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Attribution Management Buyers Guide: Part 2 and 3

Thursday, October 1st, 2009

Attribution Management Buyers Guide Blog Part 2: Products

Given the critical nature of attribution management to advertising analytics, we have created the Attribution Management Buyer’s Guide for marketers to use when selecting an advertising analytics and optimization platform. The Guide is intended to highlight key attribution management features and functionality that should be available in any advertising analytics solution you select.

This is the second blog in a 10-part blog series for the Attribution Management Buyers Guide. This second section focuses on Products, where we focus specifically on the products or services that were sold as a result of a team of ads.

The most critical question to ask here is, ‘Does the solution show the actual products/services that were sold by order, or do you just report that an order occurred along with the revenue it produced?’

On our previous post, we focused on Attribution Variables, which are the key metrics by which you are valuing conversion credit across the participating team of ads. Of the three metrics we highlighted (conversions, revenue, and profit), profit was the optimal metric to use.

In order to get to profit, an attribution management system needs to be aware of the products you’ve sold so that it can calculate profit by subtracting sales price minus the price of cost of goods sold minus cost of advertising, and then apply tax and shipping rules. If the attribution management system cannot report on products sold, then it cannot produce true profit figures. At best, it will be able to take revenue and allow you to apply a flat margin across all of your sales.

An added benefit of a system that can report on products sold is that it can provide a wealth of information about trending of products you sell, which products get sold together often, and also provide an opportunity for up sell and cross sell in the future.

Attribution Management Buyers Guide Part 3: Ad Sources Tracked

For attribution management to be done correctly, it cannot be done in a silo. Many conversions are the result of multiple forms of advertising. For example, a banner impression leads someone click on a paid search ad, then an organic search, and then they convert. If the solution you’re using is only able to capture paid search, it would be oblivious to the fact that the banner impression is what introduced that person to your brand, and that the organic listing is what eventually closed the deal.

To ensure that you’re getting a system that performs attribution management across all sources (advertising and organic), make sure you ask the vendor the following questions:

Which ad sources does your system track?
Do you place tracking code on my website?
- If the vendor doesn’t offer tracking code, they are solely reliant on information that comes through the APIs of ad sources. The only ad sources that offer APIs today are search engines; therefore, a system that does this is only capable of doing attribution across paid search.
Do you get all of your data from the APIs of the ad sources?
- Again, much the like question above, this is an indicator that they only track paid search.

Do you track direct and organic visits to my site as well?
- In order to have a full view of attribution, direct and organic visits must be included.

A foundational requirement for attribution management is the ability to track across all online ad sources, not just paid search. And the ability to do that requires your advertising analytics platform to use its own tracking code, instead of relying on the limited information provided through search engine APIs. While the search engine APIs do provide some valuable management information, the data is inadequate when it comes to tracking, classifying and accurately attributing value to the team of ads that led to the sale or conversion.

Tags: advertising analytics, amf, attribution management, attribution variables, buyers guide, forum, product, video, video blog, webinar
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Shop.org Group Working Towards Establishing Standards for Attribution

Wednesday, September 30th, 2009

On August 5^th 2009, the first meeting of the Shop.org Attribution Special Interest Group (SIG) kicked-off. The originator of the group is Anne Ashbey, who currently is an independent consultant and previously worked for Harry & David. Anne wanted to bring together people who work for eTailers, technology providers, advertising agencies, as well as online marketing insiders to establish “benchmarks” for companies to follow when doing attribution management. She was kind enough to extend an invitation to me to participate in the group based on ClearSaleing’s thought leadership in Attribution Management and attribution-based advertising analytics platform.

The first meeting included people from companies such as Range Online Media, Rosetta, Under Armour, JC Penny, Google, Rimm Kaufman Group, QVC and American Eagle. The goal of this first meeting was to establish a charter for this SIG. These are the three goals that emerged:

1) Review current multi-channel allocation methodologies for determining incremental sales, highlighting the pros and cons of various approaches;

2) Review available and emerging technologies for tracking and allocation;;

3) Recommend best practices for retailers to implement in their organizations to effectively measure the incremental impact of their marketing dollars.

