When we think of the benefits surrounding business automation, it can be easy to focus solely on operational results - things like improved first-call resolution rates in a contact centre, or increasing basket size with personalised product recommendations, or simply the removal of monotonous tasks that enable employees to do more meaningful work. In some cases, these improvements alone can make a compelling case for AI automation, but when combined with the profound customer experience improvements that come tied to these updates, the call for a solution becomes even more critical.
Every business is unique - each with different challenges, systems, and strengths, so identifying the perfect opportunity for AI automation can be difficult. Think about the flow of information through your business, and identify the bottlenecks and dependencies: repetitive, manual, or arduous tasks that your employees encounter regularly. These often hold opportunities for automation, especially as your business scales and keeping up with personalised service becomes more difficult to manage. Overlay this with the customer journey through your business, and you’ll likely find a few key priorities. To give you some ideas, we’ve pulled together a list of ways to improve customer experience through AI automation.
You’re likely already familiar with AI Email Prioritisation - email providers like Outlook and Gmail use Natural Language Processing (NLP) technology to sort spam, promotions, and updates into different categories, making your inbox easier to handle. On an individual scale, this is already significant - but if your business receives high volumes of queries - especially queries that are repetitive or have standardised answers, automating replies and triaging emails has an even greater effect.
In the insurance industry, email triage may involve linking an internal ticketing system to enquiries to automatically answer questions like; “What’s the status of my claim?” or “what is my premium for a windscreen repair?” These systems can be designed to process your unique business data, providing accurate information or distributing queries to the most appropriate department to answer a question.
Documents present another challenge for businesses, especially those that may be relying on legacy systems. Using natural language processing to automate these systems can improve customer service by decreasing the time it takes to arrive at an outcome. If your business relies on this type of paperwork to deliver customer outcomes, this may be a perfect opportunity for AI automation. If your business is still processing hard copies, optical character recognition (OCR) can be implemented to digitise and then process these documents.
In the mortgage industry, we’re helping clients process applications more efficiently using this type of AI automation. This relieves operational pressure on the business, as well as providing the mortgage applicant with an answer to their application quickly and easily.
AI Automation has a huge range of applications within contact centres - and in some business structures, the Email Prioritisation solution we’ve already discussed may fall into this category. However, there’s much more to consider! Voice or text chatbots can provide conversational support to customers, answering simpler queries so that human agents can more quickly resolve complex customer concerns. Additionally, Agent Assist programs can be developed that process caller details and provide information directly to call centre employees, giving them access to real-time answers to complex questions like “Can you tell me your pricing” and eliminating the need for agents to search knowledge bases manually for information.
In the education sector, we’re helping clients answer student queries that have multiple dependencies with a system that understands these complexities and can present the correct information in mere seconds, decreasing resolution time and improving interaction quality.
Product recommendation can take many forms - if you’ve ever shopped online for clothes or shoes, you’ve most likely come across at least one scrolling banner entitled “You May Also Like!” with related products - often more clothes or shoes. When used effectively, these tools can improve conversion rates and basket size, but they’re often manually created. This approach doesn’t scale, and it’s not personalised per customer. For example, if you’re like me, you’ve probably also received an email recommendation for something like a fitted sheet set a day or two after purchasing the very same set in a different size. AI Product recommendation engines can prevent this type of unhelpful repetition.
Recommendation engines create robust lists of products based on a list of pre-set qualifiers. This adds a critical layer of intelligence to the process - not recommending sheet sets to those who have just purchased, but identifying that they may be interested in pillows or duvet covers. They can also be built around specific gift giving occasions, with prompts like “Father’s Day Gifts for outdoors-lovers” to drive holiday purchase conversion.
We’re working with one of New Zealand’s largest retail businesses to drive conversion and improve basket size by helping them provide customised recommendations for a variety of gift recipients.
If your customer journey is largely digital, you likely already have a wide range of analytics at your disposal to outline key metrics like conversion rate and speed, lifetime value, and churn - but can you accurately predict customer behaviour? Implementing AI predictive analytics can be a valuable way of gathering critical information.
Opportunities in this area include using NLP to identify and report on critical customer feedback before a customer makes up their mind to leave, and using machine learning to analyse behaviour paths that lead to churn so that you can take steps to retain customers before they’ve even actively thought about leaving. In this digital age, companies can’t afford to let these kinds of insights go unnoticed.
We’ve included this bonus example to show you that the opportunities for AI automation truly are only limited by your imagination! If you’re just starting your journey towards automation to improve customer experience, you likely won’t land on Computer Vision as a solution - but it’s a critical step for some businesses!
Computer vision can be used to identify conditions like flow patterns in retail stores (providing data on the best arrangement of both shelving and product), as well as in object detection to enable accurate sorting and packaging on production lines. We’re working on a solution in the meat industry that automates the process of identifying and sorting meat cuts to improve packing line production. This reduces costs and also improves packaging accuracy, providing a better outcome for the customer. If you have a blue-sky idea you’d like to see implemented in your business, get in touch!
These opportunities only represent the tip of the iceberg. The ways in which AI automation can be used to improve customer experience truly are only limited by your imagination. When considering opportunities to automate business processes, we suggest starting by thinking about the most manual and repetitive aspects of your business. How could automation help your employees work more efficiently? What benefit would this provide to your customers?
In working with businesses that are new to AI automation, we’ve found that it helps to begin with projects that will work seamlessly with the rest of your processes. Introducing additional layers of complexity is exactly the opposite of what we hope to accomplish! Instead, the result should be a streamlined, effortless automation that leaves your staff - and your employees - delighted.
If you have an idea for an AI automation project for your business, we would love to hear from you! Get in touch with us here.