Three Amazing Web Data Analyses Techniques For Analysis Ninjas

ShiningDay in and day out we stare at standard tables and rows and convert them into smaller or scarier tables and rows and through analysis we try and move the really heavy beast called the "organization" into action.

It is hard.

This blog post has three ideas I've learned from other smart people, ideas that help surprise the "organization" with something non-normal and get it to take action. Each idea is wonderfully simple, and yet in its own sweet way makes us, Analysis Ninjas, think harder and deliver insights better.

Let's go.

Compute Actual Cost Per Acquisition Post-Facto Including Micro-Conversions.

I know that is confusing. Stay with me.

This idea, 100% of it, comes via my friend David Hughes. [He passed away recently and I miss his friendship, and our collaborations, tremendously.] From this post: Improve Search Marketing Conversion Rates through Email Registration. I'm going to redo the tables, just to make them fit the width of this blog.

David's idea is simple and genius.

Today when we measure our Cost Per Acquisition (CPA) for our campaigns (Search, Email, Affiliate, whatever), we just think of the macro-conversion and, perhaps worse, we think only of that session / visit.

Let's assume we are running and we got 1,000 Visitors to come to our site via a display advertising campaign. As dutiful Reporting Folks we will send this table out to reflect performance of that campaign.


$16.7 CPA might sound huge (or not depending on your margin), but on the surface it seems a lot. The flaw in this report of course is in assuming that all 1,000 visits were in play (wanted to convert / buy something). This is rarely the case.

I have repeatedly evangelized identifying all the jobs the site is trying to do (macro AND micro conversions) and then quantifying their economic value to the business. On the Macy's website of the 970 non-converting visitors, some might have signed up for a free account, some for email alerts or coupons, some opened a wedding registry etc.

If some of those 970 Visitors completed some micro-conversions, then shouldn't the CPA be on that basis rather than just the 30 orders above?

Simplifying the scenario a bit. if some of those 970 submitted an email address / signed up for price alerts and converted later then shouldn't the cost-per-acquisition include those future sales?

Say some of the Visitors did just that. What was the acquisition cost of each sign-up?


Nothing. Nice.

What will Macy's do next? Send the 100 folks the price alert they signed up for!

And what will come of it? Sales, of course.

This is a reasonable picture that will emerge.


So we got 30 orders from the original visits, and another 20 by re-targeting users via permission-based email.

What does the CPA of our original affiliate marketing campaign look like now?


A more respectable $10 compared to the original $16.7.

An immediate implication is that if at a CPA of $16.7 you were profitable, then you can communicate to your Senior Leaders that you were actually even more profitable since the final CPA is now only $10. And if you find yourself in a aggressive marketing siutation then you could even increase the bids on your display campaigns to get even more Visitors. Thanks to your clever micro-conversion and re-targeting strategy!


    1. It is important to think in terms of micro-conversions, beyond your main objective. For the 98% of people who won't convert on your site, do you have a way of engaging them again in the future?

    2. It is critical to have a robust re-targeting strategy (as in our case above). Hopefully it will be intelligent, relevant to the customers and non-torturous.

    3. If you do #1 and #2 then be a dear and ensure you compute the "final CPA" of your original campaign (search or email or affiliate or social or whatever).

    4. You can't do the above analysis inside Google Analytics (or even Site Catalyst or the base versions of WebTrends or CoreMetrics). You'll use Excel or a simple database (or possibly the data warehouse versions of Omniture, CoreMetrics, WebTrends).

Some of you might be excitedly yelling "Attribution!" at the screen. For now, just immerse yourself in the simplicity of the analysis above. I won't cover attribution here but if you have Web Analytics 2.0 jump to page 358 for my thoughts. Also remember in this case at least it was deliberate re-targeting of the initial pool of people.

Command Attention, Valuable Action, By Stating Raw Numbers.

This idea comes via Kaiser Fung, from this post: Further thoughts on the Facebook business model.

