Learn How the Webtrends and Teradata Integration Solution Paints the Whole Picture

There is little doubt of how highly critical the importance of integrating online and enterprise data is. It is necessary to analyze both types of data in order to get the whole picture in a customer’s buying habits and satisfaction, and to improve conversion rates. Knowing when a customer connects with sales staff, knowing which direct mailings and catalogs the customer received, and knowing if the customer is chatting in social networks about the company or its products – all of these are tiny pieces to the whole picture. Unifying this information helps a company fine-tune their marketing strategies, reduce their marketing costs, and helps gives the customer a more personalized shopping experience and an incentive to purchase again.

This is a marvelous idea in theory, but very challenging to achieve in practice. Unless, that is, a company is using Webtrends’ enriched online data and Teradata’s Integrated Web Intelligence (IWI) solution, a partnership that is part of Webtrends’ Open Exchange program.

On December 2, 2009, in a live, interactive webinar, guest speaker John Lovett, a Senior Analyst for Forrester Research, Inc., will discuss why bringing online and enterprise data together is crucial. Next, Webtrends and Teradata will demonstrate their collaborated efforts solution of integrating online and enterprise data, and then will facilitate a question and answer session at the end.

Sign up today for the Integrating Online and Enterprise Data webinar and learn more about Webtrends’ enriched online data and Teradata’s Integrated Web Intelligence (IWI) solution for painting the whole picture to your customer’s purchasing experience.

Integrating Online and Enterprise Data Webinar
December 2, 2009
10am PDT / 1pm EDT

For marketers managing paid search and online campaigns, data is paramount. They use data to make campaign adjustment decisions, to determine budgets, to pay channel partners and affiliates, and to justify advertising spend to their management.
 
Once in a while, a new Webtrends Ad Director customer asks us about a seeming discrepancy in their data. Our reporting may have numbers which are higher or lower than those reported by a previous vendor, or the cross-channel reporting in their Analytics UI may not match our data for the paid search channel. When this happens, our customer will frequently ask which system is wrong and how they can fix it.
 
The ability to measure the success of search marketing campaigns and effectively compare them over time is mired in how performance is tracked and how metrics are defined. As many companies change paid search tools, outsourced vendors and the internal staff who manages their search marketing campaigns, understanding and leveling the data becomes increasingly important. Even when campaign goals remain the same, changing reporting parameters or outsourced providers can lead to dramatically different perceived results.

While sometimes the discrepancies are due to tracking not being set up correctly, frequently they are the result of vendors using different tracking and attribution methods.

Examples we frequently see include:

1  First-Party vs. Third-Party Cookies

Many anti-spyware applications and default privacy settings block third-party cookies and increasing numbers of users are manually blocking third-party cookies or regularly deleting them. This cookie rejection can result in loss of and less accurate tracking and conversion information. In contrast, first-party cookies are not blocked by anti-spyware software or privacy settings, and are the preferred method for more complete and accurate data collection.

2 Cookie Windows

Setting a same-session, one week, or 90-day cookie window will lead to different reported performance.
 
If you carry high price-point products which will require careful consideration and price comparison shopping, the conversion latency may be a week or more. By limiting your cookie window to same-session or one-week, you will miss the conversions which would otherwise be attributed to your search campaign.
 
It is also important to consider the portion of your customers who are single or repeat. If you carry products which will be replenished at regular internals (such as make-up), consider if you want to end up attributing each repeat sale to the click which drove the initial sale. If users restock their supply at regular one month intervals, by keeping a 90-day cookie window open you will effectively be seeing a “lift” in how many sales are generated by your search campaign over time.

Note that your paid search tool or vendor may also have attribution window settings which determine over what period they will take credit for conversions. If their attribution window is shorter than the cookie window, they will not take credit over the entire cookie period. If the attribution window is longer than the cookie window, they will only be able to collect recorded data until the cookie expires.

3  Methods of Conversion Attribution

It is possible to use many attribution methods and rules including last-click, first-click, multi-click, same-session-only, or anytime within the cookie window. Depending on the tools and tracking systems used, each one may have a separate cookie and may take credit for the same conversion event.
 
While businesses have different reasons for selecting a particular attribution method, the most important thing is to maintain the same or similar attribution method over time and to recognize that conversion data will look different with a change in method used. It is also important to recognize how the changes in attribution will impact reporting.
 
