The Complete Digital Analytics Ecosystem: How To Win Big

Connections The world of digital analytics seems to be insanely complicated.

And, yes, some of it is. Third-party or first-party cookies anyone? And, are we tracking people, devices, web browsers or whoknowswhat?

But it is a lot less complicated than you might believe. No. Really. A lot less complicated.

I led a discussion the other day with a collection of people who were brand new to the space and some who were jaded long-term residents of Camp Web Analytics. When someone played the omg, it is all so complicated (!!) card, I took the opportunity to sketch a picture of the entire ecosystem to highlight that it really was not all that complicated. The process involved slowly laying out each piece of the puzzle and how it fit the piece next to it.

By the end of the exercise there was a lovingly simple picture, and a path to glory. In this blogpost I want to share that with you.

Regardless of your experience in the space, I believe you'll find it to be of value. Even if you are in the super-jaded category, this will help you present something to your boss's boss that will get them to finally understand what you do!

Our journey to understanding, dare I say nirvana, follow these steps:

Doesn't it sound absolutely exciting? It is. And along the way you'll find helpful tips, links for deep dives, and a ravishing amount of new insights.

Ready? Let's go!

Digital Analytics Ecosystem: The Core Elements

At the core of everything you will do in digital analytics is the concept of metrics. How do you define a metric: It is simply a number. That is as simple as it gets.

Your digital analytics tools are full of metrics. Averages this. Total that. Percentage that other thing.

[Helpful post: Best Metrics For Digital Marketing: Rock Your Own And Rent Strategies.]

metrics kpis

A very special type of metric is designated to be a Key Performance Indicator (KPI). A KPI is a metric that helps you understand how you are doing against your objectives.

This implies you cannot have a KPI identified unless you know what your objectives are. For eCommerce site X, Conversion Rate might be a KPI because their current objectives are tied to reversing key business trends. At the same time for site Y, it could be Average Order Value. The key is knowing what your business objectives are.

Remember… If you don't know where you are going, any road will get you there. And you'll be miserable.

While there are no such things as blessed KPIs everyone must follow – because everyone is not executing the exact same strategy -, some metrics can never be KPIs. A good example of this is Bounce Rate. It will never be a KPI. Simply because even if your bounce rate goes from 100% to 10%, it might not have any impact on your business. Bounce rate improvement means you just got one more page view. That is great. But hardly earth shattering.

Please remember this important caveat as you pick KPIs.

[Helpful post: You Are What You Measure, So Choose Your KPIs (Incentives) Wisely!]

Now you have your foundation, metrics and KPIs. The next layer is called dimensions . Definition? A dimension is, typically, an attribute of the Visitor to your website .

As we develop more sophistication to our measurement, they'll morph from being attributes of a Visitor to those of a Person.

metrics kpis dimensions

Traffic sources, like keywords, referring sites, campaigns, countries, are examples of dimensions. As are names of pages or videos and devices people use to consume the content. It might seem a bit odd but the number of visits someone makes to your site is a dimension, as well as, if you are tracking it, visitors who make donations or if they are a subscriber or not.

This is not always true, but in general dimensions appear in rows in a table (in your analytics tool) and metrics and KPIs appear in columns.

We now have the key ingredients. We can start to create some lovely music, and it will come via the use of custom reports – one of my favourite features in any digital analytics tool.

Notice I did not say standard reports, those barely cross the bar of data puking. I said custom reports. And the primary reason is that I want to stress the difference between web reporting and web analysis . When you begin to use custom reports, you'll be forced to stare at a blank slate and figure out what goes on it. In order to paint your symphony, you'll be forced to talk to your leaders, to your peers, to your customers and understand what the real questions are that need answering. You, then, won't go on random fishing expeditions. You'll go on focused journeys and find the secrets you are looking for.

metrics kpis dimensions custom reports

So, demand custom reports even on day one if you can. If you would like to, start by downloading three of my favourite custom reports, or these three paid search campaign analysis custom reports.

By this stage your immediate leadership is happy, and it is quite likely you are really kicking some serious butt when it comes to identifying the initial cluster of valuable insights. This will last between four to six months. Enjoy it (I genuinely mean that).

At the end of that time period, you'll focus on the singular thing that separates the kids from the adults. Advanced segmentation! There is nothing, and I mean nothing, more important for you to master to truly become an analysis ninja. The reason is simple. All data in aggregate is crap.

In order for you to really truly understand what is happening to your business, your customers and the yummy business outcomes, you need to be able to segment the data. You need to slice! You need to dice! You need to drill! Repeat after me: Slice, dice, drill!!

metrics kpis dimensions custom reports advanced segmentation

Advanced segmentation moves you from looking at the entire universe to focusing on micro-clusters in your quest for actionable insights. You can use the segmentation selector framework that outlines three broad buckets: Acquisition, Behavior and Outcomes. Each bucket has a host of specific segments for you. From there you move on to mastering Visitor segmentation, Cohort-analysis, Sequence segments, and you will feel a high at work that can only otherwise be achieved via illegal drugs.

