How does service analytics information improve the customer experience?

Customer service is arguably as important as sales itself in a company when it comes to maintaining and expanding a business; here’s how analytics can help.

SQDM shares an article published by SearchCRM portal, describing the strategy and benefits achieved through customer service analytics.

Getting customers to buy is relatively easy.  Getting the right customers to buy is less so – and keeping those customers after a purchase can be the hardest task of all.  Customer service may, in fact, be the single largest component of customer retention.  When a customer requires service, it’s because they really need it, and the company that makes that process as easy as possible is the one that will most cultivate customer loyalty.  Analytical customer service metrics can facilitate this task.

Nothing instills customer loyalty as much as being treated as a person.  There is an innate note of respect and authenticity that emerges during the process where customers need help and it is offered with a personal touch.  Customer service analytics measurements can support such personalization.

A fundamental task in effective analytics is to integrate in-house customer information (typically from a CRM system) with external data collected through multiple channels (typically social media).  From this data, unique customer traits and behaviors can be identified.

When this is accomplished, context can be created around a customer’s request or requirement: Is the customer new to the product or have they had it for a long time, is there a history of customer frustration, what makes this customer similar to others with whom the support center has dealt successfully in the past, what makes this customer similar to others with whom the support center has dealt successfully in the past, and what makes this customer different from 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 for live support, creating an atmosphere of empathy during the incident resolution process.

And there’s more; by having all this analytical customer service information available on demand, it becomes possible to anticipate expectations and address them proactively rather than reactively.

Enhance your customer’s next customer experience

Predictive analytics information does more than just describe what is happening to whom; it anticipates what happens next.  This can be of great value in improving your customers’ experience.  It is one thing, ultimately, to look back and address problems as they arise; the main purpose of applying predictive analytics to support your operations is to study the available data to be able to predict how your customers may respond.

The first task in pursuing this is to decide what should be predicted: is the idea to reduce the number of complaints, to cross-sell additional products, or to anticipate support system failures?

After these decisions have been made, the appropriate data sources (both internal and external) must be identified, goal by goal.  Next, the target audience can be segmented by demographics, purchase reactions and behaviors, and any other traces that are appropriate.  This forms the set of descriptive analytical information that is needed to feed the predictive process – carefully targeted messaging becomes possible and hidden patterns can be described that create further refinement in the process.

Careful mixing and matching of internal and external data is required to achieve this resolution.  Internal data can include explicit feedback provided by customers, transaction histories and unstructured data such as telephone contacts and free text questionnaires for support.  After all of this is integrated into the process, an important part of the customer experience becomes available-the way the customer views the company as a whole. This aspect importantly illustrates the effectiveness of the support process.

External data can and should include any data that can be gleaned from social networks to provide reaction patterns that portend dissatisfaction, which will prompt proactive support efforts.

Armed with this analytical customer service information, a company can form strategies that reduce friction and improve every customer touch point.

On how analytical information can improve field services

Most of the time, customer service takes the form of a help desk or a web page that customers access when they need something.  Sometimes, customer service is something that happens in the field – for example, equipment repairs, maintenance and crisis resolution.  Analytical information can also improve that kind of support.

Customer satisfaction with field service may even be more critical to retention than help desk success.  To be sure, the interpersonal transaction that occurs when a field team makes an on-site visit to service a product has a profound impact on how the company is perceived.  The quality of that interaction is critical and the information gained from those transactions can cumulatively affect the bottom line.

Determine metrics on the duration of visits, isolate instances of faulty scheduling and/or poor field team assignment, and provide other information that is key.  Armed with this data, a fleet service management can increase rates for a first repair, have fewer follow-up deployments made with vehicles, and implement preventative tasks to execute during a repair visit.

All of this, in turn, takes cost reduction a step further-toward customer-based operational changes.  These changes can increase the service responses made on a daily basis, with higher success rates, and can optimize routes based on service personnel – matching technician skills to required tasks, according to performance information available in the system that maps preferences to skills and location.

Ultimately, fleet management is improved by having all of this analytical customer service information available from a central point and provides better visibility for decision making.  If, for example, service teams have a high percentage of late arrivals, those percentages can be detected and then connected to scheduling failures or individual errors – and comparative metrics can improve fleet management.  Travel/arrival time estimates are improved, as well as fuel consumption and also work vs. projections are achieved with greater accuracy.

All of this translates into happier customers – who are, in turn, more loyal customers. Translated with www.DeepL.com/Translator (free version)

Read the full article, here.

For more than 11 years, SQDM -Software Quality Driven Management- has been advising countless companies with professional consulting services on IT strategies.  SQDM is an official business partner of industry-leading vendors including Salesforce, Microsoft, Oracle and AuraPortal.

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