While the Quality Score indicates whether your keywords/ads/landing pages are relevant against the competition, it does not tell much about the actual competitiveness of the market. Instead, the average cost-per-click (CPC) can help measure how competitive the auctions are, given that more competition typically causes higher CPCs. That being said, your CPC could be high because of a poor Quality Score, not necessarily because of the fierce competition out there.

In order to isolate the competitiveness component from the equation, one can measure the average delta between the max bid (what you’re willing to pay at the max) and the CPC (what search engines actually charge you). How can this delta be determined, and how actionable is it?

Calculating The Competitiveness In Your Paid Search Account

You can look at it as a ratio; and in theory, it should range from 0% if there was no click to 100% if the CPC was equal to your max bid. Obviously, the grey zone in between is what we are interested in.

Also, for the sake of this analysis, this ratio can be aggregated from the keyword to the ad group, campaign, and even AdWords label level (or any way that makes sense to your business). The aggregation is not pretty as you’ll be summing CPCs and bids; however, from my experience, it does work — especially when weighing by the number of clicks.

What should one expect to see then? Well, say you have labeled your campaigns in AdWords by trademark (your own branded terms), brands (brands that you distribute), generic (short, high-volume, non-trademark, non-branded keywords), and SKU (product-specific keywords). Then, based on multiple paid search accounts organized that way, one can expect:

• Low competition on your trademark terms: Your CPCs should be low compared to your max bids. In the below example, our competitiveness ratio shows 27%. If your max bid is $1, you’ll be charged $0.27 on average.
• Medium to high competitiveness on products-specific keywords: 53% based on this client set.
• High competitiveness on generic and brand related keywords: 66-67% in our example.

Obviously, you can visualize this ratio at the campaign, ad group, and keyword levels. too. For instance, you can look at it for your top 10 keywords from a cost standpoint – where the two first keywords on the left are obviously trademark terms:

Application #1: Bidding Strategy

Being able to determine the average delta between the CPC and the max bid is not just about putting together fancy charts — it can also be pretty powerful and actionable!

Say you’re launching new paid search campaigns, and you’re not sure about the initial bids. All you know for sure is your CPA target and your historical conversion rate for a given product category. Then, given that CPC= CPA*Conversion Rate, you can calculate your initial max bids such as:

Example:

Application #2: Identify Growth Opportunities

To simplify, a straightforward way to identify the room for growth in your paid search account is to spot those profitable keywords not already in rank 1.0 or so. You can then bid up and gain incremental conversion volume as long as profitability remains on target.

A slightly more sophisticated approach to this tactic is to also take the Quality Score into account, since you want to first get more aggressive where your keywords/ads/landing pages are the most relevant.

Similarly, search marketers can also leverage the above competitiveness indicator so that they first get the most out of those keywords where competitiveness is relatively low.

Example: if your target CPA is $40 and the average competitiveness in your account is 43%, then you might want to first bid up on those keywords with a position worse than 1.5, a CPA lower than $35, a Quality Score greater than 5, and a competitiveness lower than 40%. This logic can easily be translated into Excel using pivot table/filters and will help you get the most out of those profitable keywords where the competitiveness is below average.

Conclusions

In conjunction with a Quality Score analysis, analyzing the average delta between the CPCs and the bids can be key to determining the competitiveness in your account, adjusting your bids accordingly, and identifying opportunity keywords you want to bid up first.

Analytics Software Solutions

Below is a brief overview of 25 analytics software options (and one add-on) that should give you an idea of what each tool costs and what functionality each offers. Before you check out the chart and series of descriptions below, note that it is important to go into any analytics evaluation with a clear idea about what you need the software to do. Some important questions to ask yourself before you start to evaluate Web analytics tools are:

• Why do I need a different/new tool?
• What sort of data do I need this tool to give me?
• What actions will be driven by that data? How, specifically, will I use the data to inform decisions and activities?

Just because a feature sounds interesting and provides you with some additional data about your site doesn’t mean it’s actually valuable. You want to know the specific holes in your current reporting that will be filled by additional functionality, and more importantly, you want to be sure that that extra information will drive better decisions and ultimately more revenue and profit for your business.

Once you’ve identified what you’re looking for in these tools, you should be able to use the information below to help you make a better decision about which analytics package will be right for you.

The table below is available as an image, and you can also get the raw data in this Google Doc.

Web Analytics Software Comparison Guide

1.  Google Analytics

Google Analytics was originally designed as a tool for AdWords users. It was basically a repackaged version of Urchin on Demand, which Google acquired in 2005. It was redesigned in 2006 with additions from Adaptive Path after the company acquired Measure Map.

Key Features:

• Benchmark against all other analytics programs
• Content Experiments for A/B Testing

Cost: Free for all users, enterprise solutions available

2. Adobe Site Catalyst

Adobe SiteCatalyst is created by Adobe, best known for its Adobe Creative Suite which includes software programs such as Photoshop, Dreamweaver and Fireworks.

Key Features:

• Can be enhanced with Test&Target (for A/B testing)
• Drag-and-drop dashboard
• Embeddable reports
• Customization options

Cost: $5,000+ per month

3. IBM Coremetrics Web Analytics, Now IBM Digital Analytics

IBM Digital Analytics is part of IBM’s Enterprise Marketing Management group and works in conjunction with the optional add-on IBM Digital Analytics Multisite. The program was originally Coremetrics Web Analytics but was renamed after IBM’s 2010 acquisition of Coremetrics and subsequent restructuring of its analytics offerings.

Key Features:

• Comparative insights/benchmarks against industry leaders
• Actionable data
• Customizable dashboard

Cost: Custom quote

Optional component: IBM Coremetrics Enterprise Analytics, Now IBM Digital Analytics Multisite

IBM Digital Analytics Multisite is part of IBM’s new umbrella Enterprise Marketing Management (EMM) group and is an optional add-on to IBM Digital Analytics. It’s based on Coremetrics Enterprise Analytics; in addition to Coremetrics, IBM acquired Unica in 2010, as well.