If you are just getting started, download three of my favourite segments and go from there.

Oh, and if you mate your custom reports with advanced segments… prepare for your mind to be completely blown!

Now your core is set. Metrics, KPIs, Dimensions, Custom Reports and Advanced Segments. There is nothing else to add. All the other elements of the ecosystem will surround this core.

Digital Analytics Ecosystem: The Inputs

There are three clusters of inputs, let's start with the ones on the left.

As mentioned above, for you to move from your metrics to your KPIs (and indeed to identify valuable dimensions and advanced segments to focus on) you are going to need to know your business priorities. This one is quite straightforward.

analytics ecosystem business priorities

Get these from the highest person in your company who'll talk to you. If you have to call in lots of favors, or even your family connections, to get through to the CMO or CEO, do it! It will totally be worth it. A lot of data analysis goes directly into the shredder because the Analyst was more obsessed about what they thought was interesting rather than the boss or the boss's boss's boss. Don't make this mistake.

Then comes something really interesting, regardless of the size of your business.

The next set of input will be your competitive reality. Competitors you know about and the ones you don't know about. Start by asking your CMO and CEO who your competitors are, who keeps them up at night. Then go to Google (or Yandex or Baidu or Seznam) and type in your top category queries, see who shows up in organic and paid search listings. These are your competitors (whether you like them or not).

analytics ecosystem business priorities competitive intel

What are their key strengths? What are they doing differently then you – better or worse? Where do they get their traffic? What do their visitor trends look like? This competitive intelligence analysis will be absolutely critical input in ensuring your business priorities are more informed, you pick the right KPIs and advanced segments, which will showcase important dimensions in your custom reports.

This won't happen every day, but the last piece of your left-side inputs will be new opportunities that spring up. Say Google goes bankrupt. Ha! Brand new landscape. You need to take advantage of it.

analytics ecosystem business priorities competitive intel new opportunities

New opportunities could take your business in a completely different direction. At the minimum it will have a major influence on the analysis you will do. Both at a tactical and strategic level. Be prepared for it. No. Proactively hunt for it and seek it out. It will make you a much more savvy digital business practitioner.

Those are the three on our left.

The top and bottom inputs for our core are the ones you expect.

Analysts/Big Brains go on top. (They do, don't they? :))

Tools go at the bottom. Ok, not bottom as in bottom. Bottom just as the bottom part of the schematic we are creating.

analytics ecosystem tools analysts

From the person, me, who created the 10/90 rule all the way back in May 2006 (#omg), it should not be surprising that the importance of the tool is a bit smaller than that of the Big Brains.

As the 10/90 rule for Magnificent Web Analytics Success states: If you have $100 to invest in making smart decisions with data, invest $10 in the tool and consulting required for implementation and invest $90 in Analysts/Big Brains .

No matter how big your data, no matter how powerful your tool, you stray too far from the 10/90 rule and the promise of big data will never materialize.

People matter. Smart people matter even more. Tools just help them. Not the other way around.

That completes three sides of the our picture. You might be worried that thus far all you have see are inputs. Yes, very true. But it takes a lot to get to the good stuff!

Digital Analytics Ecosystem: The Outputs

No matter how much you and I might wish otherwise, the first set of outputs will be pure and unadulterated data pukes. Let's just embrace that fact and just bathe in its slightly muddy glory.

Data pukes! Hurray! Hurray! Big data pukes!!! : )

analytics ecosystem data pukes

It is a part of the natural evolution. You are excited about having data, you can't wait to puke it out. People might not have seen anything, they are thrilled to see % Exit Rate (perhaps the worst metric created in web analytics) or the Reverse Goal Path report (perhaps the most useless report in digital analytics). Get the data, send it out.

You'll get over it quickly, and your company will follow soon enough. Worry though if they just ask you to "produce more reports" and don't come back and start asking questions about deeper insights you can find for them. Because it might be your first big red warning sign that you are at the wrong company and your career is going to stink.

Usually, in a month or two, people will realize the data pukes are not useful and move to asking you for just the data puke they need. This is a great sign. We move to the custom data puking (CDPs) stage.

analytics ecosystem data pukes cdps

This is a good stage. The Paid Search team will ask for just the stuff they need. The Content team will ask for Page Value. The landing page optimization team will demand regular reports of all entry points to the site/app. And so on and so forth.

They will ask for a mix of not useful metrics and some really interesting ones. They will still ask for data. All ok. Where you can, throw in metrics and dimensions you think they need (in addition to what they want). And just give them data.

You want them to understand that they are not Analysts. They don't know how to do on the fly advanced segmentation, they don't know how to dive deeper to understand root causes, and they sure as heck don't know how to create the kinds of custom analysis required to answer the really hard questions.

But they will figure that out soon enough. Give them a couple months.

At the end of that period, they'll ask you for the very last output of our picture: Insights, Actions and Business Impact.

