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A Visual Blog

In previous posts, I’ve tried to explain the reasons behind the frustrating fact that some report data just won’t ever match. This week’s topic is a no-brainer. To best describe why reports created by pulling data from different data sources won’t match other reports, let’s see it visually.

So, why is this a problem? Say you haven’t configured your web analytics to track your PPC, or maybe an agency is managing your PPC, but you have not given them access to your web analytics, so you must rely on the data the search engines provide. Inevitably, someone in your organization is going to want a roll-up or an executive summary of all the engines. The result is then the creation of a report from disparate data sets.

The problem – each vendor report is not aware of the other vendors. So, in the example below, each vendor report will claim credit for the entire purchase and claim all of the revenue.

As you can see, this greatly overvalues the conversion and creates an unrealistic view of the performance.
If you do try to compare the compiled report to your web analytics, there will be a problem as to how web analytics will credit that sale, as you can see below.

The real need is to conceptually ‘divide up’ the order and revenue and give everything credit.

By thinking about dividing up credit, you more accurately value the contribution of each advertising source. With this accurate and comprehensive picture, you can really optimize your spending, ensuring that you focus your spend, time and attention on what truly is working. What you may then find clicks (and impressions) that occur at the very beginning of the ‘funnel’ are getting the credit they deserve, so you may be able to increase bids on your more general keywords or show true ROI on banner impressions.

All reports are not created equally nor are they calculated the same way.  In an attribution world, there are two types of reports, ones that factor in attribution and ones that don’t.

The two types of reports have two different purposes:

• Operational Reports
• Performance Reports

Operational Reports

Operational reports don’t factor in attribution.  Operational reports are reports used to measure how your business is doing.  Operational reports are used to ensure there is nothing ‘broken.’  Operational reports include reports run from your ecommerce system, most traditional web analytics reports and reports that are compiled from several different sources.

Performance Reports

Attribution is crucial for performance reports.  Performance reports are used to judge how well your advertising is performing.  Performance reports also take into consideration latency and latent conversions described in my last post.

Both reports are extremely useful.  The problem arises when you try to compare an operational report to a performance report.  One common example of this  that I see is when a new ClearSaleing customer attempts to compare a report from their web database, an order report or a booking report, to the ‘All Sources’ screen of the Performance tab or any other report that factors in attribution.

These two reports will never ‘match,’ nor should they.  Operations reports give 100% of credit to the last click, 100% credit to the date and source of conversion (no attribution).  Performance reports divide up the credit and attributes it to each date and source that led up to the conversion (attribution).

So before you try to compare two reports, take a step back.  Think about the report that you are trying to compare to a performance (attribution) report.  Does the report factor in attribution?  If the report is coming from your ecommerce database or booking engine, or even your traditional web analytics, it probably does not use attribution.

There are many, many, many reasons why, when comparing two reports, even from the same system, the numbers will not match.

The reasons are (and each week, I will cover one reason in detail):

1. Attribution Reporting

Attribution reports’ calculations update daily and divide credit (orders, revenue and profit) over all of the contributing ads, impressions and clicks that led to a sale or other conversion.  Attribution is a newer concept for many marketers.   The fact that there is now the idea of a fraction of an order is definitely a different way of thinking.

2. Non-Attribution

Most marketers are very comfortable with non-attribution reporting.  Common examples of non-attribution reports are reports that are used for operational decisions, for example a Daily Order Report from a shopping cart.  Problems arise when trying to compare an attribution report to a non-attribution report (comparing apples to oranges).

3. Multiple Data Source – Over Counting/Over Crediting

If web analytics have not been configured to track all of the different sources of traffic to a conversion level, marketers are left to rely on reports provided by the source itself (e.g. Google AdWords Reports, Yahoo Search Marketing Reports, as well as other vendors like Display, Affiliates and Email).  Because none of these sources are ‘aware’ of the other sources, conversions and revenue can be over counted, and therefore, over credited.