Key Features:

• Three-pronged enterprise analytics solution under EMM
• Combines best features from two original products

Cost: Custom quote

4. IBM’s Unica NetInsight

IBM Unica NetInsight is one component under IBM’s Enterpise Marketing Management group, created based on Unica’s original offering after IBM acquired the company in 2010.

Key Features:

• On-premise
• Customizable dashboards
• Drag-and-drop functionality
• Heat map overlays
• Multiple data configuration options

Cost: Custom quote

5. iPerceptions

iPerceptions was founded in 2000. While iPerceptions isn’t a stand-alone analytics tool, it’s designed to be integrated with a core analytics application, such as Google Analytics, to provide a more comprehensive look into the VoC. iPerceptions offering consists of five different components, including webValidator, 4QSuite, WASP, Mobile Website Feedback and Tealeaf.

Key Features:

• Captures Voice of Customer (VoC) data
• Live feedback
• Customer surveys
• Real-time customer path analysis
• iPerceptions Satisfaction Index

Cost: Custom quote

6. Google Analytics Premium

Google Analytics Premium is a relatively new program, introduced in September 2011. Advanced data capabilities are the biggest selling point to date, but expect most improvements to Google Analytics to hit the Premium version first.

Key Features:

• Nearly limitless data (one billion hits per month)
• 24/7 customer support
• 50 custom variables
• Four-hour data turnaround

Cost: $150,000 per year

7. Web Trends

Webtrends was one of the first Web analytics solutions to enter the market with its inception in 1993. Webtrends Analytics is the company’s core analytics offering.

Key Features:

• Unlimited custom metrics
• Heat maps
• Custom mobile and social measurement features

Cost: Custom quote

8. Mint 

Mint is an affordable, on-premise solution created by Shaun Inman. There are no official split testing tools, although testing is possible by flagging two pages for detailed analysis and comparison.

Key Features:

• On-premise solution
• Unlimited site tracking
• Extensive plugin library (Peppermill) for additional functionality

Cost: $30 per site, one-time fee

9. Reinvigorate

Reinvigorate is another analytics offering by Webtrends, which acquired the company in 2011. This acquisition allowed Webtrends to offer real-time analytics across Web, social and mobile channels.

Key Features:

• Real-time analytics
• Web, social and mobile channels
• Affordable pricing with sophisticated capabilities

Cost: Free (3 sites, 3 users, 500,000 visitors per month); paid plans range from $10 to $20 per month for additional limits, custom plans also available

10. MixPanel

MixPanel first entered the space in 2009 when the company began researching and raising seed capital. By July 2012, MixPanel was already tracking one billion actions per month.

Key Features:

• Built-in analytics component
• Save and tag pages
• Charts and graphs for sharing data

Cost: Six levels of service ranging from Free (25,000 data points per month) to High Volume for a custom quote. Mid-price plans range from $150 to $2,000

11. KISSmetrics

KISSmetrics, founded in 2008, differentiates itself with its person-centric focus. The company is headquartered in San Francisco and backed by angel investors and early venture capital funders such as True Ventures, Polaris Ventures and SoftTech VC.

Key Features:

• Integrates with CRM, email marketing and other programs
• Analyze data from multiple channels in a single interface
• Visitor-focused data instead of broad stats

Cost: $49 to $499 per month

12. FoxMetrics

FoxMetrics  was founded in 2011 by Rydall Williams, so it’s a newer player on the analytics scene and also one of the more affordable options.

Key Features:

• Real-time data
• Tracks data from any device
• Unlimited attributes
• Individual visitor analysis to create visitor profiles

Cost: $20 to $120 per month

13. Site Spect

SiteSpect has been offering A/B and Multivariate testing solutions since 2004. The company was founded by Eric J. Hansen, CEO and Larry Epstein, COO.

Key Features:

• Integrates with other analytics products
• Web Analytics Tag Transformation and Segmentation (WATTS)
• Full statistical analysis from within any third-party analytics application

Cost: Estimated $3,000 per month

14. Woopra

Woopra, founded in 2008 and offers real-time customer analytics along with integrated CRM analysis within a single interface.

Key Features:

• Watch single visitor path in real time
• Retention Analytics
• Funnel Analytics
• Segmented Analytics

Cost: Free Basic plan (30,000 actions per month, non-commercial only); paid plans range from $39.95 to $349.95 per month

15. Autonomy 

Autonomy is one of the most comprehensive customer response monitoring programs available. Autonomy Explore, powered by Autonomy IDOL, is the company’s core Web analytics solution.

Key Features:

• Products range from Voice of Customer (VoC) to customer feedback, social analytics, A/B and Multi-Variate testing
• Offers complete picture of customer experience
• Integrates data across all channels

Cost: Custom quote

16. AT Internet

AT Internet offers a comprehensive suite including Web, social and mobile analytics in real time, serving more than 3,500 customers worldwide. Analyzer III is the company’s latest core Web analytics offering.

Key Features:

• Total customization
• Soft Tagging feature
• Data Manager configuration environment
• Drag-and-drop functionality
• Heat map overlays
• A/B and Multi-Variate testing

Cost: Custom quote

17. comScore Digital Business Analytics

comScore has been in the digital landscape since 1999 with the introduction of comScore Digital Business Analytics. The company handles over 1 trillion interactions from 172 different countries each month, equal to approximately 40 percent of total Internet searches each month.

Key Features:

• Lowest-form raw data
• In-cloud or on-premise
• Integrates with third-party apps

Cost: Custom quote

18. Yahoo Marketing Dashboard

The Yahoo Marketing Dashboard is Yahoo’s attempt to produce a similar offering to Google Analytics. The initiative is sponsored by American Express OPEN. It’s brand new on the analytics scene, introduced in May 2012. The Yahoo Marketing Dashboard pulls its Web data from Yahoo! Web Hosting, Yahoo! Merchant Solutions or Google Analytics.