Examples of attribution methods can include:
·        Last click in: attribution is granted to the last channel and/or paid search listing which brought the user to the site prior to conversion
·        First click in: attribution is granted to the first channel and/or paid search listing which brought to the user to the site and eventually resulted in conversion
·        Multi-click: attribution is split across all channels and/or paid search listings which the user interacted with prior to converting

4  Definitions of Metrics

When speaking internally or with an outsourced search provider, make sure you have the same definitions of performance metrics.
 
For instance, is ROAS defined as (ad revenue – ad spend)/(ad spend) or as (ad revenue)/(ad spend)? Do these metrics include or exclude agency fees, if applicable?
 
Establishing these definitions ahead of time can prevent incomplete data later along with setting correct expectations for campaign performance.
 
 
It is important to determine early on how you plan to track your campaign metrics and maintain these settings to get the best picture of campaign performance over time. If you choose to outsource your paid search management or change reporting providers, make sure that your prior settings are maintained or kept as close as possible to how you are used to seeing reporting. If it is not possible to maintain all your settings, make sure to keep these adjustments in mind before analyzing the final data for performance.
 
By determining how you will be collecting metrics and performance internally, externally, and over time, you will have the clearest picture of how your search investment has changed over time and how it can be maximized in the future.

We will be discussing reporting and attribution methodologies, best practices, and the future of ROI-driven marketing in an upcoming webinar.
 
Please join us. Register here: True ROI-Driven Marketing Webinar

There are so many reasons to celebrate Webtrends Marketing Warehouse availability On Premise.  We’ve had a lot of success with our On Demand version over the last couple of years and wanted to better serve some unmet market needs with an On Premise version.  Here are just a few of the market needs that this product addresses.

• You choose: data mart or data warehouse – Our Marketing Warehouse product can be used in several ways to meet your organization’s needs.  You can use Marketing Warehouse as a data mart of online data to feed your enterprise data warehouse to benefit your organization as a whole.  You can also use it as your data warehouse for all you online data for your marketers.  Best of all, you can use it both ways!
• Deep integration – On Premise offers organizations ultimate flexibility in how they use their online data.  Having direct access to enriched online data means that the sky is the limit in how you want to integrate that data with other data and other marketing applications.  The goal is to drive insight to fuel actions with your customers and prospects.
• Ultimate control of the data – Whether its for privacy purposes, regulatory compliance, or  simply direct access to the data, having this level of control of this data behind your firewall is a huge benefit to many businesses.

Along with Marketing Warehouse, Webtrends Score is now available in On Premise as well.

Caution…proud Product Manager ramblings below.

I wanted to recognize the achievements of the Webtrends team who worked tirelessly to bring this product to market.  From developers writing code, to QA folks testing it, to Services folks ready to implement and support it, to Marketing promoting it, to Sales folks selling it.  A personal thanks to all of you who worked so hard to bring this product to market because you’re passionate about helping businesses strive for excellence.

Hi there everyone – it’s been a while since I last posted. We’ve been head’s down working on a Digital Marketing Maturity Model (a.k.a. DM3) that we unveiled during eMetrics in San Jose.  We released a draft of the model and are currently in the process of collaborating with industry influencers and practitioners, gathering feedback on the model and collecting real-world sample maturity profiles from organizations across multiple industries.  Details of the model and the quick maturity assessment can be found by clicking here.

We have already met or exchanged with folks from Gartner, Zaaz, and other industry influencers (inlcuding Sean Power, Stephane Hamel and Jacques Warren – THANKS GENTLEMEN!) to talk about the model and how we might be able to collaborate moving forward. We’ve also started initial discussions with Jim Sterne on how we can turn this over to the WAA and have it truly become an industry-wide standard. I am looking to continuing the dialog with the community, provide interested folks with the background and thought that went into the model and to solicit feedback from experienced analysts and marketers.

In each of a series of blog entries I will discuss a different aspect of the model and provide readers with details on the progress being made.  So be sure to come back often and comment. In today’s entry I’m going to provide an overview of the model and discuss some of the history and thought that went into it.  I’ll also talk briefly about our near-term plans for the model and how we’ll integrate public feedback into it.

As I mentioned in a prior post about our automated, interactive digital marketing scorecards, Brandon and I started the Digital Marketing Optimization practice here at Webtrends at the end of last year.  Since joining we have brought several new products/services to bear, the aforementioned Digital Marketing Scorecard, a services framework that we now use to guide all of our client engagements, an easy migration process that converts a customer’s historical data (when switching from a competitive solution) over to Webtrends so *no* information is lost, and now the DM3.  We have other offerings in the works that I’m just as excited about – but will write about those in other post.

