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This is part two of a four part article series in which we demonstrate how to identify value streams of using a virtual assistant to partially or fully automate “customer-to-business” interactions. We will detail how to measure quantifiable cost savings and track qualitative benefits in a contact center environment. You can read part one–which discusses misrouted calls–here.
In this article we will investigate the Identification & Verification (ID&V) value stream for virtual assistants. ID&V needs very little explanation as it’s prevalent in almost every call center voice interaction for any vertical. The process typically involves a caller giving a piece of identifying information–such as a phone, account, or ticket number–followed by several other pieces of information to verify the caller’s identity–such as the date of a recent bill payment, full name, address, etc.
Some contact centers have made advancements in terms of using Caller ID or Voice Biometrics to passively ID&V the caller. However, in most cases it can be both frustrating and costly, and often a misused transaction in the contact center. The reasons for this are 2 fold:
Even in a highly optimized and efficient call center, ID&V can still prove a costly transaction for live agents. Let’s assume that the average call center with 1 million calls must ID&V 75% of their callers, with the process consisting of one identification question:
Let’s also assume that agents must also verify the caller with at least 3 of the typical verification questions:
For an experienced agent, connected to a caller who has the answers to the questions readily available, each question typically takes 13 seconds for a question and answer or 52 seconds for the entire process. Note that this excludes time delays caused by “Chit Chat” / Un-Prepared Customers / Re-Prompting or Re-Phrasing of Questions / etc…
So if we put this all together we can quantify cost to the contact center based on the following information:
The quotient for ID&V results in a total of £162,500 gross cost savings if this task were to be removed from live agents.
PolyAI virtual assistants have a proven track record of automating Identification & Verification transaction types even for the difficult alphanumeric strings such as reference numbers, postcodes, and number plates.
Recent benchmark testing has demonstrated the Virtual Assistant’s capability to achieve a 94.7% success rate when “collecting” information for identification from a customer, and 96.5% success rate when “matching” information against a customer record for verification. The following table illustrates the overall ID&V success at each stage of the process:
This means that in a 4 step ID&V process, taking into account the success at each stage the Virtual Agent can generate a gross savings of £138,125.
In the articles to come, we will continue to investigate Vertical Specific Self Service transactions, as well as qualitative value streams such as customer satisfaction, retainment, & effort.