by Brian Terrell

I attended a session, at the Information Technology Alliance Fall Collaborative, today in Salt Lake City. This conference is designed to help ERP business partners and information technology leaders determine the key metrics they are keeping to manage their firms. Bob Gaby, a principal of Arxis Technology, and Dan Milne, a partner with Eide Bailly LLP, delivered an excellent presentation on why, what, and how we keep these key performance indicators. These guys brilliantly showcased their knowledge and I wanted to share their insights with you in hopes this information will be useful as you determine your organizations key metrics for success.

Bob began by reminding us that key performance indicators (KPIs) drive behavior in professional service firms. We all know management becomes much easier when we monitor and report on items we intend to manage. KPIs fall into 3 broad categories: those predicting results, those proceeding results, and those lagging results. Leading and predictive metrics are most helpful, because we still have the ability to affect future results by changing something we are doing. Bob and Dan quickly shared how important it is to pick a modest number of statistics (4 to 5) when starting out to prevent becoming overwhelmed by the effort involved in tracking and publishing results.  

Predictive indicators appear hardest to devise. However, Dan and Bob provided several interesting examples of these most valuable metrics broken down by category:

~  Days in sales cycle for won versus lost opportunities - “Time kills deals,” is what I always hear.  This predictive metric helps determine if this statement is really true.

~  Average conversion rate by sales stage - What percentage of opportunities advance from lead to discovery, from discovery to demo, from demo to proposal, and proposal to win? This helps predict revenue when coupled with estimated opportunity size. A leader identifies which stages of the sales cycle are most treacherous to the overall conversion ratio.

• ~  Time to close by industry/referral source/product - All of these variations predict, therefore focus the sales efforts in the appropriate direction.

~  Close/lead or close/opportunity - This predictive measurement influences incentive based compensation plans and marketing budgets.

~  Engagement rate per status update - LinkedIn actually reports this lagging statistic and when combined with another attribute of the update, helps drive successful social media activity (and becomes predictive in the process). By the way, I heard on multiple occasions during the conference that LinkedIn deserves to be at or near the top of every ERP business partner’s marketing plan.

• ~  Increase of followers per tweet - This predictive metric answers the question “how often should I tweet”?

Customer Satisfaction

~  Number of referrals/client base - Business partners use this ratio as a lagging indicator (am I doing well?) and as a predictive indicator (how many more referrals will I get as I grow my base?).

• ~  Client touches for A & B customers versus revenue - How does excellent customer service impact revenue?

~  Reference clients/all clients - Predictive in nature when considered with revenue, this ratio encourages me to manage my efforts to increase the number of reference clients.

Dan and Bob provided a lot of additional information in their session, “Why, What and How to Measure What Really Matters.” I wish I could share it all with you. However, the major takeaway is, good information helps us be more effective managers. Metrics drive behavior, and predictive indicators are harder to imagine and implement but are more valuable at affecting future results. If, tracking these statistics becomes your new initiative, then start with a manageable number and go from there!