Redefining
Retail and CPG ecosystem

Decoding customer data
to deliver profitable growth

Having AI integral to the Retail / CPG can help improve efficiencies across the value chain. From customer segmentation to demand forecasting, Machine intelligence can help augment real-time data with insights that drive growth and profitability

Segmentation

Accelerate growth in a given market with robust segmentation models to efficiently focus on marketing strategies and effectively tailor products to the needs of specific target customer groups. This solution leverages our advanced ML models to provide 360° view of your customer so that you can improve personalization, calculate lifetime value, segment marketing, and enhance their experience. Our ML models extends the value of this data to generate powerful insights into customer behavior for dynamic segmentation and personalization at scale. Some models are listed below

Customer segmentation models
(RFM model), market
Modular AI Solutions
Segmentation models
Retail outlet segmentation models.

Positioning

Market Positioning refers to the ability to influence consumer perception. Competitive advantages allow a company to achieve regarding a brand or product relative to competitors. The objective of market positioning is to establish the image or identity of a brand. Our Machine learning models provide exact direction for product, marketing and business strategies to compete against rivals, and develop successful market entry and growth strategies.

Pricing

Measure the price and value sensitivity of the customers and predict purchasing behaviour to optimize product tradeoffs, understand price curves, the implications of pricing decisions on the market and customers willingness to pay.

Competitive pricing of the products gathered in emerging markets and developed for several product groups to predict the reaction of their markets to national labeled and own labeled products. Assigning goods prices that are competitive enough to drive sales with a high enough margin to be profitable is a balancing act that requires constant attention to market trends and an organization’s competitors. Our optimistic solutions leverages AI and Machine Learning to analyze historical pricing, competitor pricing, and how price point impacts the rate of sale to determine optimal pricing for goods.

Promotional

Increase the wallet share, up-sell, and promote brands to sustain high brand equity, enable to compete effectively against competitors in all markets, and maximize shopper marketing ROI. Leverage machine learning to measure, manage, and optimize your promotion spend. Our promotion optimization approach is designed to help companies gain insight from historical promotion activities to understand what promotions work, and how you can optimize future promotions for getting the maximum ROI.

Trade campaign analytics
Mass campaign analytics
Brand campaign analytics
Store campaign analytics
Cross-market campaign analytics

Our Approach

Most data science problems can be divided into three sequential phases - problem definition & data discovery, model estimation & validation, insights & business application. we have broad frameworks to systematically approach a wide variety of data scinece problems to ensure business value.

Scope

Capability Analysis
Error Analysis
Baseline Evaluation
Incorporation of Subject Matter Experience

Validate

Focus On Confidence
Model Development
Model Validation

Prototype

Subject matter experience in cross-elasticities, patterns in time etc
Competitive and Macro Framework
Generalize / Leverage high-fidelity domains

Recogntion And Awards

Speaker at AI Retail Conference, May 2021

Breakthrough experiments with Consumer Brands Association

Recognized innovator in Reatil Tech

Case Studies By Industries

All
Retail
Manufacturing
Hi-Tech
Healthcare

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