In order to achieve our charter, we setup two groups during our second meeting: Group 1 is Case Studies, and Group 2 is Technology. Group 1 (Case Studies) is working with a few of the eTailers in the group that are currently performing attribution management to document the process they are using today, and the before and after affects that attribution management has had on their marketing decisions and their organization as a whole.

Group 2 (Technology) is evaluating current technology that exists in the attribution space, as well as identifying features that attribution management should contain in order to track effectively, measure accurately and produce actionable data to improve the bottom line.

Once the case studies are completed, we are going to bring in analytics experts to audit our findings and validate our processes and recommendations. These “analytics experts” will be some of the most recognizable names in the field.

The third meeting took place on September 22^nd at the Shop.org Summit in Las Vegas. In this meeting we vetted some of the challenges with creating case studies about attribution, as well as some of the data points that need to be captured to make the case studies worthwhile. One challenge that arose when trying to produce before and after results is using two different time frames. For example, a company might have been using last click attribution in Q4 2008, then switched to multi-touch attribution in Q1 2009. If we compare profit, revenue, sales figures, etc., the increases or decreases we may find may have nothing to do with attribution, but more with the time of year that we are in.

We also identified some data points that we need to have in the case study. We recognized that the companies that take part most likely will not want to share any personally identifiable information, as they don’t want to give away any findings or secrets to their competition. Some of the data points that we agreed need to be captured are the marketing mix that attribution is being compared across, the ad spend, the type of attribution, and the industry the company is in, beyond just knowing they’re an eTailer.

A the end of the third meeting, we all agreed to have firmly established outlines for the retailer case study and technology review prepared for the next meeting, which will take place in later in October 2009. The ultimate goal is to publish the retail case study and technology review sometime in Q1 2010 after the 2009 holiday shopping season has commenced.

If you would like to keep up to date with the findings of this SIG, go to www.shop.org and search for ‘Attribution SIG’.

If you would like to read more about ClearSaleing’s findings in the world of attribution, please visit www.AttributionManagement.com or contact us directly.

Tags: analytics, annual summit, attribution management, attribution SIG, shop.org, technology
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Attribution Management Buyers Guide: Part 1 – Attribution Variables

Friday, August 28th, 2009

This is the first blog in a 10-part blog series for the Attribution Management Buyers Guide. This first section focuses on Attribution Variables, which are the key metric(s) by which your advertising analytics solution values conversions and attributes credit across the participating team of ads.

Most advertising analytics packages that offer attribution will attribute credit according to one of the following three metrics: 1) profit; 2) revenue; and 3) conversions. Some packages can attribute all three metrics as well.

There are pros for each one of these variables, and cons for some:

Profit:
- -Pro - Businesses are in business to do one thing – generate profit. Therefore, anytime you can measure a business activity according to the profit it drives, the better you understand the value of that activity. The same is true for advertising – if you understand the profit a particular ad generates, then you definitively know if that ad is worth continuing to buy or not.
- -Con - There are none, however, we realize that calculating profit can be difficult for some business entities.
Revenue:
- -Pro – If you cannot get to profit, this is the next best metric. Though revenue does not definitively tell you if you’re making profit on those ads, you could infer that considerable revenue is being made on that ad, so you could continue to invest in that ad.
- -Con – Just because revenue appears to be trending in a positive direction does not mean you are also producing a profit. By not considering your margin and cost of goods sold, you could be misled into investing in ad that is not producing profit.
Conversions:
- -Pro – Allows you to see which ads were involved in the highest number of conversions. This may shed light on top of the funnel types of ads that generally do not receive conversion credit, and this can help expose the type of value they have to the success of other ads.
- -Con – Since all conversions are not created equal, it’s difficult to understand what impact any ad is having according to conversions on the bottom line. It is nearly impossible to perform accurate attribution with only this metric at your disposal.