When this last piece is in place, you will know that you have arrived. That you are working at a company where there will be constant happiness due to all the orgasmic feelings from data!

analytics complete ecosystem

Insights (I) are findings from the data. They typically look like this: "Data suggests that x happened. When we dig deeper we identified the causal reasons y and z." Most Analysts stop at the first sentence, because it is all they can see from the graph or table in front of them. The best start there, go deeper because you need to find the causal factors (y and z).

Actions (A) are steps that the business should take. They typically look like this: "Triple the investment in Paid Search for this list of keywords." "Focus exclusively on products abc, def, ghi in Florida and products klm in Amsterdam." "Invest in creating video content because of reason 453." And so on and so forth. Actions are something specific the business should do. It is your job as an Analyst to identify them (though not if you are just a report writer).

Finally, Business Impact (BI) computations are simply you quantifying what will happen should your company take the action. They typically look like this: "Tripling the investment in Paid Search for this list of keywords will increase revenue by $893k per week." "Focusing on these key products in these key geos will increase profitability by 657%."

Recently I had the opportunity to cover the IABI in a significant amount of detail in my blog post on creating strategic dashboards. You'll find it here: Strategic & Tactical Dashboards: Best Practices, Tips, Examples.

And that's the complete picture!

Not all that complicated right? Five core elements surrounded by three sets of inputs and one set of outputs. All leading up to nirvana!

If you are in a leadership role, or want to appear to be in, the next two sections will be quite valuable. We'll look at the phases in which you should set your execution strategy, and close with which teams should own what part of this ecosystem for optimal success.

Digital Analytics Ecosystem: Optimal Execution: Three Phases

Too many people try to go for a revolution when it comes to their digital strategy. They fail. On the web, evolution works. An evolutionary strategy allows not just one part of the company to get better overnight (revolutions can do that), rather they allow everyone to get better together so that the sum of the parts can be bigger than the individual parts.

This is why I love the ladder of awesomeness thinking. Do one thing, get good at it, get everyone good at it. Move to the next one. Rinse. Repeat.

If you are just starting on your digital analytics ecosystem, you'll find value in knowing what the optimal order is to execute your strategy below. If your company is already in the middle of this, you can figure out why things are so messy or you've not made more progress.

The phases in which you execute will vary a little bit on your company, country, skills available, digital sophistication and other factors. But I want to offer an optimal starting point from my years of experience from working in many different companies and countries.

I believe usually people execute in three phases as they go up the analytics ladder of awesomeness .

Phase one is all about data capture. Putting tools in place and identifying the first set of metrics. This will quickly be followed by an effort to understand business priorities.

analytics complete ecosystem numbered 1-5

That will takes us promptly to executing based on a core set of KPIs and dimensions. Steps one through five.

Take a breath. This is a nice chunk of work. It puts the foundation in place. All quality control. Checking data collection quality etc. happens here.

Phase two is all about data reporting. It begins with the pure data pukes, which will help the company realize tools don't do diddly squat and cause them to immediately prioritize an investment in Big Brains. [Remember to pay well for the big brains. If you throw peanuts you only get monkeys.]

analytics complete ecosystem numbered 1-10

This now sets you up beautifully to move into custom reports and CDPs (responding effectively to business needs!). We close out this phase by getting good at advanced segmentation (the most sophisticated kind, not the silly new vs. returning visits – perhaps the worst standard segment in any tool).

Take an even deeper breath. You are now on the cusp of glory. Few people get to the next phase.

Phase three is all about rocking the data analysis universe. It consists fulfilling the dreams you dreamed of as a little child when you looked at the stars and wanted to grow up to be a true blue business analyst.

Start by getting good at identifying insights. Try to not send CDPs or data of any kind. Just send an email with bullet items in English that describe what the data says and why it is saying that. Then move to competitive intelligence, this will bring a whole new set of bright lights into play. The illumination will be fantastic for your business strategy and data analysis.

analytics complete ecosystem numbered 1-15

Computing business impact is non-trivial. You have to get good at some predictive analysis and forecasting (basic kinds), being able to talk to other teams, working with Finance in particular, understanding macro business trends etc. Good stuff, great outcomes. And finally you'll only have to figure out how to put in place a process to identify, evaluate and monetize the relevant new business opportunities.

BOOM! Done!!

Simply put…. Phase one is about getting really good at Data Capture. Phase two is all about Data Reporting. Phase three is all about Data Analysis (identifying insights that lead to very specific actions the business should take presented with impact from aforementioned actions).

Digital Analytics Ecosystem: Optimal Execution: Timing Expectations

Timing is also dependent on many variables that will be unique to you. But allow me to share the wisdom I've had the good fortune to accumulate during my professional experience.

If you are just getting started you'll spend the first six months , perhaps a little less in Phase One. And that includes your initial analytics tool implementation (you don't need every gosh darn thing implemented right away – any alternative to that strategy is one sign your internal team or agency or tool vendor is trying to con you).

You spend month six through month twelve in Phase Two. Your leadership team will really start to value data during this time. They will give you more money to invest in ancillary tools, more savvy technical strategies etc.

