If you want to increase your odds of being considered, and even more importantly, increase your odds of winning, then you need to engage consumers as early as possible in the buying cycle…

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Testing seems to be the answer to everything advertising-related these days. When an audience member asks a tough question at a conference, a panel member inevitably says, “You have to test.”

Many of the things that we want to know or try requires that we set up a test. Tests typically need to run for a period that is twice as long as your average sales cycle (i.e., if your average sales cycle is seven days, it would be best to let that test run for a full two weeks). This allows the ads that ran on the seventh day of the test a full sales cycle, which is enough time to fairly judge the results.

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Attribution management begins with a tracking technology in place that allows you to track both clicks and view-throughs, and tracks beyond the last click. Here’s an example of the type of data available on a particular order if an attribution management technology is in place….

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Recently, we have hired two new members to our team who are on the cutting edge of attribution and media mix modeling:

Alphonse Okossi – ClearSaleing’s new Technical Director of Media Mix Modeling and Data Analytics, will spearhead the company’s technical efforts in developing and implementing creative statistical methods to solve offline/online media mix and attribution-related issues.  Okossi’s career path highlights more than 15 years of experience in marketing analytics, data mining, predictive modeling, risk management, forecasting and optimization.  His most recent role prior to joining ClearSaleing was as Managing Partner of Planning Constructs, Inc., where he led development of the company’s marketing mix and price promotion modeling platform, including managing the programming team and designing demand forecasting and simulation web tool.

Prior to Planning Constructs, Okossi was the Analytical Product Development Manager for Information Resources, Inc., where he lead and conducted hands-on statistical modeling and related software application projects for price promotion, marketing mix, assortment, seasonality, incrementality and related forecasting/simulation web tools development.  Prior to this position, Okossi held various high-level analytics consulting positions, providing web-enabled statistical analyses for clients, including Wells Fargo and JC Penney.  Okossi, whose career started in the finance industry, holds a Ph.D. in Economics and an MA in International Finance and Trade from State University of New York at Albany.

Thiyag Chinnappan – In his role as Director of Media Mix Modeling and Data Analytics, Chinnappan will be responsible for leading the company’s practice and product strategy related to advanced data analytics.  Chinnappan’s work experience is rich in analytics and media mix modeling.  Prior to joining ClearSaleing, Chinnappan served as client manager in the analytics consulting division of The Nielsen Company where he developed and managed marketing and media mix activities for Kraft Canada, among other clients.

Before his work with Nielsen, Chinnappan was a project manager in marketplace analytics for HAVI Global Solutions, where he provided tactical data analysis and campaign launch insights for clients including McDonalds.  Chinnappan, who holds an MBA from the University of Chicago’s Booth School of Business, also holds a diploma in Project Management from Northwestern University.

With the addition of Okossi and Chinnappan, ClearSaleing plans to add a host of new offerings to its service roster, including improving existing attribution performance using profit weighting from Media Mix models, performing custom online/offline media mix analyses, simulating and predicting future sales using demand forecast functionality, and conducting custom analytics studies using advanced econometric techniques. Additionally, ClearSaleing will also develop custom Analytics Dashboards to allow clients immediate access to data and insights on specific online purchase behavior and target customers.

Just as our clients are continually striving to optimize and improve their business, we at ClearSaleing are continually looking for ways to optimize our business and deliver the best services to our clients. Alphonse and Thiyag are two key components to continuing our product roadmap and developing our vision.

A/B Testing

Historically, display optimization has been done through A/B testing. You run the display campaign, you look at how that affects your KPIs (profit, revenue, ROI, CPA, etc.), and if you see lift in the KPIs you’re focused on, you can “attribute” that to the campaign working. If your numbers stay flat or go down, you can conclude display isn’t adding value.

With more A/B testing…

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Recently, I was exposed to a company that has developed a technology that allows marketers to find and market to people that are friends or “connections” to their current customers.  This company cannot do this over Facebook, so there still is a challenge in valuing a Facebook Fan in the “ultimate” way, but they can do this over many other aspects of the social landscape.

