Excellent Analytics Tip #22: Calculate Return On Analytics Investment!

BloomAnalysts: Put up or shut up time!

This blog is centered around creating incredible digital experiences powered by qualitative and quantitative data insights. Every post is about unleashing the power of digital analytics (the potent combination of data, systems, software and people). But we've never stopped to consider this question:

What is the return on investment (ROI) of digital analytics? What is the incremental revenue impact on the company's bottom-line for the investment in data, systems and people?

Isn't it amazing? We've not pointed the sexy arrow of accountability on ourselves!

Let's fix that in this post. Let's calculate the ROI of digital analytics. Let's show, with real numbers (!) and a mathematical formula (oh, my!), that we are worth it!

We shall do that in in two parts.

In part one, my good friend Jesse Nichols will present his wonderful formula for computing ROA (return on analytics).

In part two, we are going to build on the formula and create a model (ok, spreadsheet :)) that you can use to compute ROA for your own company. We'll have a lot of detail in the model. It contains a sample computation you can use to build your own. It also contains multiple tabs full of specific computations of revenue incrementality delivered for various analytical efforts (Paid Search, Email Marketing, Attribution Analysis, and more). It also has one tab so full of awesomeness, you are going to have to download it to bathe in its glory.

Bottom-line: The model will give you the context you need to shine the bright sunshine of Madam Accountability on your own analytics practice.

Ready? (It is okay if you are scared. :)).

Here's part one, let me hand you over to Jesse…


Hello my dear, dear friends fighting the good fight of analytics. I felt compelled to write about this topic because I, too, am an analyst to the core. However, I've long felt the unsettling sensation of having a tremendous impact on the business, but still having to fight for attention and resources, and always wondered why that is.

After being an actual analyst for a while, I'm now managing the Google Analytics Certified Partner program, and I now realize that it wasn't just me, our entire INDUSTRY is affected by this issue.

Why is it that we analysts feel like we have some amazing untapped ability that could revolutionize any business we touch, and yet we have to fight to be included in strategic conversations where we could do the most good, and we have to fight not to be ignored when we have something important to say?

Why is it that most analytics departments are constantly under-funded and under-staffed compared to the budget-hogs in Marketing or the herds of tech workhorses in IT?

I would venture to say it’s because we’ve made an awfully poor case for the value of what we do. Businesses, by and large, don’t understand the ROI of analytics… the Return on Analytics, if you will.

Everyone else seems to get an ROI calculation, but not us. Marketing dollars (hopefully!) get measured by their Return on Ad Spend. Product improvements are quantified in incremental sales. Even internal tools are evaluated by work hours saved. Yet the analytics team rarely has its costs measured in terms of impact on The Company.

‘But we measure (and hopefully, improve!) the ROI of other things’, you say. 'The impact of analytics is the impact we have on other teams.'

Exactly! And therein lies the problem! The absolute best case scenario is that we spend all our time making everyone else look better, only to let them take all the credit.

Do we really think that if our executives believed that every dollar invested (properly) in analytics would result in ten dollars back for the company, that we would still face the massive hurdles that so many of us deal with daily? Heck no!

However, it’s true, analytics is worth at least 10x what you invest into it. These successes are ours to claim, and it’s high time we started claiming them.

So, what is ROI?

The ROI calculation is cliché and overused because it’s so simple, even a child could do it:

* How much did you invest?
* How much did you make in return?
* Was the latter greater than the former? (And enough so that it was worth the effort?)

With that context in mind, here’s an equation I drew up to quantify your impact as the “Return on Analytics”:

return on analytics spend formula

Don’t worry, its bark is worse than its bite. All it’s asking you to do is put the ROI calculation in terms of how analytics works. The formula accommodates for the critical need to compute incremental impact from deployment of an analytics practice.

You see, the challenge with analytics is that you can’t just say “how much did you make in return?” because you were (likely) already going to make something in return. So we have to figure out the impact – the incremental return that you wouldn’t have had otherwise.

So what I’ve done here is:

* Highlight the “full incremental return” within a discrete time unit (such as a day, week, month, whatever) by first subtracting the improved ROI thanks to analytics (Ra) from the original ROI that you were getting before (Rm)

* Then multiplied that impact by the duration of those time units that it will last: (d)

* Finally divide that by the costs it took to get to that impact: Ia

The end result:

return on analytics spend formula details

‘But it’s so much more complicated than this!’, you argue. Yes. Yes it is. But so is any computation of ROI, if you really want to be honest. What this does is to package your impact into a (relatively) easy to understand way.

Let’s take an example.

Say you’re a mid-sized company who sells hubcaps. Your digital marketing team has a monthly budget of $30k, and the company sees $120k in monthly sales from it. 400% ROI, not bad.

You then hire on an analysis ninja and pay them $5k per month to “fix” your analytics (a bargain, if you ask me), and after 6 months of data-driven improvements to campaigns & landing pages, all of a sudden the same marketing costs are bringing in $180k in sales per month, a success rate which continues on for 12 months (until a new line of hubcaps come out).

To summarize: Six months of effort, twelve months of success (/gain).

Clearly some of the credit for this goes to your marketing team. But before you jump to “my marketing is now making a 600% ROI, that’s fantastic!” and then promptly give the Marketing team more money, it is important to realize that none of this would have happened were it not for the analyst who took the holistic approach to identify the optimization opportunities.

So, let's go back to our formula (above). Punch in some of the relevant numbers and you'll see this:

actual roa computation

Holy guacamole! You’ve hit a gold mine! Your six month analytics driven improvement delivered twelve months of astounding results. If every dollar you’ve invested in this team paid off even half as much, then your company would be the #1 hubcap dealer in the world in no time!

This is the potential power of calculating your ROA. Attributing success where it’s due so that you can fuel the true driver of growth.

Once you’ve taken a hard look at what your investment in analytics (everything from tools to people to professional services) has produced in terms of real business results, ask where you need to invest more in order to get to a positive ROA … and not just a positive one, but the one we all imagine ourselves to be capable of.


Simple and amazing, right?

