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Category: Use Cases

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Use Cases of Machine Learning in the Telecommunications Sector 

 

Machine Learning (ML) and Artificial Intelligence (AI) are becoming a key implementation in every industry with high value and efficiency being provided across the board. The telecommunications sector is no exception and is at the forefront of adopting Machine Learning to optimise operations, increase revenue, transfer and visualise data, enhance customer relations, and improve marketing and sales strategies.  

 

Data transfer, exchange, and analysis is key in the telecommunications sector with the amount of data increasing every day. This is why all systems and methods relating to data need to be relevant and accurate.  

Below are a few of the use cases where Machine Learning and Artificial Intelligence have provided benefit in the telecoms industry: 

 

Product & Service Recommendation Engines 

 Recommendation engines in many industries come up in our digital life. It would not be effective to market product recommendations without taking customer preferences into consideration. By analysing historical customer data (their behaviour and preferences), Machine Learning algorithms are able to predict which products a particular customer will most likely purchase.  

This is done through filtering. Collaborative filtering makes assumptions about a customer’s preferences based on behavioural similarities to other customers (assuming that people with similar profiles have similar interests). Content-based filtering makes assumptions based on previous customer product history – recommending items similar to those they have already purchased. Customers are more likely to upgrade or repurchase products that align with their needs or preferences. This is why telecom companies have implemented this ML method to increase revenue.  

 

Anomaly Detection & Fraud 

 Due to the fact that the industry attracts a significant number of users every day, this brings high chances of fraudulent opportunities. Fraud, such as fake profiles, theft, illegal authorisation, and account cloning, have a negative impact on the customer-company relationship.  

This is why telecommunications companies are implementing unsupervised Machine Learning algorithms to spot abnormal characteristics or activity in customer behaviour. 

By analysing past data of customer behaviour, ML algorithms are able to visualise and present these anomalies to analysts in real time. This is especially efficient as it allows telecommunications companies to alert their customer of suspicious activity almost immediately thus protecting the customer-company trust relationship. 

 

Customer Segmentation for Effective Marketing 

Successful marketing in any industry relies on accurately segmenting and targeting groups according to their preferences and characteristics.  

 

The key to success for the telecommunication companies is to segment their target markets and target marketing content accordingly to each group. This golden rule is relevant to the various areas of business. Machine Learning algorithms can create highly accurate customer segments based on shared customer characteristics. This allows telecommunications companies to better strategise and reach customers with more relevant marketing material (and increase their chance of a sales conversion)  

 

(Did you know that ML customer segmentation can be done in real-time. As customers change preferences and patterns, they are re-segmented to different groups based on whichever characteristics are most prominent. This means targeting the customer with irrelevant marketing material is less likely to happen – and saves time for those who once needed to re-segment groups manually).  

 

 

Optimised Product Development 

Companies in the telecommunications industry need to provide their customers with products that satisfy their needs and wants in order to continue to keep customers loyal and to generate revenue. Machine Learning algorithms can analyse data across different departments to ensure customer feedback is taken into account to provide the most customer fulfilling products are created. By analysing data such as customer feedback, marketing intelligence, and product stock, ML algorithms can present the most data-driven product suggestions for development. 

 

Conclusion 

Data science and its benefits in the telecommunications industry is not a new occurrence. However, Machine Learning has taken efficiency within the industry to the next level. Those companies that have implemented ML in their processes are experiencing fewer issues to resolve, control or occur from happening at all. 

With the right partner and expertise, telecommunications companies are experiencing these benefits for themselves. Intellify is AWS’ Partner of the Year in Data, Analytics, & Machine Learning and has worked with telecommunications companies in the past on bringing Machine Learning capabilities and culture to their organisation. Contact us to learn more about how we could help you do the same based on your own requirements.   

 

Blog Use Cases

Predictive Maintenance in the Manufacturing Industry

 

The increase in the use of Machine Learning (ML) and in the levels of automation in manufacturing has naturally led to improving asset productivity through data-driven insights that were not possible before. Now we see predictive maintenance taking the place of the “run to failure” approach where asset maintenance is scheduled once a failure in the system is observed. Predictive maintenance is a set of processes that reduce maintenance costs and prevent extra time being wasted on repairing assets – particularly in dependent machinery systems.

In order to detect changes in the system that point to potential “health problems” in machinery, pattern-recognising machine learning models are deployed. Manufacturing companies are using predictive maintenance software to reduce unplanned downtime and maximise profitability. Machine learning predictive maintenance is unique because it not only predicts impending failure but produces outcome-focused recommendations for operations and maintenance from analytics.

 

How Does This Work?

The effectiveness of ML predictive maintenance models relies on the health indicators of the machinery. Historical data from these health indicators (features) is analysed by ML models. The ML models then compute another indicator which describes the machinery’s health and an estimated remaining lifetime.