There is no end to Phase Three. But, it will take you between nine to twelve months to get to a state where people inside your company will start to recognize that this is a completely distinct phase and that you are adding unique value. Data, at this end of this time period, will be a indispensable part of business decision making inside your company.

Two years to get to the end of Phase Three – a phase that never really ends. You just get better and better at it.

I hope this helps you understand the entire digital analytics ecosystem, the phases in which you can execute your strategy and the approximate timing expectations you should put in place for success.

There is a lot to be done. It requires hard work and perseverance (and Big Brains!). But, it is not hard to understand, it is easy to realize if you are on the optimal track, and it is straight-forward to measure if your strategy is taking too long to come to fruition.

As always, it is your turn now.

I was worried about missing something from the core five elements, would you add anything there? Do you agree with the three inputs on the left? Is the order of execution of the digital analytics ecosystem reflective of the order in the 15 steps above? Is there an element, input or output that your company struggles with in particular? Would you have categorized the execution strategy into more or fewer phases? How about the buckets of time? Does phase two take longer than six months?

Please share your wisdom, critique, ideas, tips, lessons and battle scars via comments.

Thank you.


  1. 1
    Vikas Disale says

    Thanks Avinash for sharing your wisdom with us.

    I am on 8th Step right now and your post is a good guide to become the true Analysis Ninja.

    While reading this post I already open 12 other posts of yours in new tab, need to read all of them.

  2. 2
    Nelson Yuen says

    On rare occasions, I fault old tools and poor implementations for analysts not being able to get past #8 or #10. Typically older companies get ingrained with processing data one way – and in order to make things more flexible, it requires implementation resources, SQL resources, and an org change where traditional web analysts need to cross pollinate skill sets with DB marketers or devs.

    In other words, sometimes the business requirements and the foresight needed to produce the flexibility within digital properties (like mobile apps) are lost on analysts and IT.

    Enter the tag manager? dataLayer be damned. LOL Great post btw.

    • 3

      Nelson: It is undeniably true that incomplete and incorrect implementations of the current technical code can cause many setbacks. It is really sad.

      I hope that people recognize this challenge and not over-reach on day one. This is a huge contributor. "We are buying Adobe/Google Analytics Permium, now let's get every gosh darn piece of data known to mankind in the first round." This is the path of doom.

      I've made the case that you should get the standard tag with zero customization launched in a week (via GTM ideally!). Extract business value. Win love and confidence of leaders. Then do goals and ecommerce. Extract value, win L&C. Then next, then next. At each step relevance to business and knowing that you are adding value and checking implementation is built in.

      You are also right that tools like the tag manager will help. But if one chooses an overnight revolution path, doom awaits. :)


      • 4
        Joshua Uebergang says

        I have a confession to make. I have a man crush on Avinash. Disappointed I didn't get to hear you speak in Sydney last year.

        Nice idea with the gradual role out of GTM, then goals and ecommerce, then… Capture, report, analysis. Another good framework to work with.

        Your think, see, do model dominated my digital mindset in 2014. Helped strategize what goes beneath a keyword, content, and advertising. A simple explanation to understand various intents and KPIs at all stages.

  3. 5

    I like it this amazing article!

    Very simple but clever method of the introducing digital analytics to everyone.

    If I have a lot time, I will translate to Hungarian :)

  4. 6

    Excellent article, very detailed but easy to follow.

  5. 7

    Beautiful post as usual.

    Your post inspired two ideas…

    1. The iterative approach to delivering core and evolutionary data fits perfectly with Scrum. Setting this work up in that context and maintaining a prioritized backlog will go a long way to building mutual respect between technical and business teams while maintaining your metrics collection roadmap.

    2. Take steps to ensure the early core metrics are going to meet future needs. One key piece of data architecture alluded to above is the evolution from visitor to person at the beginning of the post. You must nail down the key to that data in alignment with the rest of the business intelligence architecture and legal constraints. Failure to get this right risks having to re-work/re-start your implementation. No one has time for that!

    Thanks again for your inspiring blog.

  6. 8


    I love your article. It provides structure for something I'm doing right now.

    I do have a question though.

    While your article is called digital analytics ecosystems, it's weighted in my opinion toward 'web analytics'. There's another part of a brand digital ecosystems which is what their 'customers' do off-site. It's now possible to get data on this thanks to social data.

    Do you have any experience with this and is it different from what you just wrote?

    In my opinion, social data is just another kind of data when it's attached to a customer db but it's also a totally new set of data when it's not. How would you go after segmenting people who are into, say, biking because they have talked about biking in the open?


    • 9

      Laurent: Good question, thank you.

      For me digital analytics is about everything that happens on your websites and mobile apps (owned platforms), on your social presence (rented platforms), the impact online of your offline marketing/activity, and finally the impact offline of a businesses online activity.

      That's the box. The purpose is to understand how digital drives business outcomes (for non-profits, for-profits, B2B, B2C and everyone in between). Everything in this post applies to that world.