Here's the really key part… Businesses often don't understand the ROI of analytics. In fact it is not uncommon that they often don't even understand what analytics is! Here's the hidden awesomeness of computing ROA: If we can prove that there is ROI, they don't need to understand what we do as Analysts! Just like other professions (say, Accounting – what is it that they really do? :)), the analytics practice, and Analysts, will earn the right to be left alone to add value because in a very compelling way Businesses and leaders, through ROA, will know that we are adding value!!

Yes. I hear you (and Jesse acknowledged this as well).

This part of the blog post is to deliver the specificity that you've come to expect on Occam's Razor. Practical examples and specific guidance that will give you a leg up if you are convinced that bringing accountability to your analytics effort is a good thing.

The guidance is going to come to you via a customizable model in a handy dandy spreadsheet. So to speed up your ROA computation, download: Return on Analytics Calculation Model.

The model has a summary tab, a tab full of awesome specific guidance on how to compute incrementality with pitfalls and caveats, and finally a whole bunch of tabs with sample computation of incrementality across various analytical efforts.

Let's walk through the model in detail.

Tab one contains the model for an actual client from whom you can find inspiration. The first thing you'll notice is that the formula has already been created for you. No need to touch this.

roa calculation

The second key element is the annual revenue for your company prior to the implementation of analytics (or a major expansion of your analytics practice). We are trying to establish a baseline. Type it in.

The third element is to calculate the total cost of ownership. Your cost! Ok, ok, you plus the hardware, software, army of consultants and BFFs. :)

Here's what you'll see for that element when you open the model…

total cost of ownership analytics

The numbers are realistic but by no means reflect what they might look like in your company. I've typed in as many things as I could think about connected to having a web analytics practice (i.e. Total Cost of Ownership).

So say you have Adobe's SiteCatalyst. You have a fixed fee you have to pay. You have a variable cost. You have a hourly support contract. You have an external agency helping you with implementation and online support. You have other software deployed, like tag management (all in vogue now and you know what, it costs money!) and specialized PPC tools and email or other software.

It also includes the important bits, ones we often overlook when creating an analytics strategy: The cost of people inside your company whose primary analytics job is implementation/IT (tagging, retagging, etc.), people whose primary job (greater than 70%) it to provide data but without insights or recommendations ("reporting squirrels," a necessary expense in any large company) and finally people whose primary job (greater than 70%) is to do analysis (and hence not data puke but provide insights and recommendations only).

And the $50,000 for IT resource and $25 for an analysis resource is just a joke I desperately hope is not true in your company (big or small, remember the 10/90 rule for incredible digital analytics success).

Not every single one of these rows will apply to your business. Say you use Yahoo! Web Analytics, the first two rows disappear, the third might not apply, but the rest might. Say you are a medium-sized company using WebTrends or Omniture, the first two rows might not be 100k/50k rather be 1,000k/350k. If you are a larger-sized company, well, you know the drill. If you are a large company you might have an army of consultants, if you are a small business that might be the free time you are getting from your cousin Ali.

So adapt the model, type in your actual costs. Calculate your digital analytics total cost of ownership. It will be revealing. I promise.

Then comes the magical part. What does your company get for all this investment?

The structure is simple, you identify the change you drove and then identify bottom-line impact of the aforementioned change after implementation of your data-influenced recommendation.

Here's what the various bits of impact look like the ROA computation model you've downloaded…

incremental annualized analytics impact

There are literally n number of things you could be driving inside your company.

In the model there are three clusters: 1. Media Optimizations 2. Content / Website Optimizations 3. Product / Company Optimization.

In each case, as you'll note above, there are examples of the type of activity that data might have informed and an example of the incremental impact.

By the way, incremental means incremental. The analytics team found an insight via their data analysis (at this moment you'll really, really regret if the primary function of your analytics practice is to data puke), that insight bundled with a specific recommendation for action was communicated effectively to the senior management, they in turn ensured it was implemented, and revenue went up.

At this point let me say something immensely important. We (Analysts) are NOT trying to claim credit for the entire uplift. We found the insight in the data and recommended an action, but many people are involved from that point on. Your marketing team went and got it implemented. Your copywriter created new copy. Your designer created new graphics. And so on and so forth. We are not trying to say here that we were singularly responsible for the incremental revenue.

We are just trying to say that that incremental revenue came from an insight produced by data analysis. So we are trying to give credit to the data. We are NOT trying to steal credit or undermine the team effort it takes to get things done in every company.

I sincerely hope that this section of the model serves as an inspiration of sorts for the vast net that data can cast in terms of driving change.

You'll see reduction of checkout abandonment rates from quantitative analysis, you'll see impact from improving task completion rate from qualitative analysis (which might drive offline conversions), you'll see impact from technical improvements, you'll see impact on the company's long-term value by improving brand perception or social media presence.

Let your mind roam wild. Look in every nook and cranny. And if your analytics practice is not focused on everything listed in this section (why not?), there is a lot of upside for you!

At least at the moment, not all the rows will apply to your business. That is ok. Fill out the ones that do. Improve over time.

Right now you are surely wondering: "Wait, what about that incremental bit? You ran over that pretty fast. That is hard stuff! "

: )

No. Did not forget that!

First, identifying incrementality is an incredibly difficult challenge. While getting perfect answers is nothing short of a life time effort, getting a good enough answer does not have to be very difficult.

So why not start there?

In the model you'll be delighted to discover a number of examples of how to compute incrementality. For example here's a screenshot of identifying incremental impact from your email marketing program.

incremental impact email analytics

The first thing you'll notice is that you can do this exercise in layers.

You can start with something simple. Let's say the analytics team does analysis of current email marketing metrics and identifies improvements to how your company structures the emails that go out. The recommendations are implemented and that drives an additional 100k clicks from the email campaigns. Assuming that nothing else was changed, it is now easy to measure the incremental impact of these changes.

Or maybe nothing was changed in the campaigns, but conversion rate was improved from 2% to 5% by changing the checkout conversion process for email campaigns. Well, it is easy to calculate that impact.

Or maybe you have an advanced analytics team with lots of senior management support and are able to improve the email copy and calls to action, the checkout process and do much better cross-sells and upsells and improve average order value. Well, that third cluster shows you how your computation might look.

Is it a perfect approach? Almost. Does it get you going in the right direction? Emphatically, yes!