 

The below are the base components for predictive maintenance to be carried out:

Data Sensors – sensors that collect data are installed on the machinery

Data Flow – a communication system allows machinery data to flow to a data storage centre

Data Storage – a central hub stores, processes, and analyses the machinery data either on the premises or on the cloud

Predictive Analytics – algorithms are applied to the processed data, patterns are recognised, and insights in the form of dashboards and alerts are created

Cause Analysis – corrective action is determined based on machinery health insights

 

 

When is Predictive Maintenance Being Used?

Manufacturing companies (and many other companies across different industries) use ML predictive maintenance in a number of ways. Manufacturers of machines and machinery parts use predictive maintenance to analyse and monitor the condition of motors and their levels of wear, power consumption, and productivity. Predictive maintenance is also being used to minimise production errors and reduce waste. ML models can predict when the number of faulty products is likely to exceed a set maximum percentage and give causes for the expected problems with the products. Manufacturers are also using ML predictive maintenance models to predict issues across the factory floor: by linking all monitored sections with installed machine sensors, security cameras, worker equipment, and workstations.

 

Final Notes

Manufacturing organisations who have already implemented ML into their daily functioning are seeing great benefits of predictive maintenance – an increase in information delivery speed, an increase in usefulness of information, and decreases in costs. The ML predictive analysis has allowed organisations to make smart decisions based on their own data. This results in a complete solution across operations, engineering, and maintenance. Machine learning predictive maintenance plans give managers unmatched insights into operations that can lead to optimised safety, efficiency, and decision-making. Taking a predictive maintenance approach to asset performance and reliability means manual root cause analyses are no longer needed; instead ML models will consider an entire history of asset performance and failures to identify signs of impending problems in advance.

Contact Intellify, the AWS Partner of the Year 2019 in Data, Analytics, & Machine Learning, to learn more about how your organisation can benefit from machine learning & artificial intelligence. We provide end-to-end services, helping companies reach their full potential by leveraging data proactively.

Blog Use Cases

Implementing Machine Learning in the Energy Industry

 

 

Artificial Intelligence (AI) and Machine Learning (ML) are both hot topics in business these days. These advanced analytics tools have been introduced into businesses across many sectors – from retail, to finance, to marketing, with one of the less publicised yet important sectors being the energy and utilities sector. 

 

The power of Machine Learning, data science, AI, and predictive models has made many renewable energy companies more efficient across business functions – lowering costs, increasing returns, and making more accurate predictions. 

 

Terminology 

Before getting in to how Artificial Intelligence and Machine Learning can benefit energy companies, allow the below section to provide more clarity on the terms.   

Artificial Intelligence (AI) refers to any machine or model that can conduct human-like activities.  

Machine Learning (ML) is related to AI, however they are certainly not the same thing. 

AI refers to computer-generated intelligence in machines; Machine Learning allows for some sort of artificial intelligence by using statistical techniques.  In other words, Machine Learning algorithms allow a machine to train itself based on datasets to perform a task more efficiently. 

 

Artificial Intelligence and Machine Learning in the Energy Sector 

Below are 3 use cases of these data analytics tools in the energy industry:  

 

Forecasting Energy Demand  

When it comes to delivering electricity to customers through complex power networks (grids), there needs to be enough electricity or energy available to match the demand for it. Otherwise blackouts and system failures may occur. 

 

Machine Learning algorithms can determine demand by looking at past data of daily energy consumption changes for individual customers over time. From this data Machine Learning models are able to generate very accurate energy demand and consumption forecasts. These predictions can be used by energy & utilities companies to save time, optimise operations and every storage systems, and prevent customer dissatisfaction.  

 

Predictive Maintenance for Power Systems 

In addition to matching energy demand with energy production, Machine Learning is a beneficial tool to ensure the consistency and health of power grids. In the past, massive blackouts and power cuts have occurred with no warning to energy companies of upcoming predicted faults in their systems or even warning about the power cuts currently occurring. Machine learning algorithms can implement predictive maintenance to predict when assets (power lines, stations, and machinery) are estimated to fail. These predictions are made from analysing data – collected from sensors on power lines, boxes, and machinery – against a timestamp.  This helps ascertain when assets are predicted to fail and can be used to predict the expected remaining useful life of assets. 

 

This is useful information for energy companies for saving time (replacing or fixing those assets predicted to fail before they are completely broken and have to re-ordered/replaced etc.), saving costs (allowing companies to optimise maintenance activities based on accurate predictions), and reducing customer dissatisfaction (fewer unexpected asset failures should result in more consistent running and provision of power). 

