Are Web Analytics Easy or Hard?

If you are not an insider in this little niche industry, you may be surprised to learn that this question has been a heated debate.

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This post is on the occasion of an interview to which I was kindly invited for Aug 26 at wsRadio, in Online Marketing with RSS Ray. From the show music to the ad break – this was a fun experience. Browse RSS Ray’s archives and look for podcasts from Gary Angel, Jim Sterne, John Squire, and many more fun people.
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Looking from the outside, you might think that web analysts just breed over web site usage reports all day long to dream up ways for increasing usability, conversion rates, or sales.

But you need to know the following fact:

While outsiders often feel that the topic of web analytics is so boring that it could cure insomnia, for us on the inside, there is deep passion.

The fire of passion is burning:

So, it isn’t surprising then maybe that all this passion has led to a bitter debate among the best minds in our field

The debate

Are analytics “easy” and can even be handled on the side to certain degrees? Or are they “hard”, with lots of obstacles to overcome, and require closest attention to get anything right?

Some of our brightest are on a crusade to help by making analytics intuitive and spreading their adoption to the masses. Others of our brightest are on a crusade to help by uncovering all the pitfalls that exist and that have prevented too many companies from generating ROI from web analytics.

It is a good thing the world has me to judge the outcome and reveal the answer to this epic debate now!

The answer is, of course, that web analytics are both easy AND hard.

There are aspects of analytics that are easy or at least straight forward. For example:

  • If you measure that visitors coming to you from search keyword XYZ have a high bounce rate, i.e. they are arriving at the landing page and them immediately leaving, chances are that either the landing page doesn’t fit their expectations or the keyword isn’t a good one for your offering.
  • If you create two test versions of the landing page with essentially the same content but different layout, design, etc. and you find that one leads to higher engagement and conversion rates, chances are you should keep the better performing page.
  • If you measure that visitors coming to you from search keyword ABC have a great conversion rate but there are only few people reaching you via this keyword, you probably want to check whether you should try to rank higher for that keyword ABC.
  • If you measure that visitors buying from you are all shopaholic until they reach your page where you reveal exorbitant shipment costs or a long form that they must complete, chances are that improving these items will decrease leaks from your funnel

If you did nothing but the above, you’d likely create very respectable ROI from analytics.

But there are other valuable aspects of analytics that are far from easy.

In fact, the harder you look at any individual metric the less it seems to say.

Could also add that the more you know about analytics, the less sure you become what any individual report really means.

Huh?

Well remind yourself of the following:

  • If search keyword ABC has great conversion rates, is that because of only the keyword itself or have visitors been exposed to other ads or emails of yours that led them to search for ABC in the first place? Most obviously, anyone searching for your brand or product names must have heard them elsewhere.
  • One of Unica’s clients, Braden Hoepner from Coastal Contacts was pointing out the following gotcha at eMetrics this year: If you create two versions of a landing page with different offers and you pick the one that performs better for conversion rates, you may still find that you have just hurt your company. How so? By producing lower sales or profits. That happens if you accidentally lead people towards products that are cheaper or less profitable.
  • If people leak at a particular page in your funnel is it because of something you said? Or is it the point where they have learned enough from you to stop and check first what the competition has to offer?

So given both easy and hard options to choose from, which would you pick?

Pick the deeper questions first

Tackling the more difficult questions is often critical for working towards the ultimate optimization summit whereas the easier questions may leave you working towards a local optimum.

Pick the easy questions first

But the easy questions have potentially higher % ROI because you put less effort into them and can still get great improvements. So you might be inclined to start with the easier tasks and work yourself to the more difficult questions over time.

Pick the deeper questions first

But that approach doesn’t take time into account. If you waste time on achieving a local optimum you delay the overall optimum, I.e. you incur opportunity costs. So, if you know that there is a bigger optimum to be achieved you could make your company richer by reaching it sooner than later.

Pick the easy questions first

But what if it takes a really long time to tackle the harder questions and outcomes are uncertain?

