One of life’s great joys is the oh-so satisfying feeling at the end of a project that went well:

• Thorough discovery focused on aligning requirements to the organizational goals for a project
• Reviewed solution design with sponsor and stakeholder rep who were thrilled
• Trouble-free implementation
• A few minor fixes in testing; validation came back clean
• Users are trained and excited to use the new tools

A few weeks later you connect with the users for a “now that you’ve had a chance to get used to it” Q&A.

Nothing is really wrong, but there are very few questions, and none of them are about the features they or the sponsor were excited about at launch. You ask if they are using them, only to learn that the users’ stakeholders are asking for the same reports and metrics they always have.

I have been trying to crack this nut for years, with some success. Then I took a Change Management course that provided very valuable new perspectives. I’ll describe what didn’t work, and some more effective approaches I will use going forward.

Communicating with Strategic Intent: The limits of organic adoption

When users would report back that they were getting the same old questions despite having fancy new tools, I responded based on what I would do. I’d encourage users to:

• Deliver the requested data, but when a stakeholder has an ask that could be improved upon based on new functionality, also offer related information and analysis now available.
• Offer that if the first suggestion isn’t helpful, they might be able to suggest more pertinent information if the stakeholder wants to share what questions they are trying to answer.
• Over time, users should work to be seen as SME advisors.

I maintain that this is entirely valid advice to people reporting data. But it doesn’t get the organization to use the solution to its fullest as quickly as possible. It is slow and sporadic, relying on countless repetitive (at least from the analyst point of view) conversations, with the right questions and data to provide the opportunity, all against a backdrop of a reasonably open-minded culture.

Change Management is about how to make that kind of change adoption happen at scale. To get stakeholders asking questions that take advantage of new tools quickly, we need to have an intentional strategy to communicate the change with them.

Ignoring Preferred Sender of Key Messages research

A crucial part of a change communication strategy is the concept that people tend to receive information about a change more readily if it comes from the right sources.

There are two preferred types of source, each being better for a certain kind of message:

• Messages about the big picture, like why we need to change and how the change supports our organizations’ goals, are best delivered by senior management or executives.
• Messages about what will change in people’s day to day work, like what the change will actually require work-wise and what the benefits to the individual and team will be, are best delivered by their own manager.

This raises another weakness with our original approach: not only are we relying on the sporadic instead of the systemic, we are sending these messages through the wrong people.

Maybe we’ll get lucky and already have Change Management

We’ve established the need for a strategy to help move stakeholders through analytics change, and that the communication in that strategy is best delivered by people in specific roles. But do we need to take this on ourselves?

Reinventing the wheel is a waste of time, and trying to do someone else’s job can start a turf war, so step one is to take advantage of the Change Management investments you already have access to.

If you are in-house, maybe there are Change Practitioners somewhere in or around HR just wishing that the project teams in the company would integrate with them more closely, and you are off and running. Or you might be with an org that has never heard of Change Management and doesn’t like the sound of it, in which case you might need to be more hands on.

On the agency side, this can be a bit trickier since you are working through a point of contact and sponsors that may or may not have the appetite and relationships to secure the Change Management resources you want to integrate with.

I’m handling it by making it clear that I’m enthusiastic to integrate with a prospective client’s Change Practitioners, and that my Change Practitioner certification means I’m not just winging it in that regard.

If a client answers that they don’t have a Change Office or Practitioner, I’ll offer to supply some elements they would usually provide.

Up Next: Crafting an analytics stakeholder communication strategy without the benefit of experienced Change Practitioners

So what do we do if our or the client’s organisation doesn’t have Change Management professionals to allocate to us? Ok, now we have to figure out at least some minimal plan if we want to see people get the full value our solution can deliver.

The next post will look at exactly that. If you want me to hurry up and post it, or you have any thoughts or questions, please let me know in the comments below.

The post Upgraded analytics tools, trained analytics users: same old questions? Change Management explains why appeared first on Informed Iteration.

One of the main ways people expend effort on analytics without having an impact, or at least, having less of an impact than they could, is failing to understand the business goals of what they are tracking and reporting on. Or, losing sight of them along the way.

And this isn’t just a personal observation of mine. I asked around among some leading voices in the analytics industry about what problems most hold back analytics teams from having an impact. Not aligning what we are doing to the underlying business goals was a recurring theme.

In this post, we’ll look at why it can happen, and what we can do to better align our analytics work to business goals.

It seems obvious that we’d need to understand exactly what someone is trying to accomplish if we are going to give that person the right data and recommendations to help them succeed. Unfortunately, with all of the moving pieces that go into everything from gathering data through to presenting it, it’s very easy to lose the forest for the trees. This can happen for several reasons, but major ones are overreliance on tools, and overreliance on processes.

Don’t Let the Tool Do the Work

One major pitfall that can cause analytics work to become misaligned from business goals is an over-reliance on the metrics and reports automatically provided by analytics tools.

Loren Hadley, who has a wonderful understanding of how to help organizations succeed with data, had the following to say about why the right metrics aren’t always included:

It’s often what does my tool give me? CRM systems and analytics tools (I’m looking at you GA) tend to provide what seems meaningful and is easy to calculate without much context. Not that they are bad metrics in any way. Just that they may over promise. I don’t fault marketers for reaching for this or what an agency did 4 years ago. I’d just like to help them make sure they are focusing on what really matters.

– Loren Hadley

It’s very easy to let automatically gathered and calculated metrics, that are presented prominently in the pre-generated reports offered by analytics tools, get the lion’s share of the attention. Most businesses don’t really live or die based on the total number of people who come to their website or how many pages they look at, but tools put a lot of emphasis on users, visits, and views, so it often gets more prominence than it needs in reporting.

Engagement rate is another metric that people like to use, even though remembering what it actually represents off the top of your head can be tricky, let alone educating stakeholders on it. Some might argue that the point is to give an idea of what portion of traffic is engaged. I’d argue back that the threshold to be considered an engaged visitor is too low for a lot of use cases, and that a more tailored approach to measuring engagement would be useful.

Tools will inherently push us towards defaults instead of the specificity that will enhance our ability to really support decision making, and we need to guard against that. Processes, on the other hand, can either hurt or help us align our work to business goals.

Process: Finding Your Groove vs Getting Stuck in a Rut

We all know that we can’t afford to reinvent the wheel for every ask that comes in. We use repeatable processes for efficiency and reliability, and rightly so. The right processes serve as crucial guidelines to keep things understandable and on track. Overly restrictive processes, or an overreliance on processes, however, can lead people to just repeat what’s worked before.