As Voltaire put it: "Le mieux est l'ennemi du bien." (The best is the enemy of the good.)

There are other examples in the spreadsheet that should serve as guidance/inspiration for approaches you can take when you compute incrementality of the impact you deliver via your analytics practice.

Here's the section on computing value delivered by your investment in software to do multi-channel attribution modeling and the person you hired specifically to do that work…

attribution modeling analysis incremental impact

From an impact computation perspective you can see how brutally simple the process is. Either you delivered revenue increase, or you did not.

Multi-channel attribution modeling is not easy. It has an astounding track record of failure. Identifying which model to use to attribute credit for a single conversion across multiple media channels is immensely difficult. Yet calculating whether it improved the bottom-line, whether it delivered positive ROA, is simple. You fill out the blue cells. You look at the row called Incremental Revenue. If there is something there, your digital analytics investment is worth it. If you have nothing there … well, you know … let's figure out how to say data is always worth investing in. :)

There are a few more examples I wanted to insert to really make this concrete. We cover how to compute incrementality from improving conversions, but also how to do that for the micro conversions and capture the impact of the long term impact on the business by tracking micro conversions.

Here's an excerpted version of that section…

incrementality from conversions

Excited? I hope so. I was giddy as a teenage school girl just creating these for the model!

There is also a tab to help you identify the incrementality from landing page optimization, and from improvements you make to the cart and checkout process. (You know my obsession with both, see best digital marketing experiences post.)

And we can't do anything related to data driven improvements without helping you compute the incrementality from insights we identify for our Paid Search campaigns.

I'll let you be delighted about both those tabs when you look at the model, and not spoil your surprise by posting images here.

The model contains one last present for you. Checkout the tab titled General Impact Analysis.

general impact analysis

If you are new to the field you are perhaps wondering what kinds of actions you could be taking for each focus area (PPC, Email, Display, etc.). You'll find that in this tab. Column B provides description and examples of the types of outcomes you might drive in each initiative, Column D sheds light on the implementation difficulty of various types of analyses, Column E helps you understand the difficulty you'll face when computing incremental return and finally a reality check under the column titled validity of incremental return .

You are now all set to go!

Here's the link again: Download: Return on Analytics Calculation Model.

Closing Thought #1: "I ain't got no incrementality!!"

It is entirely possible that at the end of looking at all the tabs in the spreadsheet you have nothing to type into the ROA computation model proposed by Jesse. A likely reason for that is that you were unable to identify any action taken as a result of your analytics practice.

There might be a simple causal factor for that. Your analytics practice is focused on DC and DR. And it turns out that you need to obsessively focus on DA for your analytics practice to have an impact on the company's bottom-line.

DC, DR & DA are three key components of any analytics practice. Data capture, data reporting, and data analysis.

I discussed this framework extensively in a recent blog post: Web Analytics Consulting: A Simple Framework For Smarter Decisions.

web analytics consulting framework dimensional summary[1]

As you'll note in the DC, DR, CA framework post, most analytics efforts (especially web analytics), consulting or in-house, are focused on collecting ever more data and in figuring out how to puke an ever-increasing amount of it in the form of standard reports via as much automation as possible. Sadly this rarely leads to the recipients gleaning any insights. Which in turn ensures that the organization is data-rich, but action-poor. Which, heartbreakingly, does result in zero actual impact on the company's bottom-line.

Hence your inability to type anything into the column titled Incremental Revenue/Impact.

So if you don't have anything to type into the various tabs in the spreadsheet I encourage you to read the DC, DR, DA post for specific guidance on what is contained in each area and how to ensure you have a better balance (egregiously focused on DA) for your analytics practice.

More investment in analytics (and your salary) will come from an ability to clearly demonstrate impact on the bottom line; otherwise, we will remain third-class citizens of the business world. The model outlined in the spreadsheet could possibly be a diagnostic tool in helping identify problems with your analytics practice (big data or small data) and figure out how to create a practice that is focused on ensuring incremental impact.

Closing Thought #2: Inspiration wrapped inside an exhortation!

You'll fail to attract investment in analytics inside your company (and a higher salary for yourself) if you are unable to show an impact on the company's bottom-line. You'll fail to show an impact on the company's bottom-line if you don't recommend actions your executives should take. You'll fail to recommend actions without an obsession on analysis of data. And yes, you'll fail to analyze data without collecting it.

If your analytics practice is not producing any actionable insights (hence no ROA) then it might be because the analytics practice is not focused on what's important to the business (advice: Biggest Mistake Web Analysts Make… And How To Avoid It!), or focused on reporting and not analysis (advice: Difference Between Reporting And Analysis), or perhaps needs a crash course in how to do better analysis (advice: Beginner's Guide To Web Data Analysis), or perhaps just needs to extract more value from the tool you have (advice: Google Analytics Tips: 10 Data Analysis Strategies That Pay Off Big! ). Identify and fix the problem. Promise me you are not going to settle for a lower salary and a boring job!

I wish you all the very best.

Before we go, my deepest thanks to Jesse Nichols for contributing to this post and inspiring a discussion that has been a long time coming.

As always, it is your turn now.

Does your company compute the incremental impact of its big data, digital analytics efforts? Is there a part of your effort that you are able to identify incremental impact for most easily? What are the biggest challenges you've faced to justify return on analytics? The model is centered on ecommerce/digital type businesses, what unique challenges do you face as a non-ecommerce/non-primarily-digital business? Do you have suggestions for improvements to Jesse's ROA formula? What are some salient hidden dangers we might be overlooking?

Please share via comments.

PS: An Ask from You: I feel that the model could use more tabs of incremental computation guidance. Can you help me create more tabs for various online or offline marketing initiatives powered by analytics? If yes, could you please create additions and email them to me? I'll be immensely grateful, and I'll add it as a tab to the model in this post (and of course credit it to you in the model, with a link to your blog / twitter profile / company). Please consider helping the community.

Thank you.


  1. 1

    I have a suggestion for consultants and analysts: Don't charge by the hour, charge a commision that's based on the delta of improvements. either both sides win big or you go home empty having to improve your skills.

    In my experience, the real issue is not the funding for analytics, but seeing action being taken on the insights the analysts delivers!

