Retail Innovation Lab

Convenience stores that deliver a memorable in-store experience to their customers - be it in terms of higher engagement levels, product availability at all times or reduced waiting time at POS , stand to earn higher customer loyalty and higher sales.

NeenOpal's suite of solution for convenience stores help store managers adopt a data driven approach to select the product assortment for each store. Drive impulse sales and improve Average Transaction Value through Market basket analysis. Implement a differential pricing strategy that helps capture the customers willingness to pay through Dynamic Pricing models.

Our solutions are delivered via a software-as-a-service(SaaS) model that helps us deliver immediate results and a quick ROI to our customers.

• Market Basket Analysis
• Store Clustering
• Assortment & Shelf Space Optimization
• Revenue Management: Impulse Purchase Item & Private Label
• Catchment Area Analytics: Competition Geo-Platform
• Dynamic Pricing and Promotions

Supermarkets around the world are leveraging predictive analytics to find answers to some of their most pressing business challenges.

Supermarket solutions by NeenOpal can help supermarkets identify and retain high value customers, increase in-store engagement levels, enhance personalization of offers at individual customer level, optimize product assortment, estimate demand and optimize inventory levels, etc., helping them to differentiate from the competiton.

• Customer Portfolio Analytics
• Category Management
• In-Store Analytics
• Marketing Analytics
• Store Clustering
• Loss Prevention Analytics
• Demand Planning and Sales Forecasting

Our customer analytical models help predict and minimize customer churn. Association mining models provide concrete action points for product placement in shelves to drive impulse sales and improve Average Transaction Value.

• Association Rule Mining - Planogram Effectiveness
• Pricing and Promotions Analytics
• Artificial Intelligence Chatbot - Virtual Sales Consultant
• Personalization Campaigns DSS
• Repeat Purchase Modeling using Neural Networks
• Social Media Analytics - Digital Marketing

Improve demand forecasting by integrating effect of seasonality and contextual parameters. Direct impact on bottom-line through end to end inventory optimization. Proactively identify aged and soon to be expired medicines to minimize losses.

• Shopper Analytics
• Drug Inventory Management
• Sales Forecasting Using Neural Networks
• POS Transaction Analytics

Crunching key store metrics such as footfalls, ATV help segregate and identify the performance of each department. For example, how effective was the marketing department in driving consumers to the store? Markdown management minimizes end of season left over inventory to improve revenues by over 10%. Return analytics help identify and address the root cause of product returns.

• Customer Returns Analytics
• Fashion Product Development Analytics
• Intelligent Markdown Management & Fast Fashion Sales Forecasting
• Virtual Reality and Augmented Reality Analytics
• Fast Fashion Retail Distribution Analytics - Inventory Management
• Fashion Color Trend Forecasting

Implement assortment optimization tools to select the right product mix for each region. Determine the correct levels of replenishment and allocation between warehouses and stores for optimizing network and maximizing profitability.

• Demand Forecasting
• Key-Value Item Analytics
• Footfall Counter Analytics
• Customer Portfolio Analytics
• Digital Marketing & Social Media Analytics

Retail Stores

Use analytics to minimize losses due to expiry of products while avoiding stock outs. Optimize promotions and improve customer loyalty by implementing recommendations of RFM modelling.

• Perishable Product Analytics
• Demand Forecasting
• Hyper Localized Assortment Analytics

Restaurants

By using insights based on predictive analytics, restaurants can discover the root causes and factors that drive revenue, prescribe actionable recommendations to change business operations and strategies to optimize profitability. Restaurants drive performance by applying insights from advanced analytics in the following ways:

• Menu Optimization:

  Analytics-driven Menu Engineering can help restaurants maximize profits and improve their customer satisfaction levels.

• Increase Customer Engagement

  Restaurants can segment their customers to create personalized promotions resulting in increased profitability & enhanced customer engagement levels.

• Staff Optimization

  Restaurants optimally staff their stores to meet forecasted customers' demand at different time periods & measure staff performance on key KPIs

• Operations Improvement

  Analytics can help improve the speed of service, reduce food wastage by better forecasting product usage and adjusting purchasing and inventory accordingly.

• Time of Day/Day of Week Analysis

  Cannibalization effects of new products or promotions can be detected by drilling down into different time segments rather than using only averages.

• Social Media Listening:

  Restaurants can gauge their social media sentiments and quickly figure out the areas where they should improve to attract customers making the comments.

• Picking up Store Location:

  Big data analytics is helping retailers to fine-tune how they to pick up a store location with the ultimate goal of driving more traffic and boosting sales.

Incorporate latest trends from social media in product design and assortment. Identify slow moving products and minimize losses, on account of leftover inventory, through promotions and offers.

• Dynamic Pricing Modelling
• Promotion Response Forecasting
• Demand Planning
• Location Based Analytics
• Store Clustering
• Product Trends Platform - Text Mining & Social Media Analytics