When looking at customer service and contact operations, analytics are absolutely essential to understand how to keep customers happy. In a world where customers are online on social media, providing good customer service to every single customer is of utmost importance. There is no predicting which disgruntled post from a customer could go viral, effectively blackening the name of your company, or at the very least, creating a PR nightmare.
When customer responds, especially on social media, it is imperative that your call center operations have access to real-time, or close to real-time data as well. This is where analytics come in. Not only can analytics programs, when implemented, help you understand how customers are responding to your customer care services, they can also help predict customer behavior.
Predictive programs can help you gain an edge in working with customers, and enable your customer care operatives to understand exactly what behaviors their customers exhibit, and also which ones they respond to. Having access to this data ensures smooth customer service transactions with customer.
Types of analysis and data collection
- Customer Survey Data
Make customer survey data work for your call center operations. More often than not, this crucial data analysis is missing from customer care operative training. Customer survey data can also be integrated into data analysis processes to try and come up with predictive outcomes for call center interactions.
- Agent Data
What happens during a phone call, from start to finish, can all be stored as specific data points. Storing this information properly using specific parameters and then using them to come up with a call-analysis can help your customer care operations understand exactly what is working or not working with real-time calls. Adding individual customer care call history to this data can help provide a bigger picture, and help call centers see what has worked historically, or what hasn’t, and come up with a strategic plan to improve operations.
- Analysis across multiple channels
Most customer care services have now, out of necessity and for effectiveness, branched out into social media. If your customer care operations consist of responding to people across social media channels like Facebook, Twitter, Instagram, or more. All of the analytics related to those interactions need to be complied with call-data to provide an integrated, multi-channel picture of how your customer care operations are working. Such data will help you see exactly what the ROI is for customer care, and how to increase it through operations on single and multichannel levels.
- Make analytics outcomes actionable
A lot of the time, analytics reports remain simply reports. However, with new programs that work in real-time, it is possible to have analytics inform actions quite accurately. What this means is that good data collection, and the translation of it into actionable outcomes and training for customer care operatives can actually help increase positive interactions. Such data could help an operative find a way to handle a consistently difficult customer in a more scientific manner, by knowing what does not work and trying other avenues for interaction.
- Set Goals
Beyond making data outcomes actionable at the operative levels, data outcomes need to be reflected in strategic long-term and short-term goals. Data can help you make global organizational decisions that affect manpower, industry direction, and more. If using multiple channels, it can let you know which channel works best for you and how to improve performance on the others. Data analysis can thus help you set warranted and achievable goals for your organization.
- Employee engagement
If data is used properly in predictive analysis, it might even be possible to drive certain customers to certain employees. Instead of a one-size-fits-all model, this type of personalization will ensure customer satisfaction while playing to employee strengths and preventing employee frustration. Moreover, data can also help you route calls to specific agents based on customer history. If someone interacts with familiar agents throughout their interactions with your country, they’re more likely to maintain that relationship.
- Close More
While “closing” is terminology more directly associated with sales and not customer service, it is still applicable to the latter. Data analytics can help create personalized customer profiles that can enable call center operative to anticipate the customers’ needs even before the call starts properly. “Closing” the deal in call center operations can become easier with customer engagement analytics, with agents knowing when to upsell, offer services, qualify customers, and offer varied customer service opportunities.
Using data-driven analysis methodologies in a call center can augment operations positively in many ways. That said, the need for data and analytics is now no longer an option, but a necessity. Without data integration from multiple channels or an analysis of call center processes, call center operations would cease to function effectively. To stay competitive, and to offer stellar customer service, data-driven call center operations are essential.