    • 2

      Tim: I think we are on the same wavelength. We need to show that we can drive action (and action that drives results).

      I should mention though that psychologically clients are not too keen on "a percent of upside" contracts. There are many reasons like their current contract practices (they are structured for fixed price contracts), but there is also a hint of "omg these people are going to rob us with a 10% of upside contract!"

      They don't see that the consultant/agency will bring 90% extra. :)

      So it is challenging but we should still try to show that we can work for improvements and not just for flat-not-tied-to-results rates.


      • 3
        Ian Brodie says

        Just to add my 2c of experience to the payment on results debate, one of the big problems with this approach is the it doesn't incentivise the client to take action.

        If they pay a hefty fixed fee for the Analytics, they're motivated to get a return on that investment by making the recommended improvements.

        With a payment only on results approach it's far too easy to sit back, fold your arms and expect some magic to happen, comfortable that there's no downside if it doesn't. "Hey, those Analytics guys didn't do much did they? But at least we didn't have to pay them anything".

        If the client is unsure and unmotivated by Analytics then payment by results is an easier sell. But if they're unsure and unmotivated a project is unlikely to succeed. Best to get 'em bought in and motivated first.


        • 4

          Ian: Thank you for adding this caution to the debate. Perhaps fixed fee is the way to go in some scenarios.

          I have to admit I disagree with you.

          Even in fixed fee contracts there is no money paid upfront. As a consultant you have to do the work to earn out those hourly billable hours. Then you get paid. And in fixed up front contracts all kinds of things is agreed up front, if you deviate from that (you go to optimize the site, turns out the key problem is marketing) you can't do anything because it is not in the contract, and they won't pay you more. And you sill have to calculate some ROA at the end of the contract of 1. Your contract won't be renewed or 2. Your contract will still be at $20 per hour (I kid of course) rather than $200 per hour.

          On upside contracts (results based) the client knows that you have skin in the game (or you don't get paid) and you know that you have to get stuff actioned – implementing the most complicated SiteCatalyst / GA tag in the universe won't do. I love the fact that above all else this holds Web Analytics accountable. Data does not directly impact bottom-line, you are out.

          The problem of how to get the client to implement your recommendations in upside contracts is still an issue. It does not go away. But you'll work harder on it, rather than in fixed fee contracts, because otherwise you and I won't have food to eat. :)

          Please know that I'm fully aware that neither option is a panacea.


      • 5

        Hello Avinash! Greetings from Canada!

        It was so nice to connect with you a few months ago via teleconference (remember how we both have the pleasure of knowing Liz in London ON), a great discussion on measuring ROI and progressive leadership in Canada.

        Okay, I have to say that I love your post on how to package the ROA and I am still digesting all of this great information. Proposing a % of increased revenue scenario would mean that the client would need to be open to your recommendations otherwise you may never see any lift. :) Do you have any insights on the client's willingness to be open to recommendations and change since the old way of doing things may not be working? :)

        Some helpful hints that I will be sharing with my web business community – helps our collective cause too!

        Thank you!

        • 6

          Kim: I don't think we have a choice. Without an ability to compute ROA we simply can't make money – not the client, not the consultant, not the media platforms.

          So…. it is not easy. But we have to do it. If there is a lot of difficulty then we give the client the framework and let them keep the numbers secret from us and do the work themselves. If it is a little difficult then we an help them get there.

          And if we tie to ROA then slowly over them I'm convinced the client will listen to our recommendations because unlike all other platforms/people we will tie what we recommend to the one thing the client cares about: Money. :)


  2. 7

    Great post Avinash!

    I actually have a client that asked me how can he calculate the RO-Analytics-I.

    I will tell him to read the post so he can tell his HIPPO's to pay me more $$$s


  3. 8

    Hi Avinash!

    Thanks for another great post! I have one question with regards to the first case study formula that you applied.

    Shouldn't the value of I be $60,000 considering that you multiplied by 12 months, instead of 6?

    • 9

      In the example, the improvements were over the course of 6 months – hence a '$30,000 investment'.

      • 10

        Thanks for the clarification, Jesse and Avinash.

        I generally agree that the cost to implementing solutions may not be stretched to the whole year, while the company reaps the results to beyond that. However, it may be difficult to argue against the thinking of the company that is hiring full time staff, whose wages are a fixed overhead throughout the accounting period.

        In any case, analytics work involves on-going engagements between the relevant stakeholders and there are always issues to deal with, new developments/trends, goals redefined, compromises made, etc.

        I guess the point I am trying to make is, rarely is our involvement a one-off thing and to say that we are only involved for 6 months to a one-year (or longer) project may not reflect the true nature of our work, but worse, may undermine the need to justify a need to have a full-time permanent analysis team as compared to a contracted one.

        • 11

          Azam: From a macro perspective there is no hard and fast rule for the time period. It will sometimes be dictated by the company culture, or by the duration of a consultant's contract, or by the length of a particular initiative (say doing SEO).

          If in your company annualized returns are the norm, doing that for ROA is perfectly ok.

          Ideally in-house driven or external consulting driven analytics programs will compute and provide ROA on a quarterly basis. Sometimes that might be hard as some projects take a while to yield results, that is ok. But if we shoot for quarterly ROA computation we can keep our value in the eyes of the Sr. Management.


          • 12

            Yes, I agree that it is better to do quarterly ROA computations once a baseline has been achieved and the points of references are established and agreed upon by the management.

            Thanks once again for clarifying. :-)

      • 13

        Your example assumes 6 mths of analytic investment that drives and incremental $60K per month. If you extend the example to estimate 12 months of benefit, then you must assume 121 months of cost (not 6 as you state in your response to Azam's question), so the denominator should be $60K, not $30K.

        If you are assuming that you can stop analytic investment at 6 months and continue to reap the analytic benefit for an additional 6 months. While there would be some carry over, should you stop analytic investment, but continue to apply the results from the 6 months of analysis, your efforts would have diminishing returns, resulting in an over statement in ROI (I've seen ROI reduce to zero, in a 6 mth time period, in a scenario like this latter one).

  4. 14

    Another great post, Avinash.

