Chris set the stage by describing two main situations for direct marketing:
1. Unlimited Budget: Set target ROI/CPA and maximize order volume or revenue
2. Limited Budget: Spend entire budget at best possible ROI/CPA

In both cases, there is a need to determine the “true” ROI. This can be particularly challenging in a situation where, for example, the sale started online but finished offline. Chris breaks it down with possible solutions and their effect on ROI metrics. See his presentation for the details.

Kyle Grant was up next, and focused on using analytics to tell a story. The story elements include where you are now, where you’re going, and how fast everyone else is moving in relation to you. Analysis should reveal the linkages between analytics data and its significance to (impacts on) the business.

Kyle explained how using analytics can move us from hindsight, to insight, to foresight. Next, he discussed defining Key Performance Indicators (KPIs), and avoiding data paralysis. He then went on with some examples and charts demonstrating results of an actual paid search campaign.

Very importantly for marketers, Kyle also discussed a simple formula for calculating the opportunity and justifying the cost of online advertising.

If you would like help assessing the opportunity gap in your own online paid search marketing, contact Tanya Harvey for a free consultation. Be sure to include your URL.

About the author
Karl Hourigan is a Digital Marketing Strategist at Mediative.

And yet, so many companies use a standard implementation of the Google Analytics and then ‘set it and forget it’. They fail to make the additional investment to really take advantage of the value that Google Analytics can offer in terms of identifying ways to acquire new customers and increase business with existing customers. Here are some things you may not have fully explored in getting the most out of this powerful tool:

Google Analytics Goals:

Your website exists for a reason, and it is not just to get visitors. Visitors are great, but what you really want is: customers. Or leads. Or people who sign up for your newsletter. Or watch your videos. Or download technical specifications. Or whatever it is that your website exists for people to do. There may be a variety of actions that your guests can take, but your focus for measurement should be on those actions that have the most direct relationships to your business success. Every site is different, and Google Analytics doesn’t know what your trying to do – so you have to tell it.

This may take some thought, but it is (generally) not complicated. You probably have a dedicated ‘thank you’ page that visitors see when they complete a purchase or download a document or sign up for email alerts. All you need to do is enter this URL in the goal settings and it is done. Some cases are more complicated, but there is almost always a way to set the goal, using ‘virtual pageviews’ or other means as necessary.

Also, keep in mind that Google Analytics has the ability to track the level of visitor engagement as a goal. So if your main objective is to get guests to spend time on your site, or to visit lots of pages (so you can sell impressions to advertisers), these can be set up as goals by setting thresholds of time or number of pages. Any visitor who exceeds the threshold is considered to be a conversion, so now you can track sources or content that lead to the highest value in terms of time spent and/or pages viewed.

And speaking of value, you can also assign dollar value to your to each of your goals. Even making educated estimates will provide you with another dimension of measurement that can support your decision-making in terms of maximizing the value of your site.

In some cases, it may well be worth modifying your site in order to be able to track your goals better, if that’s what it takes. After all, if you don’t know how many visitors are accomplishing what you want them to do, how are you going to make decisions to improve the site?

Conversion rates for various referring sites: it's not just about volume.

Advanced segments:

The ability to segment visitors ‘on the fly’ is one of the things that gives Google Analytics an advantage over other, more expensive analytics programs. Using ‘Advanced Segments’ you can zero in on, for example, visitors that arrive via search engines and observe their behavior. What are the trends in search engine traffic relative to overall visitors? What pages are they landing on? If bounce rates are high for these pages, what can be done to better match the content with searcher intent?

Things really get interesting when you combine advanced segments with goals, enabling you, for example, to focus in on visitors that completed your goals. How is their behavior different from average visitors? Are there particular pages or types of pages that these visitors are seeing more than the average visitor? If so, maybe it would be worth making this content more prominent.

In addition to the ready-made segments, you can also create your own custom segments by selecting or combining different criteria and then save these segments for future analysis. This way, instead of just looking at visitors as one big generic lump, you can hone your site for customer segments that matter and ensure you are attending to their needs.

