Expect the unexpected.
Predictive analytics and machine learning can help you anticipate customer support and personalize the experience.
In our increasingly hyperconnected world, a contact center’s ability to predict our likes, dislikes and problems can be the making or breaking of a brand.
That’s because poor or even ‘average’ customer service can be enough to turn customers away, according to a report by McKinsey. However, improving customer service from unexceptional to ‘wow’ can result in a 50 percent hike in a customer’s likelihood to renew, recommend or purchase another product. In addition, 70 percent of buying experiences are based on how the customer feels they are being treated.
Many enterprises are exploring how data analytics and machine learning can raise the bar on customer service. Data analytics extracts meaning from raw data by providing actionable insights to support decision making. Machine learning goes further by using algorithms to search for meaning in data without being told where to look. It might analyze inbound customer calls for content and context, or parse millions of customer emails to identify the most common issues they face.
Technology is already improving contact center performance, but the real treasure lies in the customer data. “This is where machine learning analytics is applied to customer knowledge,” explains Sean Kildea, International Sales and Business Development Manager, Orange Applications for Business. “It is where organizations can get to know customers as a whole, segmenting their customer base better and determining repeat behaviors, such as usage or spending patterns, which they would otherwise have been unable to identify.”
Mining customer insight using these new tools gives contact centers a holistic view of both their agents and customers, enabling them to improve the customer service experience, upsell and potentially optimize human resources. Orange, for example, recently helped a large business process outsourcing company which was having difficulty with employee churn. By using data analytics to spot breaking-point patterns, Orange and the outsourcer could identify when agents were on the verge of leaving and then engage with them. The result was a reduction in employee churn.
Reactive to proactive
For many organizations, the default approach to customer service is to respond to issues as they come up. Data analytics and machine learning “gives organizations the power to resolve issues for the customer without the customer having to express their need,” explains Kildea.
According to the 2017 UK Contact Center Forum Proactive Customer Service report personalized, proactive outreach is becoming the standard for modern customer outreach. The report found that three-quarters of companies said they are proactively contacting customers and believe it will save on inbound contacts, equating to big savings for large customer contact operations. Utilizing personalization and customer journey analytics technologies to improve the customer experience will also become a cornerstone in contact data center service.
“Customers will not tolerate companies that have amnesia when it comes to remembering them and their preferences for recognition,” Gene Alvarez, Managing VP at Gartner explains, speaking at a Gartner 360 Summit. “This makes it imperative for companies to recognize their customers and to serve them pertinent content that demonstrates the proper recognition and treatment.”
To do this contact centers have to open wide the data sources that are traditionally available to them. “This isn’t so much a challenge as a question of strategy,” says Kildea. “It is about convincing organizations that they can really benefit from multiplying data sources in the contact center. It isn’t difficult, but it is a decision they have to make as part of their digital transformation”.
There is a cultural hurdle, however, as traditional data sources are managed in silos such as technical, marketing, sales and customer relations databases, with very little communication between them. “These silos have to be broken down and the company tooled so that it can access data from every single point – whatever the format, data size, data location or technology being used,” explains Kildea.
Understanding the customer journey and IoT
Collecting and matching data from omnichannel sources helps organizations to predict the customer journey and create a more loyal user base.
Switched on organizations are using data analytics right from the shopping basket to better understand the customer, so further down the line this knowledge base “can help the contact center help the customer,” explains Sandra Collomb, Head of CRM Marketing, Orange Applications for Business. “The buying process is after all the first step in the customer journey.”
Orange has helped a large e-commerce and retail company in France to build a data analytics platform that provides them with a 360-degree view of customer behavior. By better understanding how their customers use technology in the purchasing process they can improve customer experience throughout the product lifecycle, as well as upsell and cross-sell. “So many big data projects fail because they collect data and don’t know what to do with it. With the right analytics you know exactly where the customer has problems and they can be addressed,” adds Collomb.
But this is only the start. The arrival of the Internet of Things (IoT) will have a big impact on the customer experience and revolutionize the contact center.
IoT will enable smart objects to communicate directly with the manufacturer or service provider, further pushing the concept of proactive service by integrating more information into the contact center infrastructure. Contact centers will have a huge amount of data at their disposal on product/service efficiency as well as user habits, enabling them to be fully prepared at first point of contact with the customer, pushing consumer service into an another stratosphere.
“IoT data and how it will be analyzed and integrated into the customer care journey will be a key differentiator for organizations,” says Collomb. “IoT will become the next channel in the contact center. The data analytics it will provide will enable organizations to get even closer to the customer”.
Collomb believes the next big challenge will be to communicate and collect data in real time and process it into actionable intelligence. “Only then will we see what it can really achieve,” she says.
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