The use of AI-based technologies comes to rescuing the revenue managers with more practical strategies to manage the outcomes.
FREMONT, CA: Artificial intelligence (AI) has great potential to register changes in behavior and business. It also can impact revenue management processes. Companies that depend on pricing as a competitive advantage need to evaluate AI on their revenue management roadmaps now. Staying competitive and turning AI-based expertise into pricing and revenue management needs to be a priority. The following is a take on how AI is improving pricing and revenue management today.
A recent survey found that 95 percent of successful digital transformation initiatives utilized one or more revenue growth levers. Seventy-seven percent of given digital transformation's financial impact was gained through the combined use of revenue growth levers. Improving pricing optimization with AI has the potential to deliver an increase in total revenue. Firms believe that automating pricing rules in revenue management systems and enforcing contractual pricing changes increase revenue.
• Capitalizing on the Insights
The patterns and insights in transaction data include new insights every firm can use to become more competitive. Unlocking those insights takes an AI-based method 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 is difficult and a challenge to combine in a unified, intuitive application. Using AI can deliver real-time price optimization driven by local market conditions, competitive intelligence, and cross-border parameters.
• Capturing More Revenue and Profits
Discovering blind spots in pricing, discount, and deal size decisions are complex to identify for customers and products using spreadsheets alone. AI can help pricing managers analyze whether existing discounts make sense by correlating deal size to discounts made, deciding outliers where discounts have been granted due to the customer's negotiating insight.
• Creating Propensity Model
Propensity models rely on predictive analytics, including machine learning, to assume the probability a given customer will act on a bundling or pricing offer, campaign, or other call-to-action leading to a purchase, upsell or cross-sell. These models have proven to be very effective at increasing customer retention and lowering churn. Every business using omnichannel relies on propensity models to better predict how customers' past preferences and behavior will lead to future purchases.