We were thrilled to have participated in The Pool: Tablet Lane and are eager to share key data from this important initiative led by VivaKi.

Presentation overview:

Based on 14 months of testing that reached 20 million users, VivaKi’s The Pool reveals best practices and key findings for ads on the revolutionary medium and game-changing device: the tablet. This research provides insights and data around how to make tablet advertising work harder and achieve scale through a combination of qualitative, quantitative, and field trial research. This massive undertaking was made possible through a collaborative process that brought together advertisers, technology companies, and publishers to work towards a solution. In this session  - utilizing still unpublished data - Helen Katz who was one of the creators of this project will also look into the differences and similarities of web and in-app advertising.

We hope to see you there!

A large portion of the research in this area is based around studies done with in-market scanner panel data.  Across all available sales data for a product category in-market, models are fit to compute elasticities for the prices that were observed for the time period evaluated. Results from this type of research are robust and reliable.

The shortcomings of this method set in when marketers attempt to reach beyond conditions that previously existed within the market.  For example, Brand Z wants to take price to a new level not yet seen in-market.  Brand X was never seen at Price A when Brand Y was seen at Price B.  Compounding this discussion even further, price is often not evaluated  in isolation.  Case in point, packages can see a change in volume delivered per SKU or a new creative package with altered messaging is introduced.

When multiple  elements are moving, the research  requires an approach to pricing  that relies on data other than syndicated sources.

One traditional survey based approach worth considering is a Van Westendorp analysis, as it is extremely useful for evaluating the introduction of new product entrants  to consumers with no prior experience for product.  However, for well established CPG products, Van Westendorp merely reflects back consumer recall for prices that currently exist in the market.  Further, the Van Westendorp insight typically identifies a wide range of prices and rarely  provides an elasticity that would allow a marketer to fine tune a price.

Also available is a more effective survey-based pricing technique that utilizes discrete choice as a methodology, and economically but effectively renders choice-based decisions within a more realistic display environment.

Modern shelf-based discrete choice techniques place all controllable aspects of an extended price evaluation in the hands of a researcher to test with respondents.  Price, packaging, volume and temporary promotion can all be accounted for to provide insight to the pricing decision.

As part of these shelf evaluations, respondents are shown several screens of shopping options which are determined by a strategic, controlled randomization process that allows the researcher to collect data in a manner similar to the policy decisions that are likely to be made by competitors in the market.  As respondents indicate their choices to the various scenarios, all the data needed to estimate the model is collected.

Unlike, traditional feature/level discrete choice/conjoint studies which evaluate the relative importance of combinations of optimal feature levels, a shelf-based discrete choice study obtains its research insights primarily through preference simulation and calculation of price elasticities.  Often times the “willingness to pay” information obtained from a discrete choice can be combined with “cost” information and placed directly into the simulator to provide a read on profit maximizing pricing strategies.

The end result can offer researchers insight as to whether a new package design is sufficient to camouflage an increase in price.  For example, it can illustrate whether a 10% increase in package capacity combined with a 15% increase in price is more acceptable to a consumer than a simple 5%  increase in price for the existing SKU.  Furthermore, if either of these moves are made, what is the likely response from other competitors in the market?  Will their reactions dictate a reaction to the reaction?

It should always be noted that preference share is not market share since distribution is unaccounted for in shelf design or simulation, however, simulations based on preference share are a powerful upgrade in capabilities over in-market sensitivity research in isolation or over Van Westendorp analysis in actionability.

If this topic has piqued your curiosity, please call your friendly InsightExpress Account Executive or email us at info@insightexpress.com  for details on our approach.

In this article you will learn:

• How to trick a marketing mix model (MMM) into giving you great results for doing just about anything
• Where mix models break down
• Emerging solutions to address the weaknesses of marketing mix modeling

Marketing mix modeling has had a great run as an “it can do it all” solution to marketers’ questions on marketing allocation.  Marketers can input their entire marketing plan into the model and voila, they get an answer on how to reallocate everything using a quantifiable, rational approach.

In 1990 it worked pretty well when we had only TV, print, and the pricing and promotion to worry about. Since then this annoying marketing channel called digital has created a  pain in the neck for the MMM. (How do I measure ROI for my display, search, video, mobile, app, social, email, website, tablet, gaming, and Captcha in a MMM?).  And that pain in the neck may soon become a migraine.

