IT TAKES TWO TO TANGO: CRM AND PREDICTIVE ANALYTICS

CRM Predictive AnalysisThat CRM has the potential to be a game changer for a business is no secret. But, not all are aware about the latest technological trends in the CRM platform. In the present hyper-competitive business environment, it is no longer possible for companies to maintain an edge over their competitors by setting targets based on past performance and data. While CRM is already helping businesses collect invaluable customer information, traditional CRM is already outmoded, and the new trend of Predictive Analytics capabilities integrated into CRM is on the horizon. Predictive Analytics goes beyond the traditional CRM methods to find patterns in the collected data.

Predictive Analytics is the use of tools of big data to make predictions related to factors such as customer behaviour. It is all about finding the insights that help us understand what might happen in the future. Predictive analytics accomplishes this by recognizing patterns in historical data, repeated transactions etc.This facilitates a more proactive business approach.

The first step in adopting predictive analytics techniques is to decide on the business goals of the company, for instance, increasing revenues, improving profitability, lowering customer acquisition costs, improving customer conversion rates, shortening the conversion time etc. Next, it should be decided how to leverage predictive analytics to meet those goals. One common use of this technique is predicting which leads will turn into customers, i.e. predicting the present. Another popular use of predictive analytics techniques is to predict the return on investment (ROI) in marketing campaigns to decide which campaigns generate the highest return.

Predictive Analytics is a very new trend, but is already being used by some businesses. It goes beyond the traditional CRM methods to find the patterns in the collected data. It helps companies to evaluate the most cost effective channels and media and also draft specialised communication messages for specific segments of the population. Existing customer data can be used to improve the customer interaction and generate additional sales by analysing the incoming customer data in real time. An advantage is that this is not a completely new and unfamiliar technology. It is essentially an off-shoot of existing business intelligence initiatives which use analytics to mine and sort useful corporate data in order to decipher patterns. CRM applications and predictive analytics tools are getting closely interconnected with social media platforms such as Facebook and Twitter to help businesses leverage information gathered from the customers. This allows companies to effectively target their marketing resources towards the customer.

Before going into the ways in which predictive analytics is going to impact businesses, it would be prudent to learn about three of the major types of predictive analytics and how they can improve our marketing acumen:

  1. Sequencing: This is related to analysing the probability and is derived from Markov’s probability theory. It uses historical patterns of one set of customers to predict the outcome of the actions of another set. However, we do not need tons of historical data to generate a sequencing pattern. Data sequencing can be initiated based on the one action that led to a purchase, i.e. the most recent successful conversion. This can be used in email campaigns. Once a customer takes an action, patterns can be generated to customize the messages in the future.
  2. Cross-Selling: We might have seen on different e-commerce sites such as Flipkart or Amazon, the different suggestions shown to us which we might most likely need or want, based on our purchase history. Predictive analytics can help in cross-selling different products together based on whether people often purchased them or not. Businesses can create bundled offers for two of their most popular products or services or automate cross-selling in an email.
  3. Lack of Action: What can businesses do when a customer who frequently interacted with them suddenly drops all communication? Obviously, in a best case scenario, the firm can reach out to that customer early enough to prevent any damage and salvage the relationship, but how does it do that without even realizing that it has a problem? Predictive analytics can help in pulling out any patterns or trends from customers who have given up. For example, if a firm notices that responses to certain email campaigns are slowing down, they can change certain aspects of it or the entire strategy.

Predictive Analytics can benefit businesses in multiple ways across several functions. Some of the domains in which this technique makes a difference are:

Customer Service: Keeping a record of customer interactions is crucial for managing successful relationships, but it is not enough. Predictive analytics tries to anticipate what a customer might do, than simply leveraging historical data to manage these relationships. This layer of intelligence aspect can extrapolate data into more actionable information. This helps improve customer relationships, acquire new customers and maintain the loyalty of the existing customer base by anticipating future trends.

Marketing: Predictive Analytics encompasses the qualities of both creativity and analytics. It can help firms design personalized marketing strategies by identifying how customers interact with online resources such as social media channels and websites. This can indicate which marketing strategies can work with certain customers, and which won’t.

Sales: Customer information is collected to assist sales representatives in their goal of closing deals. But, in the long-term, examining past data is not enough. It is necessary to forecast for months and even years in advance to make better and more intelligent decisions. Predictive analytics emphasises the practice of looking forward while still bearing in mind the past lessons and trends.

Product Service/ Refinement: It is a common experience of e-commerce stores to have returns after sales. When the frequency of returns increases, the company introspects on where they are going wrong in achieving customer satisfaction. This is where predictive analytics comes in handy. It can help the businesses effectively segment, refine and design unique experiences for customers based on the lessons learnt in the past.

To summarize, CRM predictive analytics can enable the sales staff to devote more time on customer-oriented activities. It can help generate actionable suggestions to drive the sales in the desired direction. Also, it can be helpful in anticipating pain points with regard to customers before they actually cause any adverse effects. Predictive analytics use advanced algorithms to infer data from several online sources such as websites, social media accounts and data companies to be presented to marketers in a way that helps companies develop their business goals and strategies. Predictive analytics can help identify key behavioural trends among specific customer groups. This can be particularly useful to financial services companies to assess the credit worthiness of a client.

It is high time businesses utilize predictive analytics tools to their advantage by better engaging with their clients. Predictive analytics can radically improve the CRM techniques and help gain unprecedented profits in the long run.

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