The company I learned about is called Media6Degrees (M6D).  M6D is a social targeting company that leverages the power of social media to identify new audiences to target for their clients. M6D does not use any personally identifiable information (PII) in order to build these new audiences, so it cannot carry out the ultimate tracking for a Facebook fan, as described above, but it can get us pretty close. M6D is interested in the entire social universe, which includes blogs, photo sites, sharing sites and social networks. M6D labels customers of their clients as brand loyalists, and then scours the social media landscape to find ‘connections’ to the brand loyalist without using PII to retarget. Typical retargeting shows display advertising to people that have already visited your site, but have not converted. M6D performs traditional retargeting, but also develops whole new audiences by ‘retargeting’ connections of the brand loyalists.

Here’s an example of how M6D might identify a friend of a brand loyalist to start retargeting to:

• The M6D pixel can identify the types of blogs one of their brand loyalists visits. It then discovers through the M6D network that someone else reads similar blogs. Given that some of these blogs are read by a very small audience, M6D makes an educated, data driven assumption that these two people might know each other, or at the very least, are similar in their interests. They call this the social graph. Once they identify this new person, they will then begin to target ads to this potentially new prospect using retargeting, even though this new person has never been to their customer’s site.

With most retargeting and ad networks, it is a challenge to prove their contributions to the bottom line because most marketers value the last click versus looking at the entire Purchase Path. When marketers do look at the entire Purchase Path and give value to introducers and influencers, in addition to closers, the real value of display advertising begins to show. If you are a current customer of ClearSaleing, and are also using Media6Degrees, we’d love to analyze your data to see how many conversions were introduced by M6D’s advanced targeting technologies.

AdMe is a company started by an ex Googler named Matt Ochsner. AdMe is actually an add-on to Firefox, which, once installed, will place a ‘Yes’ or a ‘No’ icon on display ads you see. If you click a ‘Yes’, you’re indicating to AdMe that you like that ad and would like to see ads similar to it. If you select ‘No’, that lets AdMe know that you no longer want to see ads from the company, nor ads similar to it. This easy-to-use, self-policing system puts a lot of power in the hands of consumers, while at the same time saving company’s lots of money by insuring they don’t show ads to uninterested people and allowing them to target ads to people that have indicated they want more.

A startup called Better Advertising, founded by Scott Meyer, a former executive at About.com, is another self-policing advertising technology. Their product is called Power Eye. Companies that employ the Power Eye solution will display a Power Eye icon in their display advertising. When a consumer mouses over the icon, they will see the data that was used to target the ad and have the option to opt-out of future targeting by those companies.

Given that I’m in the industry, I certainly would be curious to see an ad with Power Eye, so that I can discover what information was used to target that ad to me. I am not so sure that Joe Consumer would have as much interest in targeting data as I would. I am also curious about learning how I am perceived on the Internet. I have to imagine there will be many times where people are put into categories which they truly do not fit. I also wonder what affect this will have on the companies that display those ads. For example, I’m a male, age 35, but if Power Eye said I was being targeted in ad because I’m a woman over the age of 65, what would that make me think of the company that displayed that ad? Should I feel insulted or should I be concerned that my online behavior is indicative of a female over the age of 65?

I can certainly see how this type of technology can be a win-win for consumers and companies. Like with all things, adoption is the key. If consumers don’t use it, companies don’t learn anything new. It could be that these new technologies turn out to be just a preemptive strike against pending legislation trying to reduce the impact of online advertisers by limiting the amount of information they can know about consumers.

I encourage you to try AdMe, it’s a Firefox download, which is available at www.admeonline.com, and please let me know your thoughts.

For years now, marketers have been able to buying display advertising, either by cost per impression (CPM), cost-per-click (CPC), or cost per action (CPA). More companies are opting for the CPA route, which on the surface seems like a much safer bet.