      The example you share, I think it is a recent TV commercial by IBM, is interesting and valuable. The purpose is, per IBM, to mine the world's conversations and connections to make better business decisions. It is not completely clear what the purpose is, it is not completely clear what the start and end is, it is not completely clear beyond exciting us about buying IBM hardware to start to mine the worlds conversations to figure something out.

      For now, I would say it is out of the scope of the discussion in this post. Though I'll admit it is an interesting commercial, and I'm intrigued by the possibilities. :)


      • 10


        No it's not IBM ;-). It's a client of mind that wants to find new ways to do segmentation.

        Today they survey 40k people every 2 years. They sell through brick and mortars so they don't have the 'owned' digital data from web/e-commerce site. They want to explore if and how digital can help them segment better/faster.

        We chose to aggregate social data from people who mentioned a specific topic and mine both their content and network connections to find any interesting pattern.


        • 11

          Laurent: So sorry, I though you were referring to this ad:

          Your client is on a great quest. But I would encourage them to think much more expansively about the possibilities of digital than to simply aggregate social data because it presents both a sample and sampling bias. That does not mean it is bad data, not at all. But it is definitely biased data (especially on topics/products/areas that might affect or imply representation of the "broad general population").

          If they still refuse to open their eyes, then in their circumstance you have already given them the best possible gift.


  7. 12

    Avinash, thank you.

    Thanks for demystifying some things and shedding light on others.

    Phase 2 can take time if you are starting off for sure. I can openly say that for myself.

    But you only have to go through that once.

    Understanding of metrics, dimensions and segments plus experiments make you better and faster. Then you get wiser on how to communicate to the boss in a way that helps the company better.


  8. 13
    Anita Mishra says

    Thanks Avinash. I like the way you have structured the three phases.

    Sharing from my experience, most of the demand as seen in advertised jobs is for phase 1 (maybe 60%) followed by the remaining in phase 2 (more than 35%).

    Management is so pleased with 1 and 2 that many times they don't feel the need to ask for, support or invest in phase 3 (as one needs time and effort to first produce phase 1 and 2 before moving on to phase 3).

    Accordingly the job market (career growth) for many digital analytics professionals almost plateaus at phase 2. How does one choose a company that is willing to invest in phase 3 type skill sets (and likewise compensate)?

    Secondly, with big data becoming bigger, what advice would you give if one really needs to segment out gold nuggets of recommendations? Which tools/languages should one learn not just to access data but also make sense of it in a phase 3 way for leadership to take action?

    • 14

      Anita: You are, heartbreakingly, right.

      An irresponsible amount of obsession in the digital analytics space is about phases 1 and 2. This is both from people in the leadership, and from the people who currently hold the title of Analysts in our space.

      This is the reason digital analytics is still such a small world, most people who work in the space don't hold influential roles and we are still struggling for simple things as practitioners.

      The first band of wonderful folks who seeded the space were from IT, with webmaster type responsibilities, and that influence has stood strong for a long time. But it is changing. Most new folks who are coming in are coming from the business side, more companies are realizing after ten years of failure that they need to be in Phase 3. I'm extremely optimistic.

      There are a lot of posts on this blog about Phase 3 challenges, and solutions. You can also find them in other places around the web (and now you'll know how to recognize if they are Phase 3 or just fake Phase 1 and 2!).

      The way to win our leadership over is to focus on the IABI and deliver those. In companies that live there, pay their Analysts very well!

      PS: I have a broad WA Career Path development post here:

  9. 15

    I thought it is worth sharing with you that I recognised you post on FB when I was quickly scrolling down -your font types triggered my attention.

    From your post I loved that you have mentioned that Analysts are indeed an important part of the ecosystem.

    I was reading today in a book Big Data, Big Innovation by Evan Stubbs from SAS that it is predicted a shortage in Analysts – the key factor is that business experience is required to become a good analyst.

    • 16

      Karina: It is definitely a very big problem we are going to deal with, especially the people I tend to call Analysis Ninjas (people who don't just go fetch the data for you or fix your javascript code – both needed skills but easily found and inexpensive).

      The challenge is that Universities are not focused on it, tools are too numerous, the possibilities are too new.

      But this happens in every new space. We will get there. I recommend hiring managers to cast a wide net (Finance, Sales, Support and Services, Logistics, and more), and look for core analytical skills.

      PS: With, my startup, we are trying to create more Analysis Ninjas via the Master Certification course. Though we have too much demand and not enough capacity.

  10. 17

    Hi Avinash:

    Insightful and In-depth content as always. Thanks! :)

  11. 18
    Shuki Mann says

    Really, REALLY awesome post!

    You are describing the process so clearly, that it's nice to know I'm on the right way.

    Thanks for all Avinash

  12. 19

    I would add some experience-based observations about the Timing Expectations.

    * If the company you work for grew from the brick-and-mortar one, double all the time estimates.

    * If the company operates in the financial vertical, triple all the estimates.


