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Maybe you’re drowning in data but can’t answer questions from your leadership team. Or maybe your teams delay giving you an answer when you ask what seems like a simple question. If either of these scenarios sounds familiar, you might need to follow some best practices for creating and using data in a modern contact center.
I recently started capturing and organizing a family history from all the materials my mom acquired over the years. I thought it would be a simple type-and-scan exercise. But it turns out, people keep changing their names, typos occur — even in newspapers — and dates are fuzzy. The contact center is no different.
The contact center is one of the great generators of data, but names keep changing, an understanding of what data means gets lost and numbers often are fuzzy. Having the basics is foundational to modernizing your contact center. But in this case, the basics aren’t necessarily simple. And artificial intelligence (AI) creates an exciting factor of and to the reporting story.
Following are the five best practices for creating and using contact center data.
The foundation of all reporting is having complete information. You can’t understand drivers or relationships without it. And managing work requires a set of basic measures that likely won’t ever change. They are the basic interaction-level metrics (Figure 1).
The first four columns are long-standing metrics in a well-run contact center. If you’re missing any of these, I challenge you to work on adding them to your reports. However, “Results” is an area that’s sometimes ignored or shunted off to a system that’s separate from the contact center-generated measures. To lose the relationship of results to activity is to lose the ability to focus and improve the other measures for the greatest value.
Remember: The purpose of interacting with customers is not just to talk, but to achieve some goal for them. The result measures represent the outcome of that goal — that’s where you need to maintain focus without losing sight of the core metrics. A complete view across all of these metric areas drives success.
The “Completeness” stage includes measures that we’re used to seeing. In this “Consistency” stage, these measures apply across a contact center that handles multiple types of communication media and work types. You need the same measures across all channels because what you’re really measuring is work. In fact, COPC, one of the leading sources of contact center standards, lists consistent measures across channels.
Here are some reasons why having consistency, and a shared place to store customer engagement across channels, is a good goal.
Consistent measures — for activities and results — across the channels, enables you to add up factors and answer broader questions than you can if you had measured media in a silo. Keep in mind: There are some variations in channel-specific measures.
For example, abandons only apply to real-time channels; average response time between contacts only applies to messaging channels. So, be rational but avoid the mistake of creating different measures per channel.
Consistency allows agent fairness and meaningful views across your customers’ journey. It also reduces the management efforts needed to tie data together. Avoid the trap of setting up independent data silos per media channel or work type.
When I introduce this concept, I asked the following question: How many Fs are in the following sentence?
Finished files are the result of years of scientific study combined with the experience of years.
Audience answers spanned three to seven. The actual answer is six. The point here is that even having data available requires looking at the details.
This area is obvious to any data geek or to those of us who are just addicted to data. Using detailed data is the core of all analysis because it allows us to find truth that’s hidden by averages. It also allows us to find relationships that we didn’t expect by using dimensions to bundle the data in different ways. Here are examples to trigger your thought processes on this topic.
The bottom line is to have details available to use and then use details to answer “why” questions. Finally, balance your use of averages and details to effectively manage and improve your operations.
Contact centers have a purpose other than just existing. Results provide the business context for contact center activities. Setting targets to achieve, or thresholds to avoid, is the method for giving good/bad context to the numbers we see. Here are considerations for reasonable targets and techniques you can use to identify them.
Study current behavior to understand customer expectations and agent capabilities.
Remember the rules of math so you set reasonable goals.
Keep tuning so that the targets continue to drive the business positively.
Targets exist to drive the behavior you want. Use the data you have to set reasonable goals that keep the focus where it needs to be — and continue to review the validity and effectiveness of the targets.
Artificial intelligence (AI) is less about the characteristics of data and more about how it’s used. Because this series is about basics, I’m not going to go into a tutorial on AI. But this series is about basics as a foundation for innovation. So, let’s talk about AI a bit, and particularly about machine learning.
Machine learning is a type of data analysis technology that extracts knowledge without being explicitly programmed to do so.
Here’s how the basics on reporting and data translate when looking at advanced capabilities like AI and machine learning.
AI is becoming a legitimate technology — and it builds on the good work you’ve done to generate complete, consistent and detailed data.
Data and Visibility
A customer once told me, “If I don’t see it in a report, it didn’t happen.” Data and the visibility of it through reports is the lifeblood of a business and definitely of the contact center.
As the scope of the contact center expands to more channels and to work that isn’t front office, the volume of data increases and the temptation to silo it across an organizational or technology boundary is strong. Strive to create complete datasets that are consistent across media and work types so you can add up the details in different ways, as needed. The payoff is a well-run contact center with understandable and believable results — and the basis for any AI tool you choose.
Watch this analyst webinar to learn more about how to evaluate a cloud contact center.
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