Key Features:

• Integrates with Constant Contact and Orange Soda
• Unlimited visitor tracking
• 24/7 customer support

Cost: Free

19. Piwik 

Piwik is a free, open-source analytics tool with unlimited tracking capabilities and on-premise data storage. It supports more than 100 search engines and has been downloaded more than 1.2 million times since its launch in 2008.

Key Features:

• On-premise data storage
• Reports and widgets for location-based visitor data
• Easy exporting
• Manual tracking of non-Java-enabled mobile users

Cost: Free

20. Clicky

Clicky is a real-time Web analytics platform developed by Roxr Software, which was founded in 2006.

Key Features:

• Tracks up to 30 goals (paid)
• Google Search rankings
• Outbound link tracking
• Heat map overlays

Cost: Free (up to 3 million page views, single site),;Pro $9.99 (10 sites, 30,000 page views); Pro Premium for custom quote (100,000 page views, up to 30 sites)

21. GoingUp

GoingUp is a free service with a host of tracking and optimization tools to maximize your presence on the Web.

Key Features:

• Actions and goal tracking
• Visitor location
• User profile data
• Inbound link monitoring
• Google PageRank and Alexa Rank tracking
• Page optimization tool

Cost: Free for unlimited sites

22. Open Web Analytics

Open Web Analytics was built by Peter Adams and is designed to be an alternative to Google Analytics.

Key Features:

• On-premise solution
• Open-source platform
• Up to 5 custom variables
• Real-time data
• Heat map overlays

Cost: Free

23. Stat Counter

Stat Counter  was founded in 2006 and now serves two million members and tracks more than ten billion page loads each month.

Key Features:

• Unlimited websites
• Broad summary and deep-level visitor views
• Intuitive dashboard

Cost: Free (up to 250,000 monthly page views), paid plans range from $5 (500,000 page views) to $119 (60 million page views) per month

25. Site Meter

Site Meter  provides real-time data and individual visitor tracking for the 100 most recent records in the Free version. Premium accounts have limits ranging from 4,000 individual data records to 25,000 records and up to 15 million page views per month.

Key Features:

• Individual visitor tracking
• Single-visitor path view
• Broader statistical analysis

Cost: Free version; paid plans range from $6.95 to $199.95 per month

In this post, I will detail the logic behind and process of incorporating margins into bidding at the CPA and CPC level, in case you prefer manual bid management to cost-per-acquisition based management.

Depending on available data sources, you might have access to internal analytics considering margins or you may be limited to ad server data. For the purposes of this article, I am going to assume that we’re starting with gross revenue aggregated to the keyword level (standard AdWords and Google Analytics reporting). As a result, this process is applicable if you have access to basic analytics or ad server data sets.

An ad serving reporting platform will only display revenue that is captured by the associated pixel or integrated analytics platform. Because pixels are placed on order confirmation pages in the shopping cart, standard practice is to display gross revenue in the reporting platform rather than net revenue. Marketers must manage advertising performance to a return on ad spend (ROAS) or cost per acquisition (CPA) backed out from profitability goals using gross metrics.

At a high level, it’s relatively easy to apply margins to gross revenue to extrapolate thresholds for profitability. If the average margin per sale is known, then the theoretical maximum an advertiser can pay for a transaction is equal to the margin (assuming a customer lifetime value of 1x retail price of their first purchase).

The idea behind average net profit per conversion is to identify the point in time when net revenue is equal to marketing expenses. Using a 30 day cookie, if we manage to a CPA equal to average net profit, then we are establishing a cash flow break-even point of 30 days. Advertisers with more robust analytics can do this same calculation to identify the point where marginal cost is equal to marginal revenue (0 economic profit) on a sliding timeline to determine how much they want to hedge short-term losses in favor of long-term profits.

The Calculation

Gross Margin = Revenue – Cost of Goods

Net Margin = Gross Margin – Operating Expenses

Consider the following situation:

• Average Order Value: $100
• Cost of Goods (cogs): $50
• Gross Margin: $50
• Operating Expenses: $25
• Net Margin: $25

In this situation, the net profit per order is $25, meaning that the maximum acceptable CPA would be $25 on a $100 order. If all orders have the same revenue, cogs, and operating expenses, then we could easily manage advertising expenses to a maximum $25 CPA or minimum 4:1 ROAS, at which point there is no profit to the business on a 30 day basis. If the business has a CLV greater than 1x, then we can manage to a CPA higher than $25 or a ROAS lower than 4:1 and still make a profit.

In most cases, businesses have multiple products with unique margins. As a result, we can make an accurate set of assumptions by using global average margin or category average margin to more accurately calculate average profit per transaction within an account. Simply create a spreadsheet of category margins and run a lookup against this spreadsheet to calculate average profit at the keyword level.

Because CPA = CPC / Conv Rate, we can easily add in profit per click:

So, depending on how much profit must be realized in the initial 30 days, Profit / Click defines the max CPC for a 30 day economic profit of 0. If profit in 30 days is required, simply multiply the profit/click or profit/conv by the required economic profit percentage to determine the ideal CPC or CPA.

Below is the same table with a 50% economic margin and updated iCPC / iCPA:

Conclusion

What makes each bidding system unique is the process by which observed CPCs and CPAs are optimized toward ideal CPCs and CPAs. Some just update to the new ideal number, while others slowly move in that direction so as to not shock the system. There is no right or wrong way, just options and iteration upon iteration. This is a good foundation to start with, but by no means the final product.

Please comment below, as I’d love to hear about the directions different members of the community have taken with their internal tools.

