This is the detection of associations and co-occurrence between the various products with an aim to deriving important insights like knowing which products are frequently bought together, which products people buy on certain days of the week or months or during holidays and even getting insights that can help you plan your store layout, and determine the products to have on specials.
By learning the behaviour of your customers, we can generate various clusters of customers that can help you make decisions that best suit each of the clusters. This is the beginning of customer-focused decision making and helps prevent you from running the risk of making blanket-cover decisions that might hurt a quarter of your very important customers. Customer Profiling is the end game to offering tailored services and understanding the reasons why those customers behave the way they do.
The inventory or services portfolio might be so large that users only get to see a small fraction of them. Worse even, the small fraction you show the users first aren't anything they are interested in. That is the problem that The Universal Recommender solves; it crunches through your customer behaviour data to derive insights that can help get the products and services that the customer is most likely to require. The end game is not only reducing the conversion rate but it also makes your customers happy as they easily get what they want with less hustle.
Customers don't just leave, their churn is a decision arrived at after a sequence of unpleasant interactions with your business. However, it is very hard for us to understand what these factors are and even which customers are unhappy and hence are planning to say "enough is enough, I'm looking for an alternative." With Churn Prediction service, our machine learning bots act on your data to understand the factors that lead to customer churn and also predict which customers are more likely to churn out. These insights are very important in knowing which customers to talk to (tailored customer service) and also which factors to improve on so as to lower your chhurn rate.
Understanding clickstream data helps to understand how users/customers navigate your platform. We use this to show insights such as funnel analysis which is important in conversion funnel optimization, understanding cart abandonment, add-to-cart rate and checkout rates in e-commerce platforms. Others include traffic Analysis, Cart Abandonment and recovery, personalization, tracking experiments (A/B Testing), Identity stitching and user journey optimization through Markov chain models of user navigations clickstream data.
With millions of things happening in your business daily, customers get in and out leaving a trail of very important data, it is very hard to detect when something suddenly goes wrong leave alone predicting the probability of the unfortunate event occurring in the first place. This service, through its intelligent neural networks is capable of detecting anomalies which can then be flagged to help protect your business. An example of such an anomaly is a fraudulent transaction which could cause millions of money in losses.
Current analytics systems tell you how your business has performed within a given period but do not go an extra mile in telling you exactly how things will look like in the future. This is an important parameter as it helps in planning and making decisions for the future. You could be able to predict market demand up to the precision of a single product which helps in making restocking decisions. This helps reduce incidences of overstocking and understocking which leads to massive losses.