Unravel Let's start off the new year with lessons learned from a tough life on the front lines of trying to make the world a smidgen more data-driven.

This post is a collection of six things I wish I knew before I started my career in decision support systems (of which web analytics is just the latest incarnation). These lessons might have made some goals easier to accomplish, some frustrations easier to avoid and some salary jumps easier to come by.

Perhaps you are just starting out, perhaps you are in the middle of your professional journey, or maybe at the end. Regardless of where you are I hope these six lessons help speed up your journey, avoid the mistakes I made and achieve success sooner:

#1: An obsession with tools & implementations will kill you.

Everyone wants the perfect tool that bounces rates in real time while computing multi-channel attributable impact of an email sent to grandma via Facebook based on competitive intelligence gleaned from TV watching behavior of customers with lifetime value greater than $358 and FICO scores of 700 or higher.

Get over it.

The smallest part of my success, and yours, will come from having the prefect implementation of the Omniture Marketing Suite or Google Analytics.

10% of your time should be spent in implementing tools, not 15 months with an eye towards analysis in the middle of 2012.

You can win with Omniture or WebTrends or IBM or Google. Stop switching tools!

A majority of your success will come from following the 10/90 rule, hiring smart analysts and then ensuring their work day is optimally structured, weaning your management away from faith based decisions, and ensuring you have a clear line of sight and that you are following the process outlined in the web analytics measurement model.

It took me a while to realize that 100% of what makes a successful web analytics program in any company has nothing to do with tools for the first five years. After five years of success worry about tools.

Next time someone from Google Analytics or Omniture or WebTrends calls you promising to make you a data driven organization. . . tell them to suck it.

spectrum of success for a career in analytics

#2: Your magnificent analytics skills are tertiary to your success.

In our industry we believe that you have to be good at creating pivot tables in Excel. And using Site Catalyst. And possess at least some knowledge of the fundamentals of statistics. And create box plots and frequency polygons etc., etc.

It will surprise you that pretty much all of your success will not be sourced from your ability to deploy the above skills. Rather it will come from two surprising abilities: 1. Your business savvy and 2. Your soft skills, your EQ (emotional intelligence quotient).

Your real output is not the data; your real output is a set of decisions the Senior Leaders can confidently make to drive business priorities. Recommending actions requires an ability to understand business strategy, marketplace dynamics, some complex situational analysis, root cause identification, and, perhaps most importantly, an ability to identify the right business question. You don't necessarily need an MBA, but if you can't have a intelligent business analysis conversation you won't be effective.

Your real impact comes not from providing pretty pie charts from a complex Discover2 query. It comes from your ability to deal with multiple psychological personalities, from being a warm and friendly person, from constructing relationships across the aisle, from your ability to speak confidently and persuasively about hard decisions and brilliant insights, and disarm people with your charm and un-arrogant demeanor.

If you want to be a great Analyst who has an ability to have business impact with data, work on the above two skills.

[I am not saying analytical skills are not important. They are, see #6 below. But without the above two you are not going anywhere, no matter how smart you are. Prioritize.]

fetish cube letters

#3: Obsess about Outcomes.

Every other channel is so data poor that when I got into web analytics, like everyone else, I was ecstatic to see very specific customer behavior like the number of Visits! Man that was awesome!

Then I saw page views. And then time on site and unique visitors and browser types and % new visits and I became one happy data-shoveling monkey.

Here's the amazing thing: everyone else who got all those reports thought I was quite simply magnificent. After all I had all this, as they put it: "OMG data!"

That only lasts a couple of months, six at the most.

Then no one cares because no one is any wiser and the business still stinks at the web.

I remember when I first created a simple spreadsheet with 12 rows that just summed up the revenue made by the divisions of the company (even that simple task no one had bothered to do) on the web. The CMO was shocked at how big the number was. The next day he approved a job req for a VP for the Web position.

Great lesson.

Companies care about money, non-profits care about impact, governments care about costs reduced. They all care just about outcomes.

Here is the only way to succeed at web analytics: Identify the business macro and micro conversions. Torture everyone to identify economic value of non-revenue micro conversions. Obsess about this analysis: What's the economic value generated by the company? Why?

Your peers will think you are the embodiment of the messiah. Your boss will listen to your every recommendation. Your CEO will invite you to a private dinner with her/his family.

[Here is my personal recommendation in this context. Obsess about the outcomes for your customers. Use Kissmetrics or 4Q and measure primary purpose by task completion. Buy some sessions with UserTesting or Loop11. Feel the pain of your customers. Identify with their failures and hug / stalk as many people in your company as you have to in order to fix things. Deliver the outcomes your customers want and you'll ascend directly to heaven when its your turn.]

no problems only solutions

#4: Be pragmatic.

I learn this lesson every single day.

At the end of a recent keynote recently the first question to me was:

"Sure you can optimize the page and campaign based on clicks and on amount donated. But how can you optimize it for the fact that the person voted in the election?"

This was in context of a President Obama A/B test.

I can't measure if the person voted.

I can measure that they came. I can measure that they signed up for a lead. I can measure that they donated and which email campaigns caused them to donate actual hard earned dollars. But no I cannot measure the offline go into the vote booth action.

I ended my answer with:

"If after signing up and donating money to Obama the person voted for Sarah Palin then you know what? I am perfectly okay with that. I did great with my online campaign!"

Be pragmatic.

