Increased revenue generation is one of the most common enterprise uses of AI. Recommendation engines drive the business of online retailers and consumers rely on them to help with purchasing decisions. Today, machine learning algorithms are driving more than just online consumer shopping. Hyper-personalization, dynamic pricing, deal-origination, and trade execution models are driving revenue growth for Aicadium customers.
AI is raising the game for personalisation marketing. A personalised experience communicates customer understanding and increases brand loyalty, and consumers have come to expect tailored product and service recommendations. Hyper-personalisation is achieved by leveraging machine learning and real-time data to deliver instantaneous and highly relevant content or product recommendations to users. For example, as an individual reads an article online, they are simultaneously recommended similar products based on the customer’s profile and search history. In an increasingly competitive market landscape, the result is better click-through rates, greater engagement, and a direct result on a business’s top line.
Some industries require prices to be modulated based on circumstances. This might mean keeping prices high in peak times and lowering prices during off peak periods to encourage demand, or being able to change pricing structures in response to market conditions. Many organisations struggle to move beyond static revenue generation models however AI-driven dynamic pricing is now revolutionising industries such as hotels and airlines. Business and revenue strategies that are backed by AI have a clear advantage: combining strategy work done by humans with datasets based on historical data, market opportunities, and customer behaviour.
AI is shaping the future of trading. Many financial services and asset management organisations are turning to machine learning to provide greater intelligence to portfolio managers executing trade orders to maximise trade profits. Recommender engines learn various trading, market, and channel parameters that impact the costs or proceeds of a trade, then recommend the timing, volume, and price to execute a given order. Recommendations that are generated for portfolio managers increase profitability for the company and drive huge long-term efficiencies by automating trade executions.