True to its word, CPA advertising means you only pay when you get an action or conversion, which can vary depending on the advertiser’s business model. Some common actions include a lead, an order, a download, a white paper view, or a demo.

The benefit is that the advertiser doesn’t pay for a non-action generating activity. Most marketers are savvy enough to negotiate the price they pay for actions from display networks. However, they don’t negotiate the terms as to what constitutes an action.

When buying display in a CPA model, there is typically a window of time in which a display provider can claim credit for an action. That window of time is defined as a look-back window — typically 30 days — from the time of conversion. So, in essence, any customer of yours that saw or clicked a display ad, as well as those that coincidentally was on a page with a display ad that they didn’t even see, in that window of time and converted is then claimed by the display network as an action that they deserve to be paid for.

Some display networks claim to have a reach in excess of 90 percent. By virtue then, more than 90 percent of your orders will be claimed by a display ad network. Does any marketer truly believe a display network deserves to be credited for more than 90 percent of their orders?

Unfortunately, marketers without the ability to see view through or post-impression data are forced to accept these terms at face value. Other marketers that have the ability to track an entire purchase path and truly see the impact of view-throughs and clicks on display ads, have the ability to define what truly constitutes an action worthy enough to pay a display network.

Let’s look at three examples of purchase paths that involve display, and how a marketer can use this data to negotiate a better definition of an action:

• Purchase Path 1: Display ad view through promoting diamond earrings -> Paid Search ad for diamond earrings -> Paid search brand term advertised in display ad -> Conversion

• Purchase Path 2: Paid search ad for diamond earrings -> Display retargeting diamond earrings -> Conversion

• Purchase Path 3: Display ad view through promoting diamond earrings -> (29 days pass) Paid search ad for diamond earrings -> Conversion

In Purchase Path 1, it seems clear that the use of display promoting diamond earrings was the impetus for the paid search that followed and the ultimate conversion. All marketers would agree the display was integral for making this action occur.

In Purchase Path 2, the person already had interest in looking for diamond earrings, they didn’t buy immediately, then were somewhere on the Internet when retargeting kicked in, and then they converted at a later point in time. In this scenario, it’s debatable if that display ad was necessary in getting that consumer to convert. The consumer might’ve already decided to buy but was just taking their time, when a retargeting ad was shown. In that case, the display ad was unnecessary. Or, the consumer could’ve been on the fence about buying diamond earrings, and the retargeted ad pushed them over and led to the action.

In Purchase Path 3, the sequence is similar to Purchase Path 1, except in this case 29 days elapsed between the display impression and the paid search ad. Most marketers, and a lot of advertising research, would agree that an impression’s rate of decay on a consumers mind is less than 29 days. Therefore, it’s unlikely that this impression had a bearing on the paid search that ultimately led to the sale.

A display ad network would’ve received compensation for the three actions in the three purchase paths because an impression was served within that 30-day window. But marketers can clearly see that the value of those impressions in the three examples is different. Shouldn’t we be paying display ad networks on some sort of scale for actions versus treating all actions that were exposed to display as being equal value?

Some of our clients have the purchase path data described above and have successfully negotiated the definition of an action to a display network. In some cases, even though an impression was served (Purchase Path 3), they aren’t paying one cent for that action.

In other cases, they’re paying partial credit (Purchase Path 2). Finally, when it’s 100 percent clear that the display impression was 100 percent integral to the action, they’re paying the full CPA.

Attribution is about giving credit where credit is due. Attribution is also about paying for credit when credit is due. With the ability to track the entire purchase path, marketers now have the ability to negotiate and define what “A” is in CPA when it comes to display.