You will always always, always, run into people who want to achieve the impossible on day one and push you to do the same. Who refuse to fix the high bounce rate on top landing pages. Who want to measure Engagement as defined by x time y divided by z to the power of m with the resulting sum multiplied by n and subtracted from o. Why? Maybe because they are over-achievers. Maybe because they are bored with the "mundane." Maybe because they have no idea what the heck they are talking about. Maybe. . . well who knows.

It is your job to be pragmatic.  Don't focus on what should be measured in a perfect world of persistent nirvana. Focus on what can be measured in a world that is imperfect and needs improvement now. You already have data, a lot more than any other marketing channel on the planet. Use it. Make love to what you have. Produce beautiful analyses tied to business priorities. Get people to take small actions every day and some big ones every month. Go back make more love.

Achievable victories, every day. Aggregation of marginal gains!

Don't get suckered into the impossible. Not because being ambitious is bad, but because putting points on the board matters a lot.

 

rock climbing gear

#5: Embrace agility, nimbleness & a portfolio strategy.

If I am not mistaken, for the first two years I did nothing except live in the world of clickstream analysis (with WebTrends and ClickTracks). I did some of the things recommended above (others were yet to be learned). My continued gainful employment indicated that there was some appreciation for the work. But I was still dissatisfied because there was not more slashing and burning of sites going on, not enough Executives had moved beyond using faith.

It took me some time to figure out that clickstream data left far too many questions on the table. Even though we had lots of data, we were information poor.

That led me to my first online survey. Failed. Tried again. This time it worked better. Suddenly I did not have to guess why people were dropping off like flies at stage two in the funnel. They were telling me! I knew the conversion rate for search was 2.74% but now I knew it was so low because people were heading off to Amazon and Costco to buy our product (and that was okay). I knew so much more.

I remember the first time I logged into HitWise. Total data-gasm. I could not believe there was so much to learn from our competitors that so easily helped us identify gaps in our marketing strategy. (We had not even heard of four out of top five companies getting traffic for our biggest head keyword!) It helped us find new geo's to target, new affiliates to target and more. Think of how stupid we were to just focus on website clickstream data.

Then came Offermatica. Man that was awesome. A/B testing! Finally moving beyond HiPPO's trampling the user experience. (Rather than Deciders, they became just one, of many, voices on the table powering an ideas democracy.) Also finally what a fabulous way to try ideas that came up from reading all that voice of customer.

Such a valuable lesson learned (one that would end up as two books!).

Web analytics is fundamentally about using multiple tools, because the questions we have to answer are far more complex than we are used to. It is critical to develop an internal ability (and fortitude) to be agile and nimble. Use the right tool to answer the question it is good at, and then move to the next tool and then the next one because it is better at this other thing. Web Analytics 2.0.

If all you know is WebTrends, or if all you do is spend your day with Omniture, or if your face is tanned from staring at Google Analytics, all day long you'll have a short, fruitless career in this field.

long hard slog

#6: Malcolm Gladwell is right, it takes 10,000 hours.

I don't think I ever appreciated what it takes to just stay current and, in hindsight, never comprehended what it takes to become good. I mean really good.

Not to be overly dramatic but. . . blood, sweat and tears.

Most blogs die in 30 days, most Twitter accounts are full of crap and have few followers, most of us never read books, most of us rarely curate a really good list of RSS feeds and then read every post, most of us will never engage in a meaningful online debate, most of us will not start a website and care and feed it and implement 15 tools every single year purely to learn and push the universe known to us, most of us never consider taking a class or two to learn new skills, most of us refuse to work a few hours extra every week, most of us refuse to experiment with what makes us uncomfortable.

And yet it takes all that to be good at what you do.

Do the job you were hired to do as well as you possibly can, regardless of whether it is a dream job or not. Then in small and big ways, figure out how to spend an extra five hours a week on you. Just five.

Your company is not going to give you this time. Your spouse / boy friend / mom might not even give you that time. Your God might be against it.

But you'll have to figure out how to watch a little less TV, spend a little less time on social media, a little less time on dinners, a little less time watching movies on planes, a little less time launching attacks on attacks, a little less time on parties, a little less time in meetings, a little less time in the bathroom, a little less time. . . something or the other. Half an hour stolen here, half an hour stolen there, invested in learning and doing and failing at the thing you want to get good at. 

Else accept that you'll be an okay Analyst, yet another non-relevant blogger, a Twitter blowhard, a winner of promotions via political machinations rather than adding actual value, always a conference attendee not a case study, someone on the professional train to somewhere convenient to others and not you.

Not that there's anything wrong with that.

Work is not everything.

But in the context of work, know that if you want to get good the path goes through putting in the 10,000 hours of hard work. Even if you just want to get good at what you do you'll still need to put in hours and hours of effort. What's more you'll have to be self-motivated because no one can want it for you, you'll have to want it for yourself.

And yes, yes, yes, there is more to life than professional accomplishments, but that's not what this post is about! [See: Nine Rules To Work / Live By]

For me trying to get really good is a journey not a destination, one on which I am still in the early stages.

There you are, six things I wish I knew before I started my career.

I hope you'll add a pinch of passion and a dash of daring and that your journey will be rich, rewarding and resplendent with glorious achievements.

All the best.

Ok it's your turn now.

In context of digital data analytics what are some of the lessons you have learned in your professional journey? Do these lessons resonate with you? What might I have missed that you would like to add?

It would be delightful to have your comments, feedback, perspectives & critique.

Thanks.

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