As a follow-up, ask the vendor how they calculate profit. In order to be truly accurate, they should be incorporating your cost of goods sold and cost of advertising into their calculation.

Tags: ad, adam goldberg, advertising analytics, analytics, attribution management, buyers guide, clearsaleing, media mix, online advertising, profit, variables
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You Need More Than Simple Math to Solve Attribution

Wednesday, April 8th, 2009

If you’re reading this blog, there’s a good chance you’ve been in a situation like this: You and your marketing colleagues believe giving conversion credit to the last ad click is a flawed attribution method. Therefore, you have investigated or invested in technology that allows you to track beyond the last ad that is clicked so that you can perform attribution over the team of ads that lead to a conversion. With this new tracking in place, you then need to determine the correct attribution models, and it is at this point that if you are like most marketers, you get stuck.

Why do we get stuck? We quickly realize that the argument could be made for many different attribution models. For example, consider the following Purchase Path: a user clicks on an ad for ‘running shoes’, then clicks on an ad for ‘Nike Shox’, followed by clicking on an ad for ‘Nike turbo 7′ and making a purchase. One way to attribute in this scenario would be to give equal credit to all three ads. Another could be to give 20% of the credit to first ad, 30% to the second ad and the final 50% to the final ad. Or a third model could be the reverse, where 50% goes to the first ad then 30% to the second and 20% to the final ad. The point is that we can go on for a while with many different models. So which model is correct?

Marketers can find attribution very frustrating when observing the models above because there doesn’t seem to be a clear cut mathematical solution to what the right answer is. At this point, most marketers then begin to question if attribution is all that it’s cracked up to be given that there doesn’t seem to be any accurate way to solve the correct attribution models.

Attribution Models take some very complicated mathematics to develop. Recently, we conducted a webcast titled ‘Measuring the Immeasurable’ that featured our partner, Vetra Analytics. Vetra is a statistical consultancy made up of PHDs in Statistics and Mathematics that can utilize advanced mathematical modeling to create attribution models.

When solving for attribution, one needs to determine the ‘influence potential’ of each ad click, impression and site visit. In order to determine this potential, one needs to consider many factors, including but not limited to the timing of the ad, decay rate of the ad, if a conversion was made what products were sold, the amount spent of a first time buyer or repeat buyer, etc. In order to determine the influence potential, a model or models need to be built which will help to predict the consumer decisions as accurately as possible.

The statistical models account for one of the most difficult things for marketers to grasp – uncertainty. Uncertainty is all of the factors that may go into a buying decision that are not capable of being measured by your advanced technology. For example, did a friend recommend this product to the consumer? Did they see a billboard or TV commercial? Or did a sales person in the store influence their decision?

By accounting for the uncertainty and building a model that incorporates these factors, we can test the model on a go-forward basis. If the attribution in the model matches the actual results over a sufficient sample size and period, we then know the model that was built is mathematically sound. If the reality does not match the model, we then know the model is not optimal and can be recalibrated.

There are solutions to attribution management that will change the ways in which you manage campaigns. If you are serious about solving attribution because you recognize that accurately attributing credit to your ads will allow you to make more effective media buys, which ultimately lead to greater profits for you and your clients, then you will need to utilize a tool set that implements advanced modeling.

Always remember that a technology is only as good as the people behind it. When implementing an attribution solution, make sure you have the staff that understands how to calibrate it or that the vendor you choose offers services to assist you in building sound models. Your success with attribution is solely dependent on your ability to employ accurate models. If you are serious about solving attribution because you recognize that accurately attributing credit to your ads will allow you to make more effective media buys, which lead to greater profits for you and your clients, then you will need to engage with professionals that have the necessary mathematical skill sets, such as Vetra Analytics.