    I'm a little surprised there's much need for a ROA. I would imagine it's more likely that leadership would embrace whomever provided the winning insights. One or two amazing ideas, and you become the golden goose. Doesn't it work that way for some?

    I would assume that you're at the point where clients aren't questioning your value.

    • 15

      Josh: Ahh… the first opportunity to disagree with you! I'm thrilled. :)

      Ideas don't matter if they are not actioned (HUGE problem in the analytics world). Actions don't matter if they are not measured.

      Our efforts can be carried forward for a small distance based on providing what one person (or a few) perceive to be "winning insights." But if we are to matter structurally, regardless of who is looking at the analytics practice, you must quantify. Hence the need for ROA.

      Imagine how much more Content Scientists could charge, or how much easier it would be to win big contracts, if Content Scientists went into the consideration phase not with a portfolio of client names but with one simple table: Client name in rows, ROA number in a column next to it.



      • 16

        How short-sighted of me! Here I'm doing a victory dance when the real work has just begun.

        Thanks for grounding me in reality.

        I'll start working on that spreadsheet now. Speak softly and carry an ROA spreadsheet, right?

        • 17

          Josh –

          I agree with Avinash – ROA is very necessary in some cases when the person you are selling on the value of analytics doesn't completely understand what they are getting. More often than not the person making budgetary decisions is several layers removed from the analyst who made the company more money.

          In addition, analysts are notorious shoemakers children and spend much more time and thought ingrained in solving business problems over their own self promotion.

          This is why ROA is a great concept to grasp and discuss. It's also why I might just have to liberally borrow from Avinash's ROA scores idea above.

          Great post!

  5. 18

    I rarely leave a comment but it's well deserved – great read and some excellent info.

    Will def check back.

    Charl Kruger ~ Social Analyst

  6. 19
    Jean-Marie Camiade says

    Hello, Avinash

    First of all, be sure i really appreciate and respect what you do, in general.

    But in this case, as Voltaire said : "I do not agree with what you say but I will defend to the death for your right to say it".

    Unfortunately, I can't share with you at the moment, because I fly to Bangkok in a few hours. But I'll be glad to share with you when back (end of March).

    So, it's not useful to edit this message for the moment, but I really hope you will accept to share with me about what is ROI, and what is an analyst, and what is a systemic approach, etc.

    As Avinash Kaushik said "context is critical", and my ad should be "Words have meaning"

    Please contact me in private if you're interested in a deep discussion about fundamentals.

    Thanks a lot

  7. 20
    Palanivel Raja says

    Really nice formula!

    Thanks for this useful information.

  8. 21

    It might sounds strange for many, but I also use my personal accountant to help me with ROI calculations on every project.

    Sometimes not everything boils down to Internet.

    • 22
      Hyoun Park says

      Not strange at all. It's actually one of the most common types of projects we do at Nucleus, which is what led to our "Analytics provides $10.66 for every dollar spent" research document. ROI is always a personalized effort at the end of the day and it's important to compartmentalize projects into discrete inputs and outputs based on how your company actually works.

      One of the areas that I'd be cautious about is in a strict attribution of return to specific marketing tactics in a marketing mix. Often, the combination and order of marketing tactics is just as important than the pure multi-channel outreach portfolio.

  9. 23

    Dear Avinash,

    Thank you for this post and the sheet. As always – very insightful.

    I have a question regarding the ROI calculations though: why use sales and not gross margin?
    In the example above: say sales increases from $120.000 to $180.000 but the additional variable costs (unit costs + shipping & handling costs) are $40.0000. Then the ROI should be ($180.000 -$40.000 -$120.000)*12 / $30.000 = 800%.

    Am I missing something?

    Thanks and regards,

    • 24

      Frank: Asking for a focus on measuring impact on Gross Margin would have been me channeling Marie Antoinette and asking the starving Analysts/Consultant to eat cake.


      We are so far away from even the most basic of computations of our big data/analytics impact that asking for a tie to gross income would be cruel. I think.

      So we start with Revenue. The foundations we lay will allow us to progress to profitability measures.

      I absolutely agree with your emphasis, it is simply a matter of what to try first.

      PS: I do passionately recommend a focus on Net Income, I even have model I recommend for ensuring every single thing we do is tied to it: Win With Web Metrics: Ensure A Clear Line Of Sight To Net Income!

  10. 25

    Hi Avinash,

    Thnx for sharing your ideas. As you indicated . . . "The model is centered on ecommerce/digital type businesses". And you ask "what unique challenges do you face as a non-ecommerce/non-primarily-digital business?"

    Why not compare non-ecommerce sites with advertising campaigns? For an ad campaign the ROI is also dificult (or impossible) to quantify. There are some general indicators like reach, recognition and so on. Compare this with traffic on the website, conversion of #pages visited or lenght of visit.

    Somtimes I mention clients / prospects the following example; if they invests e.g. $ 1.000 in advertising, in fact they say that they hope that e.g. 2000 people will see or remember their ad. So the value of every mental contact is $ 0,50. You'll understand the parallel with websites.

    But to be honest, the comparison with advertising is not always the "killer" argument for Hippo's. On macro level they want to hear solutions for business problems. And too often we can only offer micro conversions and little insights.

    In practice as a consultancy sometimes we struggle to let clients think in terms of ROI. What makes impact than is to mention (or ask for) the costs of a website (out of pocket and internal costs). And ask the Hippo what he/she thinks the ROI will be. In most cases he/she doesn't know. As a result one category of Hippo's will reduce costs. The smart ones want to know more about it.

    And of course "do your website have goals?" is also a killer question. Hopefully the result is that you can start to define goals and measure micro conversions, carry out actions etc. In the end that will lead to estimations of the ROI.

    A killer question is also "do you have the right visitors"? And what are these "right" visitors worth and if they visit your site what do you want they do? And if they do that, what is it worth? For sure . . . Hippo's don't know but the smart ones will become your friend (and hopefully become your customer :))

    Back to the subject of ROI for non-ecommerce businesses . . .

    I think the value of non-ecommerce sites is the sum of the values of goals and micro conversions. For realistic goals / values base them on traditional offline marketing campaigns (maybe as modern online marketeers we can learn from ad agencies).
    A macro indicator could be the site value as Google Analytics can report. Here the absolute value ain't important but the trend. And if e.g. the site value increases with 5% you might count an addtional "revenue" of $ 10.000.