 

 

Efficient Energy Storage  

Besides being beneficial for traditional coal and gas energy companies, renewable energy companies can too reap the benefits of Machine Learning tools. A commonly known issue with renewable energy is its sporadic availability. When there is no wind or sunshine for a while and therefore not enough energy production or, on the other hand, too much energy produced for the amount of consumption (causing the surplus energy to go to waste), it is essential for energy companies to optimise energy usage and storage.  

Machine Learning solutions can be used to predict energy usage and manage storage decisions. For example, ML models can manage electricity shortages by briefly cutting off the supply for electricity coming from the main grid, while using stores of energy from regions. This reduces unused and wasted energy supplies. 

In the case of saving and storing energy, Machine Learning can generate forecasts for weather, energy production, and electricity demand to optimise energy usages on “overly productive energy days” and thus reducing the need for cutting off main grid supply.  

 

The Future of Machine Learning and Artificial Intelligence in the Energy Industry 

For developed countries with “green” goals for an environmentally-friendly economy with resistant and consistent power supply is of top priority, Machine Learning algorithms that predict demand, improve performance, reduce costs, and prevent system failures are a clear step in the right direction.  

 

With the right partner, your energy company can reap the benefits of Machine Learning and get accurate, insightful, and actionable information out of your data to more accurately forecast energy demand, predict asset maintenance, and more efficiently store energy. Save resources, time, and costs – contact Intellify to hear how we have worked within the energy industry to do just that. We can help you achieve your Machine Learning and Artificial Intelligence requirements.  

Blog Customer Segmentation Use Cases

Customer Segmentation & Personalisation with Machine Learning

 

Customer segmentation is a method of dividing customers into groups based on their shared characteristics such as age, gender, interests, or purchase habits. This is important for allocating company resources and targeting clients effectively. Customer segmentation influences mainly sales and marketing decisions in organisations. It allows marketers to tailor their campaigns to different target groups for promotional, marketing, sales, and product development strategies. Personalised marketing and sales efforts that are based on well segmented customer groups have much higher conversion rates and therefore impact an increase in revenue. 

 

Clustering 

Even though customer segmentation is such a vital part of organisational decision-making and machine learning is advancing rapidly, very few companies are using automation processes to optimise their efforts in customer segmentation. Organisations that are using machine learning algorithms in their customer segmentation efforts often use “clustering”. 

Clustering is a machine learning technique for identifying and grouping similar data points (e.g. customers) together. The objective of clustering algorithms is to ensure that the data-points in the same group are very similar, and data-points in different groups are extremely dissimilar. This process is “the hard part” that  42% of marketers in the USA and UK do not complete due to lack of employees, time, and funding, which prevents them from reaching their personalisation goals.  

 

Once customers, stores, or locations have been segmented into groups, managers can make decisions about how to target each group most appropriately based on their differences. For example, specific segments may be optimal targets for inbound marketing campaigns such as cross-selling and others may be better suited for outbound campaigns such as email messaging. Thanks to machine learning models, companies no longer need to spend ages defining segments and allocating customers to them. Now, their time can be used for understanding who will be best served through which channel, with what message, and at what point in time. Companies can now focus on how to maximise the impact of current capabilities and resources such as available channels, agents, content, and offers!  

 

In addition to simply automating the grouping process, machine learning can impact the “contacting” process. Machine learning models will learn (based on each customer interaction) whether the chosen communication channel, content, offer, and the point in time were “right” for a given customer. Based on the customer interaction with campaign touch points, machine learning models can automatically predict what engagement mode is most likely to delight each individual customer. The more data there is, the more accurate the models will be. Machine learning models do not even need to base their assessments on data from current campaigns – historical data from customer engagement with previous campaigns can be used to train the models.  

 

 

Other Benefits of Doing Customer Segmentation with Machine Learning 

 

Simpler & More Precise Segments  

These days customers cannot only be segmented based on their demographic or purchase behaviour data for effective segmentation results. These segments are simply too broad and deliver only suboptimal conversion rates. Organisations are now including more advanced additional criteria to ensure better segmented groups. Specific data from the customers’ sales journey is included such as whether they attended an event, clicked through to the company website, clicked on a product, and whether they bought the product or not. The desired criteria to include is always changing and often growing. If managed only by humans, it can quickly become unmanageable and inaccurate. Allowing machine learning models to do this process for you, ensures your customer segments are always optimised, saving employee time and reducing the organisation’s costs.   

 

Changing Segments 

Decisions to change the segmentation criteria to include different characteristics have perviously been a challenge. Manually reallocating customers from their initial segment to another requires constant monitoring and shifting. Machine learning models automate this process and update and shift customers from one group to a more relevant group in real-time. In essence, the customer segmentation process does not need to be time-consuming, expensive, and manually adjusted, thanks to the implementation of machine learning clustering in this process.

