What Other People Are Saying In The Business…

Jason Billingsley caught up with a number of ecommerce vendors and consultants at Internet Retailer 2008 in Chicago last June, including this month’s webinar guest, Bernardine Wu.

Bernardine is the founder and CEO of FitForCommerce. FitForCommerce is a consulting and coaching firm that helps online retailers understand their ecommerce project requirements, and match them up with vendors and solution providers that best fit those requirements. In short, when you’re planning a redesign, re-evaluating your ecommerce platform or other technology solutions, FitForCommerce helps you figure out “what you need and where to find it.”

On August 14, Bernardine will be presenting The Art & Science of Choosing Ecommerce Technology. She’ll be with us for a full hour, including time for attendee questions.

Bernardine shared a lot of good advice already in her quickie 4:30 minute interview with Jason:

Here’s a summary of the interview: (these are not exact quotes)

What is the number one ecommerce innovation?

Make sure you have your basics covered first. There are lots of great bells and whistles 360 degree views, blogging and rich imaging, but first you need to nail your basics: SEO, SEM, a functioning site, checkout with as few clicks as possible, strong branding, proper use of email and trigger emails and crisp and clean product images.

What would you tell someone who is re-platforming and not sure what process to take?

Do your due diligence! You wouldn’t buy a stock or take a job without doing your homework. The same goes for choosing an ecommerce platform. Due diligence means kicking the tires and asking the tough questions. (FitForCommerce uses a list of 2,000 questions).

It’s like going on eHarmony and choosing a mate. The devil’s in the details. There’s nothing worse than finding yourself 3 months into a project and realizing oops, something wasn’t there.

What about ecommerce RFPs?

RFPs are like religion – some people love them and some hate them. To be done well, you need to know the beginning with the end in mind. Know your requirements and what kind of site you want. Have your requirements written out, all the specs for your site and that’s your RFP. All the providers that you talk to should be able to look at your requirements and tell if you’re a fit for their solution or not.

When writing an RFP, should you do it in house or with a consultant?

Looking for outside help is important. Leveraging someone else’s expertise, an outside party who can handle the legwork can make things as painless as possible. They can also advise you on the right delivery model? On-demand, licensed software or a hosted hybrid.

So make sure you don’t miss this webinar. Even if you’re not in the market for new technology today, chances are you will be within the next couple of years. You will learn:

• How to figure out what your eCommerce strategy and needs really are
• How to research eCommerce best practices and technologies
• How to define and document your requirements
• How to assess the responses from the eCommerce solution providers; compare apples to apples
• How to select from your ‘short list’

Plus, just for registering you’ll also receive all 30 of FitForCommerce’s “eCommerce Best Practices” for eCommerce Platforms, Web Analytics and Search Engine Marketing, to name a few. After you attend, you will also receive all 30 of FitForCommerce’s “eCommerce Key Questions” to assist you in assessing eCommerce solution providers.

Sign up today. And for some instant gratification, check out our own whitepaper on How to Write an eCommerce RFP.

If you’re new here, welcome! And thanks for subscribing to Get Elastic. Friday is Blogger Digest day where we highlight posts from other blogs that are of value and interest to online retailers and Internet marketers.

• Judging by the response we get from our posts on web analytics, you’ll enjoy GrokDotCom’s Beyond the Dashboard: 5 Tips for Data Diving in Google Analytics.

• This was actually from last week, but it’s still fresh information: Avinash Kaushik’s for-dummies guide totracking cookies.

• According to Nielsen Research, 49% of consumers plan on spending less due to the economic squeeze. Need tips on how to create urgency in a bad economy?.

• Future Now also has a free white paper on how to market in a recession.

• Sitebrand’s Carolyn Gardner reminds us that you can’t focus on driving traffic through email and search and neglect customer retention. Keep the sales funnel top of mind!

• Stop selling products! Tips for communicating benefits, not just features, from Robyn at Blue Acorn.

• Should you use the word “voucher” or “coupon” on your checkout page? Does it encourage shoppers to go looking for your coupons on the web? How can you minimize this behavior and keep the coupon code field? Find out on the Rimm-Kaufman Blog.

• How do you re-engage email subscribers? Bronto has some tips, using Shop.org as an example.

• Exciting Commerce has a nice writeup on Vente-Privée (Private Sale) clubs like RueLaLa.com that are gaining popularity in the US (though popular for a while in Europe). These member-only clubs serve as online sample sales for designers and high end brands, combining the excitement of deep discounts and the urgency of a limited quantity of items available.