If your website/app has a lot of funnels, you may get into the swing of things and have a process you follow when setting up tracking and reporting for a new funnel. That process can help you be efficient when planning, tracking, and creating reporting for each new funnel that comes your way. You need to know where the funnel is, what the steps to track are, and establish technical elements like is the funnel a single page app. And you’d certainly be wasting a lot of time to start the reports or dashboards from scratch every time.

The danger is deciding that the system will work as is for every funnel we are asked to track. If we assume that completion rate is the primary KPI for anything we do involving a funnel, our reporting for some funnels won’t be telling a very useful story. Maybe one project is to modify a funnel to introduce a “You may also want to consider” element to the checkout.

Completion rate is still very important, but trying to figure out if this change is working or not means considering at least average order value. And you’ll want information on interactions with the new element, and changes to items in the cart, so that you can segment metrics based on different behaviors.

Another funnel is to open a support ticket, and success for the business looks very different here. Ideally, a content recommender will show the user some help content that resolves their problem without having to open a ticket.

Analysis, reporting, and by extension, tracking, on this funnel are going to be different than how we handle purchase checkouts. Or at least they should be. Treating a funnel drop out as success doesn’t account for people who just get fed up and leave.

We can ask that the funnel be set up with distinct steps so we can clearly track people who drop out while being shown help content, as opposed to those who drop out after indicating that they still have an issue and want to contact support. Now we can start to tell the difference between someone dropping out because they solved their problem, and someone dropping out because our contact form has issues.

Without going further into the example rabbit hole, we see how getting too comfortable with treating similar asks as identical can lead to reporting and analysis that isn’t aligned to business goals.

Making Analytics Discovery Processes That Align to Business Goals

An overreliance on an inflexible process can lead us right past crucial information we need to make sure that the analysis we eventually provide will be valuable. But, we can also adapt our processes to make sure that we gather that information before we make any other decisions.

I’ve written about this a bit before, when discussing requirement gathering for analytics projects using the 5 Ws. The difference is that in that post I tried to look at all the information you’d want to bring together when figuring out what will go into fulfilling a request. And yes, “Why” was presented first. But with five more years of experience under my belt, I feel like the Why questions deserve a deep dive.

You Need to Know Why They Are Doing It If You Want to Help

If you’ll allow an anecdote, I lived in a bit of a rough neighborhood while in university. One day I was out jogging, and a very large friend of mine happened to spot me from a nearby balcony.

He looked concerned, and shouted “Are you running on purpose?”

It took me a second to grasp his meaning, and I answered “Yup – I’m just working out. No one is chasing me!”

My friend, understanding that I was running to improve my health, and not fleeing danger, encouraged me to keep it up. Had someone been chasing me, this friend was the type who would have raced downstairs to help.

Point being, knowing what someone is doing isn’t good enough to render the right assistance. You have to know why they are doing it.

Make “Why” Questions a Discovery Conversation Priority

To fully understand the goals of a project, especially a larger one, you can’t just ask people what they are and write down the answers. You need to ask the right questions to get people talking about their goals, and have a conversation. A conversation both about their project, and about how analytics can support it in succeeding. And conversations are arguably the most important part of your job.

An analytics leader whose LinkedIn posts are laden with insight, Tris J Burns, recently shared the following:

The most powerful analysis tool we can ever hope to master and possess is:

→ CONVERSATION (use your words)

Every analysis must begin and end with conversation.

We use conversation to understand the problem we are hoping to solve.

We use conversation to gather the initial data points, whether it be to gather context on the problem at hand, or to gather highly valuable qualitative data

And finally, we use conversation to deliver the insight, recommended action and estimated business impact of the analysis we’ve performed.

– Tris J Burns

If you don’t like having conversations about analytics with people that don’t know as much about analytics as you, you are going to have a hard time figuring out how to best help them with data.

We’ll tackle the first two use cases of conversation here, and talk more about recommendations towards the end.

Why Are You Doing It?

I start every discovery with “Why are you doing it?”

If I already have a pretty good idea of why based on what I know going in, I’ll confirm that understanding and ask stakeholders to expand if I missed any points or nuances. Note that we are talking about why they are doing the underlying work, not why they want analytics for it. If someone asks for analytics help with a funnel, for now, we ask why the funnel exists, not why they want help analyzing it.

With that broad understanding in place, I move on to defining what the desired outcomes are, in two contexts.

What does success with this feature/asset look like for the end user?

What did the user come here to do? Get information? Buy something? Return something? Apply for a job? Post an ad or message? What does a successful outcome for the individual end-user involve?

What does success with this project look like for you?

This is where we get more specific about why we are doing this project. If the point of updating the checkout funnel is to reduce the exit rate on the shipping step, this is where we get into “what is the rate currently?” and “is there a target improvement we are trying to achieve or threshold we want to reach?”

Analytics Discovery Continued: Questions About Questions

By now, we have a good high level understanding of what people are trying to achieve, and depending on our knowledge of the business and stakeholders, we could start planning a solution. But we’d not have as much detail as we could benefit from. And, we’d be missing a chance to understand and manage expectations.

Liz Oke, a stellar marketing strategist, wisely suggests the question:

“What do you want your analytics to answer?”

– Liz Oke

In some cases, particularly with stakeholders that are reasonably data-savvy, this will yield some great ideas you can include in your solution with little modification. In other situations, you’ll be able to have important conversations about what questions will be possible and useful to answer.

What Inexperienced Stakeholders Want Answered

People who don’t really know what’s possible with analytics will generally give one of two types of answer:

“I don’t know, I was hoping you could tell us what would be most helpful.”

This can be a very good situation to be in, in that you are being trusted to recommend what analytics can do for the stakeholders. As long as you have the right domain expertise around what the business does, you can lead them in the right direction. If you aren’t sure if your domain knowledge is sufficient, ask the stakeholders questions until it is.

“I have no idea, but it would be cool if we could know this thing YOU’VE NEVER DREAMED OF INCLUDING BEFORE.”

These can be frustrating, but they can also be incredibly fun. A lot of the time, it’s just a matter of interpreting the desires of someone who isn’t used to analytics jargon and doing a bit of translation. Sometimes, though, the ask will be for something you’ve never considered reporting on before.

The temptation here is to dismiss it as an impossible ask from someone that doesn’t understand the limitations of the technology. And that might be the right response, in the end. But before we throw out every crazy-sounding   idea a stakeholder has, we do them, and ourselves, a disservice, by not at least giving the idea some thought.

People who don’t know what to expect from analytics, but know the business well, don’t have the same habits and biases baked into their ideas that we do. And that can be a fantastic source of outside the box thinking on what analytics can do for the organization.

