Comparison Shopping Sites Are Dead – Is Google To Blame?

In the world of eCommerce, qualified referral traffic can often be the difference between success and failure. One of the biggest drivers of ‘purchase-minded’ prospects has been comparison shopping sites, such as Pricegrabber, Nextag and Google free product search. Oh, wait…scratch that. Google’s free product search hasn’t been free for a while now, as they themselves have staked a claim in the ‘comparison shopping’ market. And in doing so, it seems they have also killed (or are killing) the competition.

But of course if we’re going to make an outrageous claim like this, we’ll need some evidence. Here goes:

Victim #1: PriceGrabber

 pricegrabber-traffic-2years

 If we look at PriceGrabber’s monthly visitor traffic from the past 2 years (according to SEMrush.com), we see a pretty dramatic drop in the early summer of 2012, continuing to decline from its high of 1.8 million visits a month to a modest 50,000 monthly visits from search. When did Google switch from free product search to paid product search? I’ll let you guess.

On to victim #2: Nextag

nextag-traffic-2010-2014

If  PriceGrabber’s drop in search traffic was a fall from the roof, Nextag’s decline is like a barrel roll off the edge of the grand canyon. At their peak in 2010, Nextag received around 18 million visits from search each month. While they did have a gradual slide in traffic for the next few years, it wasn’t until mid 2012 when things totally tanked. Coincidence?

Now let’s look at the accused: Google

google monthly site traffic 2010 through 2014

Now before you start calling out objections, let me just say that I fully realize it’s impossible to make correlations based solely on 3rd party traffic trend data. That said, I’m going to show this chart anyway. It would be impossible to determine how many product specific search queries hit google during a given month, but I’m willing to bet that number hasn’t decreased. In fact the only thing that has decreased is the amount of space given to organic search results. Since search traffic may not be the best way to evaluate this, maybe we should look instead at their earnings.

google-earnings

That money has to come from somewhere. I’m betting a large portion of those earnings have come from ads.

Let me wrap this up by saying that I’m quite aware that these comparison shopping sites may still received 100s of thousands or even millions of visits each month – from people who skip search and go directly to those sites. They may very well still offer a great experience for shoppers to find the best deals, but for how long?

Why Your Low Price Strategy Sucks

low price strategy

A race to the bottom is a no-win race.

If you’ve been selling anything on the internet for a while now, you more than likely are dealing with the same headache as everyone else. Namely, increased competition while profit margins dwindle away. You’ve been struggling with what to do – sales are falling as your customers are buying the same product at a lower cost from megastores like Amazon or unauthorized dealers who don’t comply with MAP rules.

So then, your choice is rather straightforward. You can either:

a.) Try to compete on lowest price
b.) Provide some form of added value that your competition can’t

But you’ve heard this argument before, haven’t you? Countless other ‘experts’ have said that competing on lowest price will get you nowhere, but how can you be sure unless you try?

I’ll tell you how. You run some pricing scenarios with this EBITA calculator.

But before you click that, let’s do a quick example to prove the point. Here’s the test scenario:

You’re selling a consumer electronics item we’ll call – product1. We’re not estimating shipping into this, just to keep things simple.

Scenario 1 – the way things were

* Product1 costs you $600 and you sell 100 of these with a 20% markup.
* CC processing runs you 3% and you pay 5% to your phone sales staff

Results:
* Selling price per product is $720 ($120 margin)
* Total revenue – 72k
* commission and cc fees total up to $5,760
* Earnings before interest,tax, depreciation and amortization (EBITDA) = $6,240

Scenario 2- the ‘low-baller’

Everything else is the same, except for the following.

* Product1 costs you $600 and you’re now selling 150 at 10% markup.
* CC processing runs you 3% and you pay your phone sales staff 2% commission.

Results
* Selling price per product is $660 ($60 margin)
* Total revenue – 66k
* commission and cc fees total up to $4,950
* Earnings before interest,tax, depreciation and amortization (EBITDA) = $4,050

Ouch! So you’ve sold 50% more product and you’ve still lost money at the end of the day. In fact, you’d have to sell more than double (214 vs 100) what you used to sell just to stay even with your previous EBITDA.

Still feel like competing on price?

google analytics regex goal funnels

Google Analytics and Dynamic Page Funnels with RegEx

So you want to set up a goal funnel to track the funnel path / dropoff from when a visitor looks at a product detail page  through to checkout?

For most ecommerce sites, this is a pretty basic goal and could probably be accomplished simply by setting up a destination goal page of final checkout completion. But some sites, including ours, have pretty extensive resource sections that would deflate that goal conversion rate.