  

Concluding Thoughts 

There is no longer a need to manually allocate customers to segments. With machine learning, each customer is their own segment, defined by any chosen criteria that organisations deem best. When it comes to customer segmentation, machine learning algorithms are highly effective for personalising the customer experience and leading to conversions. 

 

If you want to learn more about how Machine Learning can best be applied in your organisation, contact us to find out you could be saving time, money, and resources.  

Blog Use Cases

Supply Chain Optimisation & Machine Learning

Although it is not a media-attention-grabbing industry, the supply chain and logistics management industries have been in leading machine learning development industries over the last ten years. New technical developments have allowed supply chain and logistics organisations to leverage their data in ways that is decreasing expensive machinery failures, exceeding customer product and service expectations, and generally increasing their long-term return on investment.  

 

It is critical to have solid foundations when it comes to supply chain planning – one of the most important activities in supply chain management. The implementation of machine learning in the supply chain decision-making process has allowed for significant optimisation. Supply chain teams are now able to balance demand and supply and optimise the delivery process due to machine learning data analysis and intelligent algorithms. Machine learning in supply chain and logistics management decreases the amount of manual work that needs to be done by humans – large data sets can be automatically analysed without any natural human error. 

 

Here are a few of the areas of supply chain and logistics management where machine learning models are being implemented for optimisation: 

 
Inventory Management 
 

Both under-stocking and over-stocking are common problems in supply chain management that can ruin even the most efficient supply chain strategy. Machine learning algorithms can monitor the stock levels in a warehouse and take into account more factors than typical forecasts – this can improve inventory optimisation, particularly in businesses with multiple distribution locations. This is useful for reducing transport costs (transporting a more accurate amount of stock, fewer times) and keeping inventory at a “comfortable” level (idle stock levels can be decreased based on more accurate machine learning predictions of future demand). By analysing current inventory data, inventory management can be improved to ensure optimal business performance and allow workers more time to focus on product quality and customer experience. 

 
 Warehouse Analysis 
 

Machine learning has enabled computers to “see” and understand images and videos – this is called Computer Vision (CV). This tool enables warehouse automation. Product barcodes can be processed, warehouse perimeters can be monitored, and employee activities can be followed: preventing safety violations and theft or trespassing.  

 

In addition to CV, machine learning is enabling robots to take on mundane tasks that would usually be assigned to a human. For example, machine learning robots can pick ready goods to send to customers, can carry up to 500kgs as they make their way around with sensors to avoid collisions, and can be summoned by workers at any time. This technology is improving team productivity and efficiency in the warehouse while reducing the risk of human injury (from lifting, managing, and carrying heavy goods) and human error – like a parcel being sent to the wrong address.  

 
Logistics Route Optimisation 
 

Customers expect their deliveries to arrive at the time and date promised to them by the company. Therefore, it is important to ensure this happens without any delays and in the most cost-efficient way possible. Delivering parcels to customers on time is no simple task – for example, delivering just 25 parcels by van has 15 septillion (a trillion trillion) route options. Machine learning models can analyse existing route options and implement route optimisation – creating a current optimal route depending on road conditions, weather, and other factors. This means companies are able to reach bigger geographic locations, reach more customers efficiently, and generate more revenue. Of course, keeping the customer satisfied too. 

 
Supplier Relationship Management 
 

When you make an incorrect decision about your organisation, your business can suffer. This also applies when making decisions about your suppliers (in the worst case, a bad supplier choice can end your business). Machine learning can make the task of selecting a supplier and maintaining a relationship a lot easier. Reliable predictions about almost every interaction with an existing or potential supplier can be provided by machine learning models based on historical data sets (such as messaging records, audits, or credit scores). In this case, machine learning can help organisations avoid selecting an untrustworthy supplier and maintain a long term mutually beneficial relationship. 

 
Workforce Planning 
 

Most industries and business sectors involve team management and planning. The same goes for the supply chain and logistics management industries. Team management processes involve recruitment, retention, employee training, sub-team management, and performance analysis and more. Machine learning solutions can be applied to these areas of workforce management. For example, machine learning models can identify key traits of the most successful employees and use that information to shortlist most ideal candidates form a collection of CVs. 

 

Research shows that employees leave their jobs due to salary less often than expected. This means that employee turnover can be decreased without increasing salaries. Machine learning predictive analytics can save companies money on hiring expenses, training costs, and overall payroll expense by leveraging employee data (employee surveys, for example). This data could provide insight as to what really makes employees happy at work. This can influence a positive work culture and increase productivity without breaking the bank. 