We all want to know which sites, search engines and keywords are sending us traffic. But what about direct type in traffic? When people access your site URL by typing it in from memory, it can be a great indicator of your brand preference, success of your offline and online marketing efforts and customer satisfaction.

If you’re smart and lucky, you named your site your main-keyword-dot-com and you get search traffic from visitors who use their address bars as search engines. For example, a search on “reusable bags” sends you automatically to “reusablebags.com” which sells…you got it, reusable bags.

Other types of type-in visitors could be:

• Loyal, repeat customers 
• Late stage buyers who’ve already visited your site through search, PPC, email or affiliate link but needed time to make the purchase decision or comparison shop
• First time shoppers pre-sold from a newspaper article, blog post, social media reviews or word-of-mouth

Type in traffic may even be your highest converting traffic. This post will cover how to create a direct traffic report in Google Analytics, and how to compare direct traffic conversion against your site average. Plus, you’ll learn how to exclude IP addresses for non-customer visits.

How to Access Direct Traffic Reports in Google Analytics

We’re interested in looking at trends in direct traffic to see if the strength and awareness of your brand is increasing over time. Here’s how you get the trend data:

1. Log into your Google Analytics account and click on “Traffic Sources” and “Direct Traffic” in the left hand menu.

You’ll want to make sure you change the default date range to your last quarter, or the past year:

After you change the first date box, don’t forget to click in the second date box once to make the “Apply” button live, then click “Apply.”

2. Changing your graph to “Month” will show you an average figure per month, rather than each daily or weekly record. Believe me, this is much easier to work with. Just remember the figures are monthly averages:

Rolling over any point will show you the month average. The above image is Photoshopped to show you the year over year growth in type-ins for Get Elastic. Comparing this July’s traffic over 2007, I see the blog’s direct visits grew 330%, from 2152 per month to 7142.

3. Compare your direct visit conversion rates to your site average. If you click the little arrow beside “Visits,” you can make some pretty useful graphs.

In this case, we’re going to compare the type in segment to overall Goal 1 conversions for the past year. (Stay in “Month” mode)

The graph will even show you the spread between site and segment (not just for type in, you can use this for other segments of traffic like search engine and keyword):

Don’t Forget IP Filters

But there’s likely another type of visitor you’re tracking – yourself! One thing that can really mess up this metric is tracking your own IP address and the IPs of others who frequent your site but are not customers. These visits could represent SEO, PPC, web design and IT consultants, employees’ home computers, the office IP block, SEOs and other web consultants, ecommerce bloggers (wink) and even competitors.

Direct traffic is not the only stat that suffers when you neglect to filter IP addresses. As mentioned in 8 Stupid Things Webmasters Do To Mess Up Their Analytics, it also:

• Understates your conversion rates. Your direct type-ins could be your highest converting traffic source, but tracking visits from employees, stakeholders and consultants dilutes your real conversion rate for this segment. 
• Overstates average time on page. You think your visitors are reading every jot and tittle of your copy, when it’s really your marketing team.
• Messes up your Content stats. Your “Top Content,” “Landing Pages” and “Exit Pages” will may be skewed by tracking the wrong visitors.

How to Filter IPs in Google Analytics

Once you’ve gathered all the IP addresses you need to exclude, go into your Analytics Settings, and find the Filter Manager in the bottom right:

You can also filter a range of IPs or use an advanced, cookies-based filter in your office which will compensate for dynamic IPs.

Then add each filter, naming each one intuitively so it’s easy to make edits in the future.

If you add a filter after reading this post, keep that in mind when you create reports in the future. Your direct traffic could drop significantly after creating the filter, and you can’t apply the filter today and change yesterday’s data.

Your trends may not show an upward trend, either. They may spike seasonally or after certain promotions you ran, help wanted ads or other “buzz” about your company. It really depends on you and your business.

Big news in alternative payments this week: your friendly neighbourhood Amazon has just launched it’s challenger to PayPal and Google Checkout – cleverly dubbed Amazon Checkout. For 2.9% + $0.30 for all transactions over $10, 5.0% + $0.05 for transactions under $10, and tiered volume discounts above $3,000 per month, you too can offer patented 1-click ordering.