    So, ROI for non-ecommerce is puzzling what the best (micro and macro) indicators are.

    • 26

      Sander: I'm immensely appreciative for the time you invested in the comment, it has some great ideas for the blog's readers. Thank you.

      I concur with you that identifying some macro and micro outcomes is key for non-ecommerce sites.

      If they want people to come and read five articles, we can create a goal for that. If the client wants videos to be watched for 12 minutes, we can track that. If the client wants people to download the b2b white paper, we can track that. If the client wants the site to deliver people to a protest, we can track signups. If the client wants to track loyalty, we can create a goal for Visitor Loyalty. Of the client wants social engagement, we can track conversation rate and amplification rate.

      Just some ideas. But no matter what the site we have to get these things identified. Then will come the challenge of identifying economic value (ideas on how to here: http://goo.gl/XddFv). Not easy, but completely doable!

      And then we can show ROA! :)


  11. 27

    Welcome to the blog Jesse! I really enjoy your contributions and thought leadership in this area. You hit the nail on the head with an analysts need to prove their own value, not just provide analytical value to the company.

    Tons of great information in this post that I plan to share with several people in the company of all job titles and experience levels.

    I'll also see what I can contribute to the spreadsheet.

  12. 29
    Anita M says

    Hi Avinash,

    Great post and the "General Impact Analysis" tab was truly awesome!! very nicely summarizes the scope and issues with ROA. Have a couple of follow-up questions –

    How does one go about crediting the incremental gains to "analytics" given that there are so many other non-analytical efforts that impact any change – how does one come up with the x impact? Also doesn't complexity increase with multiple initiatives impacting each other at the same time and external factors fluctuating demand (example competitors delivering faster and better experiences/products or new/better technology changing how digital consumption occurs)

    Also how easily can this approach be used for mobile analytics initiatives?

    • 30

      Anita: My macro guidance is that you do the best you an. It is complicated, but don't let that stop you from doing anything.

      On a more specific level… The tabs do say that you should frame these as the impact from data, and not impact from the Analytics team. That is a very good way to avoid the "oh you are hogging all my credit when I pressed the Go button in AdWords!"

      If your world is truly complicated, you can use the ideas of Controlled Experiments to tease out individual impacts. Harder, but doable (and necessary in very large companies).

      I see no difference in terms of computing ROA based on platforms. Mobile is just a platform. The measurement is a little tricky (see my post on attribution analysis), but again doable with a small amount of effort (and the basic understanding of impact is very very easy, it is the full value of mobile that is harder at the moment).


  13. 31
    Ned Kumar says

    Hi Avinash,
    This is a fabulous post (and thanks Jesse to you too). I cannot agree more with the need to calculate the ROA and the spreadsheet was a really cool value-add. As much as I found the details very informative, I think the real value of this post is in the guidance it provides the reader on how one can start quantifying the returns from investment in analytics (every organizational context is unique but the post+spreadsheet is a great idea generator).

    I also like to think of the return on investment in digital analytics (data, systems, people) as a function of multiple factors. And since we are in the formula mode, I would state it as:

    Analysis= This is the return from actual analysis done by the digital analytics group and is exactly about incrementals from email, paid search, attribution, etc. This also includes incremental from new powerful content (e.g a new video embed) based on analytical insights.

    Culture= The is the return from all the work done by the digital analytics team to change the mental model and mindset of people in the organization towards analytics. This includes training, education etc. Of course the return would be calculated using a proxy from how many people you touched and how they are using the new learning on their work.

    Process=There is a return that comes from implementing things like standards and governance. How efficient has your campaign process become as a result of standardizing the tracking codes for various channels? How many 'new' efforts are you now able to track because of the better process.

    I do understand that Culture and Process is not analytics but I find many digital analytics team spending a lot of time trying to change both and figured there needs to be some measurement of return from those efforts.

  14. 32
    Tejash D Mehta says

    I have been avid reader of your thoughts (blogs are ultimately your thoughts) since i started my google analytics journey in 2011.

    I want to know one thing , why your posts initial tab image is always a flower ? this comment is on lighter part of the heavy duty information provided in this article. :)

    Cheers to you as always for making us rethink about what are we doing about web analytics.

    • 33

      Tejas: I love taking macro pictures (a gallery is here: http://goo.gl/1hhkk) and so that picture at the start is to share a little bit of my personal passion with the readers of my blog. They are all pictures taken by me.

      It is also a personal reminder to myself to look at the topics really close, from a unique angle, and create something beautiful. :)


  15. 34

    For over a year now I've been a true fan of your blog. I work at P&G and I heard that last week some of my General Managers went to a session with you… I couldn't believe it! I really wish I had been there, but the mortals like me only get to read your blog :)

    I heard awesome comments about the session, I like it that you made a strong point and bullied them with some brands… happy to hear that.

  16. 35

    First of all, let me tell you that was a great post, a great initiative to start people thinking about return on investment. And your spreadsheet is great, especially the part about the types of analysis that can be done: this can become a massive knowledge base if we keep growing it.

    Second, I am sorry but I wrote a long comment :). Hope that at least you can get two or three ideas that are useful.

    I think ROA can solve for two problems:

    1) help you sell analytics (both as an external consultant, or internally)

    2) help you evaluate your analytics practices

    In my opinion, it can be a great tool for both aspects when the company is not very mature in the use of analytics, but maybe needs to be adapted a little bit as you approach more mature companies.

    Let's look at the selling part.

    You can sell analytics to companies who've barely invested in it before, and who do not really know why they should. They look at some numbers, they feel alright, but not too much beyond the typical dashboard. For those companies, showing ROA can be a great way to open their eyes. That is, of course, provided they are smart enough to understand some numbers. If not, it may be counter-productive, since there's nothing worse than trying to sell something your customer does not understand. Finding the right balance between being too simplistic or too sophisticated, is not always easy!

