How manufacturing sales reps fix the analysis gap to maximize revenue and margins, with chat-powered, self-service analytics 

13 Jul, 2023 •

how manufacturing sales reps fix the analysis gap to maximize revenue and margins, with chat powered, self service analytics

Field sales representatives in the manufacturing industry are responsible for maximizing both volume and margins when selling to their customers, who are typically OEMs and distributors. Typically, there is a large product portfolio of product lines and component parts. Therefore, to achieve their goals, identifying cross-sell and upsell opportunities is critical. 

To find and create opportunities, field sales reps seek to understand their customers and the status of their relationships. They do so by relying upon reporting and analysis developed by their company’s centralized analytics and IT teams.  

The manufacturer’s ERP system manages supply chain logistics such as inventory stocking, shipping, warehouse management, financing, and sales. Therefore, the source data required for analysis commonly resides in ERP systems from Oracle and SAP, and the related business intelligence (BI) tools to tap that data often include Oracle Business Intelligence Enterprise Edition (OBIEE) and SAP Business Objects.  

The data landscape complexity is often complicated through series of acquisitions, leaving data silos across different entities.  

With manufacturers that have modernized, the data is often residing in a modern data cloud such as Snowflake or Databricks. The BI tools used there might be, for example, Microsoft PowerBI, Tableau, and Google Looker. 

The challenge: “in-the-moment, iterative analysis” 

Business intelligence reports and analysis often give a reasonable starting point for the investigation of a sale rep’s “in-the-moment” hypothesis. BI assets are developed by experts who understand the data well, and their efforts often solidly address the more obvious 80% of the reporting analysis requirements for sales reports. But in many scenarios, while those assets serve as a solid foundation and starting point for a question, the sales reps need to go much deeper and pivot to many other related questions – all very quickly and iteratively, and in-the-moment. 

Sales reps are often required to hypothesize and analyze on-the-fly. The company cannot afford to offer real-time access to the centralized BI team. And the rep does not have the deep technical and BI tools skills to get answers within the minutes or seconds required for a given task in the field, “in-the-moment.” 

For example, the rep might suppose that there could be cross-sell or upsell opportunity for a given customer that’s comparative to what was sold to one of their competitors. Those ideas that come “in-the-moment,” require taking advantage of the opportunity by instantly, easily, and iteratively query the corporate data to understand what kinds of products make sense to propose, which are available and at what price, volume, and timeline. 

The solution: AI-powered chat that understands the data 

Traditional BI tools described above require SQL or some technical skills. Most sales reps aren’t ever going to have the technical skills required to ask new kinds of questions quickly, easily, and iteratively. 

With DvSum CADDI reps can just ask questions naturally to their data. CADDI understands how the rep might ask a question. They don’t have to ask in a constrained way such as “Sort Product Name by Sum of Sales and Date = 4Q22- 1Q23”. They just ask, “Which products had the most revenue the past two quarters?” Or in a manufacturing scenario, “Which products with margin greater than 25% were purchased by Acme the past two quarters?” This makes the data more accessible to a wider range of users who may not be as familiar with the technical jargon or data structures. 

Take for example a rep wants to find out what other products he can sell to a customer. If he is already selling a motor, maybe he can sell some services related to the motors. Or he can upsell an IoT version of the motor that comes with predictive failure capabilities. Therefore, the rep would like to know the related products to cross-sell and upsell. 

The challenge is that the rep is not a report writer. Simply learning a relevant summary of recent order history is highly valuable. Commonly, basic information about the customer isn’t easily accessible. How do the current sales compare year-on-year, or quarter-on-quarter, are basic iterative questions. Just to be able to ask quick, iterative questions about a customer without having to sift through pages of a report – or endlessly filtering — is a great benefit. 

CADDI makes it easy for the rep to get precisely the information required, quickly, easily, and iteratively. CADDI makes it easy to find the highest-priority business issues behind data issues. For example, if the customer is not defined according to an “AB categorization,” that needs to be resolved quickly because there could be enormous opportunities for more sales waiting. 

Example 1: Field Rep asks for top selling products to Acme Manufacturing
Example 1: Field Rep asks for top selling products to Acme Manufacturing 
Example 2: Field Rep asks for orders shipped last year, and associated costs
Example 2: Field Rep asks for orders shipped last year, and associated costs

Getting started: identify the art of the possible 

While DvSum CADDI might sound appealing, what’s the right way to start? The good news is it’s easy to get DvSum CADDI up and running within hours. It’s a great idea to test DvSum with your data, and here’s how: 

 
Core questions around an account 

For a given account, CADDI can help a rep quickly, easily, and iteratively understand questions such as: 

  1. What is our current run rate of sales to this customer, and how does it compare to last year? 
  1. Which product lines have we sold and which ones are in the process of a sale? 
  1. Which products are tangentially related to the products already sold, have highest margins, and are available within 30 days? 
  1. What products were purchased by XYZ account (that’s a competitor of this customer)? 

Bonus: Here’s an opportunity for execs. 

Finding optimizations and opportunities at quarter end is a common and major challenge for executives. Particularly for publicly traded companies, at the end of a fiscal quarter, there is a lot of analysis requested to answer anticipated questions from Wall Street analysts for the quarterly calls. Commonly, an IT professional or data analyst is dedicated for 3-4 weeks to get precise answers such as: 

  1. What is my backlog of orders on the books?  
  1. What were my most profitable products? 
  1. Who were my biggest new customers? 
  1. Which region is growing the fastest and why? 

Conclusion 

DvSum securely combines OpenAI’s GPT-4 with a powerful underlying data infrastructure to query your manufacturing data quickly, easily, and iteratively. This empowers field sales reps to become more autonomous and productive in the field and “in-the-moment” with their accounts. Contact us today to see how DvSum CADDI can help you. 

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