There are a few reasons you might consider adding Amazon Checkout to your roster of payment options, including:

• Access to 81 million (no, I didn’t forget a decimal) people already hold Amazon accounts. This is roughly 30% more than PayPal. Customers would not have to create a new account to checkout with you, nor share any additional personal information. If a customer has multiple billing and shipping addresses on file with Amazon, that can be very convenient for the customer. 
• Through IP-targeting, Amazon can recognize Amazon.com customers and display the 1-Click checkoutfor them.
• Show cross-sells and upsells in your cart. And if you show Amazon products in your upsells (eww), you can earn Amazon Associate commissions.
• Call it a halo effect, but having the option to checkout through Amazon Checkout may carry some brand equity - provided experience with the Big A was positive. Plus, Amazon customers are likely aware of the A-to-Z Guarantee, which you now offer by virtue of the Amazon Checkout option.

Amazon promises increased conversion: “Amazon’s familiar checkout experience, 1-Click ordering, A-to-Z Guarantee, and tens of millions of customers who can checkout without re-entering information helps you optimize conversion on your website.”

But Scot Wingo was apt to point out that Amazon Payments comes with a price:

Amazon’s biggest weakness in general in the world of ecommerce technology like this is that they are trying to be both a technology provider to retailers and a competitor. Large retailers (TRU, Borders, etc.) have left Amazon’s third party business en masse because of this and I don’t imagine they will be jumping for joy to add Amazon’s checkout to their sites. For example, you won’t be seeing Wal-mart.com add this any day soon.

This actually plays to PayPal and Google’s advantage and I’m sure as a first response we’ll see them play up these fears: “Do you REALLY want Amazon seeing all of your transactions, learning about your top sellers and then using that data to compete with you?” The fact that Amazon has a well documented history of using partner data to their advantage in the third-party selling world will make this argument very believable.

What do you think? Would you test out Amazon Checkout or do you think the risks outweigh the benefits?

We’ve all heard of “banner blindness” – the phenomenon of completely ignoring anything that resembles an ad when surfing the website.

Image Source: Jakob Nielsen

For this reason you want to avoid sticking important links and calls to action in the right hand sidebar. You especially want to avoid colors and fonts that resemble typical paid search ads.

Home page

Product page

Silkfair product page

Same goes for navigation menus:

There are instances where even Internet Retailer 500 retailers really display Adsense on product pages:

I strongly believe reputable retailers should completely avoid paid search ads on their sites. But what’s worse, on Chapters Indigo, you can’t even distinguish the ads from the recently viewed products from the cross-sells because they use the exact same colors and fonts.

Further Reading

Yes, there is solid research to back this argument up. Thank you, Mr. Jakob Nielsen:

Banner Blindness: Old and New Findings

Fancy Formatting, Fancy Words = Looks Like a Promotion = Ignored

If you’re not familiar with Steve Krug’s web usability classic Don’t Make Me Think, it’s an entertaining and informative introduction to web site optimization. Though its screenshots and examples are quickly looking “old school” – its principles still stand. I “think” any web design and ecommerce professional should give it a read, and then give their own websites the “don’t make me think” test.

Today I’m going to apply the concepts from Don’t Make Me Think to The Source - a chain of electronics retail shops we used to call Radio Shack here in Canada, until it was acquired by Circuit City. I’m a fan of Circuit City’s web design and marketing, and have praised them many times before on this blog which is why I had high expectations from The Source’s web presence. But I found myself “thinking” very hard on this site.

This post is not intended to slam the design, but to point out areas that could be improved based on generally accepted design and usability principles.

(If you want to play along, you can click on the image to enlarge and see if you can predict which 10 issues I’m going to address in this post).

Continue Reading:
Home Page Design: Applying The Dont Make Me Think Test »

Before we dive into the link pool, I want to remind you of 2 webinars happening on Wednesday:

Christmas is around the corner, and that means cyber Monday is coming up fast. What did Top 500 Retailer Danskin do last year, what did they learn and what’s in store for holiday 2008? Join Sitebrand and Danskin for a 29 minutes of Danskin: Can It Repeat Its Cyber Monday Mega Success? Wednesday, July 30 at 2:00 PM – 2:30 PM EDT.

Marketing Experiments is offering Optimizing for PPC marketing Experiments on Wednesday, July 30 4:00 to 5:00 p.m. EDT. This is your chance to have your pay-per-click ads optimized in real-time by the Marketing Experiments team.

