“Sorry” just won’t cut it for some customers.
Even if your apology is sincere, and follows a big effort to answer their questions or resolve some other issue they’ve experienced, customer service agents may occasionally feel they have to offer something more. Or, more likely, the customer will be demanding more -- on the condition that they’ll stop doing business with the company and complain about it to their peers and on social media otherwise.
In the B2C world, the only thing better than “sorry” to most consumers is getting their money back, or at least credit towards their next purchase. In B2B, the options might be somewhat different -- from extending the life of a contract to performing the same set of deliverables over again, to discounts on their next investment and more. The catch-all way to describe them might simply be a “make-good.”
This assumes, of course, that customer service teams are even given an opportunity to make a specific offer first. Irate customers might come with their own demands, and rather than make them via a call with a contact centre they might make it in a public post somewhere online.
In many organizations there are established and very specific procedures in place to guide agents on when they can or can’t make some kind of make-good offer. In small and medium-sized businesses, however, the offers may be more arbitrary and made in the heat of the moment. When you use a tool like Service Cloud, though, you can not only get ahead of the most likely troubleshooting issues that will come up, but also draw upon data to guide how you handle a make-good situation.
Take some time to look back at historical information as well as any additional insights from your team as you create a make-good strategy that elevates your customer service operations.
If a company chose to invest in a particular product or service, it mostly likely followed a process that involved multiple decision-makers, several meetings and a list of emails longer than anyone can remember. As much as a full refund might seem like the right make-good for a particularly bad experience for a consumer brand, B2B buyers won’t want to necessarily go through the whole purchase exercise again.
What may be a better option? A “premium” version of the product with features that make it easier to avoid common problems, perhaps, or an upgrade which comes with a more decided support resource of some kind.
Look at the data in tools like Service Cloud to determine make-goods based on what you’ve learned by appeasing customers in the same industry sector, those with a similar number of employees and/or customers or the same portfolio of other products. Rather than appearing panicked, a make-good offered in such a manner will seem more like the recommendation a trusted consultant might provide.
A discount or contract extension might seem like the company is turning to a last-resort tactic when it’s offered at the end of a lengthy customer service engagement. The same goes for offers that are made in response to (or as an alternative to) what the dissatisfied customer demands.
As more organizations get familiar with the artificial intelligence (AI) capabilities of tools such as Salesforce Einstein, they will learn about patterns in customer service success that could completely reposition how a make-good is offered. For example, if you have data that shows certain kinds of problems or troubleshooting have significantly adverse impacts on customers, your agents should be prepared to offer a make-good closer to the beginning of the engagement. That way, the customer will see the firm is already prepared to go the extra mile to retain their business even as they’re dealing with the issue in real-time. One of the goals in customer service is getting all the necessary details you can, and that’s easier to do when customers aren’t seething with outrage and indignation.
Also, consider the channel you use to offer a make-good. A truly irate customer may not want to think about such details while they’re on the phone, but they might peruse the offer if it’s sent via email afterwards. Or, if an issue is being discussed on social media, an agent might want to offer a link via a private direct message. Discovering what works best is part of the power you get with advanced analytics.
A make-good is not simply an exercise in managing angry customers. They are a tactic in creating a more satisfying customer experience. That means you should think about what the make-good is ultimately designed to do based on the way you have set goals around customer satisfaction (CSAT).
Maybe you want to simply reduce churn in your customer base. Or, the goal might be increasing your potential share of business with a given customer. Some firms may want to turn a bad service call into a customer testimonial or case study after the make-good has helped to turn the situation around.
These CSAT goals may not be universal across every kind of customer, or product line or service. That’s okay. In fact, the more granular you are in thinking about CSAT across different kinds of customers, markets and so on, the better you’ll get at offering make-goods that resonate with customers.
The ideal, of course, is to be in a situation where your service team never has to offer a make-good. That may not be realistic, however. Instead, why not use tools like Service Cloud to develop a more proactive and effective approach that ties back to your overall business objectives? You may have to continue rethinking and refining how to use make-goods -- which is exactly the point of becoming a more data-driven business.