Read Time: 8 minutes

Ready Your Data for AI-Powered Pricing and Selling 

Patrick Dougherty< Patrick Dougherty February 20, 2024

Artificial intelligence can have huge impacts on business operations, but only when it’s quality and used effectively. In this article, Patrick Dougherty, Product Marketing Manager at Vendavo, covers some of the top takeaways from Gartner’s report on How to Build a Data Strategy in Manufacturing Organizations – many of which apply to any organization. 

Artificial Intelligence (AI) offers incredible possibilities for businesses, playing a crucial role in understanding customers, keeping an eye on the competition, and uncovering new opportunities. Its effectiveness depends on your data, however. AI won’t be as helpful without quality data to use.   

Every organization has data, but the question is what to do with it. What data is most important to your growth goals and how will you access it? With databases full of detail, how can that result in actionable insights?  

Data management is no easy task – so let’s start with why this fuel is critical. 

  1. AI Powers the Right Price 
    When armed with the right data, AI models can help organizations understand customer expectations, preferences, and behavior – including willingness to pay. This insight allows for identifying optimal price points, capturing more revenue, and boosting margins. The technology can anticipate customer actions by aggregating information on market changes, supply chain disruptions, and economic outlooks, and provide timely price suggestions for maximum margin. Not to mention customer satisfaction.  
  1. AI Boosts Sales 
    AI is a game-changer when it comes to improving win rates and boosting sales productivity. Automation can quickly retrieve transactional, market, and competitor information and streamline supporting processes. It can provide timely price and profitability guidance for effective negotiations, and suggest cross-sell, upsell opportunities to enhance win rates.  

There’s no question AI can bring a significant boost to manufacturers’ bottom line – and it starts with quality data.  

How to Navigate Data Complexity 

Most manufacturers have a mix of unconnected data sources, including ERP, CRM, IoT, supply chain, competitive insights, and market trends. The lack of integration and transparency is one of many roadblocks to quick, informed decision-making.  

Siloed data sources and other barriers to effective data management, including insufficient context, process, and frameworks are identified in a new report from Gartner, How to Build a Data Strategy in Manufacturing Organizations. The research recommends manufacturers build and sustain a data strategy by using the “marathon training method,” which includes: 

  • Doing the base training 
    Increase your base training as your condition improves. Use different data approaches, and go back to your bases to fine-tune your pace. You are not competing on time, but on completion and satisfaction. There are four building blocks for base training, including two critical, foundational elements to your data management strategy: establishing clear business outcomes and mapping data assets. 
  • Training and predicting based on time 
    As you get more advanced, you refine your techniques and skills to cross-train with different methods and fine-tune training plans. In this advanced mode, you are testing new approaches and different races.  

All of this requires proper set up so you can ensure your outcomes are as effective as possible. Here’s how to prepare your data for AI-powered pricing and selling.  

Start with Your ERP 

For growth and profitability goals specifically, starting with ERP data is often the best choice. Transactional, customer master, and product master data are foundational elements and generally the most accurate. It may not be perfect, but it doesn’t have to be. The most important first step is taking a step. 

Begin with basic data, then enrich it with things like customer industry or NAICS code and an estimate of customer size. Pair that customer data with product master data, such as product hierarchy (from a high-level product line down to a SKU number), product lifecycle, and fast- or slow-mover classification. 

A quick gauge of whether the data is in a good spot is when people start nitpicking results. If you find yourself adjusting small issues line-item data instead of the way data is structured, chances are you’re in a good place. 

Pilot with Key Stakeholders 

A great way to test and improve data is to use it in a real-world application. Share results with business leaders and others who can vet it. Using data is the best way to clean it up. Pick a pilot area and work with a team that is interested in moving the project forward. Have them focus on the big picture and not specific lines. Data visualization can also help identify issues. 

Put AI to Work  

Once your data is in “good enough” shape, it’s time to consider putting the power of AI to work for you. From developing timely pricing suggestions to tailored sales campaigns, Vendavo’s growth and profitability solutions rely on data-reliant AI yet leave room for human input. This unique balance results in a powerful co-pilot for driving sustained growth that is fueled by data-driven decision making and innovative technology.  

Reach out today to request a demo and speak with an expert about how best to clean and use your data for better results.