Tags: attribution, attribution management, online advertising, purchase path, vetra analytics, webinar
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Lifetime Ad Value with Related Products

Friday, March 6th, 2009

Getting a new customer is great. Getting a new customer that becomes a repeat customer is even better. When a customer makes a repeat purchase, a marketer needs to consider how to attribute the credit for the future sales back to the marketing that was used to acquire the customer in the first place. Should the original ad(s) that acquired the customer get any credit for future sales? Should the original ad(s) only get credit for the first sale? What if the second product purchased was related to the first product purchased, would that change the attribution model a marketer would use?

During the Attribution Management Forum 2.0 (AMF) on Jan 29th, 2009, we posed that question to an audience of hundreds of senior online marketers and asked them to vote on the attribution model they would use in the following scenario:

In this scenario a consumer clicked on a PPC ad on Monday and the next day purchased peanut butter from that site. A little over a week later they clicked on another PPC ad and the next day purchased jelly from that same site.

We provided the audience with 3 attribution models they could choose from. They were as follows:

If you voted for:

A) You believe the first search should get credit for the sale of the peanut butter and jelly because the 2 products bought are so closely related, the first search should get credit for the sale of the jelly as well.

B) You believe the first search gets credit for the first sale and a percentage of the credit for the next sale as well. The second search also deserves a portion of the credit for the second sale.

C) You do not believe in lifetime ad value (LAV) and think that each search gets credit for only the sale that directly follows it.

In almost all of the groups polled, over 60% chose attribution rule B, which favors giving credit for the sale of jelly to the first and second ads. The exact percentage breakdowns were not chosen in this exercise.

When looking at this scenario there are a few other factors that one should consider. One factor is the type of search ad that was clicked on prior to the sale of jelly. For example, if it was a branded term, I believe more people would have opted to give full credit for the sale of jelly back to the first ad. The logic for doing this is that this customer chose search as their preferred method to navigate back to the site that they originally bought peanut butter from vs. using the address bar, an organic result, or a bookmark.

Another factor that might change the way people voted is if they knew the second search was for “jelly”, which is a general search term. The use of a general search might be an indicator that the consumer was not thinking of the company they purchased peanut butter from at all. Therefore, if they did not have that company in mind when they did the search for jelly, perhaps all of the credit for the second sale should go to the second ad.

In AMF 1.0, we presented a similar scenario but did not show the type of products that were sold. In that forum, the majority of the audience still chose to attribute some credit for the second sale back to the original ad. I would have thought knowing the sale of the second product was complimentary to the sale of the first product would have made more people opt to give all of the credit to the first ad.

These types of scenarios and rules are why we continue to strive to generate some consensus around Attribution Management. If you would like to learn more about these scenarios or the scenarios from the previous Forum, please visit www.AttributionManagement.com. Additionally, we love to hear from our audience, so please fill us in on what your thoughts about this scenario may be.

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Is Twitter a Bona Fide Advertising Source?

Monday, February 16th, 2009

What is Twitter? You could ask 100 Tweeters that question and end up with 100 different answers. Some might say it is micro blogging. Others say it is a way to keep your ‘followers’ up to date on your comings and goings. Another group might say it is a waste of time.

As an online marketer, I want to know if Twitter is a bona fide advertising source. Sure I know I could tweet about events going on at my company or clients in hope that those Tweets drive my followers to take certain action. But is it really an advertising source that deserves to receive credit for conversion if I can tie the conversion to the fact that the consumer read the tweet?

During the Attribution Management Forum 2.0 (AMF) on Jan 29^th, 2009, we posed that question to an audience of hundreds of senior online marketers by presenting the following two scenarios, and we asked them to vote on how they would attribute conversion credit if they knew a tweet was involved in that consumers buying process.

Scenario 1:

In Scenario 1, a consumer received a tweet that alerted them to the fact that they could save 20% on Nike Shox if they bought them from the Finish Line by 12am tomorrow night. The consumer clicked on the link in the tweet, but did not buy the Nike Shox at that moment. Later on they went to a search engine and searched for “Woman’s Nike Shox,” clicked on an ad for the Finish Line and bought a pair of Nike Shox before the 12am deadline and saved the 20% that was tweeted about on Twitter.

