Assignment-2_ukti.docx

“HOW PREDICTIVE ANALYSIS WILL TRANSFORM CRM EXPERIENCE” I.

INTRODUCTION: The word “Analytics” can best be understood as the process of identification or detection of insights from the available from a data set to aid in effective decision making. There are three types of Analytics: Descriptive (to understand what all happened), Predictive (forecasting or projection of what will happen) and Prescriptive (what should be done- modelling, scheduling etc).

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PREDICTIVE ANALYTICS: It deals with using the past data and finding a relation within the fields of dataset, thereafter a fresh data set is subjected to this “already obtained relation” to predict the future happening. Most common example of usage of predictive analytics is that in e-shopping recommendations (based on past purchases of a customer, a recommendation is made for similar goods). A prediction is made that a customer may be interested in buying similar goods based upon previous purchases (date of purchase, category of products, number of similar purchases etc). CRM (Customer Relationship Management): For a company maintaining an important relationship with its existing and prospective clients/customers are important. To boost the sales growth, a continuous engagement is necessary and the same is achieved by exchange of feedbacks / complaints through social media websites (like twitter, facebook etc.) or by live chat or through e-mail communication. These days CRM software exists that displays a dashboard highlighting customer profile details and an overall purchase which better helps in targeting, retaining and going for focussed marketing techniques. Lately, cloud-based CRM has also come into existence.

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PREDICTIVE ANALYTICS AND CRM EXPERIENCE: The three stages where CRM is used are: Marketing, Sales and After-Sales Service (customer service). With the help of CRM dashboard a sales/marketing team will be able to leverage many benefits arising out of the usage of predictive analytics. This will transform the entire CRM experience in the following ways: A. SALES: 1. Lead Scoring: Using predictive analytics, It will be possible to find what percentage of leads will lead to what percentage of conversions. Different locations, different customers, different industry will have different lead-conversion rates. 2. Risk Reduction in Financial Services Industry: For financial services company, it is important to understand whether a person taking loan will default or not. With predictive analytics, it is possible to predict the expected default rate of an individual. This will make the business safer. 3. Focussed Targeting: A company into selling of luxury car business will not target a client who has been buying budget cars for last so many years. Predicting the TRP

(Television Rate Point) will help in scheduling the time slot to show the advertisement for the viewers. 4. Automated Reminders: It would help the sales team in putting up automated reminders for renewal of contracts or renewal of extended warranty pertaining to individual clients. Thus, no chance of missing the fresh opportunities. B. MARKETING: 1. Sentiment Analysis and Improving Churn Rate: By observing the pattern of all such customers who churned earlier, predictive analytics can tell what percentage of customers with certain behaviour is likely to churn again. 2. Selling of additional & by-products along with main product: With predictive analytics it is now possible to map what all by-products can be sold in combination with the main product. It will give what percentage of customer is expected to buy one good together with other good. 3. Personalized Marketing: Retaining a customer is important. Predictive analytics will help in predicting the expected time or duration for which a client is supposed to buy products of a company and then to switch-over to other brand. Such a client can be given special offers or discount. C. CUSTOMER SUPPORT & AFTER SALES SERVICE: 1. Predicting Order Cancellation Rate / Returning the Purchased Item: For e-commerce firms, logistic cost matters a lot. With predictive analytics it is possible to predict the order cancellation rate based upon previous cancellations done. This would help in delaying the dispatch of future items purchased by a customer, so as to care of chances of cancellations