 
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In conclusion, machine learning has applications across multiple sectors in supply chain and logistics management. From ensuring inventory levels are optimised throughout changing demand levels over the year, to monitoring warehouse and employee safety, to moving inventory in the warehouse more efficiently, to picking the most optimal route for product delivery, selecting and maintaining a healthy supplier relationship, and to team management, machine learning can leverage your organisational data and allow employees more time to focus on customer satisfaction. Allow machine learning models to increase your business revenue and be a long-term investment that save time, resources, and costs in your supply chain or logistics management organisation now.  

 

Maintain your competitive advantage and contact Intellify for more information on how machine learning can be customised for your company to allow you to optimise all department involved in your product or service delivering.  

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Machine Learning and the Finance Industry

 

 

Did you know that the term “artificial intelligence” was first coined in 1956? Even since then the world has rapidly evolved technologically. From the early days of radio, to television, to the internet, and now we have Artificial Intelligence and Machine Learning. Artificial Intelligence (AI) involves many aspects – including processing robotics and the process of automation by robotics. Due to large amounts of data and the increase in demand for understanding data patterns, AI and machine learning (ML) have become very popular (especially among large companies). When it comes to the financial services industry, thousands of organisations are using ML systems to more efficiently identify data patterns to gain target audience insights and much more. 

 

The financial services industry is full of data records. Hence the relevance of using ML to succeed in this domain. There are many use-cases of ML in the financial industry but risk assessment, fraud detection, financial advice, trading, and managing finance are the most popular (or obvious) business functions within the industry for machine learning systems. 

 

 

Risk Assessment 

To determine which customers are eligible for a credit card, banks use credit scores. However, this method of grouping customers is not necessarily efficient for business.  

Machine learning is now being used to scan thousands of personal financial records for loan repayment routines, the number of active loans, and the number of existing credit cards an individual has – to determine customised interest rates. Lenders can now determine those who are credit-worthy options if they do not possess an extensive credit history. For example, some machine learning algorithms use alternative data, such as mobile phone data, to evaluate loan suitability and customised loan rates. In addition to this, ML-powered models are objective – this removes any biases that a human credit officer may make. A vehicle lending company has experienced a 23% annual decrease in losses by implementing an AI system of loan evaluation. 

 

Fraud Detection and Management 

All organisations aim to reduce risk conditions. The financial industry (and banks in particular) take fraud very seriously – seeing as given loans are basically somebody else’s money. ML security and fraud identification systems are used to alert organisations of unusual behaviour by analysing past spending patterns across many transaction tools. A card being used in a faraway area from where it was previously used or a withdrawal of an amount of money that is unusual for the particular account, for example, are all detectable by ML systems. Understandably, these events may not be fraudulent. One of the benefits of ML-based fraud systems is that they can learn. If a regular transaction is marked as irregular by the system, it can be corrected and learn from its mistake. This allows for better informed decisions about what is flagged as fraudulent behaviour and what is not. 

 

Financial Advisory Services 

Pressure on financial institutions to reduce their commission on individual investments has increased. Luckily machines can give financial advice for a single down payment. Another use case for ML in financial advice is automated advisory – combining ML calculations with human insight to provide more efficient investment options to customers. This collaboration is key. An AI system is neither an all-knowing solution nor a simple marketing accessory; it is just as important to consider the human perspective along with the AI decisions when it comes to financial decision-making. 

  

Trading 

It is not such a recent experience that investment companies have been relying on technology and data scientists to predict future patterns in the market. In the trading field, investments are dependent on one’s ability to accurately predict the future. ML systems are highly useful in this regard: they can produce calculations from large data in a short time. Machine learning allows such systems to predict patterns based on past data (even taking into account – and planning for – anomalies such as the 2008 financial crisis). Depending on one’s appetite for risk, ML systems can suggest portfolio combinations: a person with an interest in high-risk shares depend on ML-calculated decisions on when to buy, hold, and sell. Those with a tendency towards low-risk portfolios can make decisions based on alerts from ML systems about when the market is expected to fall. 

 

 

Finance Management 

In an increasingly materialistic and connected world, managing finances can be a challenge to many people. Personal Financial Management (PFM) is one of the more recent ML-based developments. By analysing where consumers are spending their money, ML models build algorithms to help consumers make more informed decisions about their money. The model creates a spending graph which is personalised based on individual data from one’s web footprint. This may upset advocates of privacy breaching, but this is becoming a more popular way to manage personal finances as it removes the need to make length spreadsheets or hand-written budgets. From these small PFM suggestions to bigger investment portfolio suggestions, ML systems can be beneficial time- and money-saving tools to both customers and employees in the financial services industry. 