4. Under Counting – Under Crediting

This is a by-product of the limitations of last click.  If you are using traditional web analytics, this is likely a problem.  Because the referrer only pays attention to the last place where a visitor came from, you wind up under crediting certain sources.

5. Accuracy – Data Quality of Web Reports

Here is the big elephant in the room that no one wants to talk about.  It is possible that the data that you are relying to make important spending decisions is wrong.  The number one cause, in my experience, is a problem with the implementation.  Examples I have seen of implementation problems are code not being on every page, conversion code not being on the page or not being correct, and profiles or other settings that have not been configured properly.  It is definitely worth auditing your implementation, even if you are using a free solution.

6. Bonus:  One web vendor report to another

Even armed with the knowledge that both implementations are correct and complete, it is most often an exercise in futility comparing one vendor report to another.  The reason for this:  each company has a different idea of how metrics should be defined and calculated.  And each company has settings both internal and external that define how metrics are calculated (e.g if someone is on your site for 60 minutes are they one visit or two).

This week – Attribution

Attribution is a difficult concept.  Take pay-per-click as an example.  In order to fairly and accurately measure an ad, you have to think about latency.  On the day an ad runs, some of the conversions will take place.  But some people will come to your Web site from the ad and not be quite ready to purchase.  They may decide to return to your site a few days later and purchase.

To be ‘fair’ to your ads, you have to be able to count these conversions and credit them to the original ad (or multiple ads if several were seen along the way).

For example, say someone clicks on a Google ad on April 27^th.  They get to your site but are not convinced to purchase.  Instead they continue their research.  The next day, April 28^th, they go to Yahoo, click on an ad and purchase.

If you are attributing credit, here is what your reports look like:

April 28, 2010 – Yesterday’s Report

Here’s the tricky thing – the numbers change.  In order to give credit for latent conversions, the historic performance will then change.

April 29, 2010 – Custom Report Range 4/27/2010

There is no additional ad spend, but the .5 order is now credited to the correct day.

And this impacts all of the calculated metrics.

April 28, 2010 – Yesterday’s Report

Here’s the tricky thing – the numbers change.  In order to give credit for latent conversions, the historic performance will then change.

April 29, 2010 – Custom Report Range 4/27/2010

And even a step further, this impacts the ROI for each day.

The best thing about solutions that provide attribution reporting is that they supply this information already pre-calculated, so you don’t have to think about these types of calculations or even consider the fact that there are now fractions of orders.

Conceptually, attribution is easy to understand.  Metrics are calculated based on allocation rules.  At its simplest, attribution is based on even allocation.

But taking a look ‘under the hood’ shows that even this basic attribution model can be quite complicated.   To do this, let’s isolate one order:

One product sold to Google Affiliate Network:

Two clicks in the path:

Exclusions (‘All But First’, meaning we exclude giving credit to that source unless it’s the first click in a path, so the first click (Direct) will get ½ the credit):

Correct Calculation: ($224.00 * .50) = $112.00 (Revenue)

Then, back out 50% of the total cost of goods sold and 100 % of Ad Spend to get to the total net profit, which in this case is negative.

It may seem confusing that the calculation shows ½ the revenue, then backs out ½ the cost of goods sold, yet backs out 100% of Ad Spend.  However, this is the only way to accurately value each advertising source.  Traditional web analytics tend to under-credit some sources and over-credit the last click sources.  Search engine reports can over-credit ads, especially when people cross search engines in their research.

In the example below, both Yahoo and Google would take credit for the sale:

Taking complete Ad Spend into consideration when calculating profit is crucial to accurately value each paid media source, but there is one other consideration.  What about the clicks that don’t cost anything?  That is where Exclusions come in to play.

If you exclude clicks that don’t cost anything, the story changes. If the exclusions had been set to exclude all non-cost clicks, the earlier example would tell a different story.

Correct Calculation: ($224.00 * .100) = $224.00 (Revenue)

Then, back out 100% of the total cost of goods sold and 100 % of Ad Spend to get to the total net profit.  In this case, the conversion would have been profitable.

