How to cultivate Win/Win situations in Enterprise Sales negotiations?

[PredictiveLayer]

Corporations worldwide spend billions of dollars each year purchasing electrical infrastructure equipment, software, and digital solutions. Buyers (enterprise accounts) and sellers (electrical infrastructure vendors) negotiate prices and discounts for large ticket items such as transformers and switchgear on an ongoing basis.

In these traditional settings, equipment vendors rely on their salespeople to make pricing judgments based on experience, gut feel, and information about the customer and the competition. In most cases, the buyer does not accept the first quoted price. Negotiations ensue, and eventually both sides hopefully agree on a price. However, both sides are left not knowing whether they negotiated the best price. It is an inefficient process where up to 20 percent of a salesperson’s time is spent securing internal price discount approvals and customers can be frustrated by having to wait for a final price quotation.

For the power infrastructure vendor, the goal of the sales team is to optimize the win rate for each engagement opportunity. The salesperson wants to avoid quoting a price that is too high, in fear of losing the opportunity. On the other hand, the salesperson also wants to avoid a situation where the price discount is too deep, and the profitability of the deal is undermined.

AI Digital Solutions Drive Sales Teams to Generate Additional 5 Percent Revenue Growth

New breakthroughs in Artificial Intelligence (AI) digital solutions now make it possible for electrical infrastructure equipment vendors to use data to structure more optimized product pricing in a much more efficient manner. In fact, over the past two years our company Predictive Layer, a Schneider Electric Technology Partner specializing in AI tool development, has seen corporate sales teams who deploy such AI tools improve their revenues by up to 5 percent.

The new AI engines consider historical transactions for specific sets of products, including past win rates. The engine also factors in variables such as time of year (seasonality/cyclicality), levels of inventory, cost of goods sold, target margins, market growth trends, levels of demand, urgency of delivery dates, and purchase volumes.  The AI engine then accounts for these factors in its calculations when generating a recommended price range and explains which variables were given the most weight in the recommendation. In addition, the engine considers whether the vendor is choosing, in each particular transaction, to optimize for more volume and market share or for more profitability.

AI Digital Solutions Analyze Win and Loss Data Leading to Better Pricing

Over time, the AI engine learns more about how to structure the best deal by analyzing opportunity win and loss data. The engine sharpens its assessment of the limits of what is acceptable in the eyes of the customer and pulls back from limits when it sees it has priced too aggressively. As the system continually improves over time, the percentage of profitable deals won continues to grow.

The AI engine equips the sales team with a recommended price range. Working with a price range rather than a specific price provides the salesperson with control of the ultimate price to be proposed. Since the system also provides an explanation of the different dimensions that were considered in coming up with the price range and their relative importance, the salesperson can use these data-driven arguments to justify the quoted price to the customer, further strengthening the win rate.

AI Benefits Include Improved Sales Productivity

In addition to generating up to a 5 percent improvement in top line revenues, sales teams that use such AI pricing tools are seeing a 15 to 20 percent improvement in sales productivity thanks to more streamlined internal pricing approval processes, resulting in quicker quotations. Sales management can also use data generated from such tools to engage in more fact-based discussions with sales teams when comparing performance across territories and regions.  And unlike the deployment of large enterprise IT systems, vendors can begin with a simple “Proof of Value” project that can be delivering benefits in just a few weeks.

For More Information

To learn more about how innovative solutions such as AI pricing engines can help drive efficiencies and productivity across your business, read our blogs:

• Changes in Energy Resources Marketplace Behaviors Usher in New Era of AI-driven Price Forecasting
• Why Utilities Are Using AI Digital Solutions to Reinvent the Science of Price Forecasting?
• Use Smart Digital Solutions forecasting Tools to manage Power Grids
• How AI engines boost Grid Energy consumption forecasting accuracy?
• How to improve Industrial maintenance and safety with AI predictive engines?

We use the Schneider Electric Exchange platform to foster and nurture new ideas related to energy solutions though its community of cross-industry experts. You can also have free access to Exchange to collaborate and co-innovate on trending topics like AI and innovative digital solutions by joining any of hundreds of Exchange communities, like Power Distribution Excellence and Industrial Automation.

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Originally posted on SE Blog & Authored by Olivier Cognet

About the author: 

Oliver Cognet, CEO, Predictive Layer, is a business leader, corporate developer, and entrepreneur. He has 25+ years of experience in High Tech, Software Development, and Telecom markets focusing on enterprise and consumer markets. Mr. Cognet knows that technology and innovation can help businesses better understand the behavior and needs of consumers, thus helping businesses to grow. Predictive Layer brings the full power of machine learning artificial intelligence (AI) to businesses so they can simply and intuitively model and establish forecasts and predictive analytics of their key target indicators.