It’s never too early to register for the next Elastic Path webinar. We’ll be joined by Bernardine Wu fromFitForCommerce for The Art & Science of Choosing Ecommerce Technology.

• Buy.com’s been in bed with eBay, listing thousands of products without listing fees. What does this mean to you? Kevin Packler discusses the ramifications for medium to large businesses not using eBay as well as smaller businesses using eBay.

• Holly Buchanan gives 3 reasons why your wish list doesn’t convert. This makes a lot of sense!

• Fortune Small Business tackles the issue of handling international payments for large, wholesale orders – including foreign exchange and the cost/benefit of wire transfers vs. credit card/Paypal.

• All you design and usability experts will find Invesp’s roundup and commentary on navigation menu lessons interesting.

• New research from Nielsen Online reports 80% surveyed purchase from a store who’s site they’ve previously visited. Your online store may be drawing local traffic from visitors who don’t know you have a brick and mortar store close by. Sitebrand’s Creating an In-Store Experience Online shows an example of geo-IP targeting in action for one of its clients.

• This week’s Get Elastic posts were keyword research / web analytics / PPC focused. Rich Page contributed a post to YouMoz this week called Stop Wasting Money on SEM that continues the brainwave.

Because the AdWords system rewards keywords with high click-through history (relative to competitors) with better ad positions and lower cost-per-click, click through rate is considered an important performance metric. Along with a keyword’s relevance to ad text and landing page copy, click through rate influences a keyword’s “Quality Score.”

Every PPC campaign is bound to have a few (or few thousand) keywords with low click through rates. You can identify them easily enough with web analytics and campaign reports, but what do you do with them?

You have at least 6 options:

1. Do nothing. You’re always going to have stinkers, why major on the minors?
2. Try to improve your Quality Score, which should improve ad position, which may positively affect click through rates.
3. Add negative keywords if you’re using broad or phrase matching.
4. Create a new Ad Group. Pull poor performers out of your current Ad Group and start over with better ad text and landing page.
5. Create an AdGroup for branding purposes. You don’t expect clicks, but using your company name in the headline is free exposure.
6. Pause or delete them. Either way, you stop bidding.

But before you take action based on click through stats alone, it’s important to dig deeper as to why the click through rate stinks.

Potential Reasons for Low Click Through Rate

If your average ad position is high (1-3), it’s probably not a Quality Score issue. It’s more likely one of the following:

• Your organic rankings for the keyword are so good, people aren’t clicking on your PPC ads, and the “double listing” of your PPC ad improves your organic click through rate! You pay nothing for the additional branding, and removing the keyword may even slightly hurt you. Do nothing, except maybe “do a little dance.”

• Your keyword has low commercial intent - meaning people aren’t interested in a purchase, they want information. Are you bidding on “wii news” because it got 22,000 searches in June? Kill the keyword phrase, and consider adding “news” as a negative keyword.

• Your keyword is broad or phrase matched with insufficient negative keywords in your campaign. Use yesterday’s Google Analytics hack to expose the actual search queries that triggered your ad, and add negative keywords as necessary.

If your average ad position is medium (4-10), you may have any of the above problems, plus:

• You’re in the Automatic Match beta. You have been automatically included and your ad is showing up for synonyms to your broad matched terms, while your competitors are not. If you are part of the beta, you will see a checkbox to opt out of Automatic Match from your Campaign Settings. Just opt out, don’t be a guinea pig for Automatic Match.

• Your ad copy stinks compared to your competitors. They have tested and found winning headlines, calls to action and display URLs. They display prices that are lower than yours. They offer guarantees and free shipping in their ad copy. Customers trust their domain names more than yours. Go to the SERPs and see for yourself. And test out different ad versions.

If your average ad position is low (10+)

• You may be bidding too low vs. your competition or for the Quality Score Google has assigned you. You may have set an initial CPC that was low and performed fine, but competition has entered the picture. Or Google simply decided to raise minimum bids for whatever reason. Increase bid as long as it makes sense to, and within what you can afford.

• Your Quality Score stinks because your keyword is in the wrong AdGroup. For example, putting “learning toys” in the “educational toys” AdGroup, means your ad might display with “Educational Toys” in the headline, pointing to a landing page that never references “learning toys”. The searcher is more likely to click on results that use “Learning Toys” – it’s more relevant, though it describes the same thing. And, your Quality Score suffers when your ad text is not as relevant to the keyword. Create new Ad Group, but don’t delete similar keywords like “early learning toys” unless they also have poor history. Otherwise, you lose that history.