In a blog post with thoughts about a graph from WebTrends, that shows click-through rates (CTRs) and cost per clicks (CPCs) on Facebook. Kaiser made this simple insight:

"What does a 0.01% CTR mean? Yes, that's 100 clicks per 1 million ads shown to Facebook users."

Let me restate that astonishing number. If your ad shows 1,000,000 times, you get 100 clicks!

And of course that's clicks, not conversions.

It caused my eyes to open wide.

That is astonishingly low.

Somehow when someone tells you "Facebook's ads CTR is 0.01%" you don't quite get it. I mean, it does not feel pathetically minuscule, as it should.

I have championed the contextual use of raw numbers to deliver insights, especially when using Averages, Percentages and Ratios. [See: Actively Avoid Insights: 4 Useful KPI Measurement Techniques]

Yet the 0.01% number did not make the impact on me it should have. And that is exactly the problem when you present conversion rates (also pathetically low on every single website on the planet) and other such metrics.

So make sure you show raw numbers.


The first number might not get your management team to take any action; it just does not evoke any feeling.

The second set of numbers might get someone to scream: WTH!

They might ask:

    1. Are we showing the wrong ads on Facebook?

    2. Are we using any intelligent ad targeting strategy or just randomly showing ads?

    3. If we double our budget to 2,000,000 impressions is there even relevant inventory (desired demographic / users) on Facebook for us?

    4. Would it be worth it?

    5. Why do we suck so much? Is it us? Is it Facebook?

All really great questions — ones that you have to find answers to as a Marketer and an Analysis Ninja. Answers that will help your company improve your Facebook advertising strategy, or quit.


Makes sense? If not please share your thoughts using the comment box below.

Either way, remember that your job is to divert people from becoming lulled into a false sense of everything's okay. Scare them into paying attention and asking you tough questions.

Face Reality By Computing "Convert-able Audience" & "Real Conversion Rates."

This idea comes via Thomas Baekdal, from this post: Converting Traffic to Subscribers.

In it, Thomas postulates that even if you have 1,000,000 Absolute Unique Visitors to the website, that does not mean that your possibly "convert-able" audience is a million.

Some people will visit once and never again. That was not an audience that would have converted, ever. For example, the link above is to Baekdal Plus. I pay $49 per year to access that premium content because it is so good. Many of you may not want to pay for content on the web. So for Thomas, not all the Visitors from the above link are actually in play for conversion. [Though I wish they were.]

So it is imprudent to count those folks; better to only count returning Visitors.

Then, some content attracts traffic, other content actually is "valuable and will convert people into subscribers." Thomas's guidance is to only count the latter in the in play for conversion bucket.

Now you can calculate the "convert-able" audience. In Thomas's example here's how his picture looks:


(1,000,000 less the 63% one-time Visitors) less the 20% valuable traffic = 74,000.

Possible convert-able audience = 74,000.

Real audience you even have a remote chance of converting: 74k.

So small, right? After starting with a million.

I rarely see Web Analysts doing this simple exercise and educating their Senior Leadership of this harsh truth. We assume every single person who will visit is there to convert. Every single person who visits is there to buy. Our conversion rate calculation, Orders/Visits (bad version) or Orders/Visitors (better version), reflects that, sub optimal, mental model.

We show our leaders that we suck more than we actually do by computing conversion on the basis of All Visits (bad version) or All Visitors (better version).

If Thomas has 3,700 conversions in a month, we would normally report that as 0.37% conversion rate. [(3700/1000000)*100]

Of course, the reality is that the conversion rate was 5%. [(3700/74000)*100]

Not that 5% is orgasmically higher. But it is more reflective of the truth than 0.37%.

You would take one set of actions with 5% and a completely different set with 0.37%.

Compute your "convert-able audience." Please.

Use whatever common-sense approaches you can find.

In a post written in Nov 2006, I presented a similar thought (though in a different context than Thomas). [See: Excellent Analytics Tip #8: Measure the Real Conversion Rate & "Opportunity Pie"]

My graphics were a lot less sexy in comparison to Thomas's.


