Sanjeev Sularia, serial entrepreneur & technology evangelist. He is Co-founder & CEO Intelligence Node, a big data lab.
With the emergence of online platforms, B2B businesses have had to reconsider their pricing strategies. But, these same technologies help the organizations create dynamic B2B pricing models that bring substantial profits if implemented correctly. For example, an integrated sales and B2B pricing software can help sales reps negotiate with customers and reduce the processing period.
There are several benefits of using dynamic pricing in the B2B sector. They help improve the sales volume by predicting when to increase prices during a surge or reduce them during a dry spell. It also accelerates the decision-making process.
Take a medical-technology company, for instance. After applying an enthusiastic dynamic pricing approach, they found their profit margins increasing by 4-8 percent and overall revenue by 5 percent. Many other businesses have followed suit and shot up prices by as much as 60 percent without any reduction in sales volume.
It is evident that dynamic pricing is the need of the hour for B2B companies. But how does one go about building a dynamic pricing engine? Before adopting B2B pricing software and strategy, businesses need to understand the three key elements.
B2B organizations need to use the segmented approach when it comes to using pricing software. The AI dynamic pricing software and algorithms
use powerful analytical techniques like statistical modeling, AI, and machine learning to give minute insights if even the datasets are small.
So clustering techniques that group several products with the same sales
characteristics (such as target customers, lifecycle, level of competition in
the market, etc.) together can be applied to generate dynamic pricing.
Such B2B pricing software also crawls the web searching for data on target segments, competitors, and price trends. News articles, market prices, and many other sources are analyzed, and this data is grouped with the organization’s internal records to create a dynamic pricing engine.
The interesting thing about the pricing algorithms is that they are self-learning.
So with every price change, the customer’s willingness to pay is based on
factors like click conversions, sales volume, offer wins, and more. Another
essential role of analytics in creating a dynamic pricing engine is its
seamless integration with sales tools.
No matter what the dynamic pricing engine indicates, ultimately, it’s the
salespeople who have to close the deal on a product or service. Some of them might be hesitant to implement the price surges suggested by the B2B pricing software for fear of losing out on sales volume. It’s not surprising considering that these volumes have a direct impact on their incentives.
It is essential for the pricing committee in an organization to keep the salespeople involved in decisions from the get-go. They shouldn’t be expected to follow an ever-changing price list passively. But encouraged to be a part of the decision-making process to help them understand the dynamic pricing model. A better idea is to give suggestions and provide the reason behind the optimum prices rather than making it the law.
Also, the analytics behind the dynamic pricing engine can help the salespeople by providing fact-based advice on each product or customer. These suggestions can help salespeople negotiate better. The coaching set-ups and incentive schemes should also match the new dynamic pricing model.
Creating a dynamic pricing engine is not a one-time thing. A steady process needs to be in place to support this engine. These may include:
A Pricing Unit: A unit that exclusively deals with the pricing engine should be set up. This office could be at the global, regional, or local level. Their major tasks include measuring the impact of pricing on sales performance, tracking any deviation from models, giving suggestions on new price models, and more. This unit must include data scientists who apply their analytical skills to the B2B pricing software.
Standardized Processes: The organization must standardize the various processes involved in building the dynamic pricing engine, including the collection of market intelligence and raw material forecasts. Creating a new pricing calendar with set targets helps proactive pricing and smooth execution.
Pricing Success Management: Here, the success or failure of each dynamic pricing model is measured. And relevant feedback is given. The organization must define the target profit margin and sales volumes.
A successful B2B pricing software is essential for building a dynamic B2B pricing engine. Combining these analytical tools with people and processes can cover any lack of data and drive the organizations to set optimum prices. The fast-movers in this field are already reaping the benefits of a dynamic pricing model. Indeed, it is indispensable when it comes to staying ahead of your competition.
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