Creating a website that attracts visitors via search engine optimization is one part of the equation; engaging the users to read your content, search and come back for more is another facet of online marketing. As I explained last week, I do think how well people engage with your content may be, or may become, a type of ranking factor. It would at least be a relevancy metric that Google might use. Engaging readers just makes sense, not only from a Search point of view, but from a business point of view.

I track scroll depth using a WordPress plugin called the Google Analytics Suite and have found the information to be pretty valuable. If you don’t use WordPress, check out the advice given in last month’s article, How to Measure Content Engagement and Effectiveness with Analytics & WordPress  and implement Justin Cutroni’s method on your website.

Insert “Clues” In Your URLs

I recommend using “clues” in your URLs to help you test content. I’m sure there are other ways you can do this, but this is my favorite way – and it helps me remember to describe what a page is about very carefully. For example, for my cooking website I use the clues “Recipe” “How to” and “Infographic” to help me determine which kinds of content are popular with readers.

Note: I do not recommend going back and changing URLs, that’s not a good idea, but as you move forward start adding a clue in the URL as to what kind of content is contained within the post/page.

Once you have started to add enough clues to your URLs to gather a representative sample set of scroll data, you can then start digging in and seeing what types of content engage your users to scroll, and which types don’t necessarily instill scrolling confidence.  The first thing I do is look at the base ‘Scroll Depth’ data under events.

Once there, I look at the “Event Label” view to see the following

Now that I have the scroll depth data, I’m ready to build the custom segment. This will use our URL “clues” to show sets of data by scroll depth.  To build an advanced segment click on “Advanced Segments” and “Create New Advanced Segment”

Create a new custom segment that includes “or” statements for your “clues.” It will look something like this

Name & save your advanced segment – I like to name mine something easy to understand. Something like “content effectiveness test” so I can find it easily. Your data will now show your test URLs compared to your scroll depth data.  Make sure you’re looking at Event Label and add the secondary dimension for the page URL.  Your final report will look like this.

We can then filter by event label if you’d like. What I can see from this data is that most popular posts do not get more than a 25% scroll rate. This means we need to tweak content on these pages to encourage more engagement.

Ideas For Increasing Engagement

My previous article mentioned one way (falsely, in my opinion) to increase engagement is to use pagination. This will inflate page views, and I believe it’s a false way to measure engagement. As a user it seems counter intuitive to have to keep clicking to “page 2″ to get the content (or the rest of the content) I want. Why didn’t I just land on page 2 in the first place?

Here are some user-friendly tips I came up with for encouraging engagement with your content – I’d love to hear your ideas in the comments as well!

• Use better graphics. Great photos tell a story, much like the words on the page. If you use better graphics that encourage a reader to continue to engage with the story, you can increase scroll depth.
• For my recipe posts, I’m thinking of doing more photo tutorials that show pictures of the recipe preparation step by step to encourage more scrolling – and deliver the printable recipe at the bottom. You could add more photos to any type of post.  This could pull the reader down the page to “see what’s next.”
• You could also use more bulleted text to encourage skimming down the page.
• Bold main ideas in each paragraph – this encourages the reader to keep going to get all of the main points quickly and easily.
• Train your readers to expect something “tasty” at the end of a page or post.  I like the idea of delivering 1 or 2 action items at the end of each post that encourages them to see it through to the end to get the “reward” for reading.
• Write shorter posts. Can you say what you need to say in 400 words instead of 800? If you can, and you can get your point across, why not?  You could split your idea in to two posts and create twice as many pages for your website.

For example, /default.aspx and /Default.aspx are considered two different pages in Google Analytics. If you’re used to exporting your content or landing page reports without taking this extra step, you’re probably reporting inaccurate data.

Deduplicating With Pivot Tables

A simple solution to this would be to create a pivot table. This will automatically merge your data — and isn’t case sensitive.

However, that won’t help you in a situation where you need to do VLOOKUPs to stitch your data together; so I’m going to tell you about a cool tool in Excel that few seem to know about but is very useful. It’s the Consolidate command, and you can find it under the Data tab.

Common Use For Consolidate Command

The most common use of the Consolidate command is to merge datasets from different worksheets or even workbooks (i.e., completely different files). So let’s say you have a reporting dashboard that has a separate worksheet for each month of data. You could use the Consolidate command to combine all of the monthly datasets into one aggregated dataset. You can even create links so that the consolidated dataset is updated whenever its individual elements are.

However, it also very useful in deduplicating datasets without losing any of the data. If you just have a list of, let’s say, keywords you’re bidding on, and you want to deduplicate that list, you’d use the Remove Duplicates command. This command is also found under the Data tab. But if that list also includes traffic and revenue metrics, you do not want to merely dedupe the list; you want to merge those values. See the difference?

Consolidate Landing Page Reports

What I’m going to use in this demonstration is a list of landing pages from Google Analytics that includes visit and revenue data. You can see below that we have two instances of duplicated data. The second landing-page01 was actually the result of removing all query parameters from URLs. (Then I, of course, anonymized the data.) If your data is littered with unnecessary query parameters (the values that follow a question mark in a URL), you can use Text to Columns to remove everything after the “?” symbol. (However, you should really be filtering out any query parameters you don’t need to report on in your profile settings.)

Step 1: Copy your column headings over to where your new, deduplicated dataset will ultimately live. Then put your mouse cursor where the dataset will begin.

Step 2: Choose Data > Data Tools > Consolidate, which will bring up the Consolidate menu.

Step 3: Since we’re going to want to add the visits and revenues for the duplicate rows, we’ll choose Sum from the Function drop-down menu.

Step 4: For the Reference, click-and-drag over your dataset. Or, if you’re working with a formatted table (which you should always be), just select any cell inside your dataset and press Ctrl-A (Mac: Command-A) to select all of the cells. Lastly, you could use a named range and reference that.

Step 5: Since we’re only dealing with one dataset, we won’t need to add more references. But if you were merging multiple datasets, you would select the Add button to add them.