Of course, there are a variety of other under-used Google Analytics features, like advanced report filters, custom reports, not to mention the power that comes from extracting data from Google Analytics via the API. But chances are that more fully leveraging Google Analytics goals and advanced segments will make it easier for you to stay one step ahead of the competition.

We all know that bounce rate conveys vital information about site performance and can provide very useful clues about where to look for improvements. Especially since Avinash nominated it as ‘sexiest web metric ever‘ back in ’07. As is often the case with web metrics, though, the numbers shouldn’t just be taken at face value.

Considered in the context of your site objectives, is a high bounce rate always a bad thing? Not necessarily. If people are getting the information they need from your blog and then moving on (maybe even to another one of your sites) or if they are seeing what they need on your site and then going straight to your brick-and-mortar, there is nothing wrong with bouncing.

Also, even for a relatively straightforward metric like bounce rate (% of visits that included just one pageview), it can be important to consider implications of metrics definitions and their use by web analytics tools in order to make well-informed decisions.

Here’s a case in point that I encountered recently in a site’s Google Analytics ‘Top Content‘ report:

Clearly there is a problem here that requires urgent attention: high traffic pages with huge bounce rates! It’s important to keep in mind, though, that bounce rate is calculated only for visits that start with the page in question. Being the ‘Top Content’ report, the pageviews account for all views of that page, whether the first page in a visit or the 50th.

So the report is NOT saying that of the 2,215 views of the first page in the list, 2,076 (97.5%) of them resulted in bounces. It IS saying that of all the visits that started with this page, 97.5% of them went no further. This, then, begs the question: “well, how many visits started with this page?”

For the answer to this question, we turn to the ‘Top Landing Pages‘ report in Google Analytics (or equivalent in your tool of choice). And here we see a story that puts things in perspective:

Yeah, that first page in the list has a high bounce rate…but it is based on only 16 entrances compared to over 2,200 pageviews in total! (I know we’re mixing metrics here, but the story is valid.)

Depending on the purpose and value of that page, this still may be cause for concern. But chances are there are bigger fish to fry.

There are other ways that bounce rate – and indeed most web metrics – can be twisted by context, so it is prudent to keep in mind the nuances behind the data being presented by our trusty tools.

(If you’re interested, there is further discussion of the ins-and-outs of bounce rates in Google Analytics here on the Google Analytics Help Forum – particularly relevant if you are wondering how to reconcile content and navigation reports.)

If  you’re the CMO or VP of Marketing, what KPI’s do you really need to see so that you’re reasonably well informed about the performance of your organic search program against the half-dozen other channels you’re responsible for?

I had a great question from a client this week wondering if they should move from 2 KPI’s to 3 for their high level dashboards.

Hey Chris, quick question: other than traffic and revenue, what do you
think a good third high level KPI should be? The number of keywords driving traffic,
pages indexed vs pages submitted? Or are those too granular?

This is one of those questions that can spark an amazing discussion. I asked the rest of our team to weigh in, and here’s some of their thoughts.

• Dollar out, Dollar in. Organic investment (agency fees & in-house cost) vs the return.
• Conversions: newsletter sign-ups, whitepaper downloads, etc.
• In some cases, bounce rate (or other engagement indicators) may be relevant, depending on the strategic goals of the team.
• % of traffic from Search vs. direct or referral (break this down further to traffic from natural search)
• Ratio of non-branded vs. branded terms that are driving traffic
• Engagement mentions and social signals.

It’s tough to pick one more without flooding your exec with data they’re not likely to understand anyways.

In the end we decided on # of keywords driving traffic (and conversions thereof), which supports both general optimization, their content creation strategy and their community building efforts.

What about you? If there’s only 3 organic metrics you can show to the C-Suite or your Board of Directors, which ones do you pick?

The Methodology
Jim and his colleagues recorded real time search logs from Collecta from June to December 2009. This resulted in just over 1 million search queries that they analyzed at a term (individual words) and query (a string of two or more terms) level for frequency, length, and mutual information, which is a measure of the association between two terms.

