfall tracking to help you trace your customer’s footsteps around the store, picking up critical information. For instance, the dwell time on specific passageways, dwell time on customer engagement with ads and displays, average customer count on weekdays and weekends, the effectiveness of in-store marketing campaigns, etc. Based on this data, recommendations are shared with store managers and visual Merchandisers on the customer type, ad preferences, ad types,  placement, and time of display that will attract and influence shoppers. The effectiveness of these recommendations is measured and by applying continuous learning of AI Vision models, the recommendations are fine-tuned to attain maximum customer engagement and conversions.

Footfall tracking isn’t just a tool to measure and interpret your buyer’s data, it also imparts particulars. These particulars include conversion rates of unique customers, returning customers, customers leaving within 5 minutes (bounce rate), and so forth. All this data is sufficient to polish your in-store marketing efforts and predict stock demands and avoid stock-outs.

Intelligent experience for Intelligent Visitors

Consumers are looking for a smart, swift, and time-saving shopping experience while sellers are looking for buyer conversions or brand impact on a shopper’s mind.

 Improving their in-store marketing engagement and the ability to accurately examine the same can bestow some skyrocketing results. The power needed to kick-start these results reside within the capabilities of the computer vision applications currently being used and the opportunities to bring innovation and enhancements to them.

In some time, AI Vision won’t sound so unreal because it would be everywhere. Stores that take the leap and become early adopters of the technology would not only be striding ahead of the competition. They’ll also have enough real, on-site technology to customize it and innovate as per their unique needs to stay ahead of the competition even when they adopt it.

About Cogniphi

Cogniphi is a technology company that focuses on building next-generation vision intelligence solutions that are outcome-driven and seamlessly integrate into the existing infrastructure. Cogniphi’s AI Vision is a platform that’s built to improve operational efficiency at every level of an organization, across industries and sectors.

If you’re wondering how Cogniphi’s AI Vision can help you transform your business, get in touch with us for a free demo!

Role Of AI Vision In Improving Safety Standards In Manufacturing

Accidents and operational hazards are important concerns in the manufacturing industry. An accident during daily operations can begin with a minor violation, but end up causing severe injuries to the workers involved.

Although health and safety measures at production and manufacturing units have improved a lot over the years, many have still discussed accidents during manufacturing and production. AI Vision-powered manufacturing solutions can lead to immense safety developments in most industries. Artificial Intelligence is a technology that has constantly been growing, making way for new possibilities.

AI Vision plays a vital role in helping manufacturers to prevent accidents, get organized, and enhance the workplace’s safety operations.

Workplace safety is related to many factors. Moreover, their control is complicated by vast territories that are difficult to follow with the human eye. Automated systems analyze the movement of special equipment and workers on the production site, measure the distance between objects and the weight of the lifted loads, analyze employees’ appearance, and much more.

AI Vision Drives Efficiency And Safety 

AI Vision increases the efficiency of the people in a workplace and ensures their safety. Monitoring workers for wearing protective gear is a huge issue to take care of inside a manufacturing plant. With AI Vision at work, even minor violations like taking off a helmet or gloves are reported in real-time. This automation of detection enables the manufacturers to minimize the risk of accidents that can take place while improving operational efficiency.

While not wearing protective equipment such as a helmet, mask, or gloves could seem like minor disobedience of protocols, it is the first step leading to any workplace accident. 24/7 automated inspections through cameras can act as supervisors for the workplace’s people. With more focus on the safety of the employees and constant supervising, the efficiency of work done gradually increases. 

Moreover, there are several other benefits of including AI Vision in a workplace, these include:

Smart Maintenance

A single equipment failure can result in hours of downtime and repair costs during manufacturing. AI Vision can recognize the imperfections of products on a production line with accuracy and speed. This lowers the cost of quality controls and eliminates waste to optimize the process of manufacturing.

On-Premise Security

As we stated earlier, AI Vision can be beneficial in reducing accidents in the workplace. Other than that, it could be used to monitor the flow of people in industrial sites, warehouses, and other manufacturing units. Entry of unauthorized personnel can be restricted to specific areas with facial recognition in real-time. AI Vision can detect potentially dangerous situations for workers while controlling the access of vehicles and people in the manufacturing unit.

Anomaly Detection

AI Vision can also help you to ensure smooth functioning with Anomaly detection. It can identify unexpected events, technical glitches with its data analysis technique. Anomaly Detection will help you fix those parts that have bad conditions in the production chain by which the manufacturing costs could be reduced.

