The Evolution of the Never-Contact Contact Centre

Cameron Smith of Genesys argues that the ideal contact centre of the future will be a “never-contact contact centre”.

From one perspective, you can view a customer initiating a call to a contact centre as a breakdown in the customer experience.

Something went wrong and the customer couldn’t resolve the issue on their own — or with all the information provided to them through the web or in a chat.

Many companies are taking the position that improving the overall customer experience by implementing strategies like Design Thinking, an iterative human-centered design process, at every stage of product development will slowly reduce the need for a customer to pick up the phone.

Now imagine what the contact centre of the future — 10 years from now — will look like as the customer experience improves:

• What will contact centre work look like? Some will question if it even exists.
• What new opportunities will come from a shift in interactions?
• What drivers will allow us to get to this new reality?
• Will artificial intelligence (AI) resolve issues?

COVID-19 and the resulting move to work from home has forced traditional contact centres to rapidly evolve to a more flexible set-up to allow agents the choice in where and how they work.

Many companies with larger workforces are starting to see the cost savings and benefits in allowing for this work arrangement, making work from home a permanent implementation.

The same systems that allow for this flexibility are also built to improve the efficiency of call routing and align agent scheduling with demand.

These changes mean that contact centres can better meet metrics around reduced handle times and first-call resolution.

More impactful than the flexibility that these systems bring is how these systems are able to easily ingest disparate pieces of information and make accurate predictions on when a customer might want to initiate a call to resolve a problem.

When implemented, these predictions seem like no-brainers and show opportunities to take proactive steps to engage with the customer when:

• the customer has repeatedly — and unsuccessfully — tried to log in
• the customer has added, then removed, an item from a cart multiple times
• the customer has posted on social media about a problem they’ve experienced
• the customer is attempting a new transaction that they haven’t done before
• the customer is looking at the contact page or hasn’t found information in the “Help” section

Creating the Agent Analyst

When customers encounter these problems, it’ll trigger pop-ups, emails or text messages to resolve an issue — even before a customer needs to pick up the phone or send an email.

But what happens to contact centre agents who are no longer needed to field the same volume of inbound interactions as a result of these automated engagements?

The answer is that they become customer analysts. We can harness the creativity, ideas and analysis abilities of agents to turn them into outbound concierges.

Companies that look at user behaviour with their products and services, combined with social media input and even sensor data in Internet of Things devices, can create tailored experiences at scale.

The result is that staff who normally would guide customers to resolve annoyances like password resets can review the needs of a customer behind the scenes and apply creativity to come up with ideas and suggestions for the customer to better interact with the product.

An AI system might come up with three to five suggestions on what a customer could do; a customer service agent-analyst can craft an email, set up a phone call or message the customer via social media with the idea.

Companies can tailor the experience of proactive interaction around their culture. The tone of language and types of incentives that the agent expresses can tailor or scale the experience.

With this in place, agents may become liaisons and guides — and that improves not only customer satisfaction but also employee engagement.

Read this Genesys guide to learn how to personalise the customer experience using tools like artificial intelligence.