Is the idea actually impossible because it’s asking for something that is more magic than technology?  Or, does it seem impossible because it’s asking for something our tools don’t already do, or maybe more personally, something that you’ve never done before?

Depending on how valuable the information would be, and the resources available, maybe you need to think about extending the abilities of yourself or your tools, or adding a new tool to your stack.

What Semi-Experienced Stakeholders Want Answered

This is the stakeholder that has been exposed to analytics enough to understand what is possible in a technical sense, and get comfortable with some common measurements. But, they have trouble focussing on what they actually need in a business sense. Instead of focusing on their own business priorities, they put on their analytics hat and try to think of everything that might be useful.

If they are used to seeing it in the tools, they’ll ask for it. If they are used to seeing it in dashboards and reports, they’ll ask for it. If it was useful to them in a completely different context, they may very well, still, ask for it.

This is, in many ways, the “overreliance on tools and what we did in the past” problem, but this time it is coming from the stakeholders instead of our team. Which is entirely forgivable, as it’s not their job to avoid these issues, it’s ours.

Let’s look at some techniques to help us eliminate, or at least deprioritize, the questions that are less relevant.

Prioritizing Requirements In Analytics Discovery

Veteran voice of the analytics community, Jim Sterne, has a great method to help discern idle curiosity and old habits from the burning questions that will have an impact:

Dive into their goals and find out what they will change based on the results being above or below expectations.

– Jim Sterne

If someone has a laundry list of things they want to know, you can get a feel for how important each item is by asking stakeholders what they will do with that information. If someone insists that engagement rate (or anything else) be reported prominently, ask them what they will do if that metric were to increase or decrease by 10% – Nothing?

Ok, what about 20% – Still nothing? What about 30%, 40%, 50%?

If something can change drastically and it wouldn’t elicit a response from the stakeholders, it stands to reason that reporting on it won’t have an impact on the business. No action is being taken based on the information.

You can use this to help the stakeholders prioritize their questions in terms of value to them. Maybe you’ve only got enough resources to get to the most important ones. Or maybe you have plenty of resources, and they’ve asked for really basic stuff that’s built into the tools and won’t even use that many resources.

Either way, you need to help them focus on what’s important, both right now through prioritization, and down the road by making them analysis and dashboards that focus on those priorities.

Moving Forward

It may seem like we are skipping a lot in the middle, but solution design and implementation depend too much on the specifics of the project to get into here. And, to be fair, we have laid the foundation we need to be successful in doing those things in a way that will lead to having an impact on business decisions.

As long as you design a solution that delivers on what stakeholders want to know, as we established in the questions above, you can follow that map. Especially with a larger project, though, you can get lost in technical, documentation, or taxonomy details along the way, so it’s good to revisit the summaries you made of your discovery notes periodically.

I make it a point to do this when I move to the visualization, and again when I move to the analysis, phases of a project. Especially if it was a longer timeline, I want to make sure I remember what the point of all the work to get here was. It would certainly be unfortunate to have done everything right up until that point, then drop the ball when actually sharing the output.

Wrapping It Up

Before we can call it a day, we need to finish strongly. We need to make sure that what we get back into the hands of our users is actually going to help them achieve the goals we asked about on day 1.

Jason Thompson is not just a renowned analytics leader, but someone who often reminds us that we are human beings first, and analytics professionals second. He also had a fantastic response to my LinkedIn post asking about what prevents analytics teams from making an impact:

The biggest issue I observe in analytics teams that are struggling to make an impact comes down to struggling to move beyond data collection and reporting.

To make a true impact, analytics teams must be about more than capturing data, sharing numbers, and observing trends. To make a true impact, analytics teams must not only do meaningful analysis but must make informed business recommendations based on that analysis.

Here is some data we collected

Here is a chart attached to some data we collected

The chart line for the data we collected goes up and down

When we increase paid search budgets, the line goes up

Our analysis shows a strong correlation at a 28-day lag which highlights a monthly cycle in user buying behavior. This means for some high-value purchases, customers may take up to a month from their first visit to finalize their decision and place an order. Recognizing that customers may take several weeks to decide on a purchase we should test nurturing campaigns that present return website visitors, specifically entering into our web store, valuable information and potential incentives through a 28-day decision window…

Like most things, this is a people issue.

– Jason Thomson, 33 Sticks

The point isn’t that analysis needs to be complicated, the point is that it needs to lead to recommendations that will help the business achieve its goals. Especially in predominantly self-serve analytics environments, it’s easy to overlook that we need to make the output focused on the business questions at hand.

Go back to those discovery summaries and remember why you are collecting this data in the first place. Even in self-serve environments, we need to provide guidance and help people find the deeper insights they won’t get from looking at an automated report or dashboard.

Don’t just be a conduit for data. Where can you use what you know about the reasons for individual projects, and the business overall, to turn the data into actionable recommendations?

How do you keep your analytics work aligned to stakeholder goals and your recommendations relevant? Leave a comment and let me know!

The post Making Analytics Useful: Align to Business Goals appeared first on Informed Iteration.

There was a lot of info I wasn’t able to squeeze into my talk for the 2019 AB Testing Summit, including proper reference or sourcing. I’ve supplied that info here, along with a few comments I hope will be useful to those looking to do further reading about Bayesian AB Testing in Marketing.

Also, if you have a question that won’t fit in Twitter, please leave a comment and we can have a more detailed discussion that will be easier for people with similar questions in the future to find.

You can find the slides here.

Introduction to Bayesian Methods in Marketing

As a first look at Bayes Theorem, you could do a lot worse than An Intuitive (and Short) Explanation of Bayes’ Theorem

We almost immediately get into considering where Bayesian Priors come from and the validity of these processes, which is hotly debated. By extension, we need to consider several sources to get an idea an informed perspective.

If you don’t read any other source I provide, 5 Reasons to Go Bayesian in AB Testing – Debunked is probably the fastest way to get see a well argued perspective that diverges from the generally accepted hype. If you want to learn more, the author, Georgi Georgiev, is knowledgeable and prolific. There are several other great articles and whitepapers that explain how Bayesian methods may not always live up to the accompanying spin, which were immensely useful in building my understanding and preparing this talk. We’ll mention some more of his work later, but the one blog link above and two following white papers offer a good overview of the contention.

• Issues with Current Bayesian Approaches to A/B Testing in Conversion Rate Optimization
• The Google Optimize Statistical Engine and Approach

The overwhelming majority of discussion about Bayesian methods in AB testing was dismissed as coming from sources with a clear conflict of interest paired with an almost complete lack of transparency, and in some cases, presented with total incoherence. The pro-Bayesian materials we looked at that were useful were from referred journals or technical whitepapers with a high degree of transparency, for which the vendors should be commended.

