This month, 900 Web analytics pros and their colleagues (media buyers, marketing directors, Web developers, etc.) gathered in Utah for an annual Summit. Brands represented ranged from offline names such as Nissan and online names such as eBay.

Despite this wide range of industries, everyone shared a few commonalities -- they were with big enough or Web-focused enough organizations to be paying for top-line analytics software, not to mention T&E to send (often multiple) staff to the event. Plus, nearly everyone shared the same four frustrations …

Frustration #1. Understaffing

Web analytics has never been easy to staff for. In the recent past, most would-be employers simply couldn't find anyone to hire. We used to hear estimates of more than 100 job openings chasing a dozen qualified applicants.

Things have changed. Now anyone who's serious about the Web has at least a partial analytics staffer already on board. (By partial, we mean that person may have other job duties, such as search media buying or Web programming or marketing. …) And, it's not impossible to find new staff when you need them, especially with a crop of newbies steadily graduating up through the self-trained ranks of Google Analytics users.

However, convincing corporate to allow you to hire the full complement of analytics staff you need is another matter.

Analytics is a lot like email marketing. Once marketers dip a toe in to try it out, soon they want to staff up like crazy. The good news is, in most cases, any half-decently-run analytics department should be able to pay for itself just by site and marketing program improvements based on the data. ROI for a moderately-sized staff can be pretty darn high.

The bad news is, most CEOs feel like they just approved that first analytics position yesterday and now you're asking for more people already?! It's an internal marketing challenge to get the resources you need.

So, how many staffers do you really need? According to the staffing chart Summit organizers handed out (see link to copy below) innovative organizations need a team as large as eight.

Yes, we gasped with disbelief, too. Luckily, it turns out this is more a function of individual tasks that revolve around analytics. So, one staffer could cover multiple duties.

The only people we met who actually thought they were close to staffed up properly tended to be Web-only (or extremely Web-centric) direct response-driven companies. Think best-of-breed dot-com.

Hardly anyone in B-to-B lead generation, consumer packaged goods, media/publishing or other mainly offline companies (no matter how large) felt they had a "big enough" staff. The more offline-centric your organization, the more likely analytics are handled by a partial staffer, many times an all-in-one "Web marketer."

Often, that staffer is someone fairly low on the totem pole both in terms of age and job title. (Few Summit attendees appeared to be older than mid-30s.) Many job titles were somewhere in the associate-to-manager range. This is probably due to the sheer youth of the profession. It's been around for only a decade.

However, we suspect this is another factor that makes it tough for the C-suite to take analytics staffing as seriously as perhaps they need to. Fortune 1000 management isn't used to taking staff budget cues from 20-somethings.

Frustration #2. Too many stats and not enough useful ones

"There's so much data that half the time we don't know what to do with it all," one Fortune 500 marketer complained. "They call it a data warehouse," another chimed in. "Who has time to go through a warehouse?"

Of course, this has always been the problem with Web analytics. You turn on the tap for a few numbers and get knocked over by a gushing flood of data.

Analytics software providers have tried to solve this problem to a large degree by switching their output from mainly fat printed reports (which inevitably got dusty on your bookshelf) to easy-to-skim online dashboards.

Dashboards are a huge improvement, but users still need to tweak the way these are set up to make the data useful. The two biggest improvements most are focusing on:

o KPIs versus general site stats

Key Performance Indicators (KPIs) are numbers you can act on. Other stats no matter how exciting-sounding are by definition useless. Example: A KPI such as page conversion rate is very useful, especially if it's tracked to conversions by traffic-source. A stat such as page views isn't very useful.

So, nearly every speaker at the event advised setting up your dashboard to show KPIs (which are fairly limited) versus more general site stats (which are close to endless.) It's a great idea. But, given that many analytics pros come from technical rather than marketing strategy backgrounds, KPI implementation could be a while in coming.

o Integration

In the past year, nearly every analytics firm worth its salt has come out with some flavor of integration. Usually, there's search marketing campaign integration and often email as well.

However, given that KPIs are all that matters in the end, marketers want far more integration. Anyone spending significant sums offline -- especially in direct postal mail or promotional marketing (sweeps offers on CPG packaging etc.) wants site KPIs to tie into that offline-driven traffic.

On the B-to-B front, marketers dream of closed loop sales reporting, where they see stats on how up to 10 or more media channels, including everything from trade show booths to PR, all touch the same prospect leading over months (and months and months) to a final sale.

All this stuff is possible and already implemented in a handful of tech-savvy organizations. But now the marketing masses are dreaming an integration dream that for many is years away from reality.

Frustration #3. No attitudinal data

Despite a deluge of stats tracking what people do online, the Web analytics team usually has few if any resources on the "why they do it" front.

If you don't know why, coming up with the site tweak that moves the conversion needle can be tough. This is especially vexing because according to multiple MarketingSherpa studies the No. 1 site creative factor that affects conversions is copywriting (i.e., the text on the page.)

It's easy for an analytics person to move a button around or test image size … it's harder for them to come up with copy to test. Techies and numbers people are not the best copywriters (and absolutely vice versa.)

Plus, attitudinal data -- the best being persona-level understanding of what makes your visitors tick -- is critical for compelling copy.

Some vendors, especially those offering attitudinal tools, claim this problem can be solved with advanced technology. For example, instead of a focus group or traditional demographic research, you might be able to troll your site's user-generated content looking for attitudinal hints.

Will it work? We have reservations, not the least of which is the recently reported study that the average Amazon customer review gives a 4-star rating. (This indicates people willing to post content are more likely to be fans who are very different attitudinally from the typical prospect you're having trouble converting.) That said, we're psyched to see the data in upcoming studies on the new tools. Anything is possible!

Frustration #4. Web 2.0 fever

On top of making sense of regular Web stats plus integrating external marketing-driven stats, analytics pros are confronted with measuring Web 2.0 content. Web 2.0 tends to fall into two camps: user-generated content, such as blogs, reviews, forum posts; and rich media such as podcasts and video (which are themselves sometimes user-generated.)

We heard a lot of questions around this type of measurement. For example, how do you measure video watching as a KPI anyway?

We also heard some Web 2.0 poo-pooing, especially from direct response marketers who are not sold on hip hot new media forms such as online video until the fat lady sings. Or at least until KPIs prove it's worth adding to the site.

At the same time, on-stage visionaries, such as Tim O'Reilly, CEO O'Reilly Media, gave off buzzworthy quotes, such as, "A Web 2.0 site is one that gets better with use. Basically what you build is in beta mode." Then as your site listens to customers, it changes in reflection to their needs.

Kind of like a mood ring changing color on your finger.

Oh, we and the rest of attendees sighed, would that analytics 2.0 be that simple. What a wonderful world it would be.

Useful links related to this article

Suggested staffing chart for analytics (from event organizers):
http://www.marketingsherpa.com/cs/wacmar07/study.html

Omniture - the analytics firm that held this Utah Summit
http://www.omniture.com

Our past notes from Omniture's 2005 Summit:
http://www.marketingsherpa.com/sample.cfm?contentID=294