Consumer react to every offer, discount and pricing differently. So analyzing their behavior helps to evaluate sales and identify new preference of the consumer to recommend product that will bring value to consumer.

Demand is dependent how consumers react to different variables that affect them daily from Fuel prices, pricing of basket of products that are purchased weekly like Grocery and food, unemployment, CPI, availability of capital - Credit card offers and cashback.

Subscription model to discover food taste by sending 5 to 10 curated food products. Recognizing pattern by which consumers react with machine learning models to curate food and identify taste buds & change in taste over a period of time. This opens up platform to recommend restaurants that might have similar taste or complimentary one.

By taking point of sale data and factor that influence customers like fuel price, CPI, unemployment, climate, promotion, depth of discount, pricing etc we evaluate demand and inventory requirement with higher accuracy for perishable products that has a shorter shelf life. Overstocking will not work here, so good model to evaluate metrics is needed. Machine learning has a great solution

Consumer behavior analysis enhances sales for ecommerce environment. Trying different variables helps in finding consumer behaviors.

Personalization is about finding patterns in the consumer buying. Optimizing search results based on buying and click patterns. Finding new consumer preference to enhance sales.

Conversion rate has become the key variable to winning in ecommerce. Personalizing search results and product recommenders are winning strategy to enhance sales.

Voice technology is taking off big. Better interaction with your customer. There are numerous problems that can be solved with voice.

AI can take multiple variables that affect retail operation from pos data to weather to estimate demand and inventory with accuracy. This enhances supply chain operation reducing delivery time.

AI has proven to be a great tool to recognize patterns. By taking web analytics machine learning model can identify potential customers and interact with them through chatbots. Personalization is key.