Excluding giving credit to the clicks that do not cost you anything gives you the ability to analyze and optimize your total marketing budget.  It answers the questions, ‘Where is money being spent making the most money?’, and ‘Where is it losing money?’

The good news is that you can still see how the impact of the non-cost clicks affects the value of each ad source by switching the display option.

Tip of the Month:  Exclude your non-cost clicks to optimize your budget to your Ad Spend.  Then use the complete Purchase Path to see the impact of the non-cost clicks on the value of the different ad sources.  It is the best of both worlds.

Background

Back in the good old days of marketing, marketers made decisions solely based on their gut feelings. They’d create their marketing and media plans, and then print out a huge spreadsheet filled with marketing launches, ad buys, and creatives for the year.  The agency and the client would gather around the conference room table and debate one approach versus another until they came to an agreement on the marketing plan for the year.   The campaigns would be launched, budgets would be depleted and next year, it happened all over again.

But there were very few ways to accurately gauge the success of a particular campaign and to correlate it to increases in sales.  The marketers’ own experience and gut feelings were about the only criteria on which marketers based their decisions, as there was no effective way of gathering credible information on who actually saw their ads and campaigns and what they did as a result.  Basically, you spent the budget, and next year, if the company was still around, the budget was renewed or maybe even increased.  And then the planning process repeated itself.

Enter Web Analytics and the Last Click Mentality

Thankfully, things changed when web analytics entered the marketing picture early in the 21^st century.  Web analytics is great for helping marketers make decisions, especially those decisions related to improving the user experience once a visitor gets to your Web site.  One way that web analytics does this is by showing you the sites that are driving traffic to your Web site, also known as the referrers.

Web analytics also does a decent job of evaluating the success of your online marketing campaigns, but the information it is able to provide in this area does have its limitations.  Because most web analytics packages were built to monitor traffic once it arrives at your web site, they do not give you the full picture of everything that happened before a visitor got to your Web site—these packages can only show you the ‘last click’ referrer.

The reality is that only a small portion of your visitors do one thing–like visit one Web site, click on one ad, or do one search on one search engine–before they get to your site and convert.  The average visitor is likely to take several steps on the way to your website.  Unfortunately, web analytics is incapable of showing you the full path your visitors took before arriving on your site.

Attribution Management Widens the Funnel

By focusing only on the last click analytics that typical web analytics programs provide, savvy marketers may inadvertently be strangling the top of the funnel.  Consider a common trend of user behavior within a conversion process.  The graphic below shows hypothetical funnel statistics for a site with a well-designed checkout process:

Step 1 – Step 2                 Less than 10% conversion (add to cart)

Step 2 – Step 3                  Greater than 70% convert from this point (begin checkout)

Step 3 – on                         Greater than 90% convert from this point

Think about this:  If you could get even a slightly higher conversion rate from Step 1 to Step 2, you could exponentially increase overall conversion rates based on the conversion rate of the subsequent steps.

By counting on last click attribution that typical web analytics packages provide, most marketers cannot justify widening the top of the funnel with general keyword ads or banner buys.  This is because last click analytics focuses on the last thing that a visitor did before he/she converted.  Generally this is either clicking on a branded search result or coming back directly to the site by typing the URL into the browser or having the site bookmarked.

But smart marketers, armed with accurate attribution knowledge, can make the case for the more general keywords and the banner buys.  They know that many people need to do research before they make even a small purchase online, and they recognize that often, this research starts off with a very general search or an exposure to a banner.  Then, as the potential customer learns more about your brand and company and gets closer to making a purchase decision, they are more likely to get back to your site via a branded search when they are ready to purchase or convert.

Attribution Data Helps You Catch them Early

When the stakes are high and competition is fierce, marketers must seek out any advantage you can find.  Accurate attribution data presents one such advantage.  By having access to visitors in their early steps in the research, marketers who use attribution data are able to widen the top of the funnel AND market to potential customers earlier in the sales cycle.