During all this time we’ve had the opportunity to work in multiple roles across multiple verticals with some of the largest brands in the country, including Microsoft, Coke, Expedia, Orbitz, Toshiba, Disney, Dell, and NBC.  Throughout the experiences we’ve had with these clients from different perspectives we were able to develop the services framework, which ultimately led us to the maturity model.

Now that you know what got us here I’d like to provide an overview of the model itself.  We identified the need for a maturity model when we realized that we were pretty much informally assessing all of our client’s maturity at the outset of all the strategy projects we have done.  In order to establish successful digital strategies for our clients we need a way to understand their current competencies and opportunities for improvement.  As said by someone much more intelligent than myself, “the best map in the world is useless unless you know where you’re starting from.”  The same holds true when setting strategies and developing road maps to meet those strategies.

Once we understood the need for a maturity model we started doing research to see if there was already a proposed model that was gaining traction that would work for our needs.  We found a few proposed models but couldn’t find examples of how they were actually applied.  In addition, the models we found and researched scored an organization on a continuum where we felt what was needed was to score organizations across 6 distinct maturity pillars in order to create what we call a Maturity Profile.

Using our experience and research we landed on the six maturity pillars currently in the model with an assessment to understand ranking within each of the pillars.  We then visualize the results on a radar diagram which we did for several reasons (with an example output below):

1. The actual shape and area of the radar diagram becomes an organization’s maturity profile
2. These can then be overlaid on top of industry averages, competition, or ideal-state profiles
3. It allows for a very quick understanding of maturity deltas between a client’s organization and others

Example Maturity Profile

I’ll wrap up today’s post by briefly speaking to our near-term plans for the model and our next steps.  It is important to note that everything we do with this model is public domain – we strongly feel that the only way to even have a chance at industry adoption that it couldn’t be something that we dictate and hold close.  Also, we purposefully built this model to be completely agnostic of any tool or specific discipline within digital marketing in hopes that it could be adopted more broadly.  That said, we released this first version for a few reasons:

1. To solicit feedback from industry influencers and practitioners
2. To have organizations fill out the quick online maturity assessment in order to start building a database of maturity profiles across multiple verticals – this data will be used in aggregate to allow users to compare their current level of maturity against that of others in their industry

Our next steps will be to collect feedback and tweak the model as dictated by those who participate with us.  We will continue to actively search for individuals who would like to participate so please reach out to me directly if you’d like to be included.

In my next post I’ll go into detail about how we came up with the pillars in the model as well as how we use the assessment tool to develop a maturity profile. In the meantime please comment below or reach out to me at dm3@webtrends.com Thanks!

One of the most complex reports we have available in Webtrends is the scenario analysis report.  It’s also one of our most robust, and can provide you with worlds of good information to help you optimize scenarios on your site.  But I’ve found that a lot of people aren’t exactly sure what the report tells them.  They see the information, but they don’t understand what insights are being given. 

That’s what this series of blog posts is about: demystifying the scenario analysis report and making it work for you.

Let’s start by talking about a concept that’s bandied about a lot when talking about conversions:  abandonment.  In a lot of conversion funnels, abandonment equates to visits that did not “funnel through” to the following step in the scenario.  Not so with our scenario analysis reports; we focus, instead, on fall-out.

Here’s a sample scenario analysis report (click on it for a clear image):

At first glance you may think that this scenario has a 73.51% abandonment rate on step one, since that’s the first percentage to appear on the right.  On this, you’re right, but it’s not quite what it seems. Here’s what that number tells you:  Of the 38,232 visits to the “Product Page View” step in this scenario, 28,264 (or 73.51% of) visits did not on to the next step, but also did not entirely abandon your site.

From there, we get more detail on what happened with those 28,264 visits:

• · 619 (2.19% of the 28,264) visits did actually abandon the site entirely. The product page view was the end of their visit, hence the “End of Visit” label.
• · However, a full 27,645 (28,264 – 619) did not leave your site. Instead, they went elsewhere on your site. In the case of the first number (27,082, or 95.82% of the 28,264), they went to the Video Recorders page. How do I know this? I hover my mouse over the little blue name, and voila!

So, only 619 people from step one actually abandoned your site altogether; the others got distracted and did something else on your site, so they’re not completely gone yet.

