By Business Function
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 points about the lead itself.
Sales Script Optimisation – Using automation to learn the phrases, cadence, and processes of the best salespeople in an organisation, and model / teach those skills to lower performing salespeople.
Sales Forecasting – Leveraging CRM data, current activities, and historical performance to predict sales performance.
Sales Content Optimisation – Ensuring sales representatives have the best content for customers: personalising their upcoming sales meeting (for example, automatically matching a lead’s profile with the sales material most likely to convince that particular lead).
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 personalise 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 personalised, along with every touchpoint in the buying journey. It may seem somewhat ironic, but machine learning helps to create a more human experience. E-commerce personalisation makes customers feel more important, with the experience carefully crafted to cater to their needs and interests.
When it comes to marketing, it is incredibly useful to have a system that can quickly identify trends and actions in real-time, and then respond accordingly without any human input. This ability to “learn” on the go is what makes Machine Learning so important in marketing today, and in the years to come. In the past, many marketers launched advertising campaigns on little more than guesswork. Without truly knowing their audience. A lot of money is wasted on ads or promotional efforts that do not resonate with target customers.
Machine learning eliminates this marketing waste. Taking a scattershot approach to marketing in the digital age is not only unnecessary but mere folly. Machine learning takes the guesswork out of the process, allowing marketers to reach their audience with content and product offers that stand the best chance of engagement – and ultimately, conversions.
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 customers. Voice authentication allows you to authenticate customers without passwords: leveraging biometry to improve customer satisfaction and reduce issues related to forgotten passwords.
Machine Learning can assign a Customer Sales Agent to a specific customer. Ensuring that the agent you assign to a customer has the expertise and style which matches the needs of that customer. This can occur through Call classification systems which leverage Natural Language Processing to understand what customer is trying to achieve enabling agents to focus on higher value-added activities and enable you to better match agents and customers.
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 recognition has changed the support of physical assets across the supply chain network. Inspection of the inbound quality has also been automated by Machine Learning with the help of algorithms, isolation of product shipments, logistics hub. Efficient supply chains rely on products being in the right place at the right time. Machine learning can assess customer requirements and optimise the upstream supply chain. It matches the timely supply of goods with marketplace demands.
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, and then turn that data over to human eyes, which can subsequently investigate the story behind the numbers and evaluate whether certain patterns or anomalies may be cause for concern.
Another process that can allow finance teams to benefit from collaboration between humans and intelligent machines is fraud reduction and cybersecurity. As finance departments are embracing digital solutions for storing and managing financial data – either on-premise or in the cloud – cybersecurity is becoming a top concern for CFOs who must ensure that access to that information is monitored and regulated. Again, machines have the ability to churn through massive sets of data regarding access to and transactions upon those data sets, identifying abnormal patterns or unique access behaviours.
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 reservoir of data, that’s ever-growing. Manual management is clearly not an option. Here’s where Machine Learning comes in. ML can effectively accept, store, process and manage these giant data volumes and offer smarter insights via simple analytics.
In Recruitment: as big data comes from various sources – forums and social media – companies are struggling to decipher all the data and locate the right candidates. Machine Learning can look at a variety of key criteria – qualifications, experience, interests, professional connections, and memberships, among others.
ML helps to reduce HR’s manual efforts, streamline applicant discovery, and importantly frees up teams to focus on more strategic and productive activities.
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.
To ensure efficient operation of their business, Alinta needs to forecast the energy consumption of their commercial customers at a two week horizon. The Australian energy market requires settlement at 30 minute periods at the ‘national metering identifier’ (NMI) or site level.
To enable their customers to grow their businesses, Henry Schein makes supplies available on credit. Each business is unique, and an appropriate credit limit for each business changes with the changing circumstances of each business and market conditions.