    Then, there are companies who've invested, but did not really get a positive return. This is the typical case of the Omniture client, they paid a lot, they were promised the moon and they did not even manage to measure what they wanted to know. I find more and more of these clients. I love them. They are a challenge. They prove that no matter how much you spend, you can do a terrible job with analytics. With these clients, you need to be careful. Rather than speak about the ROA they will get, you need to convince them that getting ROA is possible. You can't assume they'll believe your numbers, they've been there before and were not successful. Why should it be different with you? So you have to prove that, done the right way, you can get ROA. It's more about the methdology you will use, the people that will help them, the technologies you will use,…

    Finally, there are the advanced companies. Those that picked the low hanging fruits a long time ago, have done a lot of testing, even some predictive analytics,… Usually, these companies follow a decentralised model. Analytics is part of every department, it's actually the way employees think about the business, even the world. It's a way of thinking. And can you measure the return of a way of thinking? It's not easy. The advanced client already knows that ROA is positive. With them it's more about the incrementals you can bring by doing more advanced analysis, or by training their employees further, or by setting up a methodology to be able to launch 50 tests a month rather than 5. Complicated problem! :)

    With these clients, I usually take more of an empathy approach to selling: we both love analytics, so let's rock even more, let's have a lot of fun trying to push it further, no matter what the ROA is in the end :) :) Of course we'll measure the impact, but we'll take more of a holistic view. "We were making this much, now we're making 20% more, and probably it's because we applied new analytics and matured as a whole company/team". "We did not know about this testing method, now all these employees can work on it and this will help us move on". etc.

    Let's look at evaluation.

    For a company that's starting, you will usually have them work with a consultant, or an analyst. In other words, analytics will be centralised in one or two people. That makes it easier to calculate the costs, as well as the returns. Hence, ROA is a great way to evaluate analytics for companies that follow a centralised approach to analytics. It's easy to isolate analytics from the rest.

    For more mature companies, you will have several people using analytics, and as I said before, it will be almost a way of thinking. For those companies, you can still evaluate with ROA, but maybe you need to adapt your spreadsheet. In other words, if the adwords team already works following an analytics methdology, can you separate what's return from analytics and what's return from Adwords? Furthermore, does it make sense? Not really. What will make sense here will be the return of improving even more the way analytics is carried on, for instance with additional technology, or additional testing, etc.

    Also, for companies to become mature, you might need to invest a lot in training, methodology, culture,… and it will be hard to calculate the results in the short run. More than ROA, you need to be very convincing, have the client understand that thinking with numbers (as well as with experience and talent), is the right way to go strategically.

    To make it short :)

    In summary, when it comes to using ROA to evaluate analytics, we probably need to tie ROA to the maturity of the company. For different maturity levels, we might want to calculate ROA in a different way. Take different things into consideration, or give more importance to particular aspects.

    And when it comes to using ROA in a sales pitch, we need to see beforehand if it's appropriate for the maturity of the company, as well as the maturity of the person we need to sell to. I would consider it another selling tool, and as a good consultant, you need to know whether it's appropriate to use it

    Thanks so much for starting this conversation. Even by just talking about it, I feel we have improved the understanding of analytics in our company, and the different aspects it helps improve.

    • 36

      Pere: You are so generous, thank you for this insightful commentary. Clearly Jesse and I should have worked with you so that this could have been a "three amigos" post. :)

      I really like the layers approach you have taken here. I agree with you on both the first two parts and then the three types of companies/culture we have to deal with.

      The advanced one is the hardest. They are spending money, but often I find that even if they are really data and process driven, the people at the top still don't quite get it. But how to get ROA across a complex set of efforts. That is hard.

      In some of these cases I've taken the approach to make "periodic heroes." Where we don't prove the overall macro value, but every couple of months we publicize one effort where were are able to clearly show data driven value. We do this often enough and the people at the top get the point we are adding real and material value – even if we don't show them the overall ROA because of the inherent complexity.

      We definitely have to adapt our approach to the company.


    • 38
      Jesse Nichols says

      Hi Pere,

      Fantastic response, and a very concise summary of exactly what I was hoping this concept could become – both a selling tool and an evaluation methodology.

      I completely agree with your points on the complexity and appropriateness of this equation depending on the company's situation. For me, it was more important to just get this concept out there for debate rather than grapple with the many subtleties (perhaps more fit for a book someday!) but they're excellent things to be aware of.

      What I was really hoping is not that this calculation would be performed regularly and for all-time, but that it would start a siphon of resources to get a coherent, advanced, digital analytics practice going at a company. As you pointed out, once the data is integrated into everyone's workflow, and analytics is just a function of everyone's job, then there is no question of its value.

      Great feedback!


      • 39

        Thanks Jesse, glad you found it useful!

        A book focused on creating successful analytics working cultures and the appropriate techniques and methodologies is much needed, so up for it anytime! :)

  17. 40

    Hi Avinash,

    Now that we are in the age of Analytics 2.0 how can a small business owner build a dashboard with actionable insights using GA.

    Using the latest techniques like Attribution, Optimization and Allocation as seen in a recent article in Harvard Business Review March 2013. The article in question was titled " Advertising Analytics 2.0" written by Wes Nichols (MarketShare).

    I have seen very little information other than your book on removing the noise from our data sets and getting to the real actionable insights.

    • 41

      Brian: Let me upack the two different elements in your comment.

      It is important to know that dashboards rarely, if ever, deliver insights. Done badly, they are massive data pukes. Done well, they are focused data pukes that focus your attention on a couple things. Then you take that and analyze (both data and ask questions about the business happenings) to find insights.

      That said, here's a post from my friend Justin Cutroni that has a few dashboards you can download and start playing with. http://goo.gl/uGbpA For a macro perspective here's my post on five rules for high impact dashboards and here's one on the action dashboard, it is not the GA type but emphasizes delivery of insights.

      With regards to attribution… it is important to use the type of features that are available in Multi Channel Funnels reports in Google Analytics. The attribution modeling tool in GA (now free to everyone) then allows you to play with different scenarios to come up with the optimal budget allocation.


  18. 42


    In your example, you multiply by 12 to get a full year, but you divide by 30,000, which is only one month's costs. It seems this would be true if the yearly budget was 30 grand, but not monthly.


    • 43

      James: This is a little confusing, I tried to clarify it in the post by adding some text earlier.