If your site uses a pretty typical url structure such as domain.com/product/productXYZ/482904/product-name-something/   or even includes a category in the url (domain.com/widgets/product/productXYZ/etc….) you could probably get by by simply adding /product/ in the goal path, as Google Analytics accepts this as a valid regular expression and will return all those detail pages that match.

HOWEVER, if you do something crazy like put the product name or id BEFORE a ‘product’ directory, time to use that noggin. In these instances, we need to setup a regular expression to grab all the dynamically created product detail pages.

Let’s use my site’s url structure as an example. Here’s a typical product detail page url:

http://www.projectorpeople.com/Panasonic-PTAE8000U/Projector/27643

How would you set up a regex to pull back all projector detail pages? Simple.

(.+)(Projector/)(.*)

You might be thinking – “you idiot, you could have just put /Projector/ in there and it would have done the same thing”. I can’t criticize your thinking as you would be correct – except for the fact that we also have a /Projector/ directory that doesn’t have product detail pages and we don’t want to include them. Besides, I could also add (Projectors/|Screen/|Accessories/) to this regex and pull back groups of specific categories.

Regular Expressions are powerful little monsters. If you want to find out if it’s going to work before saving your funnel, simply go to the content > pages section in Google Analytics and enter your regex snippet in the filter box and see what’s returned.

50 years of google analytics web data

Google Analytics Plans to Store Your Data for a Long, Long Time

 

 

 

We know that Google Analytics (free version) has a 10 million hit limit per month, but what we don’t know – for certain, anyway – is how long they store our web visitor data. Several years ago, the general consensus was ‘at least 25 months’, but I can access data from at least the last 4 years and I’m sure many of you can go back much farther.

But this caught my eye when I logged in to GA this afternoon. Is this just another glitch, or do they really plan to hang on to 50 years of data?

50 years of google analytics web data

date range problem analytics

Google Analytics Limits Date Range Reporting

Update:

Nevermind, this was obviously just a one day glitch. Phew

OH HELL NO.

I logged into my Google Analytics account this morning to pull some basic visitor data for the last 12 months and was shocked to discover that the ability to select a date range that long is no longer an option. In fact, I couldn’t select any date range longer than 192 days. Thinking something must be wrong, I went ahead and switched to a different site under my same account but the date range limit remained. What the hell Google?!

google analytics date range
note that anything after July 11th is unselectable…grrrr

I couldn’t find any mention of this on their blog, or elsewhere on the internet as of yet.

Why would Google do this? We already know that for large data requests, Google provides us with a ‘sampled’ set, or a reasonable estimation (which is debatable) of the actual numbers – so why the sudden change? My initial thought is that they want to force more businesses into using their premium service, which has a much higher data limit. But that would be so unlike Google to give us something for free, get everyone to use it, start taking things away and then forcing us to pay to get things back.

Fortunately, it looks like there is NOT a date range limit when using their API – at least not yet. I ran a quick query using Excellent Analytics within Excel and was able to pull back data for the last 18 months no problem.

Are you experiencing the same issues within your Analytics account? If so, sound off below and let me know what you think of this. Do you think it’s nothing to be concerned about, or a massive pain in the you-know-what?

 

Google Data Highlighter Tool – Brilliant Idea

I’ve been trying to implement some structured schema data on our e-commerce site for the last year, but it’s turning out to be a much bigger pain in the ‘you-know-what’ than I’d anticipated. I’m sure many of you can relate, which is probably why we’re not seeing the search results pages with an option to ‘sort by top reviewed’ just yet.

Things may be about to change though, as Google has just recently released their new ‘Data Highlighter’ tool. You can find it in the webmaster tools section, under Optimization.

google data highlighter for structured data entry

 

You can view more information on this tool, which currently only lets you highlight ‘event’ data (BUMMER) , by checking out the video below.

How to Filter Google Checkout Transactions in Google Analytics

Hey everybody!

I’ve been neck deep in work (and loving every second of it) since landing the position of ecommerce manager at AVISPL, but I need to chug out a post – even if it’s a quick one  - just so I get back into the blogging mindset again.

One of the things we’ve been working on since I arrived is trying to close the loop on transaction reporting, specifically matching up final transaction and revenue numbers to reported phone and web sales.

Google Analytics ecommerce tracking was already implemented, but we weren’t recording the Google Checkout transactions – an easy enough fix, but then we wanted to filter out those transactions vs the transactions that took place on the site.

The problem was there’s no easy way to set up an advanced filter to show only those transactions that went through Google Checkout. The only difference in transactions was the length of the transaction number.

Regex to the rescue! Here’s what you do:

Go to the Goals > Ecommerce > Transactions tab in Google Analytics. In the on-page filter box, edit the filter and select regex, then add this regex code:

^\d{1,8}(\s+\d{1,8})*$

You can choose to include or exclude depending on your needs. Then simply save to dashboard and you’re good to go.