The idea was to get you to identify your "Real Conversion Rate", by identifying your "Opportunity Pie."

My recommendations were:

Throw out everyone who bounced, just for now, and also if you use log files (ohh those were the days!), then throw out "visits" by robots / junk. That gives you a rough idea of your "Opportunity Pie" (convert-able audience).


If you have a qualitative survey deployed (with the three greatest survey questions ever), then throw out the percentage of Visitors who do not state their Primary Purpose as visiting your website to "buy" or "research products and services" (I generously assume we can convince the latter bucket to buy through amazing marketing on the site). So now you know just the people for sure in play and possibly in play.

This second path will also give you a great rough idea of your "Opportunity Pie" (connect-able audience).

My recommendations were different from the ones Thomas is using. But both reach for the same goal: To get you to understand that not every single Visitor will convert, and you should know, even roughly, how many are in play / convert-able.

Perhaps you'll come up with your own rules. You might throw out everyone who was there to check Order Status. Or those that logged into their account to update settings. Or those that only visited the /blog/ directory. Or the Social Media of course they will never every buy but eat our bandwidth daily digging diggers!

As long as they pass the common-sense filter, go for it. You'll be earning your Analysis Ninja chops, and delivering something extremely valuable to your management team (even if they perceive it to be a cold bucket of water on their faces, the first time).


    1. Don't scam your Senior Management by lulling them into believing every Visitor is convert-able.

    2. Ignore the standard Conversion Rate definition in Google Analytics, Omniture, WebTrends, CoreMetrics, whatever else you are using. Focus on People. (Unless your business model is that everyone must convert, and does convert, on every Visit.)

    3. You might get resistance when you first present the "real conversion rate" or "convert-able audience" metrics. Worry not. Charge forward. Good will come.

After the initial shock, your Management team, if they are smart, which I am sure they are, will ask you this: "So what can we do with the majority of the traffic on our website that is not convert-able?"

Preen proud as a peacock; this is your moment of greatness. Tell them why having thoughtful micro-conversions is so important on the site. Tell them you are going to compute the micro-conversion rate for the non convert-able audience. Tell them that with some of the non convert-able audience you'll hence establish a longer term relationship: with some you'll just hope to create delight and make them your recommenders, and with others still you'll do re-targeting and use David's method (all the way up top of this post) to reduce cost-per-acquisition.

All really great business outcomes.

In a nutshell. the goal is not to abandon a majority of your traffic. The goal is not to just ignore all the bouncers (fix that, tips here: Six Tips For Improving High Bounce / Low Conversion Web Pages). The goal is not to be depressing. The goal is to face reality, give it a hug and then figure out how to kick things up several notches.

Are you Ninja enough to accept that challenge?

Of course you are. You read this blog! : )

Know that I'm rooting for you.

Okay, now it's your turn.

Does your company do re-targeting to captured email addresses? If not, why not? If yes, then do you compute real CPA? Have you computed your "convert-able audience?" Is it 100% of your website Visitors? When was the last time you used raw numbers to shove a dose of reality in front of your Senior Leaders? Are there other techniques you've used that worked?

Please share your Analysis Ninja tips with the rest of us Ninjas-in-training using the comment box below.



  1. 1

    Once you have the ability in your tool to track cohorts of people over time, the game changes quite a bit. This requires a robust identity API not available in GA or Omniture, and a dedication on the part of the tool to this kind of cohort-based longitudinal study.

    If you can generate reports of this nature, think of what is possible. Let's take the ever-present "campaign" that is natural search for example. What if you could track a group of users who first learned about your site through natural search during a certain timeframe, and effectively perform a longitudinal study on their buying behavior (and total value) over weeks and months. Here it is likely appropriate to make decisions using a first-touch attribution model: which keywords should we try to rank better for? are some of our natural search keywords candidates for us to run paid search against?

    The problem is that unless you do exactly this, the data gets very muddy. The person who first googled "kevlar sweatsocks" may not buy for weeks and weeks (I'm looking at data that shows this as a matter of fact). And when she returns to buy, she is likely to instead search for "joe's family paramilitary supply outlet" as opposed to the original, unbranded query.