Search Source
Jim and his colleagues found that 60% of the searches on Collecta actually came through the Collecta API, rather than the web interface. This is very different from traditional web search engines.

Unique Searches
Studies of typical web search behavior show that the number of unique search queries can be as high as 59%, but Jim and his colleagues found that in real time search this number is much lower at 30%. They suggest that this is because many users are searching for the same timely information, particularly about entertainment, technology, and politics. An interesting corollary to this is that they also found that the same query being submitted repeatedly by the same user over a period of time.

Query Length & Type
This is one measure where real time and normal web search show similar patterns: the average query length was 2.32 terms. Popular queries, as I mentioned above, included entertainment, tech, and political topics. Unlike web search, however, real time searchers are not often looking for things that are pornographic in nature.

Search Terms
The number of unique terms and unique term pairs was similar to web search, but unlike web search, the individual terms that were most strongly associated with each other (that is, were most often found in the same query) tended to be related to people who were in the news during the data collection period.

What does it mean?
Jim and his colleagues sum up that:

• there is heavy use of real time search through APIs
• many of the searches through the API are repetitive over a period of time
• real time searchers are looking for different information than web searchers

and suggest that real time could leverage this by allowing users to save searches and employing faceted search interfaces.

It’s exciting to see research into real time search making its way to the public. If you want to read the whole paper, you can read it on Jim’s website (PDF).

Some brief notes on Search Funnels:

• collection of 7 reports that include Assisted Conversions, First and Last Click Analysis, and Path Length
• encompass only paid search activity (do not include clicks on organic results)
• provides info not just on clicks, but also ad impressions seen
• require AdWords conversion tracking or goals imported from Google Analytics into AdWords
• reports look back 30 days from the conversion event
• will be rolled out to AdWords accounts over the next few of weeks (accessible via Reporting > Conversions > ‘Search Funnels’ link on the left, below ‘All conversion types’)

Using ‘Search Funnels’, AdWords advertisers will finally be able to look beyond ‘last-click’ attribution to answer questions like:

• how many times did a visitor click on an ad to visit the site before completing a conversion?
• what keywords did the visitor search on and use to visit the site (via paid search) prior to completing a conversion?
• what keywords did a visitor search on and see an ad for (without clicking) prior to completing a conversion?
• for a given keyword (ad group, campaign), what is the ratio of assists to last click conversions? (closer to ’0′ indicates the keyword is a ‘closer’, while a high number indicates the keyword is pulling searchers in to the top of the funnel)
• after visitors clicked on a given keyword to visit the site, what were subsequent keywords that they used to get to the site prior to completing a conversion?
• how many hours or days passed before the first ad they clicked on and the eventual conversion?

Especially for B2B marketers, with relatively long (and sometimes twisted) conversion paths, often involving a string of generic and brand terms, having access to this information is golden.

To see how it works in action, check out this Google video:

Google AdWords Search Funnels Video

My own inclination is to side with those that believe this will have little impact on web measurement for those employing Google Analytics on their sites, for the following reasons:

1. Low Usage: The opportunity opt-in or opt-out is largely ignored by humans, who tend to go with the default, as Dan Ariely has so convincingly pointed out in recent years. If it works that way for organ donation, we can be pretty confident that is how people will respond to analytics tracking. Especially since Google Analytics is already set up to not collect personally identifiable information.

2. Existing Limitations: Web analytics data is already fraught with limitations caused by use of cookies, javascript, and half-baked implementation. These will likely continue to add up to more impact than any opt-out system.

3. Trending: Even if there was some initial adjustment as masses of users opted-out, there would still be enough data for most sites to establish valid trends moving forward. And data trends are arguably more valuable in web analytics than raw numbers.

4. Strategic Interest: Google has a strong interest in encouraging widespread usage of Google Analytics and has made huge efforts in the past to make this tool as attractive as possible to as many site owners as possible. Unlikely they are going to put all that marketshare at risk.

So this is definitely something we want to keep an eye on in order to determine the implications as details are revealed and the program actually rolls out. We’ll watch, but we’re not worrying – at least not yet.