• Bayesian Statistics and Marketing – Published back in 2003, this lays a lot of the groundwork about how Bayesian methods work and why they are useful to marketers, even though it predates the prevalence of AB testing that online businesses enjoy today.
• Bayesian A/B Testing at VWO
• The New Stats Engine (at Optimizely)
• If you know where I can get my hands on a Google Optimize white paper let me know.

We also looked at a non-Bayesian modern CRO tool that claims to deliver similar advantages of those claimed by Bayesian tools. We wanted to establish if the claimed advantages of Bayesian tools are only possible with Bayesian tools, or if non-Bayesian methods that are appropriately advanced and customized for CRO can deliver them as well. It may not be a huge surprise that this method was created by the aforementioned Georgi Georgiev.

• Efficient A/B Testing in Conversion Rate Optimization: The AGILE Statistical Method

Introducing Bayes Visually With Python

Introduction to Bayesian Inference is a great first look at how doing your own Bayesian analysis could work, and even if that isn’t your goal, it provides a very helpful visual interpretation to how Bayes works in marketing.

The 3d graphs were based on, like most of the code holding the internet together, a Stack Overflow answer.

Ease of Explanation

The Part to Ignore

Sorry, but I’m going to include a bit of a rant that I didn’t have time for in the talk. Skip to the TLDR if you’d like.

The whole “frequentist methods are ridiculous because [some variety of absurd claims about how frequentists aren’t allowed to discuss results or use their brains except in the strictest possible interpretation of the math they use]” such as those included by Google in their Optimize documentation are a text book example of fallacious “straw man” arguments.

Simply put, you make up a version of your enemy that doesn’t reflect reality, but completely supports your argument, then argue against that instead of what the actual enemy says.

This argument has been raised and conclusively shut down repeatedly, perhaps most devastatingly  by A “Bayesian Bear” rejoinder practically writes itself… where a mathematician gives a hilarious example of what happens if you make a Bayesian straw man to compare to the Frequentist one made by unscrupulous Bayesians.

TLDR: If you hold mathematicians to the strictest possible requirements of their assumptions and definitions, the only people who will understand are other mathematicians, and even they will think you are a jerk that is just wasting people’s time. For Bayesians to pretend Frequentist methods are the only part of statistics facing this issue reduces the credence we can give other claims made by this particular subset of Bayesian proponents.

The Part That Is Interesting, But Still Kind of Esoteric

Initially I didn’t fully absorb the importance of the debate, but the aforementioned Georgi Georgiev was kind enough to have an email chat with me and I was able to understand more of the difference in positions.

Bayesians assume that their best guess of the probability, is the probability.

Frequentists figure out how unlikely an outcome would be if the experiment were repeated a very large number of times. It’s still up to the user to decide what this result means in their real world situation.

Using a Bayesian tool doesn’t just mean trusting that the priors are chosen in a way that will work with your data, it also means trusting that the method is generating an actual probability. For people who think about these things deeply, that can be a pretty big leap of faith.

To quote Georgi: “Frequentist methods give you data and uncertainty and make it crystal clear that the decision-making part is up to you, and cannot be purely data-driven.”

Now, it’s still up to us as practitioners to decide how to explain what level of detail to stakeholders, and not everyone at a meeting about what creative to go with is going to be up for a talk about p-values. For people who are, though, it actually opens up a fascinating area of discussion and introspection.

We build human assumptions, often based on qualitative inputs, into the tools and methods we use to gather and report on data – but we often consider the results to be purely quantitative.

And we didn’t need to make a big AI project to run into that issue – even a little old t-test is an opportunity to see to what level we separate our assumption and interpretation from the data itself.

Optional Stopping and Bayesian Suitability for CRO

There’s a lot of material out there about optional stopping, but here are some excellent reads on the topic that go deeper than your average blog post without turning into heavy math and coding.

• Bayesian AB Testing is Not Immune to Optional Stopping Issues
• How Not To Run an A/B Test
• Is Bayesian A/B Testing Immune to Peeking? Not Exactly

Faster CRO Results

Information about specific products comes from either the aforementioned whitepapers or website site documentation accessed over the month of May 2019. Note that at some points while speaking (and I think even one slide), I use loss control and error control interchangeably, which is inaccurate. Loss control and error control are very different things.

Recommended Non-Bayesian Products

Optimizely, VWO, and Google Optimize, even once the hype is taken away, all make compelling cases to any org without the data science resources to do their own modelling. There are some non-Bayesian alternatives, however, that allow users to compare Bayesian tool results to traditional or more advanced frequentist menthods, for free or very little investment.

Google Analytics users can take advantage of Stéphane Hamels’ DaVinci Tools. This Chrome extension, even in its free version, makes a million and one quality of life improvements to the Analytics UI, as well as adding some useful features. One of these is the ability to take a t-test by clicking a few times on any Google Analytics report. If you also use Optimize, your experiment data is already in GA in a format that makes this very easy.

For a more sophisticated approach, have a look at the Agile A/B Testing Calculator included with Georgi Georgiev’s Analytics Toolkit. It’s very inexpensive, and Google users can connect it to Analytics/Optimize easily.

Sorry for the Google-centric view but it’s what I spend most of my time on. If there are other tools out there you feel provide a good way to double check what the Bayesian “Big Three” are recommending, I’d love to hear about it.

Please let me know in the comments or on Twitter, or reach out with any other questions or comments!

The post Bayesian AB Testing in Marketing Reading List appeared first on Informed Iteration.

Mix in a couple Radical Analytics principals from Stéphane Hamel, and you end up with a pretty good way of looking at how to best help our users with their requests.

Why – Understand It Like It Was Your Own

As the requirement recipient, you are the bridge between the person that understands the problem, and the org’s analytics resources. That means you need to understand the problem, and what your org can do with analytics. The good news is that you’ll build an ever greater understanding of your team, resources, and tools by virtue of working with them.

The even better news is that the user request side of things will be a never ending series of often totally unforeseen and fascinating topics that will keep your work interesting. It’s crucial to your performance that you understand what users are after – specifically what people want to do and why. Unforeseen questions can often be handled by someone that gets the “why” of a request without having to make that round trip to the original user.

Asking why is a very powerful  business tool, even formalized in the 5 Why method, which you might want to consider using. Getting people to talk about why they are doing something gets them away from talking about what they want you to do, and focuses the conversation on their activities and goals. That leaves room for you and your team to add value by considering solutions the user wouldn’t know about. More on this in the next section.

