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Using AI and NLP to enhance order management services

Using AI and NLP to enhance order management services
In the first of his Discovery thought leadership series, Re:infer’s Phil Hughes explores the service and capacity challenges facing order management services. He outlines key Communications Mining use cases, and shows how they work together to achieve enhanced service, efficiency and capacity liberation in order management.

If you want to enhance the customer experience, drive operational efficiency or deliver service excellence, order management has to be one of the first places you look. Large organisations in competitive industries live or die by the service they provide. It’s everyone’s responsibility to ensure service is as good as it can be.

Agents in order management act as brokers, bridging the gap between members, customers, vendors and suppliers. When a customer wants to amend an order or request an update, it’s the service agent who contacts the vendor and ensures the request is fulfilled. As a result, agents spend most of their day in communications channels - primarily email - sending, receiving and processing customer requests.

This is essential work, performed expertly and tirelessly by service agents. Yet, as with any highly manual and unstructured process, there’s scope for massive inefficiency as well as improvement opportunities.

The Challenge

Service is critical to order management, and communications is critical to service. However, the sheer volume of inbound communications can cause service in order management to buckle. To keep communications flowing smoothly and efficiently, these organisations will typically use a workflow management tool to ensure requests go to the right teams and are queued for the correct people.

Workflow tools like Salesforce are able to extract top-level information from emails - like sender domains and the important elements in a subject line - and use it to categorise and sort messages. Yet this is lacking in insight and not always reliable. Agents are forced to expend time and effort to properly sort, log and categorise inbound requests, manually performing post-call logging in the workflow system.

The resulting MI is painfully bought, pulling agents away from their important work of interfacing between customers and suppliers. Yet its value is also questionable. Logging tends to be performed hastily by agents eager to move onto their next case. Human error is common, with many requests being wrongly categorised. One Re:Infer customer discovered that their human agents were incorrectly logging requests about 20% of the time.

Fortunately, Communications Mining applications are addressing these challenge in two distinct ways:

1. Request Automation

Agents are wasting precious time on manual comms work, straining service quality and jeopardising SLAs. To bolster operational efficiency and customer service, organisations have a strong incentive to free up their agents to focus on the most important service work.

The automation of service and comms-based workflows has traditionally proven difficult due to the unstructured nature of such processes. The accurate sorting and categorisation of email depends on natural language understanding - long the preserve of human agents and out of reach for most enterprise tools. But this is a perfect opportunity for Communications Mining.

Communications Mining provides complete visibility into communications and service channels - from email to chats and even calls. Business leaders have an accurate, real-time overview of service levels and gain insight into the most demanding and wasteful service processes. This provides an invaluable starting point for automation discovery. Instead of wasting time on projects that generate minimal ROI, you can rapidly target and assess the highest-effort processes for automation.

Leveraging the latest advances in natural language processing (NLP), Communications Mining platforms like Re:infer can accurately and consistently extract the most important information from inbound communications - including email. Fast and easy integrations with workflow mean that emails can be automatically categorised on receipt and sent to the relevant teams. Post-fulfilment admin is also unnecessary as Re:infer can extract the relevant information on a granular level.

With Communications Mining, human service agents still take the lead in managing and responding to the most complex and important requests. However, transactional queries, requiring only a few contacts per conversation, are easily automated from end-to-end when Communications Mining is combined with an RPA or automation tool.

Such transactional requests can represent up to 60% of all contacts in a service function, contributing to huge efficiency gains when automated through Communications Mining. Most importantly, service agents are freed to focus on their most important clients and valuable work.

2. Vendor and Customer Management

Request automation provides fast, tangible benefits that immediately support your business objectives. Yet Communications Mining provides another use case that is arguably even more valuable - though harder to measure.

When you transform unstructured conversations into clean structured data, you create a whole new reservoir of operational insight. Automation use cases tend to deliver quick gratification through cost savings, but it’s the analytical use cases that transform businesses and make them hyper-competitive. Automation makes your service more efficient, analytics makes your business more customer-centric.

Communications Mining provides accurate, real-time insight into the most important business metrics. This delivers valuable information you can use to guide vendor and customer management.

The success of an order management business depends on the strength and offerings of its network of vendors and suppliers. However, it’s often the case that certain vendors won’t be supporting your business objectives. If they’re failing to be responsive, to make the changes customers want, it may be time to disentangle. But you have to be certain before you act, and that means having accurate data and reliable MI. Communications Mining provides this.

With complete insight into customer communications, you can analyse and extract precious insight into vendor performance. For example, you may spot a repeating pattern of one vendor struggling to complete a particular order on time, or discover that they’re commonly the subject of customer complaints. With Communications Mining you gain a real-time window into vendor performance and can make data-driven decisions with confidence. And that’s without the painful process of constant customer surveys and highly subjective stakeholder interviews.  

Communications Mining: Order management optimisation

Communications flow through every part of order management services. Communications Mining enables you to transform it into business value. It becomes possible to understand every service process, from the level of an individual customer interaction to the real-time performance of your entire vendor network.

Creating a new source of structured data, Communications Mining finally allows you to identify and automate your highest-effort processes and gain detailed operational insight that transforms your business.

Learn more about how Communications Mining advances hyperautomation, cuts costs and improves insight at the largest organisations, including Deutsche Bank, FARFETCH and Hiscox.

Using AI and NLP to enhance order management services

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