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What information do I need to complete the analysis?

Labor Data

  • Staff

    The number of full-time equivalent (FTE) employees actually doing case work. For example, if you have 20 staff working cases full time, and 10 supervisors working cases half of their time, enter 25 (20 staff + ½ x 10 supervisors).

  • Average Monthly Labor Cost

    The ‘fully burdened’ average cost for salary and benefits. For example, if the average base salary is $50,000 per year, and payroll taxes, fringe benefits, and vacation add an addition $46,000 per year, the fully burdened monthly cost is $8,000 ($96,000 / 12). If you don’t have this data, ask Payroll or Finance.

  • Utilization Rate

    The percentage of time people who do case work actually work on their cases, as opposed to meetings, training, lab time, vacations, after-call time and so on. Typical industry numbers are 60% - 80%, with highly structured / high volume / low complexity environments tending towards the high side of that range, while lower volume / high complexity environments tend towards the lower end.

Case Data

  • Monthly Cast Volume

    The number of cases closed in a typical month. If you track calls but not cases, estimate the number of calls required to close a typical issue and divide calls by that number. (For example, if there are 1000 calls per month, 75% of which are closed in one call, but 25% require a second call, the average issue takes 1.25 calls, so we close 1000 / 1.25 = 800 “cases” per month.

    If you have seasonal spikes, consider modeling both peak and average caseloads, so you can see both total benefits and the benefits when you need them the most.

  • Percent of Cases Handled as Known

    The percentage of cases that are treated at first contact with an already-known answer: either the staffer knew the answer off the top of his or her head, or could quickly find the answer in the knowledge base, documentation, job aids, or by asking a colleague.

    Estimate this by asking a number of front line staff about their experiences, discard outliers, and averaging — generally, results cluster around a small range of percentages.

    For more information, see Understanding "Known" vs. "New"

  • Time to Solve Known

    The average length of time it takes to handle a “known” issue — that is, when the staffer knew the answer off the top of his or her head, or could quickly find the answer in the knowledgebase, documentation, job aids, or by asking a colleague. In general, this comprises the time to log the case, fully understand the customer’s issue and environment, document the case, retrieve the answer and deliver it in a way that makes sense to the customer.

    Estimate this by asking a number of front-line staff about their experiences, discarding the outliers and averaging the rest.

    For more information, see Understanding "Known" vs. "New"

Web Data (optional)

  • Web Utilization Rate

    The web utilization rate is the current percent of customers who go to the web before they open an incident.

  • Success Rate

    How often are customers who come to your self-service website to search the knowledge base successful in answering their question or dealing with their issue? Recent industry data from TSIA shows that average rates have fallen to 39%, but some companies (especially with simpler questions, or more sophisticated users) do much better.

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We want to see average case handle time go down with knowledge management — unfortunately, there’s no such thing as an “average” case! New and known give us a more concrete way of thinking about how KM improves support center and contact center capacity.

Image005 Known issues are relatively quick. Either the user knows the answer off the top of his or her head or can quickly find it in the knowledgebase, in the documentation or from a colleague. To solve a known issue, one has to understand the customer’s question and environment, look up the answer, deliver it and document the case — it’s easy.

New issues take more work. The user has to actually figure out the answer, for example, by recreating the problem, performing troubleshooting, testing hypotheses, collaborating with others or even looking at schematics and code. They take much longer.

By providing the collective experience of the organization to each user, effective knowledge management lets each user treat a higher percentage of issues as known. Findable content, structured for reuse and served up with highly effective search also makes it quicker to look up and deliver answers to customers, shortening the time to resolve known.

In short, reducing new and speeding known makes operations more efficient.

Because new and known are important drivers of this benefit model, we allow you to enter the current percentage of known issues, the time to resolve known issues, and to adjust assumptions about how these will change. Few organizations track these numbers today, so the best and most accurate technique to estimate them is to ask a handful of frontline staff, either in person, in an email or using a survey tool. Excluding way-out answers (if any) should leave you with a set of reasonable estimates that you can average to enter in the model.

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