How do I know what they did?  Well, if they went elsewhere on your site, this view of the scenario analysis will tell you, and will provide you with truly actionable information.  Here, for example, I see that the vast majority of people leaving my scenario on the very first step are looking for video recorders, so why not promote those video recorders on your home page?  Or maybe you could set up a bundle:  your most popular products with a video recorder at a reduced cost.  Upsell! 

Now, let’s shift to the Step Transitions view.  I do this by clicking the “View Step Transitions” button above the report (again, click for clarity):

This view provides us with completely different information.  Instead of telling you whether a visit ended or continued someplace other than the scenario, this process shows you two things:

• · The number of visits in which the visitor did not continue directly on to the next step, yet remained within the scenario process as a whole, and
• · Where that visit went when it left that step.

So, let’s look at what the numbers on this first step tell us here:  6,733 visits did not convert to the “Cart Add” step.  However, they still interacted with the scenario:  6,731 (99.97% of the 6,733) viewed another product page (so, they were still shopping), and 2 (.03%) actually started checkout (which probably means they’d already added something to their cart and decided not to buy what was on the last product page they viewed).

So, we can say that, of the 28,264 visits that did not convert from step one to step two, 6,733 did not leave the scenario entirely.  Instead, they either skipped a step or stayed on the same step; they didn’t abandon.  That’s almost a quarter of the visits that didn’t convert to step two – and that’s a great opportunity to ensure that, now that they’re in the scenario, they stay there.  Note, for example, that 317 visits went back to the “Product Page View” after they started checkout.  Is that a result of your efforts to offer them similar items or accessories on your checkout page?  Or, if you’re not making such offerings, could you increase that number by doing so?

Of course, these opportunities may seem fairly obvious; after all, we’ve been tracking shopping carts for a long time on the web.  But imagine tracking your three-step application/registration process, or your five-step “Give me more information” process, and you can see how this information becomes useful quickly.  You might be able to reduce the number of steps and increase conversions, or note where people are getting distracted and provide them, within your scenario, the information they need to stay on track.  That’s so much more helpful than just tracking abandonment, isn’t it?

I’ll discuss the left side of this report in an upcoming post – stay tuned.

When you first start trying to get value from your web analytics tool, you may be surprised by some of the road blocks put in your path by your own organization. Internal policies and procedures may be inflexible. It may not be easy to get necessary decisions made or, worse, you may not even know who needs to make them. And it may also not be readily apparent who in the organization stands to benefit from the data you can provide.

The journey from being a data-consuming organization to becoming a data-driven one can be long and painful, especially if you don’t accept at the outset that you ARE an agent of that change. Analysts or administrators can quickly find themselves, as the person who is “closest” to the data, called on not only to support the analytics tools or deliver insight from the data, but also to drive adoption of the data through internal marketing, coordinate projects with other departments, identify opportunities for increasing value to the organization, integrate analytics data with data from back-end systems, develop standards and governance models, perform cost/benefit analysis of analytics efforts, and assist with (or even drive) budgetary planning. Ultimately, your efforts as an agent of change will involve and impact many people along the way. And not all of those people will be happy about it.

Here are some ways you can plan for the  impacts of change and be successful as an agent of organizational change. (more…)

Flash tracking as with AJAX is done through calls to dcsMultiTrack. However because a Flash applet file (.swf) is embedded into a page as a self contained object this means we must use a different method to make calls to other elements such as the logging script within the page.

We can use any event in Flash to trigger a dcsMultiTrack call; video completions, slide views, loading percentage indicators, clicks, drags, drops and more. The most common or at least traditional method for calling dcsMultiTrack within Flash uses the getURL function:

on(release){
getURL(”javascript:dcsMultiTrack(’parameter1′,’value1′,’parameter2′,’value2′);”);
}

This code would be added to each button that needs to be tracked. If you’re smart you won’t want to rewrite the multiTrack onto every event handler call for every button across your site. It makes sense as with did with AJAX to use a more modular approach. Taking the getURL method above and placing the call into its own ActionScript function, then calls to the event handlers can also be passed along with parameters to this function.

function trackEvent(value1,value2){
getURL(”javascript:dcsMultiTrack(’parameter1′,’”+value1+”‘,’parameter2′, ‘”+value1+”‘);”);
}

An alternate to getURL is the externalInterface method:
import flash.external.ExternalInterface;

function trackEvent(value1,value2):Void
{
If (ExternalInterface.available)
{
ExternalInterface.call(”dcsMultiTrack”,’Parameter1′,value1,’parameter2′,valu e2);
}
}

Where scalability may be a factor the externalInterface method is the best probably the best choice. In addition to being scalable, externalInterface is the new “best practice” method for page/Flash object interactions. It allows data to be passed in and out of the Flash object (a getURL is a static one-way command). Because of the added flexibility, this method will allow new interactions to happen as they are developed.

