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A Look At Dashboard Reporting Time Frames

Our friends at Klipfolio provided the following look at Dashboard Reporting Time Frames

An essay on time frame comparison metrics.

We take an close, in-depth examination of 3 common ways of applying time frames and comparative values with an organization’s key performance indicators (KPIs). Once your organization has adopted a KPI dashboard, the next steps – to truly make the metrics work for you – may not be as obvious. A great starting point is our article on the anatomy of a KPI, where we look at how consciously designing your KPIs can influence the way you interact and act on them.

The next item to consider is what type of time frame paradigm you want to use when tracking your Key Performance Indicators. This article will examine 3 important models: historic, X-to-Date, and moving/rolling metrics.

Historic metrics

Historic business metrics or KPIs have a fixed start and end date with a full data set – for example, last year, last quarter, or last month. This metric is locked-in and must be compared to other historic metrics.

Although this type of measure might compare values from yesterday to the previous day, scenarios typically look at monthly, quarterly, and yearly time frames. That generally means that the operative word describing historic business metrics is “strategic.” Since this type of metric lends itself to longer time frames, you will spot trends and patterns that are less granular. Historic metrics tend to answer fairly broad or strategic questions, such as the following: what is the state of my business based on this metric compared to last year/quarter/month?

X-to-Date metrics

X-to-Date measures have a fixed start date with a moving end date – for example, this year to date, this quarter to date, or this month to date. As such, you will be working with a partial data set that provides you with a story that unfolds before your eyes. This type of measure is very common, and is the traditional way of reporting sales and financial metrics.

X-to-Date metrics or measurements tend to be more tactical than historic reports. Again, this has to do with the fact that they tend to be more operational in nature simply because the last set of values generally is today (or yesterday depending on how frequently the date is refreshed). A unique, and sometimes challenging, aspect of X-to-Date metrics is that at the beginning of the period (ie: 2 days into the Quarter), the numbers can be very volatile, or even misleading. There is just not enough data there to be statistically relevant.

As is the case when comparing any metric or KPI, use Past X-to-Date, or Last Year X-to-Date, so that you’re comparing apples to apples.

Moving or rolling metrics

Moving or rolling business metrics have a fluid start and end date – for example, the past 24hrs, or the past 7/30/60 days. The data set used for this metric is complete and grants tactical insights into the operations of your business. This type of business metric can be utilized by front-line workers as well as managers and executives.

Consider the use case of a call center using a rolling metric such as Average Service Level for the past hour (taken from our KPI Examples-Call Center Metrics article). As the metric changes, say to indicate that the center is not meeting its current Service Level Agreement, agents and managers can respond to the situation accordingly. Again, rolling metrics are compared with past rolling metrics – for example, Past 30-60 days, or Last Year past 30 days.

Recap – reporting time frames

This summary chart will provide you a breakdown of the distinctions between the three time frames we’ve just discussed.

Current

Comparison

Example

Historic

Last Month (May)
Last Year (2010)
Jan 1,’11 to June 30,’11
(full data set, specified start and end date)
April
2009
July 1,’10 to Dec 31,’10
Sales of $12.6M FY10 vs $11.4M FY09 Sales. Variance of $1.2M, or +10.5% (full year comparisons)

X-to-Date

MTD
QTD
YTD
(partial data set, specified start date to moving end date)
Past-MTD
LY-QTD (Last Year)
PY-YDT (Prior Year)
Production of 2,353kg MTD vs 2,532kg PastMTD. Variance of -179kg, or -7.1% (both MTD are only partial data sets)

Moving or Rolling

Past 24 hours
Past 7 days
(full data set, moving start and end date)
Past 24-48 hours
LQ-Past 7 days (Last Quarter)
Leads of 482 past 30 days, vs 325 PY 30 days. Variance of 157, or +48% (full 30 day comparisons)

 

Notes on acronyms:
- Prev, Prior, Past: All seem to be the same and can be shortened to PY or PriorMTD for example
- Last: Used the same as prev, prior, and past. Can be shortened to LY or LQ for example

Parting shots

Developing a comprehensive and useful KPI monitoring strategy requires considering what types of business metrics you need to monitor as well what time frame paradigm you should apply. Knowing who in your organization needs what information harkens back to the old writing rule, “know your audience!” Once you know this, rolling out metrics tailor made to suit their needs – such as a historic metric showing percentage YoY of sales growth for an executive – becomes much easier and much more effective.

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