Step 6: Since the values we’re using to deduplicate the list with are in the left column, we’re going to use that option under “Use labels in.” Here’s what the dialog should look like when you’re finished. Press OK to let Excel do its magic.

Step 7: And voila! Your consolidated data.

I use this command most frequently to deduplicate content reports in GA. If your URLs don’t automatically redirect to lowercase, or you don’t have a filter in place in GA to force URLs to lowercase, it’s really important that you take this extra step to make sure you’re presenting accurate data.

While hourly bid multipliers aren’t new, they remain a crucial tactic for optimizing your AdWords campaigns. They work by reducing your ad spend at poor-performing times of the week and increasing your exposure at the best times of the week. Here, I’m going to share the steps you can take (along with a helpful spreadsheet) to determine your hourly bid multipliers for better campaign optimization.

Step 1: Pulling An Hourly Performance Report From AdWords

On the Campaigns tab in AdWords, go to Columns>Customize Columns and ensure that you’ve selected the appropriate metrics. Performance metrics required for the spreadsheet to function properly are as follows: Campaign, Clicks, Impressions, Cost, Avg Pos, and Conv (1-per-click) — all other metrics selected in the screenshot below are optional:

Once your performance metrics have been selected, hit the “Download Report” button. When prompted, add the “Day of the week” and “Hour of day” segments:

This should provide you with all the data you need to analyze hourly performance at the campaign level.

Step 2: Determining Hourly Bid Multipliers

Similar to the template used to determine mobile and geo bid multipliers, I’ve created a basic spreadsheet to help analyze hourly performance and easily determine your hourly bid multipliers. You can download it here.

Copy and paste your AdWords report into this spreadsheet as directed. From here, you can take a closer look at the following:

a. Performance By Day Of Week

b. Performance By Hour

c. Performance By Hour & Day Of Week

If you have collected enough hourly data for each day of the week, you should absolutely make bid adjustments on an hourly basis. This process can be time consuming, as it requires making adjustments on a very granular level, but the results are well worth it.

For those times with less traffic, you can still leverage daily and/or hourly trends. For instance, looking at campaign #43 in the attached spreadsheet, it appears that there was not enough data collected on Sundays from 4:00 am to 5:00 am to make a specific bid multiplier suggestion — but you might still want to increase the bids, since the data indicate that both Sundays and the 4:00 am to 5:00 am window perform well in general.

The attached spreadsheet will only address those times of the week with sufficient hourly data, while keeping in mind that “bid adjustments for locations, days, times, and any ad group-level targeting methods can be set from -90% to +900%.” Thus, it can help you to determine relevant hourly bid multipliers between -90% and +900% when there are a statistically significant number of clicks:

Step 3: Implement Hourly Bid Multipliers In AdWords

At the campaign level, navigate to the “Settings” tab; then, go to the “Ad schedule” section. The first step is to specify when you want to make bid changes. Select a day of the week from the drop-down menu:

From there, you can you can adjust the effective hourly bid multipliers, as calculated by the spreadsheet:

Conclusion

All of this is fairly straight-forward; however, your hourly bid multipliers need to be maintained over time, hence the importance of a (semi-)automated process. Also, keep in mind that once set in AdWords, those hourly bid changes do not take into account multiple time zones. For instance, if your AdWords account is set to “(GMT-08:00) Pacific Time,” and you want to increase the bids by 20% at 1 pm, then these bid changes will occur at 1 pm PST across all PST/MST/CST/EST locations. As a result, it makes sense to break down your top campaigns by time zone in order to set more accurate hourly bids.

When asked if an advertiser should consider a 3^rd party technology to help manage ad spend, I would undoubtedly say “yes” because Google Analytics, AdWords, and Merchant Center were (and remain) imperfectly bound systems requiring a lot of proprietary work to merge and make informed decisions.

Much has changed in the last year. Google has made some significant changes (PLA, rules-based and improved algorithmic bidding, attribution modeling, enhanced campaigns…), making it increasingly difficult to leverage third party tools to gain an advantage over free tools like AdWords and Google Analytics. Having been on the inside at a technology house and heavily involved in marketing third party technologies, this is a difficult reality to embrace.

I’m going to go through the historical pros and present realities of 3^rd party paid search technology platforms versus their free ad server counterparts (AdWords, Bing Ads, and Google Analytics). For the record, I have no personal bias toward or against particular technologies and all statements below are generalized toward the entire industry.

Tracking

One of the most blatant shortcomings in the Google tracking ecosystem has always been conversion tracking — AdWords conversion events and Analytics goals/e-commerce tracking.

AdWords Tracking Setup

The inability to create composite tracking events, parse conversions event types for bidding, or modify conversion data after the fact can be frustrating if you depend on AdWords or Google Analytics as the only means of tracking conversions for your marketing program.

Google Analytics Goal Setup

Third-party providers have stepped in to provide comprehensive and flexible tracking systems with deep integration capabilities. But, while it sounds nice in a pitch, complex tracking integrations are only relevant if you have complex tracking demands. Some businesses require complex tracking solutions such as composite metrics or latent conversion attribution; however, the reality is that most advertisers have simple advertising tracking demands – either a CPA (cost-per-acquisition) or ROI (return-on-investment).

AdWords, Bing Ads and GA all do a very good job with CPA and ROI. And, with a little legwork and a half-decent internal database, most tracking needs can be distilled down to a CPA: the average net present value of a conversion.

Bidding

Building on the relevance of simple tracking demands, at face value, AdWords offers a fairly similar tool set compared to even the most flexible 3^rd party platforms:

• AdWords
  • Manual CPC Bidding
  • Rules based bidding
  • Algorithmic rank based bidding
  • Algorithmic CPA based bidding
• 3rd Party Software
  • Manual CPC Bidding
  • Rules based bidding
  • Algorithmic rank based bidding
  • Algorithmic CPA based bidding
  • Algorithmic ROI based bidding

Without any additional analysis, the only way to manage to a performance metric in AdWords that’s not CPA is via rules — an option that has been around for years and refined via 3^rd party tools, but only a recent addition for AdWords.

