Companies are also jumping on the latest trends in business intelligence, big data, machine learning, and AI. They\u2019re processing insane quantities of data to augment these metrics, painting pictures of their customers in minute detail.<\/p>\n
But if the metrics are misleading or obsolete, then they may be aligning on the wrong goals and staring down a potentially dangerous path. What if you\u2019re measuring things that don\u2019t actually matter or you\u2019re producing heaps of data you can\u2019t actually use?<\/p>\n
To drive brilliant customer experience<\/a> you need metrics that are relevant, useful, and happy to do the heavy lifting. This starts with understanding what customer service success looks like in your company<\/strong>.<\/p>\n In isolation, metrics are just data points. It\u2019s up to us to give them meaning. Before thinking about whether you should prioritize analyzing Net Promoter Score (NPS) results or controlling the Average Handle Time (AHT), take a step back.<\/p>\n If your customer support functioned perfectly, what would that look like?<\/em><\/p>\n You understand your customers better than anyone. Figure out what kind of support would give them the best experience, then select metrics which effectively describe how well you deliver it.<\/p>\n Two examples of legacy metrics are Average Handle Time and First Contact Resolution. In a vacuum, both of these make sense: they accurately describe how cost-effective your service is. If you’re servicing customers faster, then the cost of each interaction drops, and the company saves (and therefore makes) money.<\/p>\n So companies should aim to solve all queries as quickly as possible: this will boost CS quality and profits simultaneously! Except, of course, we know it doesn\u2019t work like that.<\/p>\n Look at call centers for customer support. These are slowly being treated less as ‘cost centers’ and becoming more about empowering agents to adopt a service mindset. The difference is stark and something we’ve probably all noticed in our personal dealings with customer support: Agents give personal opinions, they offer discounts or less-profitable offers to keep you, they try to show empathy and retain you as a customer.<\/p>\n These modern companies are willing to keep customers on the line because they understand it\u2019s not about Average Handle Time per interaction\u2014it\u2019s about the broad, lifetime<\/em> relationship with each customer.<\/p>\n Metrics like Customer Effort Score<\/a> and Customer Satisfaction<\/a> can be insightful. But in isolation, they still only paint a partial picture: measuring the quality of a single interaction instead of the wider, more complex relationship the customer has with your business.<\/p>\n Companies should aim for the latter. Where siloed interactions are misleading, a 360\u00b0 view (i.e. considering various metrics in tandem) can reveal how successful your customer experience is.<\/p>\n Fragmented customer data can result in situations like this: A customer buys a new phone from your retail store. However, there\u2019s a technical problem. After 3 unhelpful support calls, they\u2019re irritated and frustrated but on the 4th call, a personable and knowledgeable agent solves the problem in minutes. They also answer a couple of related queries, because they can.<\/p>\n You survey their customer satisfaction, and they rave about this brilliant support agent\u2026 But you don\u2019t get any feedback on the 3 failures which amount to 75% of that customer\u2019s interactions with your team. The net customer experience was bad,<\/strong> but from management\u2019s perspective, they\u2019ve got a personable and effective team.<\/p>\n There\u2019s a similar issue with Net Promoter Score: if a customer buys 5 times a year, but is surveyed once a year, their focus will either be on the most significant memory (good or bad!) or more likely the most recent one. Isolated metrics cannot capture the 360\u00b0 experience and will inevitably mask underlying issues\u2014this legacy approach is no longer sufficient.<\/p>\n While they might take time to define, it is possible to create metrics which deliver a reliable assessment of CX success. Something like time-to-revenue-achievement<\/strong>\u2014if customers aren’t being onboarded quickly, and getting the benefits out of your product as quickly as possible…then that indicates a problem on the customer journey. What’s holding them back?<\/p>\n How about a metric that defines the post-sales experience<\/strong>? Looking not at this one purchase, but at the customer lifecycle. How well does your team handle that? How well or quickly are issues resolved? What level of engagement and support is offered per purchase or per month of service? What is the rate of successful setups with a new SaaS product?<\/p>\n In the age of big data, no matter what metrics you come up with, you will be able to measure them. You don\u2019t need to follow the crowd and just record \u2018the usual\u2019 metrics: find out what your business needs and work from there.<\/p>\n The fundamental takeaway is this: you need to figure out the key values, principles, or goals which underpin customer success in your business<\/strong>. Then work backwards and decide what you need to measure to track this. It won\u2019t be a single metric, but a combination of several factors which give you real insight into how well your customers are served.<\/p>\n We guarantee your customers will love the results.<\/p>\n","protected":false},"excerpt":{"rendered":" Companies rely on metrics to explain how well their customer service is performing. In theory, this orients the whole company\u2014from product developers to HR to customers\u2014around key goals. Companies are also jumping on the latest trends in business intelligence, big data, machine learning, and AI. They\u2019re processing insane quantities of data to augment these metrics, painting pictures of their customers in minute detail. But if the metrics are misleading or obsolete, then they may be aligning on the wrong goals and staring down a potentially dangerous path. What if you\u2019re measuring things that don\u2019t actually matter or you\u2019re producing heaps of data you can\u2019t actually use? To drive brilliant customer experience you need metrics that are relevant, useful, and happy to do the heavy lifting. This starts with understanding what customer service success looks like in your company. Start with values, not metrics In isolation, metrics are just data points. It\u2019s up to us to give them meaning. Before thinking about whether you should prioritize analyzing Net Promoter Score (NPS) results or controlling the Average Handle Time (AHT), take a step back. If your customer support functioned perfectly, what would that look like? Would 100% of customers asking to cancel their subscriptions be convinced to stay? Is it about resolving complaints as fast as possible, weeding out the loyal from the un-loyal? Is it about being ultra-personable, ensuring everyone who interacts with your brand comes away feeling cared for and appreciated? Would it be human-free, with a Google-level knowledge center […]<\/p>\n","protected":false},"author":26,"featured_media":5036,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[204],"tags":[176,105,99,116,351],"yoast_head":"\nStart with values, not metrics<\/h2>\n
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![\"best](\"https:\/\/www.sogolytics.com\/blog\/wp-content\/uploads\/2021\/02\/CX1-1-1024x556.jpg\")
<\/p>\nMany legacy metrics are intuitive\u2014and nice to use<\/h2>\n
The importance of 360\u00b0 understanding<\/h2>\n
Metrics which deliver real value<\/h2>\n
![\"Laptop](\"https:\/\/www.sogolytics.com\/blog\/wp-content\/uploads\/2021\/02\/CX2-1-1024x729.jpg\")
<\/p>\nTake a look under the hood<\/h2>\n