 

Machine learning is the future of many business functions within the finance industry. Soon enough it will be able to handle more financially sensitive and tedious tasks and provide more efficient solutions. The long-term cost saving benefit of ML systems are encouraging multiple financial service organisations to implement this technology. Although, currently, the implementation of ML systems in the finance industry is still in its infancy, the speed at which it is helping the industry progress predicts losses will be fewer, trading will be smarter, and customer experience will be better.  

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Machine Learning & Customer Service 

 

Machine Learning (ML) in customer services is viewed by directors, decision-makers, and by employees as a blessing to both the customer and employee experience. While technology is not yet able to perform all the tasks a human customer service representative could, machine learning in customer service teams is expected to increase by 143% over the next 16 months as more teams turn to chatbots, text, voice analytics, and other use cases. Currently, ML models in customer service are being used to both assist and replace human agents. This is primarily to improve customer service experiences and reduce the service costs of having employees handle simple requests and tasks.

Let’s explore how ML can be used to assist customer service agents; rather than completely replace them. 

 

Messaging Assistants 

We already know (and have probably experienced) chat bots are being used to communicate with customers online. However, many customers and companies feel that bots cannot handle all tasks that human agents can. Whilst still using ML technology – but avoiding frustration – “assistant” bots are being deployed to increase employee efficiency. This system allows the bots to deal with the simple questions up until the conversation becomes too complicated: then the chat is handed over to a human. Once the complex task is finished, the bot can continue with the simple details. The results of this system increase efficiency up to a point where human agents have been able to handle up to 6 co-existing chats at once. 

 

 

Organising E-mails 

Reading emails to decipher and respond to customer needs is a time-consuming task. Luckily, ML systems can speed up the process. ML technology is able to scan and tag emails to direct them to the right person along with suggested responses to the respective emails. Companies needing to respond to a large amount of emails in a short period of time have been able to halve the time customers wait to receive a response. 

 

Enhanced Customer Phone Calls 

When it comes to getting answers about financial questions, customers prefer to do so via phone call than online by a rate of 3 to 1. However, from a technical perspective, it is significantly more challenging to deploy ML voice-based communication systems. Background noise, unusual speech patterns, accents, and poor pronunciation make it hard for an ML model to translate voices into text. Yet even with these difficulties, companies are using machine learning to assist phone-call customer service agents. Conversations can be analysed in real-time with deep learning. These ML models listen to changes in volume, pitch, and can detect mimicking for classifying how customers are feelings and how calls are going. Customer service agents are given suggestions simultaneously to improve their calls based on their performance. A trial of this technology showed a 30% improvement in customer survey scores, a 6% improvement in issue resolution, and a significant decrease in requests to speak to a manager. 

 

 

 

The Future of Machine Learning and Customer Support 

New interesting uses for ML in customer service have only recently been deployed. This is why it is hard to tell how much ML will impact customer service and employment. Call centre employment is only one of many areas where this ML technology is being used (there are many retail salespeople, cashiers, and service hosts that will become freed up because up to 30% of what customer service agents do has automation potential). 

Thanks to ML advances, companies are now able to make quicker and cheaper responses to low-level customer interactions and improve more complex customer engagement experiences with human agents. This may lead to a significant increase in demand and standard of service. Millennials now have a 68% higher expectation of customer service than they had one year ago. Almost 80% of young customers expect the contacted customer service agent to already know their contact and product information. 

In the nearby future, ML in customer service will not cause an overall decrease in customer service jobs; companies may increase the number of ML assisted employees to deal with the new demand due to ML system-related efficiency and customer satisfaction.  

 

Concluding Thoughts 

Machine Learning in customer service is being used to improve the quality of service and reduce the cost of employees across industries such as finance, food, retail, and insurance. Companies now trust chatbots and ML speech-analysis systems to handle simple tasks – leading to a current significant investment in these systems. The benefits of ML in customer service is being felt directly by regular customers in a very clear and immediate sense. Customer service is a space where exciting ML applications are being deployed in across industries at a significant rate. While ML systems are being used to fully take on monotonous tasks – making it easier for human agents to deal with the more difficult issues – as they learn, these systems should soon be able to deal with more requests without human involvement. This means the current level of customer service being provided will be provided in the future at a much lower cost. This is not to say that companies will be able to spend less on this part of their business. As customers expect better service as a result of ML technology, more employees may be necessary. 

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Quick Read: Product Development & Artificial Intelligence

 

 
How Can Artificial Intelligence be Involved in Product Development?  
 

Let’s focus on two main ways that AI can help with Product Development. Firstly, AI can do the repetitive and boring tasks or jobs in this industry that us humans don’t usually enjoy. Secondly, AI can gather the best information relevant to the product development so that better quality products can be produced.  