At the same time attribution was picking up steam on the conference circuit, and being discussed by research firms like Forrester Research and Jupiter Research (now owned by Forrester), the search engines began to pay attention to this topic, starting with Yahoo. In the Yahoo advertising interface, they began to represent not only the conversions at the keyword level, but also what they called an assist. An assist is when a keyword is used in a purchase path, but was not the last keyword clicked prior to conversion. After that, Microsoft, through the Atlas institute, coined the term ‘Engagement Mapping’, which utilized the Atlas ad server to provide attribution data across search and display media. Then, in 2010, Google entered the foray into attribution with the Google Search Funnel product, which performs like Yahoo, but has a lot more analytics around the data to dive deeper into these paths.

One question that we often get is, “I don’t have enough money to invest in a product like ClearSaleing, so what else is out there?”  Or, “My company is still skeptical that attribution would benefit us.  Is there a way I can prove that our customers do navigate paths and attribution would be beneficial?”  One product worth taking a look at that is free is the Google Search Funnel product. Though this product is far from perfect, and leaves a lot open to interpretation, any product that shows beyond the last click can help you to improve the performance of your overall campaigns.

From there, we usually get several follow up questions:

• What would be the first thing you focus on when using the Google Search Funnel product?
  • When we look at Purchase Path reporting from ClearSaleing’s technology and focus solely on paths that involve AdWords ads, one thing jumps out at us across our entire client base: when there are 2 or more AdWords ads used in a Purchase Path, the last ad clicked is more often than not one of our clients branded terms. A branded term is a company’s name or misspelling/typo of it. When we look at the terms that precede the branded term, they are mainly general, product specific, need specific, or a model number.  Usually they are non-branded terms, as seen in the graphic below.

(Click to enlarge)

• Therefore, under a last click world, branded terms end up stealing a lot of credit from the non-branded terms that preceded them. So, if I was going to look at one thing in the Search Funnels report, I would look at paths that end in brand terms to see how often non-brand terms come before them. Then I would look at how those non-brand terms are valued under last click and determine if they should be given more credit. We have found that when consumers use brand terms at the end of a Purchase Path, they are doing so to navigate back to the site they’ve already decided to buy from, therefore, it makes sense to credit the ads that were NOT used simply for navigation purposes.
• When I look at the path length report in Google, it shows me the number of conversions that took one click, two clicks, three clicks, four clicks and so on, it shows that most of my conversions occurred with just one click. Does this mean that attribution is not something I need to worry about?
  • There are a few things needed to keep in mind when looking at these reports:
    • This data only pertains to Google AdWords, so if a client went from a Yahoo ad to a Google ad, it would be represented in Google as a one click path, when in reality, it was a 2 click path.
    • On a similar note, paid search isn’t the only advertising source out there. So, if you’re using anything outside of paid search – display, affiliates, shopping engines, etc. – these are not being represented in the paths.
    • At ClearSaleing, we use three simple categories to place ads in: Introducers – the first ad a person clicks or sees en route to conversion; Closers – the last ad a person clicks/sees prior to conversion; Influencers – the ads in between Introducers and Closers. When we acquire a new customer that has been using a last click attribution method prior to coming to us, they cannot justify spending money on Introducers and Influencers; they can only justify spending on Closers. You are likely in the same boat. Therefore, their data in the beginning looks as if attribution does not occur.  One thing that we get our customers to do that you should also test is to activate some of the more general terms in your account, and with the use of Google’s Search Funnels, see if these types of terms show their value by being an Introducer or Influencer in other paths. Over time, our clients generally discover their customers walk down more paths than when they started with us because they have the data to support investing on ads and ad sources that introduce and influence.
    • Using Google Search Funnels, I found a collection of keywords that provide a lot of assists, but barely close. What should I bid for these terms?
      • Unfortunately, this is a really difficult question to answer. If you were a company that only sold one product, or every product you sold produced the same amount of profit, you could figure out what these assists are worth. If you sell many different products with many different margins, it’s impossible to know the value of these assists. The ideal method for evaluating these would be to know how much profit was earned on a conversion the keyword assisted, so you could assign it some profit credit and then you could come up with a bid to meet your business goals. Google will most likely never be able to produce profit figures because that would require companies to share margin data with Google, which is highly unlikely. Google could, however, take the revenue earned on that conversion (assuming you are an etailer) and attribute a portion of that to the assists, so that you could make a more accurate bid decision.

