What slows down the adoption of data analytics in call centers?


Data analytics allow brands and their call centers to gain a competitive advantage over their competitors. So why are so few organizations adopting this technology?

In an industry driven by digital technology, measuring customer satisfaction through quantitative, transaction-based metrics isn’t enough. Brands need to dig through mountains of data to make sense of what customers truly want.

Poising itself a solution to this, data analytics has often been touted as a game-changer in the field of customer service. Almost every call center process is now being executed with this tech as a component. It helps managers break down bit by bit the insights gained from conversations with callers, allowing them to build a customer database.

The capacity of brands to make sense of data therefore boosts customer experience management, but why are so few call centers adopting analytics? Here are five possible reasons.


1.     It’s an uncharted territory.


Frankly, there’s still a big question mark surrounding data management and analytics. Although highly advanced multinational companies are already reaping their money’s worth off this technology, it’s still an uncharted territory for many other businesses.

There’s a need to create a holistic approach that would allow brands to easily integrate this technology in their everyday functions. That can only be done if brands hire the right people and align their processes toward a common goal.


2.     How fast must the transition be?


As brands start to incorporate data analytics into their call center, they’re actually slowly evolving into an insight-driven organization. However, bigger contact centers will find it harder to adopt analytics. Aside from securing the necessary resources, brands must also consider their core activities, goals, and the number of agents they have. To sustain the transition into a knowledge-based organization, businesses must continue to focus on the customer journey and the customer experience.


3.     Budgeting


In most cases, intelligent customer experience management tools can be costly. Brands must weigh a tool’s price tag according to how well it can drive results. An expensive tool with functions that are not aligned with a brand’s goals isn’t worth it. Brands can do better by purchasing tech tools that serve a purpose within the organization.

Furthermore, managers must also watch out for overlapping functions. To save money, ensure that every software or program has unique functions that complement one another.


4.     Understanding the role of analytics


If you want to fully embrace data analytics, you must know how it fits into the bigger company picture. Analyzing the customer database built through feedback gathering must always be done with a purpose in mind. The goal must be to understand the root cause of a problem so you can take advantage of insights and create larger impacts.

Also, analytics is not solely about big data. Even smaller amounts of data, aside from being easier to collect and analyze, can help you improve your existing products or services.


5.     Hiring the right people


The biggest challenge right now for call centers and brands is to bridge the skills gap in data analytics. There’s a need to create training courses that will allow professionals to understand everything about customer data and how they can be used to improve processes. If brands can’t find the people who can be trusted with these duties, then it will be impossible to create an intelligent customer experience management system.



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