Sales is an area in which machine learning and AI solutions can be readily applied. Demand forecasting enables the optimisation of inventory to minimise storage and provisioning costs and the opportunity costs of lost sales and profits. In addition, customer segmentation models and recommendation engines facilitate hyper-personalisation of targeted marketing for cross-selling, up-selling, and enhanced customer engagement.
The marketing business function contains ample opportunities for machine learning systems. Algorithmic marketing enables the optimisation of pricing strategies in accordance to market fluctuations and the expectation of competitor products and actions. Moreover, life-cycle value analysis and RFM (recency, frequency, monetary) analysis facilitates a greater holistic understanding and targeting of high value customers. Lastly, sentiment analysis evaluates customer engagement and the reception of various marketing strategies.
Machine learning and AI systems can be effectively applied to the customer service business function. Natural language programming models are applied to monitor customer satisfaction across call centres and survey responses. Emotion and sentiment analyses are able to capture changes in customer mood throughout an interaction with a customer service representative. Call classification can also be applied to automatically delegate incoming calls to staff based on their expertise and strengths.
The operations business function is currently being disrupted by machine learning and AI. Inventory and supply chain optimisation algorithms are able to automate and optimise the inventory management process to minimise loss of profits and storage costs. In addition, predictive maintenance models are able to forecast unscheduled equipment downtime based on historical patterns – in effect reducing downtime, maintenance costs, and increasing operational efficiency.
Machine learning and AI systems are most commonly used in the finance business function in order to forecast future expenditure for budget allocation and prediction. Fast Fourier transformations are able to generate stable approximations of even the most complex seasonality patterns while recurrent neural networks capture non-linear asymmetric cyclic patterns. However, with the advancement of sophisticated algorithms and computational power, anomaly detection systems are able to traverse internal financial databases, proactively searching for fraudulent or abnormal financial behaviour – automatically notifying the financial department when abnormalities are discovered.
The human resources space has large potential for application of AI and machine learning techniques to better improve search and operational efficiencies. Performance and satisfaction management systems are able to monitor employee engagement and satisfaction, minimising churn while providing quantifiable performance metrics. Moreover, attrition models are able to forecast expected employee turnover by department and seniority. Most prominently, however, are the use of natural language programming models to conduct resume screening processes with speed and impartiality that are unattainable by human reviewers.
Integrating AI into your software or service product can make a significant impact on the ability of your systems to provide synergistic selling, sales and preference tracking, and gain a deep understanding of your user base’s motivations and purchasing behaviour. Machine learning and AI also advances the process of physical product development to new unseen territories. State-of-the-art methods facilitate automatic product development systems that generate design patterns optimised for chosen objectives such as aerodynamic resistance or ergonomics. Furthermore, ML-based systems are able to isolate the design features and attributes that generate the greatest customer satisfaction and usage – guiding the focus of the product development team.