• Your keyword is irrelevant to your products. Perhaps you’re a victim of sloppy outsourced keyword research, or a consultant that didn’t fully understand your business. Nix that keyword, and any others that don’t belong.

Can low CTR% be a good thing?

There may be instances you want to lower click through rates. For example, if you sell high end furniture, adding “From $2999? to your ad for “teak outdoor patio set” will weed out the shoppers looking for Ikea-grade, who are thinking frugal but not expressing it in their search query. Plus, you’ll likely increase click through from luxury buyers. Your conversion rate, cost per conversion and ROI will improve. (It would make sense that Google factor conversion rates into Quality Score, since it is a better indicator of relevance than click through rate. Perhaps it’s one of the “other relevance factors” Google keeps to itself.)

What About Keywords With Low Conversion Rate or Negative ROI?

That’s a bit trickier.

Low conversion rate

Why spend money on keywords that don’t convert, right? The problem is, a keyword may have a 0% conversion rate but still be responsible for many sales. According to a 2005 comScore study, searchers who ultimately purchased online performed an average of 13 searches before converting, resulting in 12 non-converting searches for every sale. If the sales cycle exceeds your cookie expiration dates, some keywords may never get the conversion credit they’re due. (Great article on non-converting keywords by Frederick Marckini at Clickz)

What’s more, online searches can result in telephone orders, or even offline sales – which are even harder to reconcile, since there’s no cookie that tracks those.

Negative ROI

Keywords with negative ROI should be investigated. Are bids too high? Can landing pages be improved? Is broad match burning your budget and could keyword research help? They can even be a blessing. Lessons you learn from attempting to salvage negative ROI keywords may even benefit your campaign as a whole if you can apply “better practices” across the board.

If margin on the products or overall sales are low, you may decide to kill the keyword based on negative ROI to allocate budget for clearly profitable keywords and products.

The takeaway is to never kill a keyword simply because of a low metric. Always investigate the possible reasons for the low metric.

Are you a Google AdWords advertiser using Google Analytics? STOP!You MUST read this post because you are losing money daily and we are going to help you stop the bleeding.

There is a problem with the default functionality of Google Analytics when used in conjunction with AdWords. Google Analytics (GA) doesn’t report the actual phrase a shopper entered into the search bar, only the keyword phrase you are bidding on.

Let me explain:
- You bid on the keyword ’shoes’ using ‘broad match’
- A shopper searches for ‘blue suede shoes’
- The Traffic Sources > Keyword report in GA shows the search as just ’shoes’

Even worse, Google likes to use synonyms when your terms are under the broad match type (called automatic matching or extended broad match).

- You are bidding on the keyword ‘running shoes’ using ‘broad match’
- A shopper searches for ‘Adidas Gazelle’
- Google shows your ad, but wait, you don’t carry Adidas shoes

Why would Google do that?

The shopper searched on blue suede shoes, not shoes! You don’t sell blue suede shoes. You have been making decisions based on inaccurate data.

Follow these simple steps to start seeing the EXACT phrases people are using when they click your AdWords ads.

It will help you find terms to add to your negative keyword list. You can also start honing your ad and landing page copy to better reflect how shoppers search.

The Google Analytics Exact Query Solution…

This solution comes from our friends at VKI Studios, a Google Analytics Authorized Consultant and overall great bunch of people (see their analytics blog for some great tools and tips). Specifically, Brian Katz. They have evaluated various means of cracking this nut, and we have their final solution. Credit and comparison of other methods are at the bottom of this tutorial.

1. Create a new Google Analytics Profile

We do NOT want to overwrite any core data, so a new profile keeps everything intact. Even Google says it is a good idea.

Select Add a Profile for an existing domain, select which domain, and enter any name for the profile you choose – the more descriptive the better. You will not have to add any tracking code or tag anything, so no need to get the ponytail guys involved.

2. Create the first filter

Locate your newly created profile and click Edit under the Settings column. Then click Add Filter.

Field A -> Extract A: Referral: (\?|&)(q|p|query)=([^&]*)
Field B -> Extract B: Campaign Medium: (cpc|ppc)
Output To -> Constructor: Custom Field 1: $A3

3. Create the second filter

Locate your new profile again and click Edit under the Settings column. Then click Add Filter.

