Contact centers have always been awash in data and data crunchers. But based on progress in data science, there is a renewed focus on analytics and how you can use it to differentiate your customer engagement strategy. When competitors offer similar products and use comparable technologies, your customer service strategy can offer meaningful advantage to your brand.
Using analytics appropriately, forward-looking companies can wring every last drop of value from their contact center processes.
Traditionally contact centers have used metrics like Average Hold Time (AHT), First Call Resolution (FCR), Abandon Rate, Occupancy, Adherence to manage the operation.
However, now they want to leverage additional data; what are the upsell opportunity, how much customers will pay; how many items each will buy in a lifetime; and what triggers will make people buy more.
Many contact centers now want to use additional data to answer more strategic questions, such as:
- What are the upsell opportunities?
- How much will customers will pay?
- How many items will each customer buy in a lifetime?
- What triggers will make people buy more?
- How many times has the customer contacted support?
- How many channels has the customer used?
- Can we measure sentiment using both structured and unstructured data?
Contact centers have traditionally tracked how many customer service representatives are available to take customer calls. Now they also want to know when inventories are running low and to predict problems with demand and supply chains so they can staff effectively.
Increasingly, contact center operations leaders have become champions of analytics and are pushing it down to decision makers at every level.
A question that begs an answer: How will companies train traditional reporting and analytics employees to perform new tasks. How will they get data from multiple proprietary sources? And, most important, how can they ensure they can make intelligent decisions when they do have access to the data?
Using existing customer data, businesses can match customers with representatives with whom they’ve interacted. If a prospect has called several times and had better interaction with a particular agent, you could route the prospect back to the same agent.
Proper use of available data can positively benefit customer effort and improve the effectiveness of contact center professionals. When an interaction arrives with the history of cross-channel interactions, information about the customer sentiment, and recommended action, agents can perform their job with ease. And, they can take pride in what they do.
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