The ability to integrate multiple sources of information for business is revolutionized using Artificial Intelligence. Business strategy becomes a complex part due to the rise in competition. Using AI, we can perform certain tasks which fall beyond human efforts. A basic example of that is high-frequency stock trading. Let’s look at particular areas of AI which helps in revenue growth management. Although some are general techniques and can be used in any layer of business, there are some business-based AI applications like webserver management for Gaming companies or Travel agencies.

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AI-based Growth Management

Following AI applications can help in systematic and efficient growth of revenue:

Recommendation Systems:

Recommendation Systems collected and analyze the customer data to suggest the most relevant product/ service for the customer. Recommendation systems help in gaining the trust of customers as they purchase more products/services.

Techniques- Content-based filtering, Collaborative filtering, and Hybrid filtering. We can see the implementation of these systems on B2C companies like web streaming services, e-commerce, etc.

  • Content-based filtering focuses on suggestions based on similarity in customers. If two customers A and B have liked similar kinds of products in past, then A is most likely to purchase products liked by B and vice versa. We can see the same in the e-commerce website saying, people who purchased this product also purchased XYZ other product.
  • Content-based filtering focused on suggestions based on similarity in the products. If a customer purchases clothes for 8 out of 10 times then this recommendation feed will tend towards different offers in the clothing section rather than others. User search / wishlist / orders and other channel data are used to create a perception of customer’s favorite products on the website.
  • Hybrid filtering puts into consideration both filtering mentioned above along with any other techniques.

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Predictor Systems:

In business, predictive models exploit the patterns found in the direct and indirect data to identify risks and benefits. Models capture the correlation between the features to allow assessment of risk, guiding decision-making for customer transactions. For example, growth in financial institutions depends on spending the loan targets on the right customers. It helps to maintain a good track record and unusual defaulters are ignored.

Approaches – Situation-based models, Statistical based models.

AI-based Customer Support:

Customer support is an integral part of business growth. For every product/service, there are some usage issues, bugs, or training for the customer which if unsolved leaves a bad effect on the product/service. Artificial intelligence helps in predictive insights using sentiment analysis, 24/7 service, faster response, centralizing different channels of customer like email, messages, phone calls.

Techniques- Predictive analysis for an instant solution, CHATBOTS (audio-based/text-based),Multimodal sentiment analysis, etc.

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Algorithmic Trading: Complex AI systems capable of making trading decisions at speeds and efficiency greater than any human is capable of, often making millions of trades in a day without any human intervention is the objective behind Algorithmic Trading/High-frequency trading. It is one of the fastest-growing financial sectors. Many financial institutions, proprietary trading firms now have entire portfolios that are managed purely by AI systems. Algorithmic Trading systems are generally used by large investors, but recent years have also seen smaller firms trading with their own AI systems.

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Churn Prediction: Customer churn is the tendency that the customer is no longer interested to follow the product/service. Churn rate is a health meter for companies whose customers pay on a subscription basis. Increasing customer retention is a very important strategy.

Approaches- Decision tree, Logistic regression, AdaBoost, SVM, Hybrid models.

AI-oriented Advertising:

Nevertheless, the other approach for generating new customers is by using “Target Audience Advertising” and “Ad Budget Optimization”. Clustering and categorization help to analyze the level of advertising required at a particular location and budget forecasting methods help to complete tasks optimally.

Techniques for Target audience advertising- SVC, K-means clustering, etc.

Techniques for Budget optimization- Time-series forecasting using ARIMA, Holt-Winter’s exponential smoothing, Statistical models, etc.

Example – Financial Institutions

For any financial institution, it is important to study the overall growth in every product and service they provide. In the world of heavy competition, customers get easy access to compare products from different competitors. Using Artificial Intelligence, however, we can track the patterns of what changes are essential in the existing business strategy. Automated Growth Management thus plays an important role.

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Every retail bank, commercial bank, insurance companies, and brokerage firms have monthly target/yearly target systems. The sales team has a task to bring n number of customers. Products offered include current, Demat and savings accounts, personal and mortgage loans, credit cards, and business banking accounts, fixed deposits, health/property insurance. In the diagram, we can see that Algorithmic trading, loan predictor, churn predictor system are used by the company for internally handling growth. At the customer end, it helps not only in growth but the customer relationship grows stronger.

References:

  1. https://en.wikipedia.org/wiki/Predictive_analytics
  2. https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence#Finance