    • 20

      Lukas: Ha, ha!

      You are definitely reflecting reality. But I hope that you can go to them, with a printout of this blog, and say "This nice person says it should be six months for phase one, won't we be embarrassed if we took 18!"

      Maybe that will get it done in nine. :)


  13. 21

    Hi Avinash,

    I love the idea: "Do one thing, get good at it, get everyone good at it. Move to the next one. Rinse. Repeat." Is that what you are doing?

    Thanks for sharing!

  14. 22

    Avinash, this is a terrific article with a ton of value. Thank you so much for it!

    My department VP has asked me for reports on website performance since we just did a complete overhaul of our website. So, I am giving him comparative analysis custom data pukes. They have yielded basic insights on how the website itself is performing in terms of behavior and outcomes (he has never asked about acquisition). However, they haven't even really scratched the surface of actions and business impact.

    He might ask me for deeper insights, but it'll probably be up to me to give it to him unprompted. The good news is, I know he'll listen when I do.

    Thanks again!


    • 23

      Andy: It is sad that your VP is stuck in Phase Two, but as you mention your steady efforts to give him/her what he/she needs on top of what he/she wants will help with the evolution.

      If you don't have people asking you for savvy Phase Two stuff, or Phase Three, here is my post on how you can all by yourself figure out what the most important measurement priorities are and how to deliver awesome stuff:

      ~ The Biggest Mistake Web Analysts Make… And How To Avoid It!

      All the best!


  15. 24

    Hi Avinash,

    Excellent framework and of great value in any setting and easily applicable. I am a firm believer in the KISS (Keep it Simple) principle and so another reason for loving this visual :-).

    Couple of thoughts –

    Totally agree with the inputs and it may be the case that the "key" input are the Business Priorities with the other two inputs feeding into it to give it more shape and focus.

    Talking about "Business Priorities", should it be 1/Recurring than just a 3? I am stating this as over time (and sometimes suddenly) the priorities can change or new priorities added and so a periodic check-back can be useful in ensuring your KPIs and analytics are still adding value. ('Tools' can be 1 or 2 but the implementation need to be synced up with Priorities)

    Also wondering (I am thinking out loud here and so this is more a question for discussion) if the core should have another layer around 'Advanced Segments' called "Marketing Effectiveness" or something like that. The thinking behind this being once you get proficient with the Segments then move to things like digital attribution and embrace marketing at a higher level.

    The timelines are tricky as it is very contextual — depending on your legal, IT resources, analytics culture etc.

    Thank you — as always a wonderful post.


    • 25

      Ned: It is always such a genuine delight to hear from you. Thank you for sharing your guidance.

      I should have been more explicit in sharing guidance on checking in with the priorities. In my experience, twice a year reviews should be the norm. Ensuring each KPI is still providing value on a quarterly basis should also be the norm. I don't recommend reviewing any sooner than those two time frames because business priorities should not change more than that (and if they, you got bigger problems!).

      Excellent question around marketing effectiveness. There is a whole host of advanced analytical techniques that the Analyst would be able to bring to bear. So it is in the Big Orange box. I'll absolutely consider calling it out in future revs.


  16. 26

    Hi Avinash,

    Thank you for sharing very useful details.

    Can you please more elaborate "pure and custom data pukes"


  17. 28
    Marc-Antoine says

    I can't believe this article is out there for free.

    Thanks Avinash, this is great stuff.

  18. 29
    Steffen Kaufmann says

    Hi Avinash,

    Thank you so much for all your great content.

    I have learned so much from you and each time I return to Occam's Razor new gold nuggets are found. Just wanted to let you know that I appreciate you sharing!


  19. 30
    jamesbroad says

    Thank you for sharing very useful details in this post.

  20. 31
    Lucy Holmes says

    Avinash, great post.

    Here's to winning big!

  21. 32
    Lukas Grebe says

    Thank you Avinash for an absolutely excellent post!

    I've been thinking how AB-Testing fits into these phases & steps.

    In my eyes its clearly an aspect of the digital analytics ecosystem but i'm having a bit of trouble fitting it in: first data needs to be available that can be used in significance calculations (phase 1), second: at least a "phase 2 acceptance" of basing decisions on data, third – and most importantly – a respectable level of insights & "nija-isem" is needed to formulate hypotheses as a basis for an AB-Test. So that would put AB-Testing in Phase 3. Maybe right along the IABI output?

    As in: "I" forms the Hypothesis, "A" is my test design, and "BI" is the test Result? Would this be the only place to apply AB-User-Testing in the Digital Analytics Ecosystem?

    Thanks again for an amazing Post!

    • 33

      Lukas: Thank you for bringing up A/B testing. It is an important part of digital analytics.

      I tend to think of testing (A/B or Multivariate) immediately adjacent to the picture you see in this post. As the company enters Phase Three (testing would not be a priority in Phase One and a low priority in Phase Two given all else), and the Analysts start to deliver true Insights and impactful Actions (steps 11 and 12) it will be natural that some actions will call for tests. A dedicated person, or outside agency, will then assist in executing the tests (which will be measured against the KPIs identified and targeted to the Segments identified earlier).

