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Tag: customer service

Blog Informative News & Articles Use Cases

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. 

Blog Informative News & Articles Use Cases

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.