Industrial OEMs are increasingly recognizing the value of data in their decision-making processes. By leveraging insights derived from their data, they can anticipate and address potential issues and develop strategies that align with their business goals and objectives. Beyond decision-making processes, data also allows businesses to measure the success of their strategies using predefined metrics and key performance indicators (KPIs), that in turn promote sustainable growth. As a result, it is critical for OEMs to leverage their data to generate insights that support their decision-making processes in sales, marketing, product, services, and core business and ultimately drive profits.
Entytle have taken the above factors into consideration and have come up with purpose-built Data Science/AI Algorithm and analytics using installed base data to support OEMs in making such decisions by gaining an understanding of current trends, different customers’ behavior, health, and needs of their customers. By leveraging installed base data, the insights generated using these algorithms provide OEMs with opportunities to drive revenue.
In this blog, I will talk about how OEMs can take data-driven approach and analytics to solve their business issues and promote continued growth.
Propensity to buy
In the B2B world, the sales decision cycle tends to be lengthy which makes it crucial for OEM sales executives to reach out to the right customers who is a potential buyers for better management of resources and time. By leveraging installed base data and employing machine learning technique, Entytles proprietary Propensity to buy algorithm which analyzes individual customers purchasing patterns as well as customers of similar types across different industries and region and generate propensity scores that OEMS can use to determine which customers are most likely to make purchases in the near term. These insights allow OEMs to approach the right customers, optimize their resources and improve their chances of success in their sales effort.
Parts Purchase Intelligence
Once OEMs have identified customers’ propensity to buy, they may further like to know which products the customer is likely to purchase, in what quantities, and over what timeframe. To achieve this, Entytle’s Parts Purchase Intelligence algorithm analyzes individual customers’ transaction histories by calculating their purchase intervals, rates, and velocity and predicts which specific parts the customer is likely to need and when they are likely to need them. This information generates leads and opportunities for OEMs to sell those parts to the customer.
Whom to call
After identifying customers’ needs using Parts Purchase Intelligence, the next step for OEMs is to reach out to these customers. However, with various parts, services, machines, warranties, and service contracts to sell, it can be challenging to determine which persona to target at the customer base. Entytle’s Whom to Call algorithm addresses this challenge by analyzing past transaction histories and the contacts tied to those transactions. Based on this analysis, the algorithm recommends which individuals within a customer organization a salesperson should contact to sell a specific product. This helps OEMs to optimize their sales outreach efforts and ensure that they are targeting the right people within their customer organizations.
Aftermarket Intelligent Kits
During sales outreach, salespeople may want to persuade customers to purchase additional parts or services to increase the average order size and generate additional revenue. In this scenario, the Entytle Aftermarket Intelligence Kits build using installed-based data plays a vital role by providing insights into which parts and/or services are frequently purchased together by customers in order. Using these insights, the salesperson can provide parts/service recommendations to add more items into the customer’s cart, thereby increasing the order value and generating additional revenue.
Segmentation of customers based on their demographic characteristics, purchasing behavior, industries, region, type of product/service they purchase, and other various factors is crucial for developing effective marketing strategies that meet the needs and expectations of customers with different profiles. Understanding customer profiles support businesses to tailor their marketing strategies and take appropriate actions, rather than adopting a one-size-fits-all approach. Entytle’s customer segmentation workflow helps industrial OEMs divide their customers into different groups based on these factors, allowing for the planning and targeting of specific offers to each cohort of customers that is best suited to them, with better allocation of time and resources.
Customer Loyalty Manager
Keeping track of loyal customers and at-risk customers is crucial for OEMs as acquiring new customers is expensive compared to doing business with existing customers. Hence focusing on just the top 20 or top 50 customers lead to neglecting other customers, building marketing strategies based on the type of customers and increasing their engagement can help to build loyalty and prevent customers from leaving. The Customer Loyalty Manager analyses customers’ past transactions and engagement levels to identify loyal customers and those who are at risk of churning out. This enables OEMs to create targeted marketing campaigns with the right strategies to improve customer engagement and prevent them from leaving.
Business Intelligence Report/Dashboard
In addition to aiding sales and marketing strategies, utilizing installed base data can assist OEMs in developing effective business strategies and informed decisions for sustainable growth. Data can provide valuable insights, but it is important to ask the right questions to extract meaningful information. Installed base data can answer queries on performance based on usage and revenue trends, sales distribution across various regions and industries, acquisition trends for new customers, the contribution of new and existing customers to business growth, and customer churn rates. OEMs can establish predefined KPIs and metrics to evaluate business performance and create business intelligence reports using installed base data and BI tools to make informed decisions. Furthermore, data analytics can help in identifying new business opportunities, potential operational inefficiencies, and areas where resources can be optimized to improve overall business performance.