We asked the audience to select the best attribution rule out of the following options:

Over 67% in both groups we polled, according to their indicated level of experience with attribution management, believe option “A” is the best rule. This vote shows that these senior online marketers do indeed believe that Twitter is a bona fide advertising source as they are willing to give half of the credit for this conversion to Twitter.

Less than 8% in any group we polled voted for option “C”, which totally excluded Twitter from receiving any credit for the conversion. This is further proof that senior online marketers do value Twitter as an advertising source worthy of conversion credit.

Scenario 2:

In scenario 2, the tweet was exactly the same as the tweet in scenario 1. In scenario 2, the consumer clicked on the link in the tweet, but did not convert at that moment. Later on they used their address bar to navigate directly back to the Finish Line’s website, purchased a pair of Nike Shox and received the 20% discount because they bought before the deadline.

We asked the audience to select the best attribution rule out of the following options:

Over 66% in both groups we polled, according to their indicated level of experience with attribution management, believe option “B” is the best rule. This vote shows once again that these senior online marketers do indeed believe that Twitter is a bona fide advertising source as they are willing to give 100% of the credit for this conversion to Twitter and 0% of the conversion credit to the address bar.

Less than 7% in any group we polled voted for option “C”, which totally excluded Twitter from receiving any credit for the conversion. This is further proof that senior online marketers do value Twitter as an advertising source worthy of conversion credit.

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How to do Attribution Management When the Product Sold is Unrelated to the Ad(s)

Wednesday, February 11th, 2009

One of the most repeated stats about online marketing is that 44% of the purchases that happen online from advertising are for products unrelated to the ad(s) clicked on. If an online marketer were able to see the ad(s) that a consumer clicked en route to purchase, and discovered that the product they purchased was unrelated to the ad(s), how would they attribute sales credit across the ad(s)?

During the Attribution Management Forum 2.0 (AMF) on Jan 29^th, 2009, we posed that question to an audience of hundreds of senior online marketers and asked them to vote on the attribution model they would use in the following scenario:

In this scenario, a consumer did a search looking for “running shoes” and clicked on an ad for the Finish Line, but not did buy. They did a second search which was more refined and looked for “woman’s Nike Shox” and still did not buy. They then did a branded search for “Finish Line” and made a purchase. However, the product they purchase was unrelated to the first two ads.

We provided the audience with 3 attribution models they could choose from. They were as follows:

If you voted for:

A) Attribution Rule A, there were 3 ads involved before the sale. Regardless of which product was purchased, all 3 ads contributed and deserve equal credit for the sale.

B) Attribution Rule B, the product sold does not matter. However, when a branded term is used at the end of a path, it is being used to navigate back to the Finish Line. The customer could’ve found the Finish Line through the address bar or bookmark, however, they chose to use search again. In this instance, the consumer has already decided to buy and no sales credit goes back to the branded term at the end of a Purchase Path. Credit for the sale is split evenly between the first and second search.

C) Attribution Rule C, the purchase has nothing to do with what they searched for. Therefore, credit is excluded from the terms that do not relate to the product bought. All of the credit of the sale is attributed to the final ad for “Finish Line”.

The following charts highlight how the AMF participants felt attribution should be given, broken out by the indicated Attribution Management Experience Level:

What is interesting from these results?

We presented the same scenario in the first Attribution Management Forum, but did not disclose the type of product sold. All the audience knew was that a sale occurred after clicking on the same 3 search ads. Over 76% of the group with the most AM experience voted to exclude giving credit to the branded term at the end of the path and favored dividing the credit across the first 2 search ads. In the scenario above, where the product sold was not related to the ads, only 17.95% chose to vote the same way.
Almost 36% of the experienced group voted to give credit solely to the branded term at the end of the path. This was not the case in AMF 1.0 where the experienced group thought a branded term at the end of the path was used for navigation and was not deserving of sales credit.
It would be interesting to know how the audience would have voted if the 3^rd search term was not a branded search, but instead was another running shoe related term.
Why do people search and click on ads for one type of product, but then buy something very different? One obvious reason could be in the case of gift shopping. Perhaps you really wanted to buy someone shoes and when you got to the site another product caught your eye. Or perhaps your selection of shoes was not good enough or your prices were too high, but your selection and prices on other unrelated products of interest were in line.
If a retailer could isolate ads that sell unrelated products, they could try to understand why they have difficulty selling those items. It probably has to do with the 4 P’s of marketing: Product, Price, Promotion, or Place.

