Boost First Call Resolution with Intuitive Knowledge Management

Posted On: March 9, 2020 |  9 mins read 783 Views

First Call Resolution

First call resolution (FCR) is a customer’s right & organisation’s responsibility. It shows dedication and desire towards helping a customer, through preparation and ready solution parameters. Let’s see how an intuitive knowledge management system can help you achieve this and make your customers happy.

A recent study showed that 62% of the respondents consider the customer representative’s knowledge was key to successful support interaction.  Any error or confusion at this point paints a compromising picture of the company in question. The customer’s expectation management and, simultaneously, the customer journey mapping (and management) is extremely important here.

Customer journey mapping for effective first call resolution (FCR)

What do your customers want? Companies utilize extensive resources to understand (and deliver to) a customer’s need. The same dedication and effort should be undertaken towards understanding the customer’s need for support (pre, during or post-sale). Customer journey mapping is a big factor in marketing. Similar mapping is required in terms of customer support initiatives.

It begins with understanding how customers choose to interact with a brand. According to a study, 79% of adults in the US made an online purchase and about 51% of those were done on a phone.

Now, a customer would prefer to reach support through the same mode as they use to do for purchase, then the option from sales to support must be omnichannel and seamless. This is aided, in no small means with a robust knowledge management system. All relevant profiles and possible challenges (regarding the specific purchases) would be on the representatives’ fingertips when a customer chooses to call in.

First Call Resolution

This first call would go thus:

Dave from New York wants to contact support regarding the inefficient packaging of headphones he just received (through an e-commerce purchase). First, Dave used an app for purchasing wherein he was promised one-day shipping. Then he moved through the app’s interface and clicked on the support order id screen (of the headphones).

At the contact center, either:

  1.     An AI-backed chatbot resolution where the engine interacts with the customer,
  2.     A company representative is assigned for the chat (to initiate personalized interaction),
  3.     The customer is directed through optional decision-tree flows to pinpoint their issue,
  4.     The customer is directed directly (inbound or outbound call) to a customer support representative

Option 1, 2, as well as option 3, work towards filtering the essentials of the issue (and even possible resolving them at that level). If the customer is directed to an agent over a call, where the rep is in the light of an efficient knowledge management system (knowledge base), can filter out the exact solution for the customer within seconds.

Decision trees backed customer support and impact on FCR

Let’s walk through what happens here. The knowledge management system (KMS) has an extensive network of information that covers each and every frequently asked question as well as possible customer issues. The underlying knowledge base is built with no stones left unturned. Even a representative who has just taken over from exiting personnel can understand the entire first call protocol within minutes.

It’s use-case based learning, where the user (representative) gets first-hand answers (within the KMS) of each possible question the customer can have at each process point in the customer support journey.

Furthermore, the above example about the customer calling in about inefficient packaging, here customers expect empathy, acknowledge, and a quick resolution. If a KMS based decision tree structure within the chatbot or customer-facing selections has filtered the issue, then the representative’s job is half-done.

First Call Resolution

If not, it’s still simple if they follow the intuitive decision tree led process. Here’s an oversimplified textual decision tree for the same.

Decision Tree is extensive and far more responsive

  1.      In the first place, acknowledge the caller’s time and emotion.
  2.     Check if the required essential details have been pulled in: if yes, then move to step 4, otherwise move to step 3.
  3.     Ask simple questions to filter the exact issue (and its specifications in terms of order)
  4.     The system pulls up the required details of the order. Moreover, acknowledge the caller’s concern and the issues again.
  5.     Just follow the decision tree box which exactly matches the customer’s query/issue.
  6.     If the customer’s query isn’t matching, reconfirm the details (step 3) then move accordingly. Also, record the specifics of the problem for future reference (and build of the knowledge base).
  7.     Follow the checkboxes to educate the customer about the reason for the problem (the knowledge base would show snippets about how the problem may have developed).
  8.     Proceed to offer a clear action plan to timeline the resolution of the query.
  9.     Close with a smile and an assurance.

If you chose to skip over the oversimplified decision tree, here’s going one step ahead. Simply:

  1.     Identify
  2.     Acknowledge
  3.     Resolve
  4.     Assure

Problems solved by decision tree backed first call resolution

A good KMS and associated decision tree and learning management protocols improve on the tree basic metrics for contact centers and customer support.

  1. Time: It cuts down the issue identification and resolution time. This significantly reduces the overall Average Handle Time (AHT) in an unrushed and simple manner.  The pace of the call can be controlled giving the caller the assurance of having the full attention of the representative.
  2.  Quality: Standing on top of a huge knowledge bank, the representative can effectively resolve (satisfactorily) far more customer queries and issues at the first call level. This considerably increases the first call resolution or FCR likewise increasing customer satisfaction.
  3.  Consistency (replicability): This is a regular issue with contact centers and customer support. Varying representatives have different talent and comfort levels. In addition, there’s a constant inflow of new representatives along with some attrition (or knowledge drain).

Moreover, with the help of knowledge management software, companies can plug the knowledge drain and ensure consistency through standardized first call resolution protocols. It’s a level playing field, where still the talented can get ahead but the less talented would still be highly effective. The overall talent bell curve would be concentrated within the 1st deviation. This means that average performance metrics would be far higher than before.

Knowledge management is the foundation of sustainable business and customer support. It leads to possible customer loyalty, advocacy, and eventual revenue increase. It’s an investment with escalating gains over time.


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