Field A -> Extract A: Custom Field 1: (.*)
Field B -> Extract B: Campaign Term: (.*)
Output To -> Constructor: Campaign Term: $B1 ($A1)

As with almost all multi-part filters, sequence is critical and must be ordered accordingly using the “Assign Filter Order” page for the profile.

That’s It!

Here are what the results should look like when you run the Traffic Sources > Keywords > Paid report in Google Analytics:

The following set of results were obtained using an in-line filter to show bid-terms that would be different from the search terms

An unfiltered result would look as follows:

The above technique provides useful data as is but it does have some shortcomings in that it does not associate the newly overwritten Campaign Term field with Transactions, as is shown in the following screen shot:

It is probably the result of using session-based values (e.g.: all the Campaign fields) and pageView-based values (e.g.: Referral). Caught in the middle are the event-based eCommerce transactions.

In his book “Advanced Web Metrics with GA” (Page 199) Brian Clifton documents a method attributed to Shawn Purtell of ROI Revolution that uses 3 filters to show each Transaction with its bid and search terms appended.

We are experimenting with a combination of those filters and the ones described above to extend the solution to include eCommerce and will post the solution when we have it. So make sure you subscribe to the RSS Feed or by Email to be notified when it is available.

Hat Tips to Others Tackling this Problem

The original solution for this came from Brian Clifton, formerly of Google.

The solutions (Using Filters): 
- How to Get Detailed PPC Keyword Data from Google Analytics
- NUDE: AdWords Keyword Data Exposed With Google Analytics!

An updated solution from ROI Revolution (Using JavaScript):
This solution uses the User Defined variable so it won’t be appropriate if you’re using the User Defined variable (created with _setVar()) already
- Exact Keyword Tracking with Google Analytics, Revisited
- Exact Keyword Tracking with ga.js

Comparison of the two methods

I checked out the two methods (Filters vs. JavaScript) . Since readers commented saying the filters did not work or “no longer worked”, I took a closer look. The devil is in the detail. Errors in their implementations may have been the cause of the malfunctions.

JavaScript vs. Filters

JavaScript
The two methods both extract data from the and Referrer and Campaign Medium checking the latter for “ppc” of “cpc” using regular expressions. They both concatenate the bid and search terms. The JavaScript method goes 1 step further by looking for the gclid value unique to Google AdWords. That may also be done in the filters but I don’t believe it would enhance the filter solution.

The JavaScript performs its magic at run time. It uses the “troublesome” _setVar() cookie to store the bid and search terms in the User Defined field. It does so using a generally accepted “kludge” to work around _setVar()’s issues (a topic all of its own).

The greatest disadvantage to this method is that it monopolizes the User Defined Value. With all its troubles, it is an invaluable resource that most will (should ?) be using to segment visitors. Since it is stored in a domain specific cookie it cannot store profile-specific data to different profiles (well, it can be pushed to greater limits but that is a blog post all of its own).

It should be possible to rewrite the URL of the landing page before ga.js writes the Campaign cookie (again a topic of its own)

Filters
The filters run at data processing time and so, I expect those may prove marginally more reliable than JavaScript and cookies (although all subsequent visits from the AdWords campaign will rely on the keyword and other campaign data being extracted from cookies by ga.js or urchin.js) so that is no reason to choose one above the other.

By default, however, I am biased in favor of filter-based solutions because they are independent of the implementation and so don’t require updates to a site’s GA coding. Implementation is quicker and easier, as is propagation of the solution across profiles and GA accounts. In fact, in the time it takes to update the code on some sites (those that are not tagged as efficiently as they might have been) or in the time to get a site’s 3rd party developers to make the changes, a GA consultant could implement the solution for a number of accounts, regardless of the level of access the consultant has to the coding.

Note: Analysis and much of the technical write-up done by VKI Studios, Brian Katz

Our last post covered tips on using Google’s free Keyword Tool and how to apply your keyword discoveries to various aspects of marketing: SEO, PPC, site usability and even email marketing.

As promised, today we’re going to dig deeper into negative keyword research. The tools we’ll cover are the Google Keyword Tool, Google Suggest, Google Product Search and some surprises.

Negative keywords explained

If the term “negative keywords” is new to you, it refers to irrelevant or low converting keywords that you add to a pay-per-click campaign which tell the ad system not to show your ad when that keyword appears in a search.