Though the Google Search Funnel product is not perfect, it does provide a lot of valuable reports that if one takes the time to use them and analyze the data, one can certainly improve the performance of their campaigns. If you have experience using the Search Funnel product, we welcome your comments, or if you have questions about attribution, as always, feel free to contact us.

All social monitoring technologies pull data from popular social sites, such as Facebook, Twitter, Blogger, MySpace, etc. Since they all pull data from the same sites, they more or less show the same data. One area of differentiation some companies have is the ability to also pull data from unstructured data sets. For example, a company like Social Seen could also pull in information from your corporate systems, like your CRM, Call Center, website and suggestion boxes.

These Social Media monitoring tools classify each “conversation” in a positive, neutral, or negative fashion using standard language protocols. If the word “good” is used, they assume it’s positive, and if the word “bad” is used, they assume it’s negative. If it doesn’t contain a positive or negative lean, they classify it as neutral. This challenge of accurately classifying conversations is a common problem in the social monitoring space. Recently, I learned that the Hebrew University developed a technology that has the ability to analyze conversations for positive or negative sentiment.  When tested against human reviewers, their algorithm agreed with humans nearly 80% of the time. So, there is hope that this common problem will be solved.

Actions That Are Clear

It’s obvious to me that if a company is using a social media monitoring tool, one worthwhile action to take is to reach out to those who have had negative experiences with your brand. Not only do you have a chance to turn this customer’s negative experience into a positive one and to hopefully retain them as a customer, but you increase the likelihood of that customer posting a positive message next time.

Another use of social media monitoring is to use the negative comments about your product or brand and address those at a more macro level. By this I mean that you can incorporate negative feedback into future product development or to create whole new business lines to address overarching complaints in the marketplace.

Many social media monitoring technologies allow you to monitor more than just your brand. Therefore, one smart action to take is to monitor your competition.  Understanding your own competition from their customers’ point of view can create opportunities for yourself to either expose your competitions weaknesses or capitalize on these weaknesses and turn their customers onto your company product offerings.

Actions That Are Not So Clear

Do positive comments equal profit? By this I mean does increasing the number of positive sentiment on the web increase profitability, and if so, is it quantifiable and by how much? If you could clearly state that for every positive comment, I get X return in profit, then one can easily determine how much time and how many resources should be dedicated to increasing positive sentiment on the web. But I’m not aware of social monitoring technologies being able to quantify this, so it is impossible to determine how much money and man power should be invested on generating positive sentiment.

If you could determine the ROI of positive sentiment, how do you generate more positive sentiment? Ideally, positive sentiment is driven by you providing a great product and service that people want to tell others about. The only way outside of creating great products and services to generate positive sentiment without using unscrupulous methods, like hiring cheap labor overseas to post positive things about you, is to ask your customers to post things on your behalf. “If you like our new X, please become a fan of our Facebook page.” Twitter’s new advertising model is another method that could potentially help you drive positive sentiment by paying for it.

There’s no way to track the influence of positive sentiment at the customer level. For example, if a person was in the market for product X and went to Amazon to read comments about the product, and while reading the comments learned about product Y, then went to a search engine looking for product Y, clicked on an ad and bought product Y, all a tracking technology would know about this customer is they clicked on an ad for product Y and converted. There is no tracking today that would also incorporate that the user read a positive review beforehand. Without this type of tracking, social media will never be able to be accurately valued, and therefore, it is impossible to know how much time or resources to dedicate to these types of endeavors.

I am by no means an expert in social media monitoring or in executing social media strategies. I am an expert in advertising analytics and quantifying the value of trackable steps in a purchase path. I am sure there are other actions that one can take from the data that social media technologies provide today, and I would love to hear what those are. Please feel free to share those below and hopefully change my opinion on social media monitoring for the better.