Customer Behaviour Modeling :

Using customer purchasing behaviour Capyard identifies best lifecycle state for each customer and microsegments them most homogenous groups based on different parameters like Purchase Value, Recency,Risk Profile and demographic variables like Gender, Age, Location etc. Our analytics platform does dynamic segmentation and micro segmentation taking into account incremental activity by each customer. It tracks customer lifecycle journey to enable marketers to laser focus their sales and marketing actions to smallest set of like- minded customers hence enhancing return on marketing investment (ROI). Capyard’s technology and algorithm is capable to execute behavioural models at any scale of customer base meeting the scalability requirements of growing businesses.


Marketing Automation :

Our marketing automation software enables you to rapidly select a target group, create a campaign, launch targeted campaigns across your marketing channels like sales personnel, email, sms, FB, Twitter, LinkedIn, Mobile Notifications etc. Capyard’s campaign performance evaluation feature allows enterprise to measure impact of each and every campaign and take next best course of action. Capyard marketing automation provides flexibility, ease, speed and scalability for an enterprise level marketing activities and can integrate with leading email, sms, mobile engagement service providers.


Predictive Analytics :

The software analyses historical data to predict how customers are likely to behave in future, the product they are likely to buy, how much they’ll spend/invest in next visit, how often they would come. These customer level futuristic insights enables enterprises to allocate sales, marketing and promotions spend/budget in more optimized way. Predictive Customer Lifetime Value (CLV) estimates the amount of revenue or profit a customer generates over his or her lifetime to the enterprise. This technology helps marketers to spend in customer acquisition and customer retention, if a customer is 15x more valuable than another it is certainly valuable to spend more on retaining him/her.


Product Recommendation :

The technology platform has a recommendation system which uses algorithms that learns from the customers’ past behaviour and tries to make few best recommendation out of a larger pool of products which enterprise may have to offer to a customer. The approach is Customer/User based by building a profile of customers – what they have purchased in the past, their age, and any other details available to the enterprise. Customers are then grouped together based on these profiles using a variety of data mining techniques. When next time customer is to buy the recommender system will suggest items that other customers have purchased based on the similarity of profile. This will increase success rate in sales.