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In today’s market, where 80% of customers say the experience a company provides is as important as its products and services, a support partner should do much more than simply answer the phone. We must work just as hard as everyone else to improve the customer experience.
The clamor for technology in this area is almost palpable. However, technology is no silver bullet. When it comes to providing a seamless experience that your customers can rave about, support becomes nearly as important as the technology itself.
A support model which supports a great experience is not something you can build, dust your shoulders off, then turn to other priorities. A truly dynamic support structure is one that takes an iterative approach to fixing issues and empowering frontline employees with quick, accurate information in a way that minimizes impact to the customer.
Picture it like this: Just as you are implementing new technology to boost efficiency and revenue, your support partner should be doing the same thing to improve the support process. This includes technologies such as artificial intelligence (AI) and robotic process automation (RPA), which I will cover in this article.
Just as you are implementing new technology to boost efficiency and revenue, your support partner should be doing the same thing to improve the support process.
In today's complex environment, the classic support model of “call-into-a-call-center” is ripe for disruption. To provide the great customer experience that nearly every brand seeks requires a support model that takes the iterative approach of a technology company. Here's how it looks in practice.
The phrase ‘garbage in, garbage out’ exists for a reason – many business decisions are only as good as the data they are based on. With bad data, even the best strategy can quickly become a game of darts played in the dark. With good data collection, informed business decisions can be quickly made.
Research shows that while almost every company collects data, not every company uses it: 52% of companies face challenges connecting the dots between data stored across different parts of their organization.
It’s common to spend a lot of money building something, then once the shiny new thing is deployed, the focus on execution becomes murky. These days, it’s not so much about data collection, it’s about making the data available in a format which allows for further iteration, perfection and support.
For example, let’s consider a scenario where your franchise owners have adopted new audio tools. They have invested significant amounts of money and would like to know if their investment was worth it.
If case data is made available by your support partner to each franchise owner, the owner would be able to pull up a dashboard to view incidents for every store, run reports to determine how many incidents occur per store, or even a certain type of technology.
With proper data availability, you would be able to quickly determine how many cases per store or region as well as the type of audio tools with the most cases overall. Are there recurring issues? Is a certain region calling in more than others? Good data availability allows solid analysis around whether tech is bad or broken, or whether the issue stems more from training or setup.
In the end, data availability allows you to more proactively manage business, since you have access to data that many competitors don’t. In fact, 62% of retailers report that the use of information (including big data) and analytics is creating a competitive advantage for their organizations.
Think in terms of working with insights, not spreadsheets.
In fact, 62% of retailers report that the use of information (including big data) and analytics is creating a competitive advantage for their organizations.
On the customer side, you don't need me to tell you that preferences were changing even before COVID threw us for a collective loop. And nearly every business collects customer data – so what’s changed? In today’s environment, brands have less time than ever with customers, which means every touchpoint must be maximized.
People aren’t strolling the aisles anymore or hanging around the mall. So, it’s all about making those few moments matter. Especially when seven in ten U.S. consumers say they’ve spent more money to do business with a company that delivers great service.
When it comes to a great experience for your customers, I feel that elevating labor is a key element. This is also an area where dynamic support plays a key role. In my view, I believe that working to reduce repetitive tasks that bring down morale and are prone to human error should be a cornerstone of any customer experience strategy.
Here I will get into the buzzwords – artificial intelligence (AI) and robotic process automation (RPA) offer lots of promise in this area. But it’s not just about speeding up the experience but making it better. At a time when consumers are willing to spend up to 16% more on products and services with companies that offer a better experience, there’s a lot of revenue at stake.
Now, I am not saying you can give your whole support structure over to AI, or that RPA is a magic bullet for happy employees. The strategic use of these technologies should filter down to the tactical level. AI has great promise in simplifying the (usually rushed) search for information in a knowledge base, for example. Speed is the critical differentiator. Any support desk knowledge base is only good as the search terms—and how it's all organized.
Simply put, AI reduces the risk of employees or agents not finding what they need, when they need it most.
RPA offers the ability to automate mundane tasks, freeing up employee time and morale to better serve customers. Consider the frontline employee’s point of view. Processing parts orders or staring at the same spreadsheet all day will make even the best employees a bit grumpy. The more ‘complex’ things that people get to work on, they happier they are. Of course, this is not just about warm fuzzies, research shows companies with highly engaged employees outperform their competitors by 147%.
On the support side, RPA offers much more proactive monitoring of systems, for example. If a store’s broadband connectivity shows to be unavailable for more than a few moments, the system can automatically create a ticket so the support team will begin troubleshooting the issue, only contacting busy staff in-store when needed. This feeds off the same data availability mentioned earlier while also working to remove mundane tasks from the store.
At a time when brands have very little time with customers, the quality of interaction is important. In frontline roles you want people who feel challenged and have good morale, so they deserve the right tools to make customers happy. Nothing is more frustrating than wanting to help someone, only to be held back by technology issues or byzantine support processes. And you want to mitigate that feeling of helplessness over everything else.
Just as you are busy implementing new technology, your support partner should be doing the same. Success with technology in complex retail environments requires an iterative approach to fixing issues and taking care of your people, so they take care of your customers. It’s much more than answering the phone.
Connect with Chris Antonelli on: LinkedIn
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