If you ask many business executives, they would say their customer service contact center is a necessary expense and it does not contribute to the bottom line.
As such, they don’t emphasize on technology upgrades or process improvements for their in-house centers and, when outsourcing, they would settle for the least expensive options. Even worse, some companies are cutting their contact centers altogether, considering them less relevant in this age of digital self-service.
But what if you could turn your customer service contact center from cost to profit center? In fact, what if it could become one of the largest revenue generators in your entire company?
We already highlighted the reasons for investing in Customer service in our previous blog post. Check out in case you missed it
Now that I have your attention, let’s discuss why investing in your customer service contact center to turn a profit makes sense and the steps you can take to get there.
Below are the following steps to turn your contact center into a profit center.
One strategy to boost revenue is to aggressively mine your customer service data to pinpoint and capitalize upon specific sales opportunities. With the right tools at your disposal, you can uncover a wide range of such opportunities—including accessories, add-ons, up-sells, cross-sells, service contracts, and training.
This strategy may be attractive as a first step because it is the least intrusive one. You don’t have to retrain your customer service staff or modify your existing customer service processes. With this strategy, the sales cycle itself gets initiated and fulfilled by other parts of the company. Customer service here would simply provide the data necessary to generate revenue opportunities.
Here is a simple example of how this strategy works. A consumer calls a software company with a technical question. While gathering the information needed to solve the problem, the customer service agent also notes that the customer is using an older, somewhat obsolete version of the software. The service rep will complete the call as usual. Over the course of the month, customer service agents receive many such calls. So at the end of the month, the company can send a special upgrade offer to every customer who called in and was found to be using an older version of the software.
There is a Triple benefit with this kind of strategy: * the company realized additional revenue from the upgrade sale. * Customers are more satisfied because they have a better product now. Customer service costs are reduced because newer products will have fewer problems and at the end are less expensive to support.
“When a customer comes to you with an issue, you actually have a great opportunity—after you first solve the problem, of course—to initiate a highly engaged relationship with that customer,” declares Geric Johnson, who led the implementation of this strategy at Skechers. “So you can realize incremental revenue at the same time as you convert unhappy customers into highly loyal ones.”
Today’s customer values speed, so in addition to traditional phone and email, adding support channels like chatbots, live chat, social media, and mobile apps is no longer a luxury but a necessity. Make it easy for the customer to get in touch with you using whatever means is best suited to their preference and convenience.
Another way to turn your contact center from cost to profit is by training your customer service agents to make relevant offers to customers when appropriate. Even though selling is not their primary role, that does not mean that the customer service agents can’t learn the art of the soft sell.
Train your agents to get to the heart of the customer’s challenge, steer the conversation around cross-sell and upsell opportunities, and invite the customers to take advantage of the product or service opportunity being presented.
While the aim is not necessarily to create a sales culture within the customer support team, clear goals and objectives do need to be set so that the team knows what they are aiming for.
How much should your Customer Care Specialists be selling each month? Based on their volume, how does that convert into conversion levels? What’s the average revenue per call across the customer support team? With clearly defined metrics and targets in place, this level of tracking becomes easy, and by monitoring how individual Customer Care Specialists are performing, then they can be strategically motivated through positive feedback, incentives, and end-of-year performance appraisals.
Revenue-generation can only be increased by enhancing the customer service agent’s ability to convert cross-sell and up-sell opportunities. This can increase revenues, build excellent customer relationships, and increase customer awareness. However, according to Loudhouse Research, 86% of strategic decision-makers in contact centers do not think that their agents currently have the skills required to meet their obligations in terms of upselling and cross-selling. Organizations should invest in identifying skills that lead to revenue-generation, and train and coach the customer service agents to use the most important skills. Despite these opportunities, only 45% of contact centers are offering such programs, but when utilized, over 52% of contact centers reported observing increased revenue per contact. The most challenging part of implementing revenue-generating programs in your contact center is procuring sales enabling technology that can work as an extension to the existing customer support platform, a technology that can easily integrate with service and sales functionalities.
With evolving customer expectations and service complexities, contact centers are poised to deliver strategic value and profitability to organizations in the years to come. Perceiving contact centers as a cost center is old fashioned and needs to be disposed of if organizations wish to see the real potential of a contact center.
In an era when customer experience is the make or break criteria for business success, not investing in your customer service contact center is the biggest mistake. If maintaining a center in-house is no longer feasible due to the low unemployment, higher wages, and the inability to scale, choosing to outsource can be a viable/ better option.
Never select an outsourcer based on cost alone, however. Price is a critical metric, but the ultimate goal should be to provide high-quality service that reflects the value of the investment.
If outsourcing is an option, consider Team MAS. We provide customized solutions, experienced leadership, and an open model that lets you see exactly how your contact center is working at all times. Contact us to learn more.
Customer experience and contact centers, in particular, are great starting points to implement artificial intelligence (AI) solutions since they are a large source of customer information, generating enormous quantities of data that is impossible to process manually. Here’s how AI can be used in contact centers:
Your customers may contact you for a multitude of reasons. Some of these reasons are fairly straightforward, others are intricate, yet, rarely are they entirely novel.
With the immense data that you are collecting with every recorded phone call, chat interaction, and email, you have a strategic asset that can be used to train machine learning models to understand customer intent within conversations.
Once you understand true customer motivations, you can then use AI to optimize interactions through
Customer effort is one of the leading indicators of loyalty. Analyzing customer effort can guide companies in identifying emerging issues before they explode into major issues.
Traditionally, the effort has been quantified through structured questions on a survey. However, AI and machine learning techniques, combined with text analytics, can aid in evaluating the level of effort expressed in any piece of unstructured customer feedback.
AI can do this by interpreting word choice and sentence structure, as you can quickly understand which aspects of the customer experience cause friction in any feedback source – not just in surveys.
Before you deploy AI, it is worth considering that one of the biggest risk factors in any IT implementation, system upgrade, or system change is the human users of that system.
By failing to communicate in an open, honest, transparent way how this technology is going to benefit them, you will meet resistance.
If you simply say, we are rolling out this new robotic-led approach on Monday, your employees will inevitably be negative towards the technology and may even actively sabotage it.
Instead, you need to get people involved in the process. Ensure they can test out the technology in a safe environment and make sure they are comfortable with it before you even start rolling the technology out.
Any AI application will only ever be a good as the knowledge at its disposal. You need to ensure that when a question is answered in the contact center, that knowledge is captured and delivered into the knowledge management system (KMS), so that customers, bots, and advisors can feed off it.
After all, how can AI be used to make decisions when it does not actually know anything? It can learn but it needs relevant data to do that.
This is why it is so important to have processes and procedures in place that enable you to feed accurate data and intelligence into the KMS.
Many businesses are too reliant on their employees as a source of knowledge and therefore run the risk that if people leave the business, they take the knowledge and understanding that they have gained with them.
Any strategy that uses AI and machine learning should be considered within a broader customer experience AI strategy that considers how data will be leveraged across both the customer and employee journeys.
There are many opportunities to apply AI and machine learning across the customer engagement process.
For example, knowing the right moment to proactively engage with customers online, routing to the best agent based on the desired business outcome, and assisting them to accurately handle inquiries – AI and machine learning can help drive all of that.
However, AI applies to more than just customer journeys. It can also help identify why specific agents are better than others at certain contact or customer types, increase the speed and accuracy of workforce planning and scheduling and automate task completion post-contact.