    It's overwhelmingly common to see a pile of opaque, branded search traffic that reveals no insights whatsoever.

    For paid search, this type of report allows the search marketer to say "I spent $X and made back $Y" where $Y is the *actual* money generated by the cohort of people who were acquired by $X dollars, not the bits of revenue that are attributable to those SEM activities during the time range of the report. More rantings here:

    One other great use of this kind of longitudinal cohort study is retention. Are your site optimization efforts actually increasing the stickiness of your site/app/product? If you take slices of people over time (say, new visitors who entered during week 1,2,3,4, etc) and see how long they stay around, or continue to use a certain feature or set of features, you immediately gain insight into the effectiveness of your macro experience improvements.

    In summary, my belief is that 1) choosing the right attribution model for the *decision* you are trying to make, and 2) measuring actual behavior of users through the course of their customer lifecycle together combine to provide a whole new world of actionable data.

  2. 2

    Breaking it down as this micro thing is impressive. I think it will be wise for me to take a better look at my conversion these days..

  3. 3

    Thanks, Avinash, for the tips on how to find the convertable audience. Last month I spent time talking to a senior leader about conversion rates. His metaphor to me was that if only a handful of people that went to a store purchased something, the store would go out of business.

    We then talked about the level of commitment required to drive to a store relative to navigating to a website. Driving to the store requires so much more commitment, so it's likely that the decision to make a purchase happened at some point well before the customer even entered the store.

    Going beyond this metaphor, I can use the approach you outlined in this blog post to arrive at our convertable audience using data and numbery goodness.

    Thanks as always. PS – I got my beta for the new GA. Really cool aside from losing my custom reports!

  4. 4

    Hi Avinash,

    Good thinking, simple language, fine ideas. My comment to your post.

    A little secret for you folks there!

    Concerning the part: 'Command Attention, Valuable Action, By Stating Raw Numbers' I've developed a new advertising technique my company now is using regularly.

    It's not bad to focus on low CTR ads on FB (Facebook) for a new product, brand or company. If the CTR is 0.01 or lower, it's 1 click per 10000 views. The FB CPC is in Croatia about 0,10-0,25€ so I can assure my client that he is paying very little for a huge number of impressions (0,25€/10000impressions).

    But the point is not in low FB ads CTR. It's later, in the Google AdWords campaign. In Google AdWords we focused on high CTR and high conversion rate keywords and ads and achieved a 3-15% better ROI.

    So, cheap ads with low CTR in FB, can help you achieve better ROI in Google AdWords. I have tested this in more than 120 campaigns. It even worked for old and existing campaigns in Google.

    It looks like the great number of cheap impressions in FB is helping in the AIDA purchase process (Awareness, Interest, Desire, Action). I would like to compare my results with yours. Is there anybody willing to participate?

  5. 5

    Hi Avinash – These perspectives are interesting! It is sad that more people responsible for interpreting data do not think beyond what their analytics tool tells them, and try to get inside the reality of the data. It's amazing how, when you start to calibrate with realistic assumptions, the outcomes can change – and so can the decisions you take as a result.

    A couple of points: you can take the insight from your "microconversions" idea and apply it to your representation of Facebook ad clickthrough data. A low clickthrough is only alarming if your goal is clickthroughs – but if your goal is low-cost highly targeted impressions (i.e. you are more interested in branding than in conversions) then you want as low a clickthrough rate as you can get. It's possible to get a targeted branding ad seen a couple million times for only a hundred dollars, making Facebook ads one of the best value ad media around.

    Secondly, you can further enhance your conversion data from repeat visitors, by factoring in the likely cookie-deletion rate. If half your visitors routinely delete cookies, your number of repeat visitors can be double what your analytics tool tells you it is, dropping your conversion rate somewhat.

    We emarketers love to believe that everything we do is so amazingly measurable – but the reality is that most of our datapoints have some fuzziness and are subject to assumptions. Be smart (and consistent) about applying your own assumptions, and the numbers pull reality into sharper focus.

