Complex manufacturing has fundamentally changed in the past decade. Businesses in the industrial, power generation, elevator and other manufacturing sectors have had to change how they operate. Global competition, supply chain interruptions, and labor shortages are just a few of the challenges businesses have faced.
These challenges have left manufacturers no choice but to digitally transform or disappear. Our latest survey found that 64% of manufacturers are either in the early stages, or well into their digital transformation efforts.
Many of these companies are spearheading their digital transformation efforts with CPQ (Configure, Price, Quote) systems because these tools dramatically enhance the efficiency and accuracy of their sales processes, leading to increased customer satisfaction and higher revenue. While customer satisfaction and higher revenue are always on the top of mind for manufacturers, another key feature of CPQ is the wealth of data it provides. This data encompasses configuration selections, sales BOMs, pricing levels, won-lost data and more.
Let’s explore seven use cases that demonstrate how manufacturers can leverage CPQ Analytics to gain valuable insights and drive business decisions. These use cases highlight the unique nature of CPQ data, which is not typically found in other enterprise systems.
What are some common CPQ Analytics Use Cases?
1. Sales Pipeline Analysis
The first use case focuses on sales pipeline analysis. Opportunities can be visualized at various stages of the sales process, from design solution all the way through to order.
With CPQ analytics, it is possible to identify where opportunities are getting stuck, particularly in the final negotiation stage. By examining both the number and the size of opportunities in different stages, efforts can be prioritized to unblock high-value deals. Additionally, the API allows filtering this data by year, providing a temporal perspective on the sales pipeline.
2. Price Positioning Analysis
Next up is price positioning analysis. This analysis helps understand the margin and size of different opportunities.
For instance, patterns in high-margin deals can be seen—whether those deals tend to be won or lost. This analysis can be segmented by application, industry, and year, offering a granular view of pricing strategies. These insights help refine the approach to pricing and ensure competitiveness in the market.
3. Parts Reduction Analysis
The third use case is parts reduction analysis. Here, the choices made by customers during the configuration process are analyzed, focusing on various components and materials.
By examining these choices, parts that are rarely selected can be identified. This insight allows for streamlining product offerings, reducing inventory costs, and focusing on popular options that drive sales. Additionally, visualizing these choices in a 3D environment provides a clearer understanding of customer preferences.
4. Product Market Fit Analysis
Product market fit analysis is the fourth use case. This analysis compares customer requirements with the solutions provided, highlighting any over-dimensioned products.
Using a slider tool, solutions that exceed customer requirements by a significant margin can be filtered out. With Tacton, This helps determine if new variants need to be developed that better match customer needs, ensuring products are neither overbuilt nor under-equipped. Refinement and aggregation of the data is handled by the manufacturers preferred Business Intelligence tool such as Qlik, Microsoft BI or Tableau.
5. Service Upsell Analysis
The fifth use case is service upsell analysis. This analysis examines additional services chosen by customers, such as express installation, and their relevance across different industry segments.
By identifying which segments—like medical or public transport—opt for these services, marketing strategies and upsell efforts can be tailored more effectively. Understanding these choices during the configuration process helps align service offerings with customer needs.
6. Customer Value Driver Analysis
Finally, customer value driver analysis focuses on high-importance questions within the configuration process, which are crucial to customer satisfaction.
Analyzing whether customers are actively choosing these options or simply accepting default suggestions reveals how relevant these value drivers are across various industries. This insight helps refine the configurator and improve customer engagement.
7. Quoting Analytics
The additional section is quoting analytics. This use case involves analyzing the quoting process to identify trends and inefficiencies.
By leveraging CPQ analytics, data on quote generation times, approval rates, and conversion rates can be gathered and examined. This analysis can reveal bottlenecks in the quoting process, highlight the most and least effective sales tactics, and provide insights into quote acceptance patterns. Understanding these factors helps streamline the quoting process, ensuring faster turnaround times and higher quote acceptance rates.
In closing
Uncovering and utilizing the wealth of data that comes from CPQ analytics can be a true game changer for manufacturers. With this data it can be easier than ever to understand products, customer needs and more while creating actionable insights to make it easier for your teams to sell their products.