Getting customers to buy is relatively easy. Getting the right customers to buy, not so much -- and keeping those...
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customers after buying can be the hardest task of all. Customer service can, in fact, be the single biggest component of customer retention. When customers need service, they really need it, and the enterprise that makes it as painless as possible is the one that cultivates real loyalty. Customer service analytics can make it easier.
Nothing inculcates customer loyalty as much as being treated like a person. There's an innate note of respect and authenticity that emerges in a process when customers need help and it is offered with a personal touch. Customer service analytics can support that personalization.
A core task in effective analytics is integrating in-house data on customers (usually from a CRM system) with external data gathered across multiple channels (usually social media). From this data, unique customer traits and behaviors can be identified.
And when this is achieved, context can be created around a customer request. Is the customer new to the product or a long-time owner? Is there a history of frustration? What makes this customer similar to others with whom the support center has dealt successfully in the past? All of this information can be aggregated and made available in real time to live support, creating an atmosphere of empathy in the troubleshooting process.
Moreover, having all of this customer service analytics information available on demand makes it possible to anticipate expectations and deal with them proactively, rather than reactively.
Improving the next customer experience
Predictive analytics does more than describe what is happening and to whom: It anticipates what happens next. This can be invaluable in improving the customer experience. It is one thing, after all, to look at the past and deal with problems as they arise; the point of applying predictive analytics to support operations is to study the available data in order to predict how customers are likely to respond.
The first task in pursuing this is to decide what it is that needs predicting. Is the idea to reduce customer complaints? Is it to achieve crossover sales of additional products? Or to anticipate failures in the support system?
Once these decisions have been made, appropriate data sources (both internal and external) need to be identified, goal by goal. Then, the target audience can be segmented by demographics, sentiment and buying behaviors, and whatever else is appropriate. This forms the body of descriptive analytics that is necessary to feed the predictive process: carefully targeted messaging becomes possible, and hidden patterns can be discovered to create greater refinement in the process.
It takes a careful mix and match of internal and external data to achieve this resolution. Internal data can include overt customer feedback, transactional histories from previous customer support activity and unstructured data, such as phone calls and free-form text queries to support. But once these are integrated into the process, an important part of the customer experience becomes available -- how the customer views the enterprise as a whole. This can greatly inform support effectiveness.
The external data can and should include whatever can be obtained from social media to provide patterns of sentiment that foreshadow dissatisfaction, which will spur proactive support efforts.
Armed with such customer service analytics, an enterprise can form strategies to reduce attrition and make improvements at every customer touch point.
How analytics can enhance field services
Most of the time, customer service is in the form of a help desk or a website to which customers reach out when they need something. But sometimes, customer service is something that happens out in the field, such as equipment repair, maintenance and crisis resolution. Analytics can enhance that support, too.
Customer satisfaction with field support can be even more critical to retention than service desk success. Certainly, the in-person transaction that occurs when field service personnel make a site visit to troubleshoot a product has a profound impact on how the enterprise is perceived. The quality of that interaction is critical, and data gathered from those operations can cumulatively affect the bottom line.
Descriptive analytics, applied to fleet operations, can yield great efficiency. In-vehicle data, fed to dispatch, can produce more accurate service commitment success, determine metrics on length of visits, isolate instances of poor scheduling or inefficient field staffing, and provide other key information. Armed with this data, fleet service management can increase first-time fix rates, have fewer follow-up truck rolls and implement preventative tasks on repair visits.
All of this, in turn, takes cost reduction a step further, to customer-centric operational changes. These changes can add more runs per day at higher success rates and optimize routes based on service personnel -- matching techs to tasks based on logged performance data that maps preferences to skills and geography.
Finally, fleet management improves, with all of these customer service analytics centrally accessible, and enhances visibility into decision-making. High percentages of late arrivals can be tracked and then traced to scheduling failures or individual errors -- and comparative metrics can then enhance workforce management. Travel time estimates improve, as well as fuel usage, and actual work vs. projections is more accurate.
All of which translates to more satisfied customers -- and more satisfied customers are more loyal customers.
Organizations still grapple with customer data analytics strategies
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