Tags: attribution management, blog, paid search and attribution, SEO
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Attribution Management 2.0

Wednesday, January 7th, 2009

Attribution Management 2.0

By Adam Goldberg

On October 28, we held the first Attribution Management Forum (AMF). We created the AMF to bring together the most influential minds in the online advertising community and to better identify, define and ultimately recommend improved valuation practices and methodologies for measuring an ad’s value.

The AMF is a thought exercise that has several objectives:

facilitating the discussion of emerging trends in Attribution Management
enabling online marketers to take steps toward being more profitable
identifying consensus on certain attribution models
establishing industry benchmarks

In the AMF 2.0, to be held on January 29, we will be exploring many of theideas and discussions that have surfaced since the first Forum. Specifically, based upon audience results and feedback from the first Forum, we will focus on the following:

Time Sensitive Attribution. One area in which we established consensus in the first Forum was that over 65% of our audience believes that when three search ads are involved in a conversion, all three ads deserve equal credit for the conversion versus crediting only the first or last ad. In AMF 2.0, we are going to explore this scenario even further by introducing an element of time. For example, if the three search ads appeared in a conversion over 30 days, would we still have a consensus that all ads deserve equal credit for the conversion?
Product Sensitive Attribution. A widely known industry fact is that over 44% of online purchases that are a result of online advertising are for products not related to the ad(s) that were clicked. In AMF 1.0, we did not pay attention to which product was sold, but only that a conversion occurred. In AMF 2.0, we will specifically call attention to the fact the product sold was unrelated to the team of ads that produced the conversion. Will this new data point change the consensus opinion that all ads deserve equal credit?
Search – Display Interaction. In the first Forum, we saw that when two banner impressions were served to a consumer who then clicked on a corresponding search ad for the company, 88% of our audience believed that the banners deserved some credit for a subsequent conversion, but not as much as the search ad. In AMF 2.0, we are going to reverse the order of this Purchase Path to see if people think that banner impressions that occur after a search ad are still worthy of getting some form of credit.

As was our hope in AMF 1.0, AMF 2.0 will continue to drive bigger ideas, establish some consensus and create benchmarks that online marketers will be able to put in action. We had a wide range of audience participation in AMF 1.0, including Fortune 500 and large international agencies. If you are interested in participating in the AMF 2.0, please Register Here.

Tags: attribution management, blog, clearsaleing, forum, webinar
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Attribution Management: Good Theory or Good Practice?

Wednesday, December 3rd, 2008

Attribution Management: Good Theory or Good Practice?

Most businesses that advertise online set some rule as to what they can and cannot afford to pay for a conversion. A common practice is multiplying their average sales price times their average margin and setting that as the maximum cost per acquisition that can be allowed. In our example below, we are looking at Joe’s TVs, which has an average sales price of $1,200 and a margin of 35%, which leaves $420 to acquire a sale at breakeven. Any advertising source producing sales that cost more than $420 is in need of change. Below you will find an example of two keywords from JoesTV.com, one for the general keyword ‘HDTV’ and another for their branded term, ‘Joes TV’. The two keywords have combined to produce 86 orders, and within those 86 orders, 12 orders were produced as a result of a Purchase Path (two or more ads) beginning with ‘HDTV’ and closing with ‘Joes TV’. With these rules in place, let’s take a look at the performance of their keywords to determine which are acceptable and which are not, and then take action on which of these keywords works.