Arggghhh…!^%$^%@!!!

See what I mean. Analytics are both easy and hard. And the more you think about them the worse it can get!

Tags: Web Analytics by akin
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Is Amazon really that cool as we keep saying?

For all that buzz around Amazon’s sophisticated analytics and its targeted book recommendations, it is worth asking in Kevin Hillstrom’s priceless, heretic style: Is it just hype or does it really make the big difference for their business?

Do we really buy from Amazon because of the recommendations, personalized emails, the behavioral targeting through widgets?

Or do we buy from Amazon because they always have the cheapest price? (Not to the least because of the 3d party vendors and used books that are linked in.)

If they stopped being the least expensive would we still be buying from Amazon?

In other words, are they really competing on analytics? Or are they competing on price?

What is our willingness to pay extra for the kind of “marketing as a service” that Amazon has perfected?

Of course … this question isn’t really about Amazon, in the end. Much rather I am trying to double check what the true value of sophisticated analytics and targeted marketing are. All hype aside.

Separate things: What you will buy vs. where you will buy it

There is no doubt that Amazon is the go-to place for doing your research on books.

But being the greatest place for researching books doesn’t necessarily mean that people will buy the books there if they can get them cheaper elsewhere.

I imagine we all go to many web sites to research what car, electronics, gear, etc. we should buy. Where we will buy the item that we settle on tends to be a different question though. We might check on eBay or Craigslist, for example.

So are we giving too much credit to Amazon’s sophisticated analytics and marketing?

The answer …

As Anil Batra was joking yesterday when I saw him at the OMMA Metrics and Measurement in San Francisco, a typical consultant’s answer to such a question could be: “That depends on … what it depends on.”

It depends on …

Of course, I don’t have Amazon’s data. But I think that the answer will indeed differ by buyer segment:

  1. High value prospects that buy books frequently and in higher quantities will likely appreciate the convenience and time savings. But given that these people buy so many books they would also be the segment that could get the biggest total savings by being disloyal to Amazon.
  2. Infrequent buyers that are strapped for time will value the convenience over a few bucks of savings per book.
  3. Infrequent buyers that are strapped for money but not for time would be more likely to put in the extra 5 minutes for buying the book at the cheapest vendor. Most books aren’t big ticket items. So this effect will likely be much less pronounced than, say, with electronics. But it would be there to a certain degree.

In the end, maybe the answer has not all that much to do with frequency but is a function of

  • how much money vs. time buyers can save.
  • how much value the individual puts on money vs. time

Time is money and money can buy time

The more time Amazon can save its book researchers, the more of them would buy their books on Amazon (assuming price is fixed). Still, those buyers who value money a lot more than convenience and time would be the hardest, if not impossible, to keep.

Anything else they could do?

Barnes and Noble has (or had) essentially a frequent buyer card. You could pay an annual fee and would get x % discounts on anything you buy.

But that really is just a return to the strategy of competing on price.

Could Amazon withhold access to book research features unless a buyer … purchased something in the last 12 months or joined some kind of for-pay club? That would be a return to subscription based content. Seems like it would backfire badly.

Bottom-line

It appears that the true value of all those analytics and targeted marketing for the retailer are in drawing the crowd into their (online) store. They get a shot at making sales (and cross-sales) that they wouldn’t otherwise have.

But converting researchers into buyers requires more than just targeted marketing. It also requires convenience, a “good enough” price, and of course customer satisfaction with previous transactions.

How would you go about measuring the value of targeted marketing effort XYZ?

If you have to measure ROI of targeted marketing effort XYZ you would probably do it through controlled testing.

That would be easy for Amazon’s targeted emails.

It would be harder for their book recommendations because you would wonder where they disappeared to if you fell into the control group.

Oh … but you could make them deliberately untargeted. Say you find that dinosaur book that the individual bought and shipped to somebody as a gift 3 years ago and recommend more dinosaur books. 8-)

Tags: Customer Centricity, Direct Marketing, targeting, Web Analytics by akin
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