Many businesses are disconnected from their end-users, but particularly manufacturers, because they sell through numerous third-party distributors and retailers. The dynamic nature of their supply chain prohibits them from engaging directly with their specific customers, making it more difficult to grasp precisely what their customers want and need, as well as how they behave.Consumer experience research for these businesses can look at different data feeds to provide a valuable image of customer patterns that may otherwise have been overlooked by the manufacturer.
Customer relationship analytics, also known as predictive CRM, makes sense of data producers gathering mounds from their data warehouse from CRM applications, databases, and transactions. Analytical tools for consumer interactions will provide consumers with a 360-degree view, helping suppliers understand what customers tell you, who they are, what they need, and most importantly, what they can do in the future. Instead of anecdotal evidence or “gut feel,” these systems make fact-based decisions based on hard data and data mining. The position of a highly sophisticated marketing department is taken up by customer relationship analytics. These tools classify the most important clients, group these clients on the basis of buying habits and other characteristics, and target them with promotions and promotional efforts aimed at increasing customer satisfaction and sales revenue. You can then concentrate sales and marketing strategies on the most profitable segments by knowing each customer’s relative value.
For example, the introduction of CRM analytics for a leading cell phone manufacturer lets them monitor previously unknown customers in the cell phone industry. The organisation only knows a fraction of one percent of the millions of consumers who have used its products worldwide without CRM analytics to help, as the data of most distribution partners only includes sales figures and regions. The producer needs to better consider not just their dealers, but who the repeat consumers are, the motives and purchasing habits of end-users. The organisation will create a data warehouse to collect data from its ERP framework, as well as sales data from partners, using CRM analytics. Since they are now able to monitor consumer transactions, the manufacturer may know that the company loses 12 percent of its subscribing customers each year because of its churn rate.
This means they spend more than $1 billion per year buying back the U.S. market alone, which is $1 billion from their profit margin.
A strong link between customer relationship analytics and company revenues is found in research by Accenture (formerly Andersen Consulting). By improving its CRM capabilities with in-depth data mining, a typical $1 billion company could add $40 million in profit.
The business will concentrate on transforming into a more consumer-driven enterprise and growing its bottom line at the same time by collaborating proactively with partners on end-user research.
Businesses need to better consider their clients in today’s intensely competitive world, which ones are the most successful, and how to best keep those customers. While businesses invest millions of dollars in CRM systems, they only produce data and fail to tell the business what the data implies. Analytics of consumer relationships and data mining help manufacturers make better sense of customer needs, help businesses more intelligently handle these relationships and help forecast the future.