The first view of the keyword set below is using your traditional last click model. The keyword ‘HDTV’ does not perform below the target $420 CPA, so that keyword is up for elimination:

Last
Keyword	Conv	Convr	CPA	Revenue	Profit	ROI
Joes TV	58	2.22%	$33.74	$69,600.00	$22,403.25	1145%
HDTV	28	0.40%	$563.23	$33,600.00	$(4,010.52)	-25%
Total	86	0.89%	$206.13	$103,200.00	$18,392.73	104%

In this next example, the data displayed is using an even attribution model, meaning that both keywords are being given even credit for the conversion. The keyword ‘HDTV’ is still costing above the targeted $420 CPA, so that keyword is up for elimination:

Even
Keyword	Conv	Convr	CPA	Revenue	Profit	ROI
Joes TV	52	1.99%	$37.63	$62,400.00	$19,883.25	1016%
HDTV	34	0.49%	$463.84	$40,800.00	$(1,490.52)	-9%
Total	86	0.89%	$206.13	$103,200.00	$18,392.73	104%

In this next example, the data displayed is using an even attribution model with exclusions, meaning that we have selected to exclude the branded term from credit since the user initially found the site by searching for ‘HDTV’. We’ve found that users search a branded term at the end of a Purchase Path for navigational purposes, so we exclude it from receiving credit. The keyword ‘HDTV’ is now performing at an acceptable CPA and is producing a 7% ROI:

Even w/ Exclusions
Keyword	Conv	Convr	CPA	Revenue	Profit	ROI
Joes TV	46	1.76%	$42.54	$55,200.00	$17,363.25	887%
HDTV	40	0.57%	$394.26	$48,000.00	$1,029.48	7%
Total	86	0.89%	$206.13	$103,200.00	$18,392.73	104%

In this final example, the data displayed is showing that if you use the metrics in the first and second example you may move forward with eliminating the ‘HDTV’ keyword, which will then result in a large loss in revenue and profit that could have been prevented by using an even attribution model with excluded terms.

If HDTV Eliminated
Keyword	Conv	Convr	CPA	Revenue	Profit	ROI
Joes TV	46	1.76%	$42.54	$55,200.00	$17,363.25	887%
HDTV	0	0.00%	$ -	$ -	$ -	0%
Total	46	1.76%	$42.54	$55,200.00	$17,363.25	887%

A lot has been written about the theory of attribution management, but very little has been put into the practice and types of results it yields. The previous examples took you through a scenario with a view of a campaign, and another view of that campaign where attribution management is applied. As you can see, attribution is not only a good theory, but a best practice that all online marketers should implement sooner than later if their goal is to increase profits.

Tags: Articles, Weekly Observation and Analysis
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Two Years of Learning about Attribution Management

Thursday, October 23rd, 2008

Attribution management is the practice of allocating credit (revenue, profit, etc.) across the team of advertising responsible for a conversion (sale, lead, download, etc.) versus giving the very last ad clicked all of the credit. Any good Internet marketing professional knows that assigning all of the value to the last ad clicked before the conversion is inherently flawed. Most Internet advertisers, however, lack:

1. The tracking technology required to determine the actual team of ads and their sequence that lead to the conversion, and

2. The valuation methodology to properly assess each ad’s true contribution and value to the conversion.

There are a few technologies emerging that are able to effectively track and assemble the team of ads leading to conversion, including our own Purchase Path^TM. technology. With over 2 years of market presence with our attribution management technology, we have been able to capture millions of “purchase paths” across dozens of companies and numerous industries. The remainder of this blogs highlights some of the major “learning’s” and insights we have been able to draw from the massive amount of attribution management data we have collected.