As previously mentioned, any revenue-based metric can be distilled down to a CPA, and as the ad server, Google has a massive advantage over any other algorithmic bid management provider.

Consider the number of variables a 3^rd party system can leverage for bidding in AdWords: in the enhanced campaign environment there are 2 bids per keyword (Computer/Tablet & Mobile). If the platform is robust, it may offer up to 8 day-parts, making 16 possible bids per day per keyword.

On the other hand, AdWords conversion optimizer can manipulate bids for all three devices, across both Google.com and search partner networks per unique impression. That’s several orders of magnitude of variance in possible bids per day!

The most common argument against conversion optimizer is that it’s a black box – but so are 3^rd party platform algorithms. The inherent advantage of the ad server is too much to ignore.

Large Accounts

Prior to Enhanced campaigns, marketers had a clear incentive to segment campaigns by as many values as possible to best understand unique click and conversion behaviors by device, network, etc. Take a comprehensive account with 50 campaigns and break it apart by three devices (computer, tablet, mobile) and three operating systems (windows, mac/iOS and others), and you’ve got a 450-campaign behemoth that’s borderline unmanageable due to size constraints.

Accounts that get too big become extraordinarily difficult to manage and manipulate as even the simplest tasks require mind-numbing repetition leading to human error.

As a result, 3^rd party platforms are built around automated processes and scripted bulk changes evolved to bear the burden of repetition.

For better or for worse, Enhanced Campaigns force marketers to reduce that load back down to, in my previous example, 50 campaigns – effectively negating the benefits provided by the 3^rd party tool sets designed to streamline bulk processes.

Bing

With all this talk about AdWords, we can’t forget about Bing Ads. A fleshed out Bing Ads account can make up 15-25% of overall volume in a paid search effort, so it’s nothing to scoff at. While Bing Ads lacks the same feature-rich design of AdWords, it does offer a very capable on- and offline editor. With a little elbow grease and offline analysis, AdWords bidding practices can be incorporated into Bing Ads.

With no proprietary bidding technology, we cannot rely on an algorithmic conversion optimizer to set bids for us in Bing. Fortunately, the very same tool we use to identify goal CPAs can be modified to calculate goal CPCs.

Consider the following known formulas:

• CPA = Cost / Conversion
• CPC = Cost / Click
• Conversion = Conv Rate x Clicks

Doing some quick substitution:

• CPA = Cost / (Clicks x Conv Rate)
• CPA x Clicks x Conv Rate = Cost
• Cost / Clicks = CPA x Conv Rate
• CPC = CPA x Conv Rate

So, we can take calculated goal CPA (net present value AOV) multiplied by conversion rate to identify a goal CPC at a keyword level.

Concluding Thoughts

Third-party software solutions exist to fill in the gaps where proprietary software falls short. For many years now, tools like Marin Software, Kenshoo, Adobe Omniture, and countless others have delivered enterprise-level tool sets designed to make up for shortcomings in the AdWords and Google Analytics tracking and management systems.

But, with the bevy of recent updates and changes, these platforms are struggling to maintain the edge that has made the search technology industry relevant. Keeping up with AdWords/GA has become the primary objective. Google is laying down the challenge to yet another industry: add value or move on.

Going beyond the search and keyword into how those keywords bring the visitor into content they either read or don’t read, is the next step beyond basic Google Analytics. Many in the industry believe a fast bounce from your page back to the search results is a negative in Google’s eyes. That user is basically voting your content down, saying “this isn’t what I wanted when I entered this specific query.”

If this is true, and I happen to believe it is or it will be, reducing bounce rate by investigating user behavior is a great way to increase your chances of a nice ranking for any given phrase. Nice ranking plus amazing user-pleasing content equals goal achievement. You can picture how this works in a circle.

How long are folks staying on a page, and how much scrolling are they really doing? I’d like to explore some ideas I have for measuring visitor engagement with a page of content.

First, you have to have good content. If you’re struggling along to get 300 words on a page that contains 3-4 keyword phrases and no care as to what the user does after they land, then you probably really don’t care about this. If you’re writing with an eye toward engaging the reader and retaining them as a reader and a consumer of some sort, then this will be of interest; read on, MacDuff.

Google Analytics

There are simple and difficult ways to track page engagement with Google Analytics. Event tracking is the easiest way; however, there are easy and complicated ways to implement and trigger the events you desire.

Scroll Depth

You can set events to trigger as a user scrolls to or past a certain point on the page. I love the idea, but I haven’t gotten it to work yet without using a WordPress plug-in. That doesn’t mean it can’t work, it just means it’s a bit too complicated to implement for a coding novice like myself.

If you’re interested in implementing this type of tracking on a non-WordPress site, check out this article by Justin Cutroni – and this one at Savio.no. They go through setting up the event step by step. If you have WordPress, you’re in luck. Keep reading!

Rating Events

I like the idea of creating a button at the end of your content that contains a simple thumbs up or thumbs down voting capability. You could also use a star rating system if you like. If the reader scrolls down to the buttons and clicks either one, that can trigger an event in your Google Analytics dashboard. This tells you that they at least scrolled down the page.

It can also help you understand what content the reader might like or dislike. Getting these votes is not the easiest thing in the world, so you would need a good amount of traffic to your site to ensure a representative sample vote.

Pagination Of Content

This is probably my least favorite way to deliver content. Many big news magazines do it, and I’m convinced it’s purely to show a low bounce rate and a high “pages-per-visit” so they can charge more for advertising. That being said, putting event tracking on the “page 2″ of the article can help you understand how many people are reading at least to the bottom of page 1.