Should the externalinterface method be attached to all event handlers enabling you to collect information on any and every interaction? Or prehaps limited only to duistinct content loads/views within the applet? And what should you be passing as parameters? Why not join us to discuss this at Engage 2009 in our Workgroup. Look forward to seeing you there.

I’ve been in the software industry for over 20 years and in that time I have seen plenty of term confusion and misuse.  But it’s hard for me to remember a time when I saw so much of it from a single space.  Let’s clear the air on a couple of definitions (for now), beginning with the liberal use of the term multivariate.

Regardless of the terms, there are really two fundamental forms of testing and optimization in our space: split tests, as I refer to them, and multivariate.  Split tests, a.k.a. A/B tests or A/B/n tests, apply when you have a small number of variables and values.  For instance a single variable, such as an image, may have two values, Image A and Image B.  This most basic example is where the term A/B test comes from.  Add in Image C, Image D and Image E, and you can see why the A/B/n shorthand is used.  In these instances the number of values is low enough to permit a large number of trials to be performed on each, providing us with a clear statistical winner with a reasonably sized population of trials.

Now let’s throw in another variable, such as a text block.  You might have two versions of the text to go along with two versions of the image, making four combinations.  This is where the confusion enters in.  Some vendors have gotten into an annoying habit of calling this multivariate.  It’s not.  You can use the exact same split test approach as before – it just so happens there are two variables involved.  I like to call this multi-variable split tests, but frankly I don’t care what we call it, so long as it’s not confused with multivariate.

So what is multivariate optimization?  It’s a form of statistical variance analysis.  The Taguchi methods (named after its inventor Genichi Taguchi) is one form of multivariate statistics reportedly used in several behavioral targeting, testing and ad optimization solutions today.  Where multivariate statistics come into play is when the number of variable/value combinations is so large that it prohibits more than a small number of trials being run against a single combination of values (i.e. it would be too time consuming or too costly).  Multivariate statistics permit us to infer, statistically, the singular values and/or combinations that lead to the desired outcome (e.g. conversions) based on a relatively small population of trials.

Now that we have that cleared up, let’s move onto the term “portfolio” as it applies to PPC campaigns. Back in the ‘70’s and 80’s, Harry Markowitz published his works on Modern Portfolio Theory (MPT).  His work earned him the Nobel Memorial Prize for Economic Sciences in 1990. Essentially MPT provides the body of mathematics used to create diversified investment strategies, and in so doing providing the greatest possible return within a given risk tolerance (notwithstanding global economic calamities, that is).  Or conversely, the lowest amount of risk for a desired return.  It’s all on Wikipedia, so you can read all about it there.

More recently some really smart people from Efficient Frontier, Inceptor (RIP) and WebTrends applied some of the principles of MPT towards the problem of optimizing large-scale PPC campaigns.  In fact Efficient Frontier derives its name from one of the main concepts of MPT.  It is also how the term “portfolio” came to be used in the context of search marketing.  Today I’m only aware of three vendors that use MPT-style mathematics in their PPC optimization solutions: Efficient Frontier, eSearchVision (I think) and WebTrends.  If there are others, please comment below and let me know.

So now enter the confusion.  Several vendors advertise “portfolio-based bid management” capabilities.  What is meant by this is that you can apply a bid rule against a group of keywords.  The term portfolio, in this instance, is used as an English synonym to group or collection.  Grammatically accurate?  Yes.  Intentionally designed to mislead?  Absolutely.

Of course vendors get away with this because the detailed understanding of these technologies is locked in the heads of a relatively small number of people.  But hopefully they are influential people who have a belief in transparency and truth in advertising.  So if you’re one of those people, please help educate the market, starting with me.  If you see errors in fact or have differences in opinion from my comments or in WebTrends’ messages, let’s hear it.

You Asked, We Acted.
Today we launched a few improvements to WebTrends Tag Builder. Based on input from this blog we were able to quickly make some changes to a couple of issues you called out.

•   We didn’t update referrer appropriately when off-site links were clicked. Now these are passed with every off-site click.
•   We were a little too picky about file extensions. File extensions you specify on the Click Event Tracking tab are now case-insensitive.