      If I had to visualize it, testing might be to the right of the IABI arrow. I think.


      • 34

        Yes, truly if you have done everything you will attract a lot of traffic. The key is then to segment the relevant traffic to that particular page, homepage, landing page etc that is most relevant to your business which has the most probability of the conversion metric that matters.

        Then the Web reports but more importantly the analysis you have done will help you set up A/B or multivariate and keep honing that segment. Finding issues, fixing , testing again etc.

        Not just to sell more but to get info to people they need faster and in a better way.
        Just a better end user experience where everyone wins.


  22. 35
    Brent Sitterly says

    I have a simple question how do you prove causality?

    • 36

      Brent: Sadly, your simple question has far too numerous answers. Many of which are dependent on the situational elements.

      For now, let me simply direct you to the top results here:

      I want to underline how important the quest to get good at this is!


  23. 37

    You perfectly noted the evolution of analytics, starting from row data with basic analysis and segmentation to the current huge flow of data we fight on a daily basis to get conclusions and secure solutions for the benefit of our organisations.

    This developed like crazy and only ones that can draw real conclusions and predict trends will be successful.

  24. 38

    Hi Avinash,

    We miss you at Superweek 2015 …. :(

  25. 39
    Nikhil Ganotra says

    Hello Avinash,

    Yes, It's truly very complicated than I expected.

    I am just a beginner to SEO, Analytics and the digital marketing world. I never thought that learning analytics would be so much complicated LOL.

    I read the article and understood just few terms and feeling like drinking a kettle of coffee after reading this articles LOL.

    But I have subscribed to your blog and I hope that I would be able to understand the metrics easily after reading your further articles regularly.

    Thanks ! :)

    • 40

      Buddy, this is a journey like no other. One step at a time and I can assure you ,, it's going to be fun.

      It's amazing that you found Avinash ' s blog , Def an asset.

      work you way to market motive certification.

      All the best,


  26. 41
    Michael Ellison says

    Hello Avinash, great article.

    With the exception of "some" experience using Google Analytics, I am very new to the discipline. I would love to learn more, and move into a career in digital analytics, but I would want to do so under the wisdom of a mentor.

    In my research the only opportunities for this kind of learning is through the Analysis Exchange program by Analytics Demystified, but it looks as though that program is no longer active. Do you have any advice, or resources for me?

  27. 43
    Irina Johnson says

    Thanks for sharing this brilliant article with us.

    It makes very good reading.

  28. 44
    Mazid Umar says


    This is Mind Blowing Information and very interesting to read.This is very useful post.

    Keep up your good work!

    Thanks to share this useful post with us.

  29. 45

    Hi Avinash,

    Very interesting article! I´ve been working on setting up my own eCommerce company for the past year, I´m currently knee deep in creating my Marketing Plan. I realise how important it will be for my business yet I have no previous experience with Digital Analytics.

    After reading this post, I have a better handle on what is required, however I am a one man band, I will be doing all of this alone, at least in the beginning and right now it feels slightly overwhelming.

    On the one hand, I do not need to convince management to buy into my plan, as it is only me, however, I am somewhat restricted by limited resources(time, money).

    For someone like me, who has to fulfil all roles you describe above, including Big Brains, do you have any words of direction/advice in terms of where to begin with this "large" topic!? (apart from absorbing your book Web Analytics 2.0, which I´ve just ordered).

    Many Thanks!


  30. 48
    Gaurav Sethi says

    Thanks for the excellent stuff.

    My friend recommended me to visit this blog, and I have to say that I have learned something new which has definitely enhanced my knowledge.

  31. 49

    Hi Avinash

    Thanks so much for sharing such useful information you share with us for free!

    I love it, and look forward to learning from your experience. :-)

  32. 50
    Deepika Tanna says

    Hello Avinash,

    Great post!

  33. 51

    Superb article as always!

    Avinash, can you please provide a link where you wrote about single page web analytics. When i say single page, it is single-page application (SPA).

  34. 53

    Hey Avinash!

    Quite an elaborate post and a very informative one too!

    You have not only covered the basics, but have done a spectacular job in simplifying technical jargon for even those who have been in the online marketing game for some time.

    Thanks for sharing!

  35. 54

    Such a valuable post.

    I am just getting started on deeper analytic research and currently doing some A/B testing to see what works better overall.

    You suggested to someone before to use google analytics. Is it able to to give adequate information in terms of A/B testing?

  36. 56
    Rama Krishna says

    Yes you are right Avinash without learning we can't do any thing that's why firstly we have to understand about these topic.

  37. 57

    Thanks for the awesome post Avinash!

    It would also make a great video if you every found the time ;)

  38. 58

    What a post, Avinash. You have not only covered the basics, but even made complicated technical terms easier to understand for people who haven't been in the game for that long.

    Keep the great work coming!

