Great digital support is a moving target – what was okay yesterday isn’t even close today. To ensure that you stay ahead of the competition and your online support is future-proof – a shift in mindset is due. Delivering effective online support is a process, not a one-time project. It doesn’t end at “go live”. It requires ongoing adjustments, driven by data and customers’ feedback.

Similar to conversion optimization in e-commerce, online support needs continuous optimization based on customer behavior and action.

In this blog post, we explore the process of continuous support optimization that enables your organization to stay ahead of competition and customer expectations.

Let’s dive right in.

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Consolidate data to see the journey – from the customer’s point of view

 

A customer journey is anything but linear. It’s often long and complex, stretching across multiple touchpoints and channels. It moves from a community forum, to a self-service website, to a chat, to a phone call. Rarely are two journeys identical.

To improve your customer experience, you need to see your support from the customers’ point of view. The trouble with focusing on single touchpoints is the limited customer experience story it tells, in many ways. For example, a customer may call an agent to clarify a confusing bill, and the agent may provide a quick and simple explanation. Problem solved. But the underlying issues here are still left unaddressed.

How many channels did the customer try before calling? What could be improved in the online bill to make it more simple and easy to understand for customers? Could this phone call have been avoided?

Data from single customer touchpoints fails to 1, uncover the root cause of avoidable issues and 2, describe the cumulative effect on customer experience.

The answer is to consolidate data from both online & offline channels to synthesize insights and understand the entire customer journey.

Consolidated data will help you see how customers are navigating through touchpoints as they move across the journey. A process that will reveal the friction and systematic support issues that isolated data would otherwise fail to identify.

Once you have the entire customer end-to-end journey in front of you, it’s easier to see the bigger picture and spot the friction that makes customers escalate their issues.

Experiment and optimize

Combining data from online & offline touchpoints enables you to gain an end-to-end customer journey overview and identify high impact support areas. With that, you’re able to start the second phase of the process – experimentation and optimization – and focus on delivering quick wins.

Digital natives like Amazon and Google have long realized the importance of data, tests, and optimizations. This is the engine that drives their innovation and helps them remain successful in the digital age.

The purpose of experimenting is finding out what works best for your brand and your customers. This stage provides an opportunity to redesign underperforming support journeys, finding new opportunities, and taking purposeful actions to seize them.

But remember, it’s not an experiment if you know it’s going to work. 

Fast forward a few weeks and a number of experiments later, and you’ll have a better understanding of your customers and what type of support experiences serves them best.

Implement new journeys

 

After you’ve built an understanding of what is working and what is not, you can now implement the new support journey based on the data of your experiments. This is where you put your recently acquired learnings into action.

By replacing the underperforming support journeys with the new and improved ones, we typically see improved customer service metrics and support KPIs in a matter weeks, not months.

Continue to monitor the progress and look for additional opportunities. Once the desired support KPIs are reached, you can identify a new optimization area and start the process again.

Summing up

The ability to change quickly and adapt determines your success in the digital world. The key to constant improvement is creating a culture where data guides you, experimenting is natural, inexpensive and even failures deliver learnings.

 

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