After all, as Mark Twain said: “Worrying is like paying interest on a debt you don’t owe.”

Both numbers are important (although they may not be critical – depending on the outcomes you need to measure) and they provide similar information, but there are some important differences. Leaving aside the argument over whether either of these satisfies the criteria to become a real KPI, let’s consider the uses of each metric in the context of this client.

Visitors

I’m pretty sure that since the reporting was done on a one month period, that the tool the client is using reports ‘unique visitors’. (i.e. People – or at least browser cookies – that are only counted once during the period.)

[Side Note on 'visitors' in Google Analytics:

For Google Analytics, apparently the term 'visitor' is not enough and they even go beyond 'unique visitor' to insist they are reporting on 'absolute unique visitors'. Of course, this over-states the case, given the limitation of cookies. But, ok, we get it, this is your best count of individuals visiting the site during a given time period. More confusing terminology is used in the 'New vs Returning' report. This is reporting visits, rather than visitors (as explained on the Google Analytics Blog) but the term 'visitor' is also used. So maybe it would clear things up to refer to 'New Visits vs Return Visits'. ]

It is good to know how many people have come to your site, just as it is good to know how many people walk into your store in the mall. It gives you an idea of the total number of customers/potential customers that you are drawing in, and allows you to compare trends over time to spot opportunities or problems.

But there’s a big difference between a person poking their head into your store on their way to the food court, then never to returning again, and a person who repeatedly makes the trip to your store, even if they don’t purchase something every time. And this is where I think visits may provide more relevant, actionable information than visitors for this client.

Visits

As always, metrics that warrant attention vary depending on the nature and goals of a site. The client I’m talking about has a B2B ecommerce site that sells a broad mix of commercial products, including many that represent ‘repeat’ or ‘modified repeat’ purchases in Buyersphere terms. So, yeah, it is interesting to know how many people visit the site and to hopefully see this grow over time, but more critical in this case is the number of visits.

We are looking at organic traffic, and we are trying to use search engines to drive as many visits on as many relevant search terms as possible to the site. New visitors, certainly, but if we can capture visits from searchers who already know the site, so much the better, giving us the opportunity to further build on a relationship already established.

Further, we are already using this logic in measuring paid traffic, by counting ‘clicks’. Not necessarily the same as visits, but likely to be closer to visits than it is to unique visitors. So comparing organic visits to paid clicks may not quite be apples-to-apples, but it is at least apples-to-pears and pears are more like apples than oranges. (Visitors being oranges…you get the idea.)

Where the Rubber Meets the Road: Conversions

We all know – because Avinash has drilled it into us with his trinity approach – that it is essential to move from clickstream data to outcomes. (And, to be fair, virtually all leading web analytics advocates promote a similar philosophy.) So the number of visitors is interesting, the number of visits may be more so, but we need to get to the real reason our site exists: conversions. In this case, purchases. And to make decisions about optimization and resource allocation, we need to understand the efficiency of various channels bringing visits to our site and this means: conversion rate. And to get a conversion rate that makes sense, we need to have the most appropriate denominator.

Which brings us back to visitors vs visits. Yes, it can be useful to know what percentage of unique visitors in a month made a purchase, but wouldn’t it be more useful – in the case of this B2B ecommerce site selling repeat purchase products – to know the percentage of visits that resulted in a purchase? For a lot of B2B sites, the purchase pattern may resemble that of a car dealership: long consideration phase involving multiple visits, probably multiple decision-markers, (hopefully) culminating in a purchase that will serve the buyer’s needs for a lengthy period. This particular client has a site that is more like an industrial grocery store.

So in terms of organic traffic, it is quite possible that the same visitor may return to this site several times during a month searching for different products (in fact, there could be several different searches during the same visit, so visits are not the same as searches, but probably close). If we really want to understand how efficient our site is in converting organic traffic, we should be calculating conversion rate = orders / visits.

This also helps us compare organic search engine traffic with paid search traffic, where conversion rate = orders / clicks.

