- Conversational AI Blog
- 8 Minutes
- April 30, 2021
The 10-Step Guide to Scaling Customer Support — with Conversational AI
We’re at a tipping point for customer support operations. In the past year, you must have witnessed a drastic increase in customers contacting your business over a greater number of channels for quick access to reliable information.
To handle such sudden fluctuations in support requests, we have been contriving to an unsustainable strategy of increasing the headcount over the years.
But with the unprecedented spread of COVID-19, it’s impossible to scale the support operations with a human-only agent team. This has led businesses to examine future-proof and scalable conversational AI solutions for automating their support system.
There has been an acceleration in global deployments of conversational AI solutions post-pandemic, especially in the financial, hospitality, and retail sectors — to manage unexpected customer engagements, provide consistent information, drive efficiency, and maintain business continuity.
If you are getting started on the journey toward deploying conversational AI, I can understand that you might be struggling to define a strategy for gaining true business benefit from it. The steps I’ll walk you through will ensure you’re able to leverage this technology for delivering the best customer support experience while saving cost, rather than just having it for vanity.
So let’s get down to it.
Step #1: Identify top reasons for customer contact and the channel preference
As per the Gartner survey, the most preferred channel for customer service interactions is phone (44%), followed by chat (17%), email (15%), company website (12%), and search engine (4%).
Phone being the dominant channel, you must understand the root cause that generates these inquiries in the first place. As per Gartner’s research, there are six resolution types into which customer service inquiries can be grouped in the phone channel: Transact, Confirm, Discuss, Workaround, Validate, and Vent.
A leading grocery and meal delivery retailer in North America was struggling to meet the sudden surge in customer support requests over the phone, fueled by COVID-19. On analyzing their calls, it was found that most customers inquired about order status, delayed deliveries, coupon redemption, cancellations, and refunds. They realized that for most issues, they could provide resolution to customers over voice in a self-service mode and easily scale their support operations.
Step #2: Define the business objective
Define what business problem you are trying to solve. It could be,
“To reduce the total volume of calls handled by the customer service reps by X%, by implementing conversational AI to handle Y% of initial inquiries. This is to deliver high value to the customer base and optimize costs.”
Step #3: Choose the right use cases to automate
Once you identify the top reason customers contact you through the phone, identify the right use cases to automate. You need to :
A. Understand why these use cases are important, the mechanism that they currently run, and the volume handled. You could easily collect this information from your contact center.
B. Determine the complexity of the use case inquiries:
- If the customers are calling in for general information, confirmations, and transactional queries, resolve it with a conversational AI-powered virtual voice agent.
- If the call intent is complex and requires deeper human support, redirect users to the right live agent via conversational IVR.
- If phone calls could be deflected with proactive support, identify such shortfalls and resolve them via outbound calls, text/SMS, etc
C. Extrapolate the baseline metrics like how long it is taking to resolve customer queries, what is the average waiting time, and how to achieve success for these use cases
D. Analyze how these calls and variations are handled by the agents on a day-to-day basis
Step #4: Design, build, and train conversational engines
Once you have all the answers, start creating conversation workflows that mimic the way agents deal with queries. You need to consider many elements to build a conversational AI engine — speech recognition, NLP/NLU, a dialog manager, and text-to-speech. You can use one out of many DIY platforms such as Dialogflow, Lex, Rasa, etc. to quickly build one. But they have limitations, are expensive, and require specialized talent.
I suggest using a solution like Agara, which has overcome these limitations and build a truly intelligent and effective conversational voice AI engine. Agara is built with a singular purpose — high-quality, natural, autonomous conversations over voice at scale. With Agara, your spending is 60%-80% lower than current manual spending, highly predictable and in direct proportion to the volumes processed.
- It includes pre-built workflows to handle industry-wide use cases and can be deployed right away! But you can always build an entirely new workflow for your needs with its self-service Conversation Builder. Using the intuitive drag-and-drop interface, you can create, modify and test new workflows within minutes. All of this without any coding knowledge.
- You can choose the preferred voice that your virtual agent should use when interacting with callers. They are custom-built to sound more human-like!
Step #5: Deflect and automate the resolution of customer requests in seconds
Start deflecting transactional, confirmations, and other routine queries. You can leverage conversational AI-powered virtual agents to:
- Provide end-to-end resolution for select use cases instantly, with zero human intervention, and eliminate the need for creating additional tickets on other channels
- Focus experienced and expensive agents on sensitive, high-value conversations
- Instantly scale up phone support capacity while continuing to deliver exceptional customer service
I would suggest you have Agara as one of the choices in the current IVR. You can invoke Agara for a specific use case while keeping the rest of the flow unchanged. For example, press 4 for loyalty card replacement.
You also have the option to have Agara available on a separate phone number.
Step #6: Personalize every conversation to the caller
Integrate with your technology stack that enables AI to curate data, pull information in real-time, and deliver tailored responses.
When it comes to integrations, Agara works great with:
- CRM systems (Zendesk, Salesforce, etc.)
- Telephony systems (Amazon Connect, Nice, Genesys, etc.)
- ERP systems
Step #7: Seamless transfer to human agents based on the issue, sentiment, and customer
Your USP should be how you handle every call in the best possible way. Well, for that, I recommend that you define call transfer scenarios beforehand so that AI can seamlessly hand-off conversations to human agents based on sentiment, your business rules, and customer profile.
Call transfers are a critical part of Agara’s design. It will transfer the call if the caller explicitly requests the same at any point during the interaction or fails to capture or verify any information.
Step #8: Scale proactive communication
Proactive communication is one of the most important pillars for scaling customer support. Not convinced? Here are a few stats to get you interested.
- Proactive support increases customer retention by 3-5%
- For 12 months, being proactive has seen a 20-30% reduction in customer support calls, lowering contact center operation costs by 25%
- 87% of customers want to be contacted proactively, in issues related to customer support
You should use AI to send out automated messages or schedule outbound calls to keep your customers updated in ways that save them time and effort. You may keep your customers informed about ETAs, order status, new promotions, payment dates, loyalty points, and subscription renewals.
Many retail companies are using automated outbound calls to notify customers when wishlist products are back in stock or on sale. This also comes useful when product deliveries are delayed due to lockdowns or weather conditions.
Step #9: Engage beyond voice conversations across channels
You should also think about integrating your conversational AI-powered virtual agent with other channels of communication. For instance, use emails and SMS to provide a seamless experience to your customers. So if your customer is calling in to update his address for an upcoming delivery, it is a good idea to send a confirmation email too!
Step #10: Analyze, measure, and optimize
The last step for you is to build a dashboard to monitor the call performance and the ongoing support. It should include metrics related to the use of your virtual agent and the conversations it engages in. Is the call volume going up? Are customers spending more time on calls? What are the most common queries?
Within the Agara platform, you have access to all call recordings, transcripts, clustered and labeled metadata, deep insights, and more.
Conversational AI over voice is a relatively new and dynamic trend. You need to learn from the ongoing support experience and measure the effectiveness of your conversational AI-powered virtual agents. If you follow these ten steps, you will be well on your path to leverage conversational AI for scaling customer support!
Want to learn more about conversation AI and virtual voice agent to create customer service experiences that lead the pack. Get in touch.
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