  6. 6

    Great tips Avinash. It is definitely easy to just determine the conversion rate by just comparing visitors to acquisitions. But as you mentioned, segmentation creates a more targeted and actionable approach.

    Another great segmentation to add to your Macy's example is determining the email open rate because not all uninterested customers will unsubscribe, most will delete. Great post!

  7. 7

    Look at conversion for individual segments, one segment at a time. Makes sense. Have multiple levels of conversion. Makes sense. Thanks for pulling this together.

  8. 8

    Great point by stating Raw Numbers! So simple, but can make a big difference. And keep us away from widespread CTR obsession.

  9. 9


    You seemed to have missed how you calculated the valuable traffic. That is how did you arrive at the figure of 20%?

    Or did you just apply Paretto's Law? (The 80/20 principle.)

  10. 10

    Christopher: For some websites it is undoubtedly true that there are really long sales cycles during which consumer behavior will be complex and that it is important to know what that is for a person. Person as in a certifiable PII data captured individual.

    Here are things to consider that I've collated from my experience:

      1. It has proven sub optimal to believe that tracking people, individuals, makes for better analysis for any business except those that get 100 orders a month. If you track people, how do you get insights at 50,000 orders? And, more importantly, what do you do with 50,000 individual customized understandings of people behavior?

      I am a fan of micro-segmentation. Small buckets of logical groupings by source, behavior or outcomes. The analysis scales, actions sale.

      2. It is utterly disturbing how it is impossible to find any attribution model that does not fall into the MCU category (see the section starting page 358 in the book). We tend to overlay our own biases and it is hard to then turn around and find that that analysis improves Net Income (because it never reflects reality).

      3. It is amazing how answering this question: "What is the best mix of media I can advertise in to maximize Net Income (or have the lowest CPA)?" is a trillion times better than: "How much credit I can assign to each touch point prior to this person's conversion?" In my humble experience I've found the right question to open up a set of new impactful approaches.

    I hope this is of some value. Oh and I am totally with you on calculating Customer Lifetime Value!

    Thanks so much for adding your commend and giving us something really meaty to chew on.

    Josh: The issue with the Sr. Leader's metaphor is that people can go to a store for only one purpose and, as you say, even that is a high commitment task. But people come to our websites for so many reasons (from applying for a job to submitting a lead to downloading a spec sheet to writing a review to… well so many things).

    If of the people who come to the site with an intention to purchase, only a handful purchase then of course that would be bad. Hence we should always have a handle on the size of the convert-able audience and then work really hard, as I know you do (!), to convert every single one of them. :)

    Mvarga: Great feedback on stressing ROI. If you only get 1 click out of 1,000 impressions (0.01) or you get 1 click out of 1,000,000 impressions (0.01%, the average I referred to) and that it converts then that is perhaps a success. In these scenarios I encourage people to include two important things (regardless of where the advertising is being done):

      1. Cost. Both of doing the campaigns (people, creative etc), cost of the click, and of course cost of products and services. If you take all the costs into account and the 1 click converts (so 100% conversion) then, as you point out, it is well worth doing.

      2. The opportunity cost (not doing something else when engaging in this activity).

    My stress on showing the raw numbers is not to question the value of Facebook, rather to simply prod you to ask yourself the four questions in the post and ensure that you are making the best use of the channel.

    Godfrey: Thanks for sharing your thoughts, it is always so fabulous to have many different points of view.

    I completely 100% agree with you that running a million impressions of an ad for branding purposes is a good enough exercise in advertising. But I would never, and perhaps you would not either, accept simply the raw count of the impressions, or the low click through rate, as success. There are very very good standard test and control methods that can help us quantify the impact of our branding campaigns (there are seven measurement methods outlined in the link included in the post).

    That test and control method should prove that Facebook ads is the best value ad media, and not the fact that it is cheap or that one can run up many impressions.