  
Testing Product Features 
The shortage of quality assurance engineers and analysts in the workplace means that the quality standard of products is not always up to scratch. Testing the features of products can make up 50% of the work being done on a product before it is even approved for production. This is seen as a huge time-consumer – especially when aspects such as customer and competitor research are being overlooked. 

An AI automated approach can help in this regard. By assimilating, machines can meticulously mimic human behaviour. This means that a team of testers can move beyond the traditional route of manually testing products and progressively move forward toward an automated and precision-based continuous testing process. AI models can test the features of the product to find faults (with a higher chance of finding faults that a human product manager may have missed). Not only are product faults lessened by using AI models, but human error is minimised too. Even the most meticulous tester is bound to make mistakes while carrying out monotonous manual testing. By performing the same steps accurately every time, AI models never miss out on recording detailed results. Testers are freed from repetitive manual tests and have more time to create new automated software tests and deal with sophisticated features. 

 
Designing for Ultimate User Experience 
Successful products are generally those with uses that resonate with the users. The product needs to be user-friendly and, in some cases, even fun to use. Product design calls for creativity across the board. The design team must consider the product uses (regular and unusual) and support those uses that embrace the business objectives as well as those that give the user value.  

 Artificial Intelligence can use behavioural data to determine how a product is used and how it can be built to best fulfil its use. The most useful part of this determination is that AI can alert the design team before the manufacturing phase as to which designs will and what won’t be successful. By analysing the intended steps to use a product, AI can determine whether a user will be successful in getting the desired action from the product or not. This means that it is no longer necessary to build different prototypes of a product for testing. Simply run all the different product designs through a simulation and let AI models determine which is best. This is ground-breaking for companies that are heavily reliant on manufacturing: saving them time and money previously spent on product development and research. 

 

 

As Artificial Intelligence has become relevant in the product development cycle, organizations are faced with the decision whether they should adopt it wholly within their practices. Initially, the set-up costs of an AI system may seem unjustifiable in the product development industry, but in time organisations will produce greater testing rewards for less money.  These new savings can be redirected towards quality assurance efforts, testing uncovered areas, or more exciting and creative parts of product testing. In the future, AI in product development won’t only be relevant in product testing but will be applicable to all roles across product development involved in delivering top-quality products to the market. 

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Artificial Intelligence in the Retail Industry – 5 Common Uses

How Can Artificial Intelligence be Implemented in the Retail Industry? 
 

 

If you ask any business leader or expert in the retail industry what the future of shopping looks like, you’re bound to hear something about “bots” shaking up the way things work. Artificial Intelligence (AI) in retail is being used across the entire service and product life cycle – from manufacturing to post-sale interactions with customers. This means that retailers need to change their strategy and thinking in order to include this technology and remain relevant. 

 

In this blog post we will look at 5 ways in which AI is being used in retail:   

 

1) Product Recommendation through Personalisation 

 

Products are now being recommended to customers based on their personal buying habits. Our online buying habits create data categorised into, for example, products bought, preferred brands, money spent and frequency of buying. Machine Learning algorithms use this data to predict which items a customer is likely to buy. Once this technology anticipates the intent of the customer, it provides a recommendation in real-time with incredible accuracy. These recommendations come in many forms – for example, links to other items (“customers who bought that also liked this….”) or marketing emails that contain the chosen items that are suited to your buying patterns. Whatever the form of the recommendation, customers are more likely to buy products or services that require little extra thinking and align with their already formed self-concept. Machine Learning algorithms and personalised product recommendation allows for just this.   

 

2) Customer Service and Complaints Resolution 

 

Artificial Intelligence software can help build a strong sense of brand trust through communication – by answering questions and resolving issues. Brand trust is earned when customers are satisfied by answers to their questions and by the outcome of their complaints. Artificial Intelligence software manage multiple communications at one time – meaning no customers are kept “waiting in the queue”. In addition to less time wasted, Machine Learning “operators” can answer customer queries more accurately by avoiding human mistakes (that a “mere human” operator would make in fetching answers). Artificial Intelligence software is also able to make decisions to solve customer queries or raise customer complaints with higher management immediately – giving customers a solution without delay. 

 

3) Inventory Management – Demand & Forecasting 

 

One item out of stock can bring a business to its knees. Machine Learning time series prediction models can estimate future demand levels for all items in an inventory. Although this is useful for retailers to know, Machine Learning models are even able to make decisions based on the demand information that they produced. This is a more advanced approach and is done by rewarding and punishing the model for acting incorrectly. For example, a model is punished for letting a particular inventory item run out of stock or for stocking a higher value for too long. A model is rewarded when in-demand inventory items are ordered within a safe window before it’s too late. These reinforcement systems produce outstanding results – more than 30% reduction in inventory operations costs in many cases! 