What – Never Ask, Alway Propose

People might try to tell you what analytics solutions they think they need, or even how to do what they think they need, instead of telling you what they are trying to accomplish. Remember that you and the analytics team can figure out how to track, report, and analyze just about anything, but you can only do a good job of that if you know what the analytics user is trying to do, and why.

Depending on the ask, goal, and need, the solution might be obvious to both of you. If it’s not, present some options you can discuss. The point is to get people talking about solutions without inviting them, let alone forcing them, to think of a solution without your help. You are the one that knows about using analytics to help people succeed at marketing – don’t rely on your users to take the lead.

This principle comes from the Never Ask, Always Propose, part of the Radical Analytics series by Stéphane Hamel. The series is a fantastic look at things that are wrong with our industry, and what we can do about it. I enjoy doing analytics more now that I’m applying it.

Note that you don’t have to come up with great options on the spot. You can let someone know you’ll get back to them and give yourself a chance to do some research or talk to your team. If you are just spitballing ideas on the spot, make sure you tell people that, and avoid promising something you aren’t sure you can deliver.

Who – The Good, The Bad, and The Ugly

We aren’t going to categorize user requests with those three terms, and certainly not the users making them! They are just to break down reasons it’s important to consider and pass along who is asking for the analytics work.

The Good

Understanding who is asking is often the first step to understanding why they want to do something, and a crucial part of scoping a question. The quality of communication you have with a person is greatly influenced by how well you know them. Sometimes, someone on your team will have a relationship with the asker, or at least experience working with them, and can leverage to find a better solution. Of course, in a larger org, you’ll need to keep track of this just so you remember who to tell when something is done.

The Bad

Who do you reach out to if there’s something to clear up, or a deadline that will be missed? Perhaps more importantly, who does whoever is working on that ticket reach out to when they get your auto-repsonder about recovering from surgery for the next couple weeks?

The Ugly

Different people in your org have different levels of ability and inclination to make you and the rest of the analytics team happy or miserable. As much as it can hurt people like us, who value transparency and accuracy, it is in your interest to take this into account when prioritizing who’s stuff gets done first, and how to frame your response. Faking report results to humour a CxO is unethical. Doing something you don’t think is the best technical solution, use of resources, methodology, etc, once your politely but clearly disclosed objections are noted, overruled, or ignored, is part of working with others.

Before you run off to get the “c-suite buy-in” often presented as a silver bullet to every analytics problem, Stéphane’s Radical Analytics lets some of the air out of that myth, and presents a great, sustainable strategy for analytics pros to grow influence in an organization.

Where – Online Is a Big Place

Make sure you clearly understand the scope and targeting that are called for. Very often, people (including me) with decades of experience working in places that have dozens of websites, a bunch of social media accounts, several agency partners, and huge numbers of campaigns, will refer to whichever piece of the puzzle they happen to be thinking of as “the site/account/agency/campaign.”

Are the new content and forms we need to track going up on all the properties? Or just on the English part of the US site for now? Or it’s actually going on a new subdomain?

It’s incredibly easy for someone working hard on something to leave out a detail that is obvious to them, but unknown outside the people really tucked in to that project. Help avoid misunderstandings by double checking on context.

A standard request form or ticket format can help make sure this is clear in every case. Don’t use it as an excuse to avoid a conversation though: it’s there to make sure all the important stuff is covered, not to allow the important stuff to be glossed over and assumed.

When – Preferably Before the Campaign Ends

People are usually pretty good about letting you know when they need something. Unfortunately, depending on resources and culture, not every ask will get done, and it can take a lot of diplomacy to help users understand when their needs can’t be the priority. Try not to leave people empty handed: if new interactive dashboards aren’t in the cards for this quarter, can you at least get the data into a custom report to tide them over?

A lot less thought and discussion goes into how long something will be needed, which is almost as important. Tags that only needs to be around for a short campaign or promotional event don’t require the same degree of thinking about maintenance consequences. It might get added in a faster, simpler way than if the team has to worry about how the tags will have to behave as the site and analytics solution evolve.

Something needed now that doesn’t need to last a long time is usually ok, and something that needs to last a long time but isn’t needed right now is usually ok. Trying to get something that requires long term planning done in a hurry is where you run into problems.

Consider a phased approach if the priority and timelines justify it, particularly if your org or team use Agile. Get a temporary and easily reversible solution in place quickly, then make a new request out of making a long term solution.

Sometimes, it’s a lot easier to wait and do something as part of a larger update or new build. Sometimes, when you go back and ask the stakeholder how things are working out and if there’s anything they want tweaked while you set up the long term solution, you find out that the campaign was a bust and they don’t need the tracking anymore.

Either way, the user got what they needed while your analytics team judiciously conserved resources, and kept the tag container from turning into martech’s answer to the Mutter Museum of Medical Oddities.

How – That’s 6 Questions and You Don’t Start with W

You’re right, we’re done! How is a different thing entirely, in that it’s not up to business to figure out how you are going to implement their requirements for you. That doesn’t mean you should be dismissive if they mention something technical: the last thing you want is a culture where people don’t tell you stuff because they are worried you’ll be frustrated with their lack of understanding.

With less analytics knowledge, they are even less able to determine what is obvious to everyone and what has to be mentioned. And it’s not like we’re 100% accurate when we are deciding what to assume and what to ask. You’ll usually fair better in an environment where people freely mention their concerns and don’t get a bunch of eye rolling in response.

Be reassuring about stuff that is obvious, and be thankful when people give you info that turns on a light bulb. Outright tell them how that info is useful and changes things, without getting technical. Over time, you’ll build a shared concept of what is par for the course and doesn’t need discussion, vs what might be out of left field and require some extra notes.

Let me know in the comments if you have any questions about how this could work in your organization, or if you have some tips to share about how you better understand your users’ requirements.

The post The Five Ws of Analytics Business Requirements appeared first on Informed Iteration.

Why Didn’t You Check With Us First?

It’s usually obvious that your first step to analytics happiness would be talking to your analytics person, team, or agency, depending on your scale and situation. There is one exception I see a lot, though, when people are working with advertising agencies. And, it’s not an unreasonable one in the slightest. After all, if an ad agency is going to do your campaign reporting for you, why would you bug the analytics team to look at this first? We just need to add the tags the agency sends over, it will take us a few minutes.

Right?

Not quite. A lot can go wrong with this kind of ask.

What Goes Wrong

Less Functionality

The agency running the campaign generally has no idea how your analytics are set up, and it’s almost never a matter of just sticking the tags in. It’s like telling a mechanic who hasn’t seen your car about the weird noise it makes and buying the part they recommend, then asking your regular mechanic to install it without telling them what is wrong with the car. Depending on the complexity of the part, the complexity of the situation, and blind luck, your car could be in the shop for a while, and you might end up with a surprising bill.