To take advantage of the “long tail of search” (longer query combinations and product searches that are performed infrequently but can convert like crazy), marketers will bid on broad keywords using “broad matching” or “phrase matching.” If you phrase match “learning toys” your ad would appear for “learning toysfor 3 year old toddlers.” Broad match would include even more searches like “best toys for motor learning.” The problem is, often times these match types cause you to show up for searches that have nothing to do with your products, or that don’t have a high “commercial intent” behind them. This hurts your overall campaign performance.

Why Google wants you to research negative keywords!

It’s in everyone’s best interest to prevent results like this:

Google makes more money if ads are relevant and searchers click on them instead of organic results, retailer can attract more clicks when there are less competing ads (especially from high budget software companies), and customers don’t get so confused.

Here’s how you use various Google tools to build out your negative keyword list to avoid your ad from appearing for irrelevant searches when using broad or phrase matching.

Google Keyword Tool

When using AdWords, you can access the Keyword Tool from inside your account.

This section assumes you understand how the Keyword Tool works. Yesterday’s post advised you to switch your match type to “Exact” to see exact search counts. Today we don’t care about keyword popularity. We just want to find as many negative keywords as we can.

If you switch the match type to “Negative,” all that changes is the square brackets from exact match are changed to the “-” sign before the keyword, so you can build your shortlist of negative keywords and import right into an AdGroup or Campaign, or save in text or .CSV format. This doesn’t change the keyword suggestions,

But remember that Google’s Keyword Tool does not give you enough negative keyword data, you still have to go digging further. You can manually add additional keywords, and then create one text file or .CSV

(Note that if you add “wooden” once, your ad will not appear for “wooden puzzles,” “wooden blocks,” etc. You don’t need to add “wooden + keyword” to your list. If you do carry some wooden toys, you should consider creating separate AdGroups for only the wooden toys you carry (better landing page selection, higher quality score, better ad text), for which you would add negative keywords for the wooden products you don’t sell – “blocks,” “puzzles” etc).

Google Suggest

Type in your keyword, e.g. “learning games” and Google will drop down suggestions.

Keep in mind 2 things:

1. The numbers that show are not keyword counts, but results of pages in Google’s index. The higher the number, the more competitive the keyword is, actually. But, because Google suggest shows long tail search terms, you can use this tool to pick out additional negative keywords the Keyword Tool didn’t bother to show.

2. You can’t see all the suggestions when typing in your broad match. You’ll need to go through the alphabet, first typing a space and then “a” – if no results, you continue until you hit a letter with suggestions:

Sometimes you have to apply this going-through-the-alphabet system on top of a suggestion, like “learning toys for a…, b…”

You’ll have to make notes on which keywords to add, maybe on a notepad. Make sure you add them to the appropriate campaigns – and you may discover new keywords to bid on in the process.

Google Shopping

Google Product Search is the shopping engine formerly known as Froogle, and confusingly labeled as “Shopping” from the links across the top of Google’s home page, or when you’re in Google Reader, or Gmail…

You can use Google Product Search to find negative keywords in a couple ways. Perform any keyword search, and scroll to the bottom to see more links, and check out the “Brand” and “Related Searches” links.

Clicking “More” expands the lists:

Adding brand names you don’t carry as negative keywords is very important. When a search query involves a brand name, it’s a strong signal that someone is looking to research or purchase a specific item, not check out other brands. So your general ad will have lower click through, which lowers the click through rate of your entire AdGroup, hurting all your keywords’ ad positions and possibly raising your cost-per-click.

Not to mention your landing page quality score will be lower if it doesn’t reference that brand. And, even if you do attract clicks, there a much smaller chance of conversion, though you still pay for the click. And if your broad matched keyword is very competitive, it could be an expensive click!

You can also turn to a review site like Buzzillions to glean brand names. Buzzillions aggregates reviews from retailers using Power Reviews, so there’s a good chance most if not all brands are represented. Simply go to Buzzillions, type in your keyword, and check out the brands listed in the left hand navigation:

(Numbers indicate the number of branded items with customer reviews, not number of customer reviews or keyword popularity.)

Or, use eBay for negative keyword research, as I wrote about for SEOmoz’ YouMoz Blog last year.

Google Analytics

Of course, using your Referring Keywords report, you can mine your Google Analytics data to weed out referral keywords that don’t relate to your business. And you can segment out non-paid and paid searches from your reports.

But wouldn’t you like to know which “long tail” terms your broad match and phrase match terms are bringing in? You can identify them with Google Analytics, but it requires a hack, which we will cover in depth tomorrow…