Improving Pricing And Revenue Management With AI
Here's how AI is improving pricing and revenue management
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:
1. Smart Discounts
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.
2. Automating Helps Improve Total Revenue Growth
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.
3. In-depth Analysis to Identify Pricing & Trends
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.
4. Allows Pricing Managers to Work Smarter
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. Here is where AI and ML help capture more revenue and profits by finding different aspects of a given customer: how or why is a customer willing to pay? These technologies also allow in analyzing if existing discounts make sense by correlating deal size to values made. Plus, it helps identify outliers where discounts have been granted due to the customer's negotiating insight. For instance, Salesforce Einstein is one such leader in this area, capable of predicting optimal purchase price and location.
5. AI Makes it Possible to Create Propensity Models
In simple terms, the propensity model is based on predictive analytics, including ML, to predict the probability a given customer will act on a bundling or pricing offer. It tracks the customer's reaction to an e-mail campaign or other call-to-action initiative leading to a purchase, upsells, or cross-sell. It has also been proven to be an effective way of increasing customer retention. Those businesses excelling at omni channel rely on propensity models to better predict past customer behaviors and how their preferences have been changing that will lead to future purchases.
6. Helps Improve Price Segmentation Strategies
When it comes to paying for a product or service, each customer segment has a different price they are willing to pay. Each customer segment has a different price they are willing to pay for a given product or service. Artificial Intelligence and Machine Learning help gain companies in-depth insights in knowing the price by segment customers are willing to pay for a given product. Further, it can provide suggestions to sales and revenue managers on how to working in enhancing price segmentation strategies. It also helps automate segment-specific pricing guidance using CRM (Customer relationship management) and CPQ (Configure, Price & Quote).
It is also during sales cycles when sales and revenue managers face more significant pricing pressure. AI-based insights become imperative as it provides them with more accurate deal price guidance than was available in the past. This is somewhat true, especially in the case of enterprise software.
7. Helps Monitor Risk-based Metrics and KPIs
It is an issue if sales and service teams don't identify a reduction in the sale or loss of customers immediately. AI-based monitoring and alerting on crucial revenue, pricing, and quoting metrics can save a customer and help pinpoint a specific product issue. These risk-based metrics and KPIs alerts are valuable because they provide greater visibility, thereby assisting relevant teams in getting to the root cause of any concern. Customer behavior, competitor's pricing, sales, growth, and revenue, as well as seasonality - all are important and dynamic factors that contribute to a company's pricing success. And as aforementioned, it is clear that Artificial Intelligence and Machine Learning can identify almost all these factors leading a pricing success for the companies.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house
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