Similarly, your tracking might not be in place by the time you need it, or even be feasible from a technical standpoint.

Less Priority

There’s a whole team, if not a whole organization, of people asking for analytics help and functionality improvement. Anything you need just for your channel/campaign is hard to make time for against asks that improve capabilities across channels/campaigns. Giving the analytics team a chance to understand your goals and challenges with you means they can fit that into the rest of the organization’s needs and strategy, then build a solution in that framework. If the first time I hear about something is tagging instructions from an agency, it’s an abstract mystery competing for attention with more clearly motivated requests.

Less, or No, Consistency

Different agencies, different channels with the same agency, even different channels in your own organization, each supplying their own highly specific tagging requirements without asking analytics for guidance, makes a big mess. You end up with lots of duplication of effort and ongoing data inconsistency that makes comparison very difficult and report automation largely impossible.

But You Just Tell Us To Get the Tags From the Agency Anyway

I know, a lot of the time, when you ask us about this stuff, we ask you to have the agency send over tags. Sometimes, the chat we have with you gives us all we need to know: This is something simple, and we really can just drop in the tags. A lot of the time, based on what we learned from you about what you need to do, and what we know about tags for that channel, we know that we’ll be able to figure it out in a reasonable time frame. In some cases, literally the only part of the tags that we use as is are account or event ids, and we push the information in a totally different way.

The tag examples have value, but it’s the context you provide that makes this work.

If the analytics team knows what you want to achieve and how the agency wants to do it, we can usually figure out something that works. If we just have tagging instructions with nothing but “can you add this for the new campaign” as context, we’re going to ask why. It’s one of our more annoying habits, but we need to figure out if what we are being asked to do is going to do what you think it will, without doing anything you haven’t predicted.

Good Analytics Pros Ask Why a Lot

I’m not the originator of that idea, but I can’t for the life of me remember where I heard it. I’ll gladly update with a source if someone can help me remember in the comments.

It’s very true: we might ask you why several times, and again, we aren’t just being difficult. We need to see how this fits into the bigger organizational and analytics picture so that we can line it up with other priorities, or why it deserves special attention as its own priority.

Remember that it’s like talking to developers in IT, or any other skilled professional: they should not need to be told how to do things. You might tell a surgeon about allergies, your medical history, current condition: what and why type information that you know best. If you tell a surgeon how to do your appendectomy, you are being ridiculous. If you don’t trust the surgeon to know how to do it, you need to find a different surgeon, not hope you can walk them through it.

The same goes for your analytics team and tagging. We figure out how to tag all kinds of stuff, all day long, for our entire careers. The part we need help with is understanding what you are doing and why.

That understanding is a key requirement of helping you with reports and analyses that really unlock insight.

The post Can You Just Slap These Tags on the Site? appeared first on Informed Iteration.

Google WMT Account Creation

We used a dedicated Google Account to set up Analytics to a make sure we could later control and transfer access to the data and settings. Because Webmaster Tools stores similar data and settings, and the goal is to integrate the two tools, we’re going to use the same email we made for GA to set up WMT. Make sure you are logged in under it before going to, or log in under it when asked by, the WMT homepage www.google.com/webmasters/tools/

The first step is pretty straight-forward: enter the website’s address. The only thing to note here is that Webmaster Tools thinks that the www and non-www versions of your site are two totally different things, so type in the one that you usually go by and click the button to continue. Ideally, you have a redirect on your website so that people can only get to one version, and you can use that one. We can use WMT to tell Google which one to use as well, but we’ll leave that for another day.

The next screen will offer a Recommended Method tab, and Alternate Methods tab. The recommended method will change based on your situation: if you already have GA set up and the Tracking Code on your site, then verifying through Google Analytics will be the recommended method. If it isn’t, just click the alternate methods tab and select the Use Google Analytics button. Whichever tab you are on, click the Verify button when ready.

You should be on the Congratulations screen with a green checkmark. GA and WMT both need you to put something on your site, but since we can use the GA one to make WMT work, but the WMT code won’t make GA work, it makes more sense to set up Analytics first. Then you only have to add one thing to your site.

But we aren’t quite done. I almost forgot this myself (seriously I’m doing this along with you to make sure I don’t miss anything and get screen captures).  There’s one more thing to do before we log out of our data only email account. Click where it says Webmaster Tools in red (yes it’s a link) twice, once to get to the home page for the given website, and again to get to the page shown below.

Adding Users to Webmaster Tools

From the WMT website selection page, click the Mange Site button to the right of the site we’re adding a user to to get the dropdown menu shown in the green square on the picture above. Select Add or remove users. On the next screen, click the red Add a New User button at the top right, or the blue Manage Site Owners link just to the left of it.

WMT permissions come in three flavors: Restricted Users can just look around; Full Users can change a few things about how Google sees your website (enough to do some damage), and Owners can do everything, including locking out other Owners and telling Google that you don’t want your site to show up in results anymore. Be careful who you give Full or Owner access to, and if they stop working for/with you, remove them from WMT promptly. Remove them immediately if it was an acrimonious split.

The Add a New User button will bring up a pretty self-explanatory pop-up window. Enter an email, select the permission level, and click Add. The Manage Site Owners link takes you to another screen. Click the Add an Owner button at the bottom, add an email, and click continue.

Integrating WMT and GA

A lot of the data that WMT gathers is actually a lot easier and more useful to look at in Google Analytics, so you should always link them together. And it’s just a few clicks. Click the red Webmaster Tools text link at the top left to get the website selection page, and hit the Manage Site button again. This time, select Google Analytics Property. Click the radio button for the GA Web Property that gathers the data for the website we’re managing, then the Save button. Note that you can only have a website associated to one property, and doing this will cancel any previous links to other web properties in your Google Analytics account.

There’s a lot more that you could, and probably will, do with Google Analytics and Webmaster Tools, but all of that could arguably be called configuration or even customization. As far as basic setup, you’re done. Congratulations!

We’ll eventually look at good first steps to take now that things are set up, like that www vs non-www thing we mentioned above, and setting up conversions on your contact pages. For now, I’m ready for a break. Please leave a comment if you have any questions.

The post Google Webmaster Tools Setup and Analytics Integration appeared first on Informed Iteration.

This will save you having to log in or out of your main account when you want to look at your analytics. If you have several websites, or manage the sites of several clients, this is a lifesaver. It’s also the procedure you use to give consultants, clients, employees, and so on, access to your account.