      It is six months of effort, twelve months of success (/gain). Hence the use of the 30k (six months of investment).

      I hope this makes sense.


  19. 45

    I read this article and found that this quote stood out most to me, it definitely has had an effect on my own perspectives,: "You look at the row called Incremental Revenue. If there is something there, your digital analytics investment is worth it."

    Our SEO clients keep a close eye on their ROI through website inquiries, conversions and traffic.

    Thanks :)

  20. 46


    Thanks for sharing this, I can see myself using this.

    I will make the following tweaks:

    Referring to 120k-180k example and the example in the model, I would call this "Revenue Opportunity from Analytics" (still ROA!) instead of "Return on…".

    This is because, at this stage it is revenue change and not bottom-line change that is being measured.

    I would then apply a profitability factor to compute net profit. I will then use net profit for Return on Investment.

    Thanks again for sharing this extremely useful and very well explained information.

    • 47

      PS: I concur with you that there is a difference between revenue and profits.

      In this case, actually for most of marketing, advertising, product releases etc, our first attempt is to measure revenue as a return on investment. Hence in this case I was comfortable with Return on Analytics.

      As we get good at this, I believe that we should indeed try to take the next step and understand profitability.

      Thank you,


      • 48

        Hi Avinash,

        This was a great article to solve ROA. Although I have a slightly different problem.

        Lately during penguin update my website SEO ranking was hit which I think was due to bad backlinks. Now I am removing bad links and building new good backlinks. I just don't know how should I quantify the benefits of all the efforts I am putting to build new links.

        (After one month of effort and spending 2000$, how will i know that the benefit is 500$ or 5000$)

        • 49

          Sunny: It is a bit difficult in this type of a scenario, but you do know in this case what your traffic looked like prior to the change happening.

          Say in 30 days prior to the change you were at $10,000 from SEO. Now it has gone down to zero (exaggeration). You can wait for the duration required for your good links to start working and then see from impacted date how long it takes you to get back to $10k. You'll know how much you spent during that time period. You can measure ROA.

          Things are rarely this simple, there are always other variables that might impact your computations in the post-change area. But try and isolate as many of those new variables as you can.


          • 50

            Hi Avinash,

            Thanks for your reply but I think I missed one variable in my problem. Now there are 2 teams who are trying to improve the benefits of SEO – Offpage and Onpage. Now how can i quantify that out of total benefits of 10K, X $ revenue is due to Offpage and rest from onpage.

  21. 51
    Stephen Polizzotto says

    What do you think about using A/B multivariate tests to help tell the story of ROA?

    If we look at the current data and come up with experiments to test, we can then measure the impact of our work through revenue/conversion rates.

    Granted this is on a smaller scale, it still helps show the impact that analytics can have on a website.

    • 52

      That's an excellent way to add a control group to the test and clarify the impact of acting on the insights. Great recommendation!

  22. 53

    The ROA calculator on the second sheet of the file doesn't seem to follow the equation you highlighted in your post, it calculates the return as Ra / Ia without taking Rm into consideration.

    Return on Analytics investment as highlighted in sheet: 2,646%
    Return on Analytics investment when using Rm: 704%

    • 54

      Hi Jacob,

      The column in that sheet was labeled "Annualized Incremental Impact". Although I suppose it would have been a more consistent to have that page in the spreadsheet replicate the exact formula, the intention was to have already subtracted Rm from Ra.

      Good catch, we'll consider how to make this more intuitive.

  23. 55
    Ramakrishnan Parameswaran says


    Its a good read and thanks for the post. It helped a lot. However this ROA is more focussed around web analytics where returns are little easily understandable in terms of click views and few other parameters you mentioned.

    Can you please suggest how can we do a similar thing fr big data analytics for an enterprise?

    • 56

      Ramakrishnan: From my perspective the broad parameters of the approach you would take for any analytics project would be very similar to the one shared in this post.

      Some of the specifics shared in the spreadsheet of course would be different, but you can glean insights from the choices we made here.



  1. […]
    Would this work for measuring topic map ROI/ROA?
    What other measurement techniques would you suggest?

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    What is the ROI of Digital Analytics? In the end it is about the website's goals. In most cases it'll be revenue, but you may have virtual price tags for other conversions as well. Here's a direct approach to compare related costs and outcome: https://www.kaushik.net/avinash/calculate-return-on-analytics-investment/

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    Most businesses invest on using Google Analytics to boost their online presence and attract more traffic and customers. However, rarely do they make the effort to actually calculate the impact of using Analytics for their business. Most importantly, they need to be able to find out the return on investment (ROI) for Analytics and whether they should continue using it. You have to learn about the formula you can use for working the ROI on Analytics. Read more at Kaushik.net.

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    Avinash Kaushik írt egy kiváló bejegyzést arról, hogy hogyan tudjuk kiszámítani az Analytics befektetéseink megtérülését (ROI-ját). Minden analitika projektnek van legalább emberi erőforrás költsége, ezért egy bizonyos szint után fontos hogy azt is vizsgáljuk ezekre fordított pénzbeli és humán erőforrások hogyan térülnek meg. https://www.kaushik.net/avinash/calculate-return-on-analytics-investment/

  10. […]
    Still need some convincing before you buckle down and track the numbers? Avinash Kaushik, author of Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity has some great tips on calculating the ROI of time spent on analytics. With the huge availability of data from so many sources (Google, Facebook, email campaigns and now even Pinterest), it’s worth learning how to find the information that will propel your sites to the next level.

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    Excellent Analytics Tip #22: Calculate Return On Analytics Investment!
    Coraz więcej mówi się o zwrocie z inwestycji. Podobnym zainteresowaniem cieszy się analityka internetowa, mimo że wciąż jest często rozumiana jako licznik wizyt i odsłon serwisu. Jak zmierzyć ROI wydatków na analitykę internetową?

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    Dilerim bir gün Türkiye'de de ölçümlemenin geri dönüşünü ölçümlüyor oluruz: https://www.kaushik.net/avinash/calculate-return-on-analytics-investment/

  16. […] Original Blog Posthttps://www.kaushik.net/avinash/calculate-return-on-analytics-investment/ […]

  17. […]
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  18. […]
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  21. […]
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