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. Machine learning has a clear role in automating or enhancing the following sales business processes: Lead Generation – Creating automated contact lists for outbound communication. Lead Cleansing – Verifying that contact data is accurate (automatically referencing existing CRM data with new updates across various outside / inside information sources). Responsive E-mail Cadence Automation – Automatically following up with unresponsive leads using targeted campaigns that vary depending on the lead’s behaviour, or CRM data point
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. People want brands to care about them. So much so, that 52% of customers are likely to switch brands if they don’t feel a company is making enough effort to personalize their messaging. Machine Learning can be used to analyse huge reams of customer data on their customer’s online behaviours, interests, and past purchases to tailor the online shopping experience. Everything from the emails to the product offers is personalized, along wi
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. Using Machine Learning to identify customer issues with social listening and ticketing solutions wherever they arise is the first step to resolving them. Social listening and ticketing vendors help you to leverage Natural Language Processing and machine vision to identify customers to contact and respond to them automatically or assign them to relevant agents increasing customer satisfaction. Artificial Intelligence can seamlessly but securely authenticate custome
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. Logistics Control Tower operations are utilising the power of technologies to get new insights for the improvement of warehouse management, collaboration, logistics, supply chain management. Machine learning can analyse timings and handovers as products move through the supply chain. It can compare this data to benchmarks and historic performance to identify potential holdups and bottlenecks and make suggestions to speed up the supply chain. The advent of visual pattern recogniti
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. Machine Learning algorithms can be useful in using patterns in data sets to highlight potential areas of discrepancy and double-check human work. Machine-learning technologies can sort through high volumes of data from financial reports at an exponentially faster pace than humans
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. With regard to Current Employees: remember, this is the age of Big Data. Managing employees means gathering data on a host of areas – that span employee attitudes and feelings, qualification verification, employee approach towards policies, compensation management, and addressing relevant external developments. This means a giant re
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.