Adding Users to Google Analytics

If you’ve come right from the last post, click the account name at the top of the page, just under the orange menu bar, directly to the right of where it says Account List. If not, click the home button at the top left, then the Admin at the top right, then the name of the account. Once you’re on the screen pictured above, click the Users tab I’ve highlighted in green. The Users Tab will have a New User button, just below the tab itself. Now click it to get to the screen shown below.

This is pretty straightforward: enter the Google registered email you want to give access to, decide if they need Admin or User privileges, and if they need to be notified by email. If you are giving someone user privileges, you can restrict which profiles they have access to. When you click the Admin radio button, the profile selection area disappears. You don’t have that control with Admins because Admins can see all the profiles in an account. That is why we go to the trouble of making a separate account for each business.

Don’t forget to click Add User at the bottom when you are done. Eventually you get in the habit of scrolling down to look for these buttons any time you do things in tools made by Google. You can make sure everything worked by checking under the Users tab for the emails of the people you are adding.

Adding a Master Profile

Google Analytics (GA for short) comes set up with one profile that collects all the data it can. Eventually, you or another user will want to filter the data. Filters are one of the most powerful features in GA, and they are used to modify data in all kind of situations. But like anything that is powerful, filters can get pretty complicated, and the outcomes of using them aren’t always what one anticipated. Beyond this, data that gets filtered out can never be recovered.

As in many other technology applications, the smart thing to do is have a back-up: a set of data that has everything, and that no one ever puts a filter on. Ever. And if you do it while you are already logged in it could take under a minute. Click the home button at the left of the orange menu bar, then the Admin button at the right of said bar. Now click the account name we want to work on from the list, then the property name from the next list. You should be in the Profiles tab for the website we want to make a back-up for. Click the New Profile button.

This is again, pretty straightforward (it’s not unusual that GA makes something harder to find than it is to actually do once you find it). Profile names like Master, Back up, All Data, or so on, will be immediately recognizable to frequent Analytics users, who will know not to filter them. We can guard a bit against someone given Admin access getting a bit too ambitious by explicitly including Do Not Filter in the profile name. I go one step further and prefix the name with a “z” so that it will show up last in lists.

You’re Done! Kind of…

There are still a couple or three things that you’ll have to do before you’ve done good job of basic Google Analytics setup. Most importantly, you need to get the tracking code up on your site. Then you can integrate your GA account with Google Webmaster Tools and get information about your how people find your site in Google search.

The post First Steps in Google Analytics Configuration appeared first on Informed Iteration.

I’ve finally found a moment to sit down and post though, so with no further ado, we finally get to the Google Analytics part of this series about Google Analytics (which I’m going to call GA from now on).

Analytics Account Creation

I’m going to assume you aren’t still logged in to the website data holding Gmail account we made seven months ago, so let’s get back in there. Head over to the GA home page http://www.google.com/analytics/ and click on “Create an account” at the upper right . You’ll likely be asked for your password again. Then a pointless screen that tells you it’s really easy to use GA. Come to think of it it’s not just pointless, it’s dishonest. Then comes a new and more daunting screen that asks if you are setting up a website or app, we are doing a website.

The next selection is whether to use Universal or Classic Analytics. One day, we’ll likely be using Universal Analytics, but the only thing beta products should ever be used for in a professional setting is testing, so we are going to choose Classic. The bottom sections are more involved:

Website Name is just something that you’ll recognize the website by. I’m setting up GA for a tourism site called Destination Niagara Falls, and I know my client is considering doing the site in different languages, so I’m going to go with Destination NF English today.

The dropbox allows a choice of secure or non-secure domain. Obviously, choose whatever is appropriate for the website you are tracking. Note that you don’t have to include the www: Type your domain name in the way it usually appears, or the way you want it to appear. If your business cards, store signs, and newspaper ads all have a www, then so should your url, and vice versa. If you haven’t yet decided, non-www urls are generally preferred from a branding and design viewpoint.

Industry Category and Reporting Time Zone are pretty self-explanatory. It’s worth pointing out that getting your Industry Category right is more important to Google’s data-gathering, and improving GA, than it has to do with your data-gathering and improving your website.

Setting Up Your Account

As with the Website Name, the Account Name is just what you call something around the office, so to speak.  It’s the next level up from a website property, meaning you can have several websites in one account. You should only ever do this if they are part of the same business though, like in our case, versions in other languages. If you own several websites or businesses that aren’t part of one business entity, they go in separate analytics accounts.

If you are a web firm, DEFINITELY give every client their own account, at the very least. Less than that and if ever a client wants admin access to their account, you are completely and utterly screwed. You either have to tell them you can’t, or give them access to every web profile in the account. Pick your poison.

Ideally, you are giving every business, even if owned by the same client, its own account: what if a client wants to sell a business one day? So I’m calling the account Destination NF, and it will hold the website with the name Destination NF English, and other language versions as time goes on.

It’s Good to Share

The Data Sharing Settings are similar to the Industry Category section from above, in that they are more for Google’s research than yours. Still, if you are using the tool, you may as well help them make it better, especially given that there aren’t any documented instances of misuse (to my knowledge, if you know of any please leave a comment).

Brian Clifton, in the 3^rd Edition of his excellent Advanced Web Metrics with Google Analytics, makes the interesting point that if you are a large monopoly or don’t have much competition, even if your data is annonymized, people looking at aggregate reports could make inferences about your business if it plays a big enough role in the industry. But hey if you are a large monopolist you probably aren’t in the habit of sharing anyway.

Terms of Service and Privacy

After clicking on the Get Tracking ID button you’ll have to choose the country you’re in, and agree to the privacy policy. Yeah, we are actually going to think a bit before clicking without reading like we always do. There are two things that bear special attention (assuming you are in Canada or the US, EU is a bit harsher, Germany is very strict).

1. Don’t use GA to try and track or figure out personal information like names, emails, or street addresses.
2. “You must post a Privacy Policy and that Privacy Policy must provide notice of Your use of cookies that are used to collect traffic data,”

Internet privacy is pretty complicated, but really all you have to do, as far as GA terms go, is not try to figure out exactly who any of your visitors are, and have a little link at the bottom of every page of your site to a privacy policy that says you use cookies to collect traffic data. It’s a nice gesture to include a link to the site allaboutcookies.com in case people want more info.

Note that I’m not a lawyer and the above doesn’t represent a considered legal opinion for your situation. When your lawyer gives you the ok, click I Accept.

