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Retail

We help retailers implementing machine learning solutions to optimize their processes, anticipate their customer needs, predict sales, optimize supply chain operations, and more.

AI Retail Solutions ilustration

Retail is going through a digital revolution started by giants like Amazon, eBay and Alibaba. This has led to huge challenges for the traditional retail business model. As technology evolves, consumer behavior does too, pushing retailers to find new solutions to stay ahead of the curve. Given the large amount of data in hand, it is extremely valuable for retailers to dig through it to find significant insights.

A study from McKinsey suggests that “U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years.”

At LotusLabs we help retailers implementing machine learning solutions to optimize their processes, anticipate their customer needs, predict sales, optimize supply chain operations and more.

With machine learning it is possible to automate data analysis and go beyond the surface to really get to know your customers, discovering trends and patterns hidden in the data, and transform them into profitable actions. This offers tailored solutions to your customers without the need of looking at your competitors.

By combining customer data with market trends and patterns in your data, you will be able to target your customers better by optimizing pricing and predict buying behavior.

At LotusLabs we can help you drive revenue growth in a more efficient way by implementing machine learning solutions that improves decision making by finding patterns in data used to take crucial business decisions.

With the increasing use of machine learning, retailers can boost efficiency and productivity while actively engaging with consumers via digital and mobile platforms. There is a real need for automation and deeper data analysis to identify patterns that support cost-effective and accurate decisions. Moreover, retailers have to figure out now how to meaningfully connect with consumers who expect intuitive and convenient shopping experiences. Ultimately, retailers need to focus on bringing customers back by providing them with a complete shopping experience.

Face detection

LotusLabs can help your business with an advanced face detection system design to perform interesting analysis such as face blurring for privacy, unique visitor counting, demographics analysis (age, gender, emotion, ethnicity), visual sentiment analysis, access control and theft monitoring.

Inventory optimization

Inventory optimization through item assortment and machine learning ensure shelves are stocked and best products are always available for purchase. Machine learning systems can detect “freshness” of perishables, wear and tear on machinery, empty shelves and predict demand in advance for ordering stock.

Personalized product recommendations

Offering highly personalized product recommendations for advertising and promotions (for automated upselling and tailored, complementary product suggestions based on previous purchases). Personalized offers to improve the customer experience by offering relevant information which in turn provides retailers with improved data about the customer’s brand engagement.

Price optimization and demand prediction

Demand prediction is important to meet consumer demand related by creating a demand forecast at various price points and business constraints to maximize potential profit. Optimizing your pricing strategy with dynamic prices using algorithms that can take key pricing variables into account, including supply, seasonality, and demand and offer you insights on how to adjust your prices accordingly. Combining machine learning and optimization techniques into a pricing decision support tool can make substantial financial impact to your organization.

Smart customer service

Integrating machine learning into customer care opens doors to more flexible automated solutions. Sales and customer service forecasting systems can predict customer behavior and allow retailers to deploy sales and customer service staff where they will be most effective. It also frees up live agents to focus on handling complex or revenue-generating tasks. With the growing challenges and volume of customer interactions that most companies must handle, that flexibility, efficiency, and accuracy is highly in demand.

Fraud detection

Fraud detection to detect anomalies and other errors that signal dishonest behavior. LotusLabs can help you create machine learning models that detect with high accuracy the ever-evolving methods of fraud. Transactions scoring high for probability of fraud can be further investigated, increasing the accuracy of your fraud detection system.

Customer churn prediction

Improve strategic decision making regarding customer engagement and lifetime value. Companies that constantly monitor how people engage with products, encourage clients to share opinions, and solve their issues promptly have greater opportunities to maintain mutually beneficial client relationships.If your company has been gathering customer data for a while, we can help you use it to identify behavior patterns of potential churners, segment these at-risk customers, and take appropriate actions to gain back their trust.

Sentiment analysis for brand visibility

Sentiment Analysis can help companies improve their products and services by better understanding how their offering impact customers. The sentiments expressed by the customer, with LotusLabs help, can be classified based on the interactions with the product or the services provided by the retailer.Sentiment analytics uses language processing to track words bearing a positive or negative attitude of a customer. This allows the retailer to improve the marketing and sales strategies which as a result leads to a better customer retention rate and a higher profit margin. If your brand is getting a lot of criticism online, machine intelligence will help you identify that in real-time so that you can take appropriate actions.