What is Predictive CRM and How to Implement it
Traditional CRM is now considered outdated. Essentially just a databank, CRM was for a long time seen just as an automation tool. To be really useful CRM had to go an extra step. Not just record data, but also show us how that data could be useful. This is where predictive CRM comes in. Some would say that this is the next obvious concluding step in CRM development where the data we record helps us in predicting consumer behaviour.
Actually, predictive CRM is far more than its predecessor. Predictive CRM uses both internal and external data to predict sales behaviour. In an era of Big Data where a salesperson has access to such a large pool of information, this is critical. It tells us not just where we may get maximum leads, but what are the trigger factors. By studying data we can predict what will appeal to a customer and modify our strategy accordingly.
Predictive CRM also effects a business organisation in multiple ways. Not only does it effect the person-to-person contact where a sales rep interacts with buyers, it can also define bigger policies like advertising or product design. In this highly consumer-oriented market predictive CRM has become vital.
Interestingly, it is also observed to have a much wider functionality than traditional CRM. Where the traditional model was largely management oriented, predictive CRM has a larger impact on person-to-person contact. Sales rep are using it to find out more about the people they are interacting with. In contrast, the traditional mode has become outdated because it relies heavily on just internal data.
Implementing Predictive CRM
In its scope predictive CRM is wider than just CRM. Because it works with so much data — a mind boggling surfeit — implementation is critical. But like CRM, effective implementation can only occur if you integrate it with your current systems.
Predicting sales calls: Sales calls are often the first contact with the customer. When implementing predictive CRM, we focus not on the contact and its immediate results, but on the call itself. We are looking for trigger phrases, demographics, timings and so on. Predictive CRM here focuses on the next step — what should be done, what worked and what did not. We focus on the relevant tactics as a database on what can be followed in the future and not just for a particular call.
Personalised targeting: Predictive CRM is customised, meant to pick details of the customer relationship. So, we focus on creating content that is extremely personalised. When we tailor our questions, we can gather information that is not just personalised, but highly targeted for our own purpose. We can use different means — from online quizzes, polls to mailers for gathering this information. Feedback is then collated and forms the basis for predictive CRM.
Automating information: Once we have the data, we create a framework to automate it. It is simply impossible to go through every detail of Big Data collected or consumer journey recorded. So, data is automated to include specifics like trigger phrases, preference, demographic, gender and so on.
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