The Google Analytics Tracking Code (GATC)

The next screen will be in your new account, congratulations! I’ll do a post at some point about the on/off switch options you see above the code for multiple Domain/Subdomain tracking, and might even get into the other two at one point, but those all complicate things. For now this account has just one property.

Before the account can gather data, you have to add the GATC to your website, specifically every page of it. Don’t worry as most content management systems make this very easy to do, and I’ll give examples of doing it in some popular CMSs in a future post.  There are a few things we should do before we even leave Analytics though, and we’ll cover those in the next post.

Please leave any questions or suggestions in the comments, and remember to add that privacy policy!

The post Creating a Google Analytics Account for Your Website appeared first on Informed Iteration.

Head to https://accounts.google.com which will send you to the screen pictured below, or right to any Google Account you might currently be logged in on. If you use Gmail as already, I’d recommend opening a different browser, as that will save you a bunch of logging in and out of accounts later. If that’s not possible for some reason, click on your portrait or email address in the top right to get the pop up with the Sign Out button and reach the login/create account screen.

Then, click the Sign Up button top right or the link in the text at the left to get started. Give the new account an easy to remember and recognize name, like datatools.yourbusinessname@gmail.com

I use a gmail account because they are free, and as a core Google product, if ever there is a  really serious problem with gmail, there will probably be a big problem with many of it’s other products, including Analytics and Web Master Tools, anyway. Using a Google Apps for Business address would cost you 60 bucks a year, and creates one more link in the chain for notification email, in that your registrar needs to be getting your DNS right.

Two Tips for Making Your Google Accounts

I’m not going to go through the whole process of setting up a Google Account, but I will offer two points of advice:

1) Unless your business has been around for more than 18 years, overcome the temptation to use the founding date of your business as a birthdate. You have to be 18 to open a Google Account, and if you submit your form with a birthday that makes you younger than that, Google will put something in your browser that will keep you from making the account until you clear your cache and cookies from them (or use a different browser).

2) Set up forwarding from this account to your main email account. You could add your main account to the notification lists for Analytics and Webmaster Tools, but that is two things you’d have to do instead of one. And Webmaster Tools in particular will send out important emails, like if there appears to be something wrong with your site as far as Google is concerned, and you don’t want to miss those. To set up forwarding, click the Gear button in the top right (just under your email address), then Settings, then the Forwarding and POP/IMAP pseudo-tab, and finally, the Add a forwarding address button.

After entering and confirming the target address in the two subsequent popups, you’ll need to verify the connection to your main email account. The easiest way to do that is to log into (or switch browsers to) your main email account, and click the link in the verification email that Gmail sends there. Then, you need to log back in (or switch browsers back) to the new account, head back to the Forwarding screen in the settings, select the button next to Forward a copy of incoming mail to…, and click Save Changes at the bottom of the screen.  Don’t forget this notoriously easily to forget step.

Next Step

Congratulations: the new Google Account is made, forwarding email to your everyday account, and ready to create Analytics and Webmaster Tools accounts for your website. We can take care of that while we are still logged in to the new account. It’s crucial we get that right or this whole extra account hoop we jumped through will be a waste. We’ll get to the Analytics setup part in the next post, see you there!

The post Getting Google Going: Making a Gmail Account to Manage Analytics and Tools appeared first on Informed Iteration.

Or we can do it right the first time!

You are going to need a new Google Account. No, you can’t use the one you already have.

Don’t worry, we’ll set up the accounts so you can do whatever you want from whichever Google account you want,  we just need a new one to set it up. If you want to take me on faith just wait for my next post and we’ll get going on the actual setup. Those wanting to know why can read on.

Don’t Mix Email and Data

Google Analytics and Webmaster Tools (and AdWords for that matter) all give some kind of special consideration to which Google Account originally set up the tools for a given website. With AdWords it’s a real pain, if not impossible, to change the main email on an account because Google doesn’t want people driving up bids with fake accounts, but Analytics and Webmaster Tools have more subtle issues. They boil down to two problems: giving people access to your email account, and giving people access to tool data they shouldn’t have.

We all know you shouldn’t share logins for things, but sometimes it can save so much trouble, often in an emergency, that you end up doing it. With a separate Google Account only used for technical stuff related to your website, giving login access to someone helping you with the site or its marketing becomes a much smaller privacy threat. If it’s also the account you use for email, you are risking information they don’t need to see at all.

What if you already have a Google Account you use just for tools related to another website? You should still set up a new one.

Don’t Mix Data From All Your Websites

Ok, to be honest, there are some exceptions where it makes more sense to put several websites on the same Google Account. This almost always has to do with the websites being different parts of a single online presence though. If your multiple websites or domains are different subdomains, translations, localizations, or what have you, related to one business, it often makes more sense to have them all under the same Google Analytics Account. That’s a horse of a different color though, and complicates setup considerably.

For now, we’ll assume your website has only one domain tied to it.

If you have websites for different businesses or organizations, then those should be added to Analytics and Webmaster Tools from separate Google Accounts. Here’s why: even if, as a savvy owner of several online businesses, you never ever find yourself giving out your login info because you can easily add users if anyone else needs to get in, there isn’t much granularity in how you can set up their permissions. In Google Analytics, if you need to add someone as an Admin (which, if you’ve hired an analytics consultant, you’ll probably do), they’ll then be able to see all of the websites in the account.

It’s not as big a deal as having people see all your email, but if you are involved in partnerships or try to sell a business, it can become a massive source of confusion and confidentiality issues.

Webmaster Tools has its own way of attaching itself to the account that connected it. Removing it can remove some, or even all, of the other site owners you added since, taking valuable data with them.

I Don’t Need to Have a Separate Account Because…

…It’s just you running the website and you just have the one business and won’t be starting another website, ever, and are somehow sure of this? Maybe because you aren’t super interested in online stuff and just needed the site for your established offline business? That increases the likelihood that you’ll one day need help with your website, and possibly share a login to the related tools – all the more reason for a separate account. You can use it as the contact account for your hosting plan as well and keep everything for your website in one place.

Conversely, the more sophisticated a tool user you are, the more likely you are to have several websites, and to take on a variety of collaborators, and perhaps sell websites. Obviously not having full flexibility in who sees what of your Analytics data won’t be ideal as your online empire grows. So one business to one account works for you too.

And don’t worry, we’ll set up email forwards and admin access for your regular account so that the next time you have to see this extra one we made, it will because you need it for something, and you’ll be glad you’ll have set it up. We’ll tackle the new Google Account and setting up Gmail forwarding in my next post, so see you then, unless you have some questions or comments to share below. Thanks!

The post One Account per Site: Set Up Google Analytics and Webmaster Tools, Right, the First Time appeared first on Informed Iteration.