 

4) Pricing Strategy & Product Bundling 

 

Most of time, sales made depend on the prices of the items or services being sold. Customers will wait for prices to drop for the items they want or for items to be bundled at a better price. This leads to times when sales generate high revenue for short bursts and potentially overall unprofitable sales because of special price deals. Artificial Intelligence models can be used to drive sales by targeting a particular set of customers at their “optimal price” – leading to immediate sales. Different customer sets can be targeted by AI systems by determining which product combinations and at which price will lead to immediate or more likely sales. When it comes to non-urgent items, these methods are proven to make sales more consistent. 

 

5) Strategy Testing 

 

Artificial Intelligence allows retailers to test different strategies against each other without having to implement any of them – saving them potentially lost resources. By using AI and Machine Learning models to monitor customer behaviour (through computer vision and activity maps), individual shopping experiences can be created and resources can be better allocated to areas most needed. Retailers can find answers to their common questions, for example: 

 

Which rack is most explored by customers? 
Is it popular because of location or products? 
Can the most profitable products be placed on the most popular rack? 
Will people buy if prices are increased? 
Should high priced products be shown prominently? 

 

 

These 5 ways in which AI an ML are being used in retail situations are only a few of the possibilities that are available to retailers at the moment; and best believe there will be more to come! Despite these new technologies available in the retail industry, it will continue to remain heavily competitive. Retailers need to be aware of this technological shift and consumer trends that could drastically impact their business and the industry as a whole. As always, the key to success in retail is that the customer needs, wants, and expectations are understood and accounted for. As long as this is kept at the forefront of the retailer’s mind, then a changing shopping landscape is no cause for concern.

 

Informative Use Cases

Natural Language Processing in Business – How Is It Useful?  

What is Natural Language Processing? 

 Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. 

While natural language processing isn’t a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.  

 

 

While we, as humans, may be able to understand and speak more than one language, most people do not have fluency in “machine code” or “machine language” – millions of ones and zeros that produce logical actions. In the past, early programmers would use punch cards to communicate with computers but now “Siri, what is the temperature today” triggers a real-time response in a human voice. This interaction is made up of multiple sections: device activation from your voice, an understanding of your command, and a response in a fluent human language. This common “conversation” is made possible by NLP thanks to machine learning and deep learning elements. 

 

Common Uses for Natural Language Processing in Business 

Chatbots 

Chatbots are one of the more recent NLP examples. Most business bots are used in Human Resources. Some NLP tools are used to deal with common employee questions, for example, “how many days leave do I have” or even deal with workplace satisfaction requests such as “we need more milk in the breakroom”. Other chatbots are used to improve employee retention and morale – employee chatrooms are monitored by bots and when employees use certain complementary words like “kudos” or “cheers” then staff get rewarded. 

 

Conversational Search 

Other non-bot tools are voice-activated. For example, tools are being used to listen in on company meetings for trigger phrases like “what are” and “I wonder.” When it hears them, a search function activates and retrieves an answer. It would work something like this: “What was the ROI on that last year?” Silently, the tool would scan company financials and display the results on a screen in the room. This can save the 30% of the day that employees spend searching for information and up to US $14,209 per person per year.  

 

Hiring tools 

On the topic of HR, NLP software has long helped hiring managers sort through resumes.  

By using the same techniques as Google search, automated candidate sourcing tools scan applicant CVs to pinpoint people with the required background for a job. This is difficult however, as humans we try to stand out from the crowd and may use creative terms to describe our skills and this can cause CVs to be ignored by NLP scans. More recently, NLP technology has branched to include synonym searching on keywords. In addition to this, as women and minorities use language differently, this synonym-searching keeps qualified candidates from slipping between the cracks.  

 

Call Centres 

Natural Language Processing (NLP) technologies are increasingly active in the labour-intensive world of call centres. Both inbound call centres (usually administer customer and product support or information enquiries) and outbound call centres (used for telemarketing, debt collection, soliciting charitable etc.) are implementing NLP tools. 

The technology used in call centres roughly falls into four categories: real-time speech recognition; intent analysis, which classifies conversations based on context to predict customer intent in real time; conversation management, which ensures simultaneous processing of multi-pass conversations; and conversational analysis, which broadly analyses users’ dialogue. These previously extensively human-intense operations (demanding training, time, and money) are now more efficient and effective – thanks to NLP. 

 

These are only a few examples of ways in which Natural Language Processing can be implemented in business situations every day. While such Artificial Intelligence technology may seem to pose a threat to employee’s jobs; this is not the case. At closer inspection, these tools are simply allowing employees to work more efficiently by having the tedious and time-consuming aspects of their roles sorted out by machines and bots. Thus, ensuring that time spent is well-spent, that employees are satisfied, customers are being heard, and ultimately, businesses save time and money!