85% of B2B management teams believe their pricing decisions need improvement, and just 15% have effective tools and dashboards to set and monitor prices according to a recent Bain global survey of more than 1,700 business leaders. For the many companies that rely on pricing as a competitive advantage, they need to start evaluating AI and machine learning on their IT platform roadmaps now.
Staying at competitive parity and turning AI- and machine learning-based expertise into a pricing and revenue management strength needs to be a priority. Data is a proven panacea for fear, and given the new market dynamics many companies are facing, it's the most reliable way to make decisions. The following are ten ways AI is improving pricing and revenue management today:
Using AI to identify then eliminate the most unproductive customer discounts and segments, freeing up more financial resources and time for those that contribute to profits . A recent Bain & Company research brief, Bringing Order to Discounts Gone Haywire , provides an excellent example of how AI can be used to determine the effectiveness if discounts by customer segment and type of discount. The brief mentions how focused analysis of discounts can help stop revenue leakage due to suboptimal, expensive customer investments. The following two graphics summarize the brief's key findings:
Automating pricing rules with AI in revenue management systems increase total revenue by 5%. Boston Consulting Group (BCG) found that 95% of successful digital transformation initiatives utilized one or more revenue growth levers. 77% of given digital transformation's financial impact was achieved through the combined use of six revenue growth levers. Improving pricing optimization with advanced techniques including AI has the potential to deliver a 5% increase in total revenue. BCG believes that automating pricing rules in revenue management systems and enforcing contractual pricing changes increase revenue. Source: How to Grow Revenue Quickly and Sustainably in Transformations , Boston Consulting Group, August 18, 2020.Picture 2
Capitalizing on the many insights transactional data can provide by using AI and machine learning to look for patterns in pricing, volume, and mix analysis is delivering measurable results today. The patterns and trending insights in transaction data include new insights every business can use to become more competitive. Unlocking those insights takes an AI-based approach to interpreting the price, volume, and mix fluctuations often locked within the constraints of transactional data. Combining transactional data analysis and price, volume, and mix fluctuations have proven difficult and a challenge to combine in a unified, intuitive application. One of the companies having success combining transactional and product mix data using AI is Vendavo . Their approach is noteworthy in how it solved the usability challenges, so many other price optimization vendors have struggled with. They've been able to deliver real-time price optimization driven by local market conditions, competitive intelligence, and cross-border parameters. Corning Optical Communications identified price, margin, and profit opportunities using an AI-based Profit Analyzer, delivering a $10M contribution in the first year. The following is a screen of Vendavo PricePoint:Picture 3
AI and machine learning are helping pricing managers capture more revenue and profits by finding how what a given customer is willing to pay or optimizing price across their customer and product mix. Identifying blind spots in pricing, discount, and deal size decisions are difficult to identify for customers and products using spreadsheets alone. AI and machine learning help pricing managers analyze whether existing discounts make sense by correlating deal size to discounts made, identifying outliers where discounts have been granted due to the negotiating insight of the customer. One of the leaders in this area is Salesforce Einstein , which is capable of predicting optimal purchase price and location . Source: Harnessing Pricing Power to Create Lasting Value , Bain & Company, February 24, 2020. Full report available for download here (96 pp, PDF).Picture 4
AI is making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, propensity models rely on predictive analytics, including machine learning, to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign, or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers' preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO .Picture 5
Price optimization and price elasticity are growing beyond industries with limited inventories, including airlines and hotels, proliferating into manufacturing and services. All marketers are increasingly relying on machine learning to define more competitive, contextually relevant pricing. Machine learning apps are scaling price optimization beyond airlines, hotels, and events to encompass product and services pricing scenarios. Machine learning is being used today to determine pricing elasticity by each product, factoring in channel segment, customer segment, sales period, and the product's position in an overall product line pricing strategy. The following example is from Microsoft Azure's Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution .Picture 6
AI is improving Configure, Price, Quote (CPQ) effectiveness by bringing greater accuracy and control to price management and price optimization, which increases margins, reduces costs, and increases profitable financial performance. The following is an AI roadmap to value based on its contributions to improving pricing management, optimization, and long-term performance as part of CPQ selling strategies. Commercial Excellence is defined as the ability of an organization to attain stable gross margin, revenue, and profitability performance year-over-year.Picture 7
Fine-tuning price segmentation strategies with insights gained from AI is helping to stabilize and increase margins and revenues today. Each customer segment has a different price they are willing to pay for a given product or service. By using AI and machine learning to know the price by segment customers are most willing to pay for a given product, AI applications can suggest them to sales and revenue managers. Automating segment-specific pricing guidance using CRM and CPQ systems is pivotal to pricing segmentation strategies' success. The following graphic illustrates how pricing segmentation works. The more high-quality sales and transaction data and AI systems can obtain, the more its models can accurately predict willingness to pay levels. Source: How to Capture More Value with Price Segmentation . Alex Hoff. CFO.com. July 6, 2017AI is providing sales and revenue managers with more accurate deal price guidance than was available in the past, leading to more effective use of pricing discounts. Facing greater pricing pressure in sales cycles, they want to close quickly, and sales reps are quick to provide deep discounts that sacrifice margin. This is especially true in enterprise software. McKinsey found that using dynamic deal scoring indexed to discounts provides the guidance sales reps need in determining what level of discounting will win the deal and not sacrifice margin. Source: Advanced analytics in software pricing: Enabling sales to price with confidence , McKinsey & Company, June 14, 2018Picture 8
Relying on AI to monitor risk-based metrics and KPIs to gain greater visibility into the root cause of potential risks to revenue. Lost sales, accounts, and customers often happen because sales and service teams don't know soon enough; there is an issue. AI-based alerting on key revenue, pricing, and quoting metrics can save a sale, customer, and help pinpoint a specific product issue as well. AI-based risk alerts are customizable for specific metrics and conditions and are sent to relevant team members, supporting customers. The most valuable aspect of these alerts is getting to the root cause of any issue. Of the price optimization vendors providing these today, Vendavo's Business Risk Alerts are among the most comprehensive and easy to use.
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