A blackswan crisis like COVID-19 can create a yin and yang situation to the economy and businesses around the world. While some industries like FMCG witness an all time high demand and others are walking in the desert with a thirsty throat, there are third category of industries where business operations and demand are not binary. Hitech and Digital natives are the two verticals where the SITREP is not black and white, rather it is a spectrum. A good example would be video conferencing solutions which are on the rise while a trader for Giffen goods is suffering. Regardless of which side of the spectrum you are, these are the times to introduce innovation to drive sustainable growth and stability Implementing these use cases could be your first step:
Forecasting the appropriate future sales for the top device brands based on previous history and similar sales pattern within the same brand for new products with limited or no history will help the company to achieve target and also fulfill the customer’s demand on time.
To make digital sales more successful and longer run, few aspects like quick service, immediate issue resolution, and providing personalized support can be automated with the help of Contact Centre AI’s Bot provided by Google. This bot has conversational powers to identify customer intent and determine what to say and do next.
Forecasting the appropriate future sales for the top accessory brands based on previous history and similar sales pattern within the same brand for new products with limited or no history will help the company to achieve target and also fulfill the customer’s demand on time.
Automated flexible pricing mechanism can be built around various product categories, this feature helps in setting appropriate price for the products based on the market situation , type of inventory run (slow moving item or fast moving item), cross products and demand for the respective product in the market.
Based on the transaction history the customers can be clustered into high or low value customers. They can be segregated into different demographic regions to further refine them. This different band of customers can be used to market the products and personalize the content for them accordingly
Considering the number of purchases , purchase value and purchase frequency of each customer the lifetime value or the maximum amount the customer would likely spend in the coming future can be predicted. The higher the number more is the profit to the company, also this helps in retaining old customers by suggesting them the right deal. And AI is already ramping up to provide you with most creative ways to predict the CLTV.
The customer behaviour pattern, purchase history and search details can be used to make a machine learning model predict which customer will purchase. Also the model can be connected with other details like in store transaction history to get to know whether the customer will buy online or in store.
Personalized recommendations based on purchase pattern or online sales for stock clearance can be held to exceed the sales.
To fulfill all these customers’ demands on time and not to go over stock or under stock proper inventory calculation has to be made along with demand planning. Properties like varying market conditions, new product release should also be considered in calculation.
Based on the several personalized offers made to the customers, the recommendations which got converted into deals can be considered and similar kind of offers can be structured to improve sales.
Further considering the buying patterns of the customers we can also suggest the frequently bought together items or combo of a product and a subscription plan together can be suggested.
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