Who Owns Web Analytics? A Framework For Critical Thinking.
It is rare for me to work with a organization where the root cause for their faith based decision making (rather than data driven) was not the org structure.
It is almost never tools. Not any more.
Surprisingly it is often not their will to use data, that is there in many cases.
Sometimes it is that they don't follow the 10/90 rule.
It is always the organization structure.
Specifically: Who owns web analytics / who it reports to from a org structure perspective.
[Let me hasten to add that this, web analytics ownership, does not exist in a vacuum. If your overall web business is misaligned from an org perspective then honestly there is no hope for you, regardless of where analytics sits.]
This is a topic I cover in my new book, Web Analytics 2.0. Chapter 14: HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture.
In this blog post I'll share a unique "case study", more like one person's problem, and my advice to them about how to think about the organization problem.
Here's the question / challenge:
I’m facing an issue I’m sure many large organizations struggle with: where should an organization place its web analysts? Currently, I lead a small team of analysts at a medium-sized bank. We are part of the Web Sales division, along with an e-commerce (online media) team and the content crew.
Web Sales is considered a channel in the same way our call-centre, local branches and customer account managers are. As such, we are not a part of the central Marketing (and Marketing Intelligence) teams at corporate. I see a few different options but would be happy to hear your opinion.
You will all agree that it is really hard to answer a question like the one above without spending time with the company and understanding its strengths and meeting the political players involved.
In this post let me share with you a common sense framework I use in my consulting engagements to figure out a home for web analysts.
Each facet of the framework also contains a peek into what I am thinking, best practices I have developed from all the bruises I have (as a Practitioner and a Consultant) and how I end up making the choices I do. I hope it is of value to you all (and now you don't have to pay me large sums of money to do this for you!).
The four pronged real world tested probing and loaded with politics framework to find a home for Web Analytics:
1. How long has the company been doing web analytics, what is the landscape of tools?
Are there standard tools deployed? Or is it all cowboy country with "Analysts", if any, running with as much freedom as free range chickens (which by the way I highly recommend!).
I use this as the first filter because I am trying to gauge how to have the highest impact, quickly.
[A] If there is some level of standardization of tools, if there are some analysts (an analyst!), some reports going out on schedule (even if data pukes) then an optimal path might be to centralize some where (see item #2 below).
[B] If it is free range chicken cowboy country then the fight might not be worth it, I lean towards identifying "accelerators" with the goal of finding the best fit division / site / HiPPO and getting them, just them, to embrace web analytics and show the macro organization how value flows from moving from faith based to being data driven. I call "them" (combination of analytical marketer, analyst, HiPPO, Google Analytics, small site – or atleast two of those things) accelerators because rather than waiting for the CEO to save the world, my optimal path is to embarrass the CEO and VP's by showing proof.
That breaks log-jammed discussions and politics like nothing else.
2. What's the state of analytical maturity of the organization (either the center or the division/silos)?
I am trying to get a feel for three things with this:
* How hard to fight?
* How long will the struggle be to move away from faith?
* Should I go with a centralized or decentralized or some other strategy (more on this below)?
If the overall organization is not very savvy analytically (and it is large) then the strategy will be very different. I don't have much patience and I am not going to try and rebuild the entire darn organization in one day. 
When I consult with large companies when they are in this (messy) state my deliverable is a 90 day plan (that relies on the aforementioned accelerators) and a 180 day plan and a 365 day plan.
If you make the mistake of just creating a 365 day plan for your company that is not analytically savvy then…. well you are making a mistake.
If it turns out that the org overall is not savvy but a division / silo is, then they are my new BFF's and any analytical resource that I might have I am going to send their way, even if that analytical resource is a Marketer or a Salesperson who knows how to log into Google Analytics and interpret bounce rates and analytics intelligence.
If it turns out that the org is savvy then this becomes a discussion where I try to interview, chat, unearth the politics, identify the true power centers and make a recommendation about centralization, decentralization or (centralized decentralization).
I wish there was a standard option for every organization, even one that is analytically savvy, but there rarely is. Every business I have delivered the 90, 180, 365 day plans to has gotten something unique.
3. Who owns the power to make changes to the site (not who owns updating pages or hosting the site)?
This is a nuance to the discussion above. But a very important nuance.
Web Analysts (or call them data driven missionaries!) get crushed (and ignored) very often because they end up sitting in an org, reporting to people, who actually don't have the power to make authorize changes to pages, campaigns, acquisitions strategies, testing paths, surveys etc etc.
The Analysts / Marketers / IT dudes keep churning data and sending the insights but nothing every changes.
It matters who your boss is and how much power she has to make stuff happen.
So… not a surprise… if you can align Web Analysts (and based on #1 and #2 above the Web Analytics program) with the actual human being who has the power.