Branded Keywords:
- Whether you are an established or new brand, an eTailer or lead generator, in the B2C space or B2B space, the most common Purchase Path we have observed is where the last ad prior to a conversion is a branded keyword from a search engine.
- Ask yourself, what is your best performing keyword? I would be willing to bet that your answer is some derivative of your company’s name. The reason why companies believe these branded terms are best is due to their inability to track and view a Purchase Path. Our data has conclusively proven that users are generally not searching for your brand at the start of the buying cycle; rather they are searching for the types of services and products you offer. It is during these searches that they discover or become acquainted or re-acquainted with your brand. Once they get to this point, the user may look at other options, or your competition, and after doing so, they “navigate” back to the site that has the best offering and value. The most common way to “navigate” back to a site is to type the company name into a search box and click on a paid search advertisement. It is this common search behavior that inaccurately inflates branded keywords and makes companies improperly conclude they are their best form of advertising.
- Because of this branded keyword bias that exists today, your other forms of advertising are often undervalued, such as banners, emails, and category-level keywords that generally occur early in the customer buying cycle.

Window of Time:
- Shortly after we developed our Purchase Path technology, the question of ‘How far back in time should we track the path when doing attribution management?’ arose. For example, if you have a website that sells toasters and someone clicked on an ad 2 years ago, then clicked on an ad today and bought a toaster, technically that is a Purchase Path. However, most marketers would agree that the ad clicked two years ago had no impact on the sale that occurred today. Given this intuitive issue of time relevance of an ad’s effectiveness and impact, it becomes imperative that any advertising analytics technology that focuses on Attribution Management needs to include a time-based parameter that controls how far back in time one should go when assembling their purchase paths.
- One method that we developed to help answer this question analytically is to plot your sales on a spreadsheet from the time of first click to sale. Once a large enough sample of your sales has been captured to be statistically sound, take a look at the time that it took to get 80% of your orders from first click to buy and also look at the time it took to get 90% of your orders. You can then rationally set your Purchase Path time parameter as the average time it took to get between 80% and 90% of your orders.
- We have found that one time window does not fit our entire client base. The types of products/services you sell, their price points and average sales cycles are the three biggest factors that will influence the appropriate purchase path time parameter for your company.

Introducers, Influencers, Closers:
- Our Purchase Path tracking technology showed that it often takes more than one ad to earn a customer. For most of our clients, over 50% of their customers clicked and/or saw more than one advertisement before becoming a customer. To help understand the interactions and roles of ads in a Purchase Path, we created some base classifications to categorize these ads:

§ Introducer- the very first ad the prospect saw or clicked

§ Closer- the last ad clicked prior to the conversion

§ Influencer- any ad that is seen or clicked between the Introducer and Closer

o When we work with a new client, we find that the majority of their ads fall in the Closer ads, and they have very few Introducers and Influencers. If you think about it, this makes a lot of sense. Companies can only justify buying advertising that demonstrates it has value. Without the benefit of being able to see a Purchase Path, the only ads that appear to have value are the Closing ads. The introduction of this classification system allows us to rationally group ads into logical buckets and in doing so, more accurately value them, no matter where they appear in the buying process.

Comparison Shopping Engines and Search:
- Perhaps the form of advertising that is undervalued more than any other is Comparison Shopping Engines (CSEs). CSEs more often than not fall in the Introducer or Influencer stage. We have discovered that consumers use CSEs to compare prices, click on the listing they deem to be the best, leave the site that had the best offer, then go to a search engine and search for that same company by name (branded term). From there, they click on the company’s branded ad and complete their purchase, thus giving all the credit to the search engine and none to the CSE. Our analytics do not tell us exactly why consumers do this, but one theory is that consumers are uncertain as to how CSEs make money. Consumers may believe that CSEs jack the price up a bit; therefore, consumers think if they go directly to the site through a branded term or direct visit, they may find a lower price. When they realize the price is the same, they do not go back to the CSE and simply remain at the site to make their purchase.

If you are a marketer that is currently tracking the Purchase Path and performing attribution management, then we would love for you to share your findings to our own data. In effort to advance the knowledge of attribution management, ClearSaleing will be co-hosting a webcast with Search Marketing Now on October 28th that will survey experienced online marketers about different attribution models, or various ways to properly allocate revenue and profit to each contributing ad in a conversion. If you would like to understand how other marketers are handling attribution, please sign up for this webinar: www.clearsaleing.com/attribution.

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Attribution Management

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