How does analytical service information improve the customer experience?

Customer service is possibly as important as sales itself in a company when it comes to maintaining and expanding a business; here is how analytical information can help.

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

Getting customers to buy is relatively easy.  Getting the right customers to buy is not so easy – 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 needs 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 likely cultivate customer loyalty.  Analytical customer service measurements can make this task easier.

Nothing instills customer loyalty more than being treated like 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.  Analytical customer service metrics can support such personalization.

A fundamental task in effective analytics is to integrate in-house customer information (usually from a CRM system) with external data collected through multiple channels (usually social networks).  From this data, unique traces and behaviors of each customer 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 has he 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?  All of this information can be aggregated and made available in real time for live support, creating an atmosphere of empathy during the issue 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.

Improve your customer’s next experience

Predictive analytic 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.  One thing, in the long run, is to look at the past 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, cross-sell additional products, or anticipate support system failures?

After these decisions have been made, the appropriate data sources (both internal and external) must be identified, goal by goal.  Then, the target audience can be segmented demographically, purchase reactions and behaviors, and any other traits that are appropriate.  This forms the set of descriptive analytic information that is necessary to feed the predictive process – carefully targeted messages are 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 sees the business as a whole. This aspect illustrates in an important way the effectiveness of the support process.

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

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

On how analytical information can improve field services

Most of the time, customer service comes in 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 in equipment repairs, maintenance and crisis resolution.  Analytical information can also enhance that type of support.

Customer satisfaction with service in the field can be even more critical to retention than success at a help desk.  Certainly, 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 obtained from those operations can cumulatively affect the bottom line.

Determine metrics on the duration of visits, isolate instances of faulty programming and/or poor allocation of field teams and provide other key information.  Armed with this data, fleet service management can increase first-time repair rates, have fewer follow-up vehicle deployments, and implement preventive tasks to execute during a repair visit.

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

Finally, fleet management is improved by having all this analytical customer service information available from a central point and providing 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 power management.  Trip/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 more satisfied customers – who are, in turn, more loyal customers.

Read the full article, here.

For more than 11 years, SQDM -Software Quality Driven Management- has advised a number of companies with professional consulting services on IT strategies.  SQDM is an official business partner of leading manufacturers in the industry including Salesforce, Microsoft, Oracle and AuraPortal.

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