Predictive Maintenance

Malfunctioning parts or equipment breakdown leads directly to stoppage in the work structure. Businesses that rely on physical components need to do constant maintenance of machinery. With AI Vision, you can optimize equipment usage with a better understanding of its lifetime andnreduced performance. This won’t only save a lot of time, but also a lot of cost reduction would take place if one knows about the condition of machinery beforehand.

Quality Control

With AI Vision, you could easily control the finished product’s quality and the complete production process. Defects in finished products also mean that the production process lacks the needed accuracy. AI Vision can detect a failure during the production process to ensure the quality of produced goods and recommend options to correct the errors made during the production process. It can also analyze employees’ work during the production process and report if they are not adapting correct actions.

AI Vision eliminates the human factor and errors, providing a 24/7 data analysis and surveillance, which results in the production and manufacturing processes becoming more efficient and safer for the employees at the workplace.

About Cogniphi

Cogniphi is a technology company that focuses on building next-generation vision intelligence solutions that are outcome-driven and seamlessly integrate into the existing infrastructure. Cogniphi’s AI Vision is a platform that’s built to improve operational efficiency at every level of an organization, across industries and sectors.

If you’re wondering how Cogniphi’s AI Vision can help you transform your business, get in touch with us for a free demo!

AI in Retail: How it’s Changing Forecasting for the Better

Every retailer is looking for the next big thing in the industry to gain an edge in the current market scenario. With data coming closer to the logistics, consumer goods companies, and retailers – this is where the next storm is brewing.

For decades, the traditional levels of analytics have been used in the data-driven retail industry. But the recent advancement in artificial intelligence and machine learning have brought a new level of data processing that uncovers hidden business insights.  It opens new avenues to business operators in case of anomalies, forecasting, and correlations.

The pulse point of data influx is determining consumer behavior and catering to their actual needs. By mining insights from the global market and buyer data, AI business intelligence systems predict industry moves and make proactive modifications to a company’s marketing, merchandising, and corporate strategy. This can also be defined as demand forecasting.

In short, demand forecasting enables the right product to be at the right time in the right location. It ensures meeting customer demands and manage costs efficiently.

With advancements in AI, demand forecasting can optimize stock levels, increase efficiency and elevate customer experiences to an automated level.

Here are 4 areas that AI Vision has transformed in retail demand forecasting-

Data consolidation

AI Vision corroborates and centralizes internal data such as information on sales, product characteristics, metadata, marketing, and promotional activities; external data such as sales data from distributors and market research reports; contextual data such as insights on demographic or geographic data and seasonal market – all in one place.

AI-powered vision intelligence forecasting systems are capable of using data from multiple locations to derive complex and hidden relationships between them which can be substantial predictors of demand for the future.

Demand anticipation

Demand forecasting is about predicting future sales based on historical sales data of a given product. With AI Vision-based systems, you can use the most sophisticated vision intelligence algorithms to predict demand accurately. These solutions and algorithms are self-evolving, deployed once and improved in accuracy continuously.

AI Vision can moreover provide new products with no historical data, a base to compare characteristics to the particulars of the previous products. Thus, saving costs and providing customers with their utmost needs.

Impact prediction

AI Vision-driven demand forecasting can categorize demand into ‘real’ demand and ‘promotional’ effect. It can catch the difference between when a customer is purchasing a product for a genuine reason or due to an influx in promotions. It can achieve this by measuring dwell time on customer engagement with ads and displays and tracking customer behavior consistently.

These analytical insights can be utilized by decision-makers to plan and target marketing to direct the target audience.  They can assess the impact of a certain campaign on sales and, as a result, choose the best pricing point.

The intelligent vision monitoring solution is also capable of forging different scenarios, such as gauging how a new clothing line will be received by the existing customer base if it is launched in the next season’s fiscal year collection. Essentially, this can aid the retailers in planning new product launches.

Manage demand irregularities

AI Vision solutions equip business operations to precisely analyze and improve results on overstocking and understocking to increase profitability and avoid wastage. The accuracy in AI Vision complements the business intelligence to give you precision in operational management.

It also helps monitor current activities as well. Vision intelligence detects anomalies such as safety hazards and administrations such as storage capacity,  etc. for immediate action by integrating an understanding of object identification and analysis. It enables visualizing the bigger picture of business in real-time and strategizing accordingly.

To conclude, Retailers must rethink their old supply chain in favor of adaptable and flexible ecosystems that can swiftly adjust to consumers’ evolving behaviors to meet a larger range of customer needs that are going from mainstream to niche. They must filter through the noise to translate diverse data sources into consumer-first strategies when confronted with an influx of information from all elements of their organization- from supply chains to stores to consumers.