  39. 59

    Such a informative post. In a simple way you define digital analytics. Thank you

  40. 60

    Hi Avinash!

    Excellent Article as Always!. These are really a Win-Win Strategies for Digital Analytics.

    Thank you For sharing Such an informative article with us.


    Vishnu Kumar

  41. 61
    shaikh tanjeeb says

    This is what how can we achieve big success in analytics…….

    Thanks for the article…

  42. 62

    This is an incredibly valuable article Avinash, thank you for bundling so much goodness into one article.

    I'm a big fan!

  43. 63

    What is the (business) difference between dimensions and advanced segments as defined here?

    I'm getting stumped because I am confusing them…clarification would be great.

    Thanks for a very thorough and actionable post!

  44. 65
    Mark Bertrand says

    It's always good to set a systematic approach to science processes. At the agency level I typically inherit a Google Analytics or Adobe Analytics account that has many years of data collection and zero efforts in data cleaning and tidy controls. It's often an uncomfortable conversation to have with clients when discussing analytics, analysis, and gambling.

    Adding filters, cross domain tracking, data layers, calculated metrics, custom dimensions, custom alerts, etc. And then getting campaign parameters consistent, server errors, and finally a month or maybe a few months later you are collecting usable data. Sweet, now we can get into data mining, attribution models but not with years of data.

    I feel like attribution models are missing from your data outputs.

    • 66

      Mark: I have to admit that your experience is not atypical. Systematic attention to all three facets, DC-DR-DA, are rare. But, we have to strive to get there!

      You are right about attribution, it is missing. I'll add it to a future version. In the visual language I'm using here, it would be a another blue ring outside Advanced Segments. The outputs stay the same.



  45. 67

    This was a great read and i learned a lot.

    Now if you can just tell me how to get on with a team to get hands-on training.

  46. 69
    Tessa Floreani says

    Hi there Avinash! Really enjoyed the post! Made an infographic in its' honor.



  1. […]
    But it is a lot less complicated than you might believe. No. Really. A lot less complicated.

  2. […]
    The Complete Digital Analytics Ecosystem: How To Win Big –

  3. […]
    Avinash Kaushik’s blog Occam’s Razor is a great place to start. In particular, have a look at his piece titled “The Complete Digital Analytics Ecosystem: How to win big”. It may not change the way you run your analytics, but it breaks everything down so neatly that the field begins to look less imposing, and gives real purpose for particular activities.

  4. […]
    The Complete Digital Analytics Ecosystem: How To Win Big by Avinash Kaushik

  5. […]
    If most digital marketing programs or campaigns have a weak area, it’s analytics. One recent study identified that the biggest talent and hiring gap in online marketing is in the analytics space. 37% of companies surveyed said that they desperately needed staff with serious data chops. If you take a look at the image below, courtesy of Avinash Kaushik on Occam’s Razor, you’ll see a similar emphasis on “big brains” and there just aren’t enough of them going around.

  6. […]
    Il mondo dell’analisi digitale sembra super complicato, ma a sentire Avinash Kaushik nel suo ultimo articolo non lo è affatto, a patto di implementare il corretto approccio alla web analytics. La base sulla quale tutto poggia è formata da 4 elementi fondamentali: Le Metriche, le Dimensioni, i KPI ed i Rapporti personalizzati.

  7. […]
    The always amazing Avinash Kaushik recently wrote an excellent article explaining The Digital Analytics Ecosystem. As you can imagine by the title, it’s not a quick read. Despite that fact, it contains information that is absolutely essential for both digital marketers and their clients alike.

  8. […]
    The Complete Digital Analytics Ecosystem: How To Win Big by Avinash Kaushik

  9. […]
    The Complete Digital Analytics Ecosystem: How To Win Big by Avinash Kaushik

  10. […]
    With such a large overwhelming quantity of data, how on earth does a digital marketer know what to focus his or her attention on? The core element of any measurement model is Key Performance Indicators (KPI). These are derived from client information– breaking down their business and drilling down their goals until the very reason for their existence is apparent.

  11. […]
    The Complete Digital Analytics Ecosystem: How To Win Big(by Avinash Kaushik)

  12. […]
    Kaushik, A. (2015). The complete digital analytics ecosystem: How to win big. Occam’s Razor, retrieved from

  13. […]
    I believe, as perhaps common-sense would dictate, that you should have a content strategy first (and content investment first). Then, at least after you have a critical-enough amount (yet, not complete) of content, you should invest in marketing. Once you have a critical-enough marketing budget (this could be $10k, really the bar is quite low), you should have measurement strategy. [Phase one, steps one through five, here: The Complete Digital Analytics Ecosystem: How To Win Big.]

  14. […] ผมก็เลยอนุญาตขอแนะนำโพสนี้และโพสนี้ ที่ทางเทพเจ้าแห่งการวิเคราะห์เว็บไซต์ของผม Mr.Avinash Kaushik ได้เขียนเอาไว้ มาแนะนำให้พวกคุณได้เข้าไปอ่านต่อกันครับ […]

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