Auto Detection of Paid Search for AdWords
Are you interested in tracking paid search for Google AdWords in your WebTrends reporting without having to deal with additional setup? Now our tag detects if referrals from Google originate from paid or organic placements without requiring you to set an additional parameter. This does require that you have the “Destination URL Autotagging” option enabled in your AdWords account. We will keep you posted as we automatically detect other engines in the future.

Integrated Tagging for Quantcast Publisher
Tag Builder now includes Quantcast as an option when you are setting up your WebTrends tagging. Quantcast is a no-charge solution that provides access to detailed geographic, demographic and lifestyle data about visitors to your site (including age, race, sex, income range, children status and other sites visited).

With WebTrends and Quantcast you can:
•    Compare your traffic acquisition strategy to competing sites
•    Get insight into how your competitors are getting traffic
•    Use insight on high-value populations to move ad dollars
•    Adjust your ad buy mid-stream – see what demographics are responding

For more details check out the Quantcast site.

Please keep the feedback coming. We are listening! Whether you are a software or On Demand customer, we’re always working hard to improve your experience with WebTrends.  Let us know how we’re doing -  from within the products,  here on the blog, user forums, Twitter or contact any of us directly.

Hi Everyone,

Last week I had the pleasure of listening to Eric Peterson speak not once, but twice. The first time was during a Coremetrics webinar on campaign attribution and the second later that evening at the local Web Analytics Wednesday where Eric delivered a longer presentation that included the same attribution material. And while I have a great deal of respect (and even friendship) for Eric and an equal amount of respect for Coremetrics, I feel a need to challenge the content.

For awhile I’ve been speaking about the emergence of the third generation of web analytics, as I call it. For those that haven’t heard me present this before, the first generation was characterized by IT departments measuring web site activity via software installations of log file analysis tools. The second generation was dominated by marketing departments utilizing hosted solutions and page tagging. The primary value these two generations of solutions provided were aggregate reports, along with rudimentary ad-hoc analysis capabilities (rudimentary, that is, compared to modern business intelligence systems).

Whereby the first two generations were characterized by reports, the third is certainly about the data – the open access to un-aggregated visitor detail data and the endless forms of true analysis that can be performed with it. Knowing that Coremetrics is one of a few major vendors to store un-aggregated data in an industry-standard database (along with WebTrends) I was expecting a thoughtful discourse on statistical modeling. Alas, what we were told was to utilize not one, but three flawed attribution models (last, first and equal), in hopes that three wrongs would make it right I suppose.

Since our high school statistics classes we have been taught the difference between correlation and causality. Statistics show that as ice cream sales increase, so do drowning deaths. Therefore, ice cream causes drowning, right? Of course not – it is the onset of warmer temperatures that indirectly leads to both. As trite as this example may seem, it is no different than the fallacy that a campaign’s inclusion in a visitor’s click-path prior to conversion means that it had a causal affect on the conversion, or that it belongs in our campaign portfolio. The same campaign may have been clicked on by many more non-converting visitors … at substantial expense.

True, if a visitor clicked on a campaign prior to conversion, it’s certainly more likely to have had a causal impact. That’s especially true for the last campaign. But if we’re going to finally break away from the flawed last-click attribution model, why not do it correctly? We have the data – let’s use a statistical model.

Now for the less-than mathematically savvy user of web analytics, no, this doesn’t mean your solution will be more complicated. Quite the contrary. Before credit card companies implemented mathematical models to detect fraud, we consumers would first learn of fraud only after we received our statement. And then after weeks arguing with our vendor we might have gotten the charges removed. Today we get a phone call within hours of the questionable transaction and a new card sent overnight to us, no questions asked. Math made our lives easier.

So will it be for campaign attribution. Imagine a campaign report that tells you, in a statistically valid way, which campaigns and campaign attributes actually had a positive contribution to conversion and to your campaign budget, versus those that didn’t. Then imagine that same report telling you how to improve results. I propose the following report:

Don’t sweat the details – I just punched some example data into a spreadsheet. Instead, focus on the bigger picture of having a report that shows you how your campaigns truly performed and recommends to you an adjusted mix based on the current set of campaigns. Then imagine the data for auction-based networks being automatically passed to an automated campaign optimization system. Now that would be progress towards true optimization of campaign budgets while also making the marketer’s job much easier.

Note that at the moment WebTrends doesn’t provide the above report either (but we do have the requisite data in a readily accessible format). My point is that it’s time to embrace the third generation of this industry and start truly leveraging the data in mathematically and scientifically valid ways.

- Barry

P.S. Please send me your thoughts on the dream campaign report.