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Better customer service via machine learning

Advanced analytics helps Rogers Communications become more customer-centric, cutting customer complaints in half

A customer calls his telecommunications provider. The call center representative immediately knows that this mobile customer also has internet and landline service, giving them relevant details they need to serve the customer. The representative also knows how critical this customer is – they are, in fact, a customer many times over. The result: The customer gets the right service delivered at the right time in the right way, all due to a complete, reliable set of data and insights.

For large companies, with individual business units operating from different data sources, this can be a tricky goal. But that’s exactly what Rogers Communications does, and it has the results to prove it.

Founded in 1960, Rogers built Canada’s modern mobile communications network. In addition to cellular services, Rogers delivers cable TV, internet and landline services to millions of customers across the country. The company uses high-performance analytics from SAS to create a data-driven approach to customer service. The goal: Enhance customer satisfaction and preserve its leadership in Canada’s media and telecommunications sector.

“Companies are competing on analytics,” says Chris Dingle, Senior Director of Customer Intelligence at Rogers. “My focus is improving the customer experience, particularly in how Rogers can make its communications, products and channels work better for our customers.”

Every customer interacting with Rogers is at a specific point in their journey. They may be considering buying a new product, or they could be looking for support in how to use one they already own. “We wanted to use analytics to gain a better understanding of each customer’s needs,” Dingle says. “We need to be as intelligent as possible in terms of what we present to that customer.”

The challenge for a customer-centric organization like Rogers is that many teams within the company have the potential to support that customer experience. But regardless of the function they support, these employees must all have access to the necessary insights.

Rogers wanted to encourage cross-team collaboration while delivering insights to improve the customer experience. Building on a 25-year relationship with SAS, Rogers decided to modernize its SAS® Analytics suite.

Predicting customer satisfaction

One outcome of Rogers’ analytics effort is the Net Promoter Score, a sophisticated data set based on data extracted from customer survey information. “The Net Promoter Score gives us an understanding of what those customer journeys are, which ones are working well, and which ones can be improved,” Dingle says. “We can then do all the process improvements based on that.”

It’s really spectacular to see the culture of analytics in the collaboration among teams. And despite our different roles, we’re all driven to improve the customer experience, and analytics is getting us there.
Chris Dingle Rogers Communications

Chris Dingle
Senior Director of Customer Intelligence

This effort also creates an opportunity for another innovation using SAS products: machine learning. This allows Rogers to build models that predict how likely customers will be to promote or detract the company’s services to others. “We can then communicate with our customers based on that,” Dingle explains.

Rogers’ analysis spans multiple communication channels. Using analytics, Rogers customizes voice call scripts to suit a customer’s situation. On social networks like Twitter and Facebook, it can respond more precisely to individual customers based on what it knows about their journey.

“Customers are engaging with us on our social networks,” Dingle says. “They talk about how Rogers is really becoming a customer-centric organization and how we can quickly solve their problems.”

A focus on collaboration

SAS Analytics enables multiple teams covering different channels to collaborate with each other and base their conversations on practical data. As this data becomes easier to access across different teams, analytics becomes more a part of the company’s culture, naturally fueling improvements to the customer experience.

“SAS makes the visual environments accessible for what we call ‘citizen data scientists,’” Dingle says. “It’s about the democratization of analytics – making analytics available for more of our employees.”

This has resulted in some notable benefits at the company. In fact, the use of machine learning lowered customer complaints by 53 percent in the last year.

Now, Rogers has begun an internal analytics challenge, bringing together employees across different departments to work with SAS Analytics. “It’s really spectacular to see the culture of analytics in the collaboration among teams,” Dingle says. “And despite our different roles, we’re all driven to improve the customer experience, and analytics is getting us there.”



Gain a better understanding of each customer’s behavior, preferences and situation, and use it to refine the customer experience during interactions.



  • Developing a culture of analytics, which fuels cross-department collaboration and ultimately improves the customer experience.
  • Customer complaints dropped by 53 percent.
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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