The closer you can get to her (direct report?) the better off you are. It does not matter if she (or he :)) is in Sales or Marketing or …. anywhere.
Getting access to data is easy. Finding insights is harder. Taking action on insights is nearly impossible.
If you need to sleep with someone to get your data folks/tools directly aligned with the person than makes decisions, take one for the team and do it! [Ok, only if it's legal where you live. ;)]
4. Which physical organizational model will work best for you? Centralized? Decentralized? Something else?
Every large or small company has to deal with this. Atleast when they a implementation roadmap from me (or you) that looks beyond 90 days, and certainly beyond 180.
Before I go on let me point out that I very deliberately talk about this here, #4. And that's regardless of how analytically savvy your organization is, from pathetic to magnificent, you'll want to come to this last (even as in #2 you are collecting data that will influence you here).
My organization redesign plans have recommended either one of the three models. I have come to realize that from my humble experience that it is the trajectory of the arc of evolution that makes one model better than the other (and, amazingly, independent of the first three questions!).
These models are discussed in Ch 14 of the book but let me give you a hyper fast summary here:
Centralized models (where there is one analytics team, usually in the center, and it serves the entire organization and every need from an ad hoc report to when to go to the bathroom) are a fit for organizations that are earlier in their evolution arc. They are exceptionally good at standardizing tools, best practices, teaching, getting everyone in the org to rise to a local maxima.
They have a nasty tendency to become, and I use this word in its dirtiest possible uses, bureaucracies. Slow moving, disconnected from reality (they are rarely on the front lines and even rarer still connected to anyone's particular business goals) glorified data pukers. Sorry. Had to be said.
If you are executing on a centralized model be aware of the pros and cons.
Decentralized models (free range chicken cowboy land where everyone is doing their own thing) are fast moving, directly aligned to someone's (a division / business unit's) P&L and contain people who can get fired pretty fast if the data is not adding value. Just try to implement a paid tool for half a million dollars and dare to not deliver actual usable insights. You are out man!
They also tend to generate inefficiencies (everyone's doing their own thing after all) be it with tools or work or metrics definitions or testing platforms or….. Decentralized organizations optimize for a local maxima and it happens all the time that while individual divisions in a company win, that the company as a whole loses. Pantene and Tide win but P&G as a whole still gets screwed.
I share in the book that the best model in the universe for an analytics team is a hybrid, something I call Centralized Decentralization. There is a lean (# of people) and agile central tem that is responsible for all the pro's you see mentioned above and also satellite lean team (of one or a very small number of people) in the BU's / divisions, that are responsible for the pro's you see mentioned above for decentralized teams.
Everyone wins.
There is a way to structure the leadership of the organizations, there is a way to align incentives and bonuses, there is a specific method to picking the skills required in each part, there is a perfect time to create such a centralized-decentralized organization. But that's for another post.
Oh and one more thing…
Before you get upset (if you are in IT) please please know that the tweet above comes from someone has spent three years in IT, lived the life and paid the dues. It sadly simply does not work. A mismatch of skills, motivations and what the core existence is supposed to deliver. I'll reluctantly agree with you that there are perhaps exceptions to the rule, I'll believe it if you show them to me. :)
Which division / department offers the best possible home for Web Analytics?
After a lot of experimentation and failures I have come to realize that often (if above conditions are met) Marketing is the best organization for Web Analytics to be in. It is optimal because Marketing is in the business of raising awareness, connecting with customers, presenting the company's value proposition etc etc.
Unlike say Sales that is there to make a quota at any cost each quarter. Or PR that is there to pimp the company and it's greatness to the world (not that there's anything wrong with that). Or Corp Comm whose job it is to share information and where folks are not hired for their business savvy. Or…. other divisions. In my humble experience Marketing tends to have the right set of skills, motivations and their core existence is around current and future customers.
If they have the power in the company, Analytics will be happy there.
Caveat: Remember Marketing ownership is not a panacea. You'll have to go through the questions in the framework above and ensure that there is a strong business leader who owns driving changes on the site and that the company is on the right evolutionary path and…. all the things you read above. And even if Marketing owns web analytics the ideal you are shooting for is Centralized Decentralization.
[Update: Please see Jim Novo's thought on value of Finance as an option for owning Web Analytics.]
Now you know.
I hope you've found the four pronged real world tested probing and loaded with politics framework to be of value and that it helps you make better decisions about how to organize web analytics in your company. It is one of the hardest things to pull off right, and with all my heart I wish you all the very best in your journey.
Ok… your turn now.
What is the organization structure like in your company? Where does web analytics fit? Does it work? If not why not? What would you do differently? What do you think I am missing in my four pronged framework? From your experience how would you make it better? What is one thing I got completely wrong?
Please share your feedback via comments. Thank you.
PS:
Couple other related posts you might find interesting:
I am absolutely thrilled that my book 
