Improving Pricing And Revenue Management With AI

New-age technologies, including Artificial Intelligence (AI) and Machine Learning (ML), have started enabling nearly all business processes in the last few years. From tracking buying trends, determining data and pricing solutions to reinventing sales, organizations are today leveraging AI to automate tasks, drive more revenue, and improve efficiencies and beyond. While this development is in progress, only 15% of B2B management teams have useful tools and dashboards to monitor prices, as recently highlighted inBain Global Survey. The remaining 85% still believe their pricing decisions need improvement. After all, pricing provides companies with a competitive edge.

It would also be fair to mention that analyzing Data using AI is the most reliable way for companies to make effective decisions. This is precisely what leads them to turn to AI- and ML-based expertise to infer data that can help them strengthen their pricing and revenue management. In this regard, let’s have a look at a few ways how AI is improving pricing and revenue management:

Every customer loves discounts, and companies invest their time and effort to offer them the same. But, unfortunately, all discount offers may not give impactful results. Leveraging AI, companies can identify the most unproductive customer discounts and segments, eliminate the same, thereby freeing up more financial resources and time for those that contribute to profits. More so, a recentBain & Company research brief mentions how focused analysis of discounts can also eliminate revenue leakage due to suboptimal and expensive customer investments.

Using AI to automate pricing rules in revenue management systems can deliver a 5% growth in total revenue. Boston Consulting Group also found that 95% of successful digital transformation initiatives utilized one or more revenue growth levers. Along with automating pricing rules, it is also possible to enforce contractual pricing changes that lead to increased revenue.

Integrating advanced technologies such as AI and ML can help identify current and changing customer behavior concerning pricing. For instance, if an online customer’s action shows that he/she barely spends even minutes comparing prices within a specific price range, ML algorithms identify this pattern and take this into account next time.

Also, it helps in analyzing patterns and trending insights in transaction data. An AI-based approach for unlocking new insights will help businesses interpreting the price, volume, and mix fluctuations often lost in the constraints of transactional data. These new insights, therefore, can be used by companies to become more competitive.

For pricing managers, identifying blind spots in pricing and discount and deal size decisions using traditional ways like spreadsheets is challenging and an exhaustive process.