3. Optimize pricing based on demand

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Predictive analytics uses machine learning to analyze data, both internal (sales and customer data) and external (current events, competitor data, review and social media comments) for insights. loan data These are critical for anticipating market trends and informing decisions around inventory control, marketing spend and other investments.

For example, alcoholic beverage company Diageo uses AI to get real-time forecasts of customer demand, commodity prices and creditor payments. It also uses AI insights to inform investment decisions based on factors like the timing, length and reach of a marketing campaign.

Forty-five percent of business leaders say that AI and ML will be critical for building dynamic pricing models in the future.

This is not surprising given that dynamic pricing is common in industries such as hospitality and tourism with fluctuating customer demand (e.g., the popularity of a flight/destination) and seasonality (weekends or weekdays).

Screenshot of a tweet from Bloomberg Markets about travel spiking amidst the drop in airfares

AI algorithms analyze both historical and real-time data (e.g., inventory, demographic-based sales, competitor pricing and social media posts) to pull highly relevant, time-sensitive insights. With this information, teams can customize product pricing and messaging proactively so you can increase your competitiveness and meet revenue goals.