    Good point on the cookies. Especially if you are using sub optimal third party cookies which have a high erosion rates. Mercifully most web analytics tools not standardize on first party cookies which have low single digit erosion rates in the first 30 days, with slightly higher in 45 days.

    Mark: Were you referring to 20% Valuable Traffic in Thomas's picture? If so then he had derived it from an analysis of his own content. He identified how much of his content was geared towards "viral" and driving traffic vs. content that was geared towards the purpose of converting Visitors. Please click through on the link to Thomas's post I have provided if you would like to get even more context.

    In your case this analysis might yield a different number. Doing the analysis, if you are a primarily content business, is pretty important.


  11. 11

    Great post Avinash !

  12. 12

    Great article Avinash! And thank you for including me in it.

    A few notes about my conversion diagram. Baekdal Plus is, as you know, a content subscription service. I have to convince people to turn into dedicated readers, before I have any chance getting them to subscribe.

    This is why I am immediately discarding non-returning visitors. They are simply not convertable.

    There are a potential for micro-conversions. As in getting people to come back or follow the site (via different means), but even that is extremely low. People are not visiting the site, they are merely browsing their social streams. Most people do not even notice where they are. They just click a link on Twitter, see the content, and move on.

    But other companies might see a completely different user profile. There are a lot of products out there that can convert people on the very first visit. We see that all the time with apps on our mobile phones. We see something priced at 99 cents, and just buy it.

    The point is (which you also explain) is that we all need to figure out what our convertable audience is, what type of content/product/experience is convertable, and what path they take.

    Another that thing is very important for me to leave out, are people who are already subscribers – as in people I have already converted. They are insanely valuable, but I can't convert them twice (they already paid) – macro conversion. And they are already following the site via email/RSS/social – micro conversion.

    But this group now fits into the 3rd conversion type – which is sharing. How many of your customers share what they bought. In my case, how often do my subscribers share PLUS content (like you did in this article – and thank you!!).

    This is a really important metric (and one thing that is hard to track in GA :)). It indicates customer satisfaction, and how well I am doing. I can then track conversions based on shared conversions… which is an interesting exercise in itself.

    This metric also helps people to better understand their macro conversion. Do you have problems converting people because of the product (low customer satisfaction), or because of the path to conversion.

    It all fits together :)

    Great article!

  13. 13


    Great advice on conversion rates, the only downside is if we all start customizing our conversion rates then that will make it impossible to compare CR's from one company to another or even on a national or global basis.

    (It's probably unwise to do such comparison's even now, but you know what us humans are like.)

    The other issue is, as I've been arguing for awhile and you have as well, is that conversion rate is not the ultimate measure that everyone seems to obsess about.

    Average order value, repeat business and of course profitability are perhaps better metrics.

    EG One of my client's recent sites has an average order value around $10,000. (No that's not a misprint and I'm not making it up.)

    But their conversion rate is around 1%.

    But with an AOV so high they are clearly doing much better than many online businesses with a CR of 10% or even 20%. IE a 100 times better.

  14. 14

    Thomas: Thanks so much for adding your perspective, and stressing the two important issues.

    I am amazed at how often even sites who do exactly the same thing have completely different consumer behaviors. A study by Microsoft a little while back had a table that showed that the Media Time to Conversion for Expedia was 17 days and for Orbitz (an pretty much duplicate business) was 22!

    The "network effect" (existing customers driving new ones) is an important concept to track. I can think of a couple ways to track it using GA (since you do create customized url's with the "long number string" at the end), but it will require some gyrations to report in a clean way. For a rough way you could create a custom variables (scope at a Visit level, though you could do Visitor) if someone comes via a link (like mine in this post) and then segment it and apply it to your conversions. Should give an aggregate number correctly. Challenge will be diving into individual referrers. :)

    Mark: You are absolutely right, we should not obsess about conversion rate. I hope that people don't develop a new obsession about conversion rates based on this pots.