Conclusion

Focus on visitors or visits, as appropriate to site type and objectives, but do so consciously. Recommendation for this client: switch from a focus on organic visitors to organic visits.

It might even be worthwhile to consider tracking and analyzing conversions against visits for some keywords (‘repeat’ purchase) and against visitors for other keywords (larger, less frequent, or ‘blank-slate’ purchases).

For other sites, visitors – or a visitor segment – may be more relevant. With reference particularly to non-ecommerce sites, Anil Batra has a great blog post on how to dive in and select the appropriate denominator for your conversion rate.

Before I go on, I would like to say that I am by no means an expert on attribution. The purpose of this article is to really open up a dialogue and help take some of the confusion out of attribution as it pertains to marketing specifically online marketing.  People have been trying to figure out attribution for years and quite honestly, very few have a good handle on it.  Of course there are different types of attribution and with the digital age there are new types of attribution emerging such as last click attribution, 30, 60 or 90 days back attribution, multi-channel attribution, multi-campaign attribution and the list goes on.  Where to begin?

Let us start by sharing some common definitions of attribution:

• the process of awarding different marketing events different levels of commission
• placing value on various marketing efforts that lead to a sale
• the process of assigning value or credit to marketing sources that result in a conversion
• a method for assigning a worth to an action, a person or event

Really at the end of the day, attribution is about giving credit where credit is due.  The definitions above are all correct.  Attribution is about assigning a value to a marketing action that results in a reaction from your target audience (i.e. customer).  Again giving credit where credit is due.  Conversely, attribution is also about being accountable for.

The Recipe for Attribution

For attribution to work you need a couple of key ingredients:

1. Business Objectives – the clearer the better.  You need to know what the end goal of your campaign is.
2. Accurate Analytics/Tracking – you need to be able to accurately track all of the attributes that are present in your conversion process.
3. All Touchpoints Identified – if you miss one, and just one, your attribution distribution is negatively impacted and becomes less accurate.

Let’s look at a typical scenario.  This past weekend many of us watched the Super Bowl (congratulations to the New Orleans Saints, on a great season…).  Viewers are tuned into some of the most expensive advertisements ever produced.  So let’s say that you are looking to purchase a new car.  While you were watching the Super Bowl, you happened to the commercial for Hyundai Sonata.  You had previously done some research and Hyundai was one your shortlist as part of your consideration set.  Then you perform a search in Google for “Hyundai Sonata”then happen to click on the top organic listing which is for www.hyundaiusa.com/sonata/.  You bookmark the site.  You do some research and then jump off to another unrelated site.  Then on the weekend you meet with a friend who hands you a magazine with an ad or a review for a Hyundai Sonata.  You go back to the site that you bookmarked.  You then go online that night a perform a search for “Hyundai dealers” in <insert your town/city here>.  You happen to live in Seattle, so you click on a sponsored listing for Hyundai of Kirkland.  You then click back and find that there is a local listing for a Hyundai dealership not 15 minutes from your home.  You visit the dealership and obtain more information.  The deal presents you with a DVD outlining some of the features of the Hyundai Sonata.  You review and a week later you go back to the dealership and purchase a 2010 Hyundai Sonata.

As the marketing in this case, the folks at Hyundai can in fact attribute pieces of the sale to the various items that you were exposed to.  The original Super Bowl ad, your previous organic search tasks, the magazine ad, the sponsored listing(s) that you clicked on etc etc.  While this may be an extreme example, you get the point.  Attribution is complex at the best of times, so taking the time to clearly identify a typical conversion path becomes important for the purposes of attributing a value to each of the marketing efforts.

Something to keep in mind is that attribution may not ever be 100% accurate.  People are different and while there may be some common reasoning as to how or why they make a purchase, things like semantic mapping and experience are not the same for everyone.  As a result, it is hard to determine what influenced a person to make the decision they did at that single point in time.  Having clear business objectives, proper tracking and analytics and being able to accurately assign a value to your marketing efforts can provide a more useful method for attribution.  You will have a better idea of where your marketing dollars are best spent.