In-Page Analytics

I don’t think this feature is used enough by even heavy Google Analytics users. Granted, when it was first available, it was a hot mess and not very useful. Today, it’s a different animal. There are still some weird features that you need to be aware of – if more than one link goes to the same page from the page you’re looking at – every link to that page reports the same number.

For example – my home button, the WordPress admin bar home button, and my header logo all link to my homepage, but all are used very differently. Google reports that 8.3% of users – an equal number – used all three ways to get to my homepage. That’s not a true statement; Google is combining their clicks because they all point at the same page. This is a feature that needs to be fixed, but doesn’t completely render this tool useless.

I know from In-Page Analytics that nobody is using my social media links in the upper right hand side of the page. I need to redesign that feature to promote engagement. No clicks means nobody is using them, and I need to do something about that.

WordPress

While there are plug-ins and other content management systems that many people use to create and update their blogs, I am strictly a WordPress user. If you use Drupal or Joomla, sorry, I can’t help. I have found there are some great WordPress plug-ins that can help you track content engagement; some even report that right into the events section of Google Analytics; and, all are free.

Google Analytics Suite

The Google Analytics Suite plug-in will hook into your Google Analytics and AdSense accounts and report page views, scroll depth, page speed, downloads and AdSense links as events. It is really simple to set up, and it hooks in easily to your Google Analytics account. The results are nice.

Below is information on scroll depth in Event tracking, just a few hours after installation. Now, you can see how far down your page the reader got. You can also see this data in Real Time event tracking.

Google Analytics For WordPress By Yoast

I really enjoy this Google Analytics plugin by Yoast de Valk. It’s easy to install and configure, and it reports on a variety of events.  Justin Cutroni stated in his blog post referenced above that scroll depth would be implemented in the Event Tracking on this plug-in; but, I don’t see evidence of that yet.

What I love about this tool is the ability to track exit links and affiliate links. If you link out to a lot of sites, you can easily see those as “exit links” as opposed to exit pages – where your visitor just disappears. This is something that Indextools/Yahoo Web Analytics used to do very well, and Google never did very nicely.

Outbound clicks are tracked as page views, so they’re easy to isolate in your Analytics dashboard under “Site Content” and “All Pages.”

Knowing how users interact with your pages is as important as knowing users are getting to your pages. Whether you share recipes or sell computers, understanding how your users behave once they land on any page of your site helps you write the content that best suits their needs.

You cannot know or understand user behaviors without analytics. Try one, two, or all of the methods shared above until you find the piece that fits with your site and your setup. Once you understand the type of content users engage with, and what form or shape they’re most likely to read, you can write and share more content more efficiently.

Postscript: It was pointed out in the comments that I missed a few configuration steps that make these tools even more valuable.

While the Yoast plugin is really useful, he actually recommends tracking outbound clicks as events, not pageviews. Under Advanced Settings in the Plugin Settings — make sure you have not checked the box that says “Track Outbound Links as Pageviews.” My installation had it checked.

When looking at InPage Analytics, Google announced a fix that sets attribution by link, not by landing page. You will need to alter your analytics code slightly — the directions are fairly easy to follow on this blog post.

Thanks so much to SearchEngineMan and Paul Thompson for helping me ensure the accuracy of my article.

However, there are some nice tools that go a long way toward making life easier.

If you are questioning the data you’re getting out of Google Analytics; if your e-commerce reporting doesn’t match your sales; if you really thought there’d be more downloads of your whitepaper on the fonts used in movie credits; then you can use these tools to find out if Google Analytics is broken or if the error lies somewhere else.

This is how we do it.

Things That Go Wrong

Typos

Sometimes we just get things wrong. The Google Analytics developers site references some of the most Common Tracking Code Errors.

Fancy Quotes

We do a lot of our data collection in Microsoft Word. This means the IT departments of our clients are cutting and pasting our code from Word.

If you know what I’m about to say, you’ll have an evil grin on your face.

In its effort to be helpful, Word likes to add fancy quotes to everything you do. Microsoft calls these “Smart Quotes.”

We call them “Fart Quotes” as in “brain fart,” and they turn this:

'product category'

into this:

&rquo;product category&lquo;

You can turn them off after a Dante-like descent into the nine rings of Word configuration, as the MalekTips blog will demonstrate.

Wrong Google Analytics Account

We find all kinds of strange configurations when we start optimizing a website. Often, we’ll be granted access to a Google Analytics account, only to find out that a completely different property ID (as defined by the “UA-#######-#”) is being used.

Viewing Page Source: Two accounts on one page.

Other Google Analytics Accounts

If you use a content management system like WordPress, you might find that some of your plugins also use Google Analytics. The most common one we find is the Disqus comments plugin, which you can only detect with some of the tools I introduce below.

View Source

The most common place to start for debugging your Google Analytics Tracking code is to simply open a key page and view the pages source.

In almost any browser, you simply right click on the page and select “View page source”:

Unfortunately, Chrome’s “Translate to English” option doesn’t help in this scenario.

Once you have the page open, you can search the page just like any webpage (Ctrl+F or F3) for some common Google Analytics strings.

• Searching for “Google” will find the domain that the tracking code uses to download the Javascript files. It will also find all of your AdWords-related tags and code.
• Search for “gaq” to find a common variable found in Google Analytics implementations.
• Searching for “UA-” will help you find out the Property ID found in the tracking code.

You should try this on the following pages:

• Your home page
• Your PPC landing pages
• Your “Thank You” or “Receipt” pages
• Your shopping cart, registration process, or subscription process

Ghostery

An “easier” way to see if Google Analytics is on a page is to use the plugin Ghostery. There is a version for all of the popular browsers.

Ghostery reveals that Google Analytics is on the page. It also reveals other tools that are installed, making it a great way to spy on your competitors.