    My overwhelming emphasis was on recommending that we understand, and then communicate clearly, the size of the "convert-able" audience. Is that 10% of the people who come to the site? Is it 70%? Most people have no idea. Once you know what the audience looks like you can rethink your marketing strategy, your site content strategy, and even, sometimes, the business you might be in!

    PS: You've correctly stated that I do not care if computing a "real conversion rate" makes our numbers incomparable to someone else's. We are in this for us right? :)


  15. 15
    Andrew Blank says

    "For now, just immerse yourself in the simplicity of the analysis above." — Does "for now" mean you will address it in the blog? (BTW, I have the book, read from page 358. Makes sense.)

    I love the third point. Thomas's example was a really great specialized application of what you were talking about below it. He really knows his audience and his site dynamics well.

  16. 16

    Thanks Avinash! I will play around with it when I get GA 5, and see what "gyrations" I can come up with :)

    The problem is that people do not subscribe on first visit. So people clicking on e.g. the link in this article doesn't convert immediately. They read the plus content, and then (maybe) micro-convert into followers on Twitter. Then, a couple of weeks later, a small percentage might convert into subscribers.

    The problem is that I cannot track micro-conversion in GA. I cannot track if a person reads a an article on the site, and then decides to follow me on Twitter. I can guess. Like, if I see a person linking to me, and I then get a number of new followers, who are also following that person.

    But that is highly manual approach (and wouldn't scale)

    It is easy enough to track Plus referrals. E.g. this article produced 86 unique page views, and a 50% bounce rate. Compared to another Plus member who also posted a link this week, who produced 15 unique pageviews and 100% bounce rate.

    So your blog (and you as a person) are clearly much more influential :)

    Andrew, Thanks for the kind words! Although it is one thing to know your audience, the real trick is is to get the audience to know you :)

  17. 17
    Ricky Ahuja says

    This is great insight – often times our publishers go through their campaigns with blinders on and have to look at this as a holistic approach.

  18. 18
    Krishnan says

    Slightly out of topic and a general question.

    For a startup in online apparel retail, I'm weighing the pros and cons of hiring a full time 'analytics ninja' vs hiring a 'core' online marketing type with some analytics knowledge to make sure all marketing decisions are data driven. I'm planning to use GA only for now. Thoughts?

  19. 19

    Krishnan: It is very hard to answer this question with the detail included in your comment because the answer is unique to each company.

    Here are some things to consider…..

    1. Without good marketing the data you are going to analyze is going to be "sub optimal" because it will just reflect the "poorness" of your activities.

    2. Without good analysis it is possible to do good marketing and survive as a business, but you won't always know what is working and what is not.

    3. Hire the person who will make you the most amount of money.


  20. 20
    Matthias says

    Micro Conversions:
    Another great example! It is so important to show precisely how to avoid often meaningless averages, or what else can flaw (actually successful) campaign data by just not having the right perspective.

    I still think that people use it for private reasons mainly and secondly, not necessarily for shopping and product research (though the sheer amount of users may easily prove me wrong). Also I often experience that Facebook does not seem to learn from what I dislike: I often get the same ad displayed again and again, regardless of the reason I selected when skipping it (my favorite is skipping an ad because it's "repetitive" and then getting it displayed in hot rotation).

    I also saw presentations where agencies reported to have "30 mio views generated" – in fact the ad was only displayed that much. The message was first understood as those were page views on the linked website. Dangerous mistake (but easy to clarify).

    However, I would assume that _precise_ targeting would lift the CTR up significantly (this probably requires to go beyond demographic data as it needs to include something like "general internet behavior" and this related to the current mood. Certainly not easy, if any possible).

    PS: I would click on almost any add if it's by one of my 3-4 preferred brands (but I am not telling which these are :)

    Facing Reality:
    Sorry for the confusion I created at Texco and Petsy by following the links. They hopefully do not invest too much time trying to understand why I didn't buy..

