Home

 

From the outside looking in, call centers don’t seem like a burgeoning frontier for data science.

 

But, then again, the outsider perspective has never done call centers much justice. The media portray call centers as dimly lit, caffeine-fueled hives of bottom lines and call lists.

 

At Clearlink, we’re punching holes through that old stereotype. We not only work closely with our data science team but also incorporate data into nearly every facet of our sales and marketing.

 

We’re striving for a truly connected customer experience. That, and turning our call center into a finely tuned engine, humming along on all cylinders.

 

Sound far-flung? It’s already happening, and it’s powered by our very own in-house data science division.

 

Every call center gathers numbers. How many calls they receive, how many sales they make, which products are the most popular, and frequently asked questions, just to name a few.

 

To be truly successful and efficient, however, call centers need to learn what to do with those numbers.

 

Our in-house data science group blended our billions of data points with our machine learning capabilities. Now we can constantly iterate forecasts for our major brands.

 

The best part? These iterations happen without any single person looking over the algorithms or tweaking the formulas. Machine learning means the forecasts continue running and correct themselves as we add more data.

 

Let’s break it down with a simple example.

 

Let’s say your call center sells soda, and you’ve got three brands: Regular, Diet, and Lemon-Lime.

 

The thing is, when you’re constantly gathering customer data, patterns emerge. Your agents might not notice this on their own, especially if your sales teams don’t share information with each other.

 

In this case, the data shows that Diet sells best during the day when parents are contacting you; Regular sells best on the weekends; and Lemon-Lime sells best to older adults.

 

Machine learning optimizes future reports to sharpen your sales team. Thanks to your data, sales can optimize their pitch to sell the perfect products to the perfect audience at the perfect time.  

 

But data science isn’t just for product.

                                                                                                                                                                                             

The same tools can create baselines for marketing and workforce management. They can predict how deep a campaign needs to go to be effective, as well as how many people need to be on-call at any given time to efficiently operate the call center without overages.

 

Business efficiency is one of the main arguments for data science. After all, if you don’t use your data, why are you collecting it in the first place?

 

You already have the data you need to improve your call center. Wondering how many employees you need during peak hours?

 

Your data has done the work for you. The real problem is that most call centers underutilize their data.

 

Here are three specific benefits we’ve experienced by using data to optimize our call centers.

 

One of the biggest challenges of running a call center is staffing.

 

There are dangers to being both under-staffed and over-staffed. On one hand, you need enough representatives to help your customers. On the other, you don’t want to pay for more agents than you need.

 

Then there’s the headache of scheduling employees two weeks in advance. Traditionally, call centers adjust schedules two weeks out, roughly estimating call volumes. The result is inevitably over-scheduling or under-scheduling.

 

We schedule differently, thanks to data science.

 

Clearlink has a real-time employee scheduler that talks to our call forecasting software. This way, we can bring flex agents on and off on an hourly basis, depending on business needs.

 

We no longer have to hope that our scheduling is correct. Thanks to data science, we can most accurately predict and change our agent schedules in real-time. The less manual work required to run our call center, the better we serve our customers.

 

But scheduling is just one piece of the puzzle. We also need to understand how our agents serve customers, and how that affects the business’s KPIs.

 

Traditionally, call centers calculate all of these metrics separate from revenue. But our data science team is actually finding correlations between staffing and efficiency metrics with customer conversions.

 

Clearlink records metrics for scheduling, staffing and attendance, and business revenue. As a result, we can see how staff changes directly affect sales.

 

It’s time to ditch the scripts. Data allows our salespeople to make informed, intelligent, real-time decisions instead of just reading off a screen.

 

Your customers call because they want to talk to a real person, not a script-reading robot. Use data to empower your agents to make independent decisions.

 

For example, we give our salespeople direct access to data tables. These contain all kinds of information to help them make smarter decisions for customers.

 

To provide better service, we invest in our salespeople. When your workforce feels knowledgeable and empowered, they’re able to deliver amazing service without the scripts.

 

Invest in your employees, provide the right tools, and give them your trust.

 

 

Most people think user experience begins the moment an agent answers the phone. Nothing could be further from the truth.

 

The time spent before a customer reaches a “real person” can be anything from efficient to enraging. Think about how many keys that customer has pressed, how many interactions they’ve had with your automated menu, and of course, how many times they’ve looped through your hold music.

 

Wouldn’t you feel annoyed?

 

Take a compassionate approach to your call center. Remember the humans on the other end of the line with data.

 

Our PPC automation tool helps us streamline the phone queue, optimize potential sales, and improve the customer experience overall. This tool analyzes our call center traffic and ad analytics to estimate the number of calls anticipated compared to agent availability.

 

If our agents are busy and can’t take the call, we won’t show the customer a phone number that will inevitably lead them to frustration.

 

Instead, our site will show a chat option, a leave form, or some other communication option so they can still get in touch with us. If the customer intends to make a purchase, they can do so without waiting in a queue.

 

The automation tool uses data to decide what calls are the most likely to convert for specific brands, and which campaigns are likely to turn into sales. It’s great for agents because they can take the best possible opportunities to convert.

 

Integrating data science into your call center takes time, but the results are worth the effort.

 

Start with a smart data integration team to use the data you already have. As resources become available, can use machine learning to develop algorithms that harness the power of that data.

 

Thanks to smarter data, we can have happier employees, satisfied customers, and a thriving bottom line.