In the image above, we can see that Google Analytics is installed on the page, but that doesn’t mean that the tool is installed correctly. We can also see that this site has CrazyEgg and Optimizely installed, two tools of the conversion specialist.

If you find these on a competitor’s site, be very afraid.

Generate Data & Look In Google Analytics

Once you feel that you’ve got Google Analytics installed, you can use the tried and sometimes-true method of simply logging in to Google Analytics and seeing if it is reporting data.

If you’re the only visitor to your site, this just might work. Otherwise, keep reading.

Firefox Debugger

I’ve just discovered this debugger for Firefox by Keith Clark, called GA Debugger.

I like the simplicity of this plugin. It shows what Property IDs you come across, which pageviews get generated, Events, Custom Variables and more, even if you navigate across sites. For those new to Google Analytics, you will like the hierarchy of the listing. It shows you how Events and Custom Variables relate to Pageviews in the system.

It doesn’t let you save a log of the data you collect, however, and this can prevent more detailed analysis.

Google Analytics Debugger For Chrome

Google’s debugger is only available as a plugin for the Chrome browser, but it provides the most detailed information of any of the tools I’ve found.

Google provides a debugging version of the Google Analytics Javascript code that generates messages for you as it works. This allows you to see exactly what is being written to your Google Analytics database, and what is not.

Install the plugin and an icon appears in your extensions list bar. This extension works in conjunction with a built-in feature of the Chrome browser, called the JavaScript console, which you can open by clicking the “Customize and Control Google Chrome” button, and selecting the “Tools” menu. See the following figure.

The information you collect is substantial. Every call is logged along with every parameter. The data collected by Google Analytics is also logged. You can see the Property ID, the URL of the pageview, the domain and referring URL.

You can also QA campaign information, such as source, medium, content and term.

You will be given information on Events, including Name, Type, Label and Value.

Your Custom Variables will be listed, complete with Label and Scope.

It’ll disclose what you are reporting to GA Ecommerce Tracking.

Here’s a helpful tip: If you right-click in the console area, you can select the “Preserve log upon navigation” option, which keeps the console area from being cleared with each new page.

Now, you can cut and paste the contents into a text editor and use filtering and regular expressions to zero-in on just the information you want.

But, we’ll save that kind of analysis for another column.

Anyway, this post is about helping search marketers determine those mobile and geo bid multipliers based on historical data.

1. Pulling The Data From AdWords

In AdWords, first go to the ‘Dimensions’ tab, then select ‘View: Geographic’ and update the column set as follows :

Depending on the size of your account, you might want to use a filter in order to limit the amount of data you want to look at – using a filter such as ‘Impressions>=100’ will help do that:

When downloading the report, use the ‘Device’ segment:

Now, we’ve got all the data we need to analyze device and top locations’ performance.

2. Determining Mobile Bid Multipliers

I have shared an Excel spreadsheet in order to help go through this process. You can download it here: Device and Geo Bid Multipliers.

Provided that mobile bid multipliers can only be set at the campaign-level and are relative to Desktop/Tablets bids, one should first aggregate Tablet and Desktop together, then compare Mobile against it.

One simple way to go about it is to use the formula below  based on the ‘Device’ field: =IF(LEFT(B3,6)=”Mobile,” “Mobile,” “Desktop/Tablet”)

Now, you can look into your historical performance by Desktop&Tablet vs. Mobile, which will help you determine your mobile bid multipliers for each individual campaign:

What is your Mobile CPA target? You can relate it to the Desktop/Tablet CPA target, such as: Mobile CPA target = X *Desktop&Tablet CPA, where X is a multiplier which reflects the role of mobile impressions for your business.

For instance, you might be able to determine that you are ok with your mobile CPA being twice as great as your Desktop&Tablet CPA since those mobile impressions are more about brand awareness, not so much about immediate conversions.

Then, for a given campaign, say your Desktop&Tablet CPA is $29.46 vs. $118.28 on mobile, and you are ok with the mobile CPA being twice as great as on Desktop&Tablet, then your mobile bid multiplier can be defined as Mobile Bid Multiplier = (Mobile CPA Target * 100 / Historical Mobile CPA )– 1, such as 2 * $29.46 * 100 / $118.28 – 1 = – 50%.

As a result, your mobile bid multiplier should be negative 50% for this particular campaign to achieve your mobile goal.

The attached spreadsheet should help apply the same logic to all campaigns with historical data across all devices. Note that you all want to make sure you’ve got enough data to determine those mobile multipliers, hence the ‘Click threshold’ field in the attached spreadsheet.

Note that you should rename your mobile-only and tablet-only campaigns just like your primary desktop campaigns for this tool to work in case you had broken down your campaigns by device.

3. Determining Geo Bid Multipliers

Using the same report and same spreadsheet, you should be able to easily visualize your historical performance by geo – for instance, by country to keep it simple – the ‘Most specific location’ field is way more granular:

Then, the same Excel spreadsheet can help suggest some geo bid multipliers for those locations with a statistically significant amount of clicks, assuming you want each geo’s CPA to converge toward your campaign-level CPA – which basically means you want to invest more where over-efficient, and cut the spend where under-efficient.

Your geo bid multipliers can be determined such as Geo Bid Multiplier = (Avg. Campaign CPA / Geo CPA) – 1.

For instance, if your campaign-level CPA is $18.40, while your New York CPA is $13.27, you might want to invest more in New York and set your New York bid multiplier to ($18.40 / $13.27) – 1 = 39%. And again, you want to make sure each location drove enough traffic to be able to draw any kind of conclusions; hence, the ‘Click threshold’ field in the attached spreadsheet.

In a nutshell, Enhanced Campaigns require some in-depth analysis by device and geo — hopefully, this post will help you get started, or at least put you on the right track if you were not sure how to go about it.

Obviously, those device and geo bid multipliers will need to be updated on a regular basis as consumer behavior evolves over time.