    I often face discussions where marketers "find it interesting" to know "who else" (than the target audience) visits a website. Usually I then switch to a "so what"-mode (one can learn by reading this blog!). I have to stop that once it is interpreted as aggressive behavior ;-)

    My approach (for non sales websites) is to filter out any traffic which is clearly not belonging to the defined target audience. This is often internal traffic and agency traffic, but could also be traffic from another country than the target audience (a typical "traffic distraction" for websites in Spanish, English, German, Portuguese and a few other languages).

  21. 21

    Am I missing something? Isn't .01% equal to 1 out of 10,000?

    1% is 1 out of 100, and .01% is 1 one-hundreth of that, or one out of 10,000.

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    Josh: In his article Kaiser says "100 clicks out of 1,000,000 impressions". That does work out to the math you have in your comment, one out of ten thousand.

    So all is well! :)


  23. 23

    Avinash would you say % New Visits is valuable for an ecommerce site or only for a content driven site?

    Of course I use this to access loyalty, but oddly never find any great correlation between this metric and conversions. I'm tempted to drop it altogether.

    Does anyone have any person feelings on this (i.e.: do you derive a lot of value from this metric). Perhaps I am misusing it.

  24. 24

    Great Post Avinash, as always you rock!

    My conversion rate is 2% what does 98% of visitors do on my website? Will this be the right approach to measure the 100% visitors by segregating them into:

    – 2% Actual Conversion
    – 40% Bounce Rate
    – X% Abandoned Cart
    – X% Engaged in Review
    – X% Subscribe the blog
    – X% Became Members
    – X% Others

    The above segmentation are categorized into micro conversion and macro conversion. Measuring both conversion would allow us to deliver the HIPPO the overall picture.

    Nevertheless, it could be an ideal report to measure the total visitors on the site which then are segregated towards their goal.

    Measuring CTR:
    But when it comes to measuring the CTR, the CTR would vary among different channel. It would be necessary to then have a benchmark and associate it by multiplying with the weighted value the company would consider.

    Measuring CTR:
    Paid Channel: 2.00% with 24000 Visitors
    Email Marketing: 1.33% with 15000 Visitors

    Having known the CTR it won't be of much value until it is measured against the company benchmark or industry benchmark.

  25. 25

    Levi: No metric is good or bad by itself. (Ok maybe % page exits is. :)

    It depends on what your marketing strategy is. If you are focused on new user acquisition then that is a great metric. If you are not then maybe not.

    Hence I am such a huge fan of having a Web Analytics Measurement Model. Let's the business priorities dictate metrics (and segments) to focus on.

    Rabin: If you draw a Venn diagram you would notice that your list (for 98% conversions) has many overlapping (and indeed nested) groups (circles). You want to keep them as distinct as possible.

    One thing that might help is to think of the list by consumer intent and not by outcomes (as you have in your comment).

    To your second point, CTR will vary. But you don't always need an external benchmark (sometimes they are hard to come by as well). You can use your internal past performance and you can use your financial goals / targets to get the additional context you need to identify what CTR is good and what is bad.


  26. 26

    Thank you for your insight. I guess maybe I was thinking of the metric backwards.

    I always wondered why it wasn't % return visitors. I thought maybe because the label was shorter :). I was trying to assess if certain keywords were bringing repeat traffic by looking for a lower % new visits. Being as how the original referrer gets credit for repeat visits until the traffic is not of a direct source, I'm thinking now that's probably why that metric is so vital. Because on the other hand if I show 10 visits for a keyword, 9 of them can be the same person, and I might get a false idea as to the popularity of the keyword.

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    Michelle says

    One of the greatest post with detailed info I have ever read. Thank you for taking time sharing it with us.

  28. 28

    I am not sure that I agree with your contention that only returning visitors count as valuable, or possible, buyers. Some people will inevitably make a purchase on the first visit. Some products lend themselves to this more than others (think small vs big purchases). Otherwise, I like your train of thought.

  29. 29

    Dear Avinash;

    Do you know the international Branding Guru David Brier?

    He is one of my absolute favorites. You and he are speaking the same language. Your methods are the same, but you use different tools. You both approach the customer in like manner. Your sameness is amazing. That is why I took to you so quickly.

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