Let’s begin by de-mystifying marketing mix models. Experts will try to baffle you with Bayesian statistics, robust regression using s-shaped decay and Adstock to blow your minds. Marketing mix models are very, very fancy regression models, but after stripping out the smoke and mirrors, they fundamentally are drawing insight from correlating weekly sales data and marketing.    Herein lies the fundamental problem.

The first issue here is that “correlation is not causation.” For example, let’s look at what happens to Orange Energy Drink in a hypothetical MMM.  Orange Energy runs quarterly extreme sports sponsored events. Being a fan of extreme sports and energy drinks, I attend each of these events with my cat Felix. At the events, the combination of excitement and the drinks are too much for me, causing me to feed Felix a cat treat at each one.

As an analyst for Orange Energy, I am asked to run a marketing mix model. I decide to code up a variable called “Feed_Felix_Treat” and include it in my model.  The model finds that sales go up around the time when Felix eats the treat. The conclusion is that feeding Felix has an infinite ROI and so I recommend that Orange Energy spend 100% of its marketing budget on cat treats.

Felix the cat

As ridiculous as that scenario sounds, it’s exactly what happens if you try to include some new forms of media in a marketing mix model.  A good example is search, which is special because it’s both a marketing tactic and a return object of great advertising. In fact, search positively interacts with virtually all advertising. Marketing mix models cannot distinguish the difference between the impact of search, the interaction of search and the “navigational search effect” of traditional media.  Don’t get me wrong, search is as revolutionary to marketing as the television. However a MMM is not the appropriate way to measure its value.

A second issue with MMM is that it does not provide the apples-to-apples marketing comparison as would appear at first glance. Just because you input all marketing into one system doesn’t mean they are comparable.  Getting back to the details of the level of observational modeling in a MMM, we see that store level, week level, and market level observation favor store level, week level, and market level marketing. As a result, MMMs tend to paint a more rosy view of pricing and promotion strategies . Note that pantry loading and products with semi-inverted price elasticities exist (e.g. Chanel raises the prices of its hand bags almost every year and drive sales by doing it).

The opposite can be true of media, which has been proven to have a “long term effect.” IRI’s seminal “How Advertising Works” study compellingly proved that media pays out over a very long period of time, yet marketing mix cannot capture that effect.  The issue of uneven treatment is a problem for TV but even more unfair for magazine print and the emerging digital tactics.

The third fundamental problem with marketing mix modeling is that it measures only one of the return objects of advertising. Great advertising pays out in many ways, but MMM only measures sales. Marketers also need to understand the return on Audience, Brand and Consideration (at InsightExpress, we call this the ABCS of Brand Advertising Effectiveness).

Great progress is being made to address these issues using what I call Single Source Media Mix approaches (SM^2) which were cost prohibitive or impossible in the past. These techniques are blending more conservative experimental design frameworks with econometric concepts from traditional marketing mix.

Ultimately, though its face may change, marketing mix modeling rightfully remains one of the most valuable tools in a marketer’s tool box.  George Box, a pioneer in the areas of quality control, time series analysis, design of experiments and Bayesian inference, once said “All models are wrong, but some are useful.”  I ask myself, “If I owned the brand would I use a MMM?” and the answer remains a strong “Yes.”

That being said, there are ever-increasing needs to understand the marketing mix before the model is available using leading indicators, to find supplemental learnings for digital with more appropriate methods, and to understand the entire purchase funnel not just sales. InsightExpress has been at the forefront of this innovative research and Cross-Media Insights, AdInsights, and SM^2 (Single Source Media Mix ) approaches using our Ignite Network are excellent ways to do just that.

For more information on how Cross-Media Insights, AdInsights and SM^2 techniques can give you a leading indicator view on how your mix is performing, or information on emerging MMM and attribution techniques that addresses issues with traditional, please contact me at jshimizu@insightexpress.com.

The findings detailed below were drawn from InsightExpress’ Tablet InsightNorms™, a normative database containing results across 43 campaigns comprised of 83 ad executions that show  the branding effectiveness of advertising placed on tablet devices.

Thanks in great part to InsightExpress’ involvement in a 14-month long research study conducted by VivaKi’s The Pool, an ongoing initiative to uncover advertising solutions of the future that revealed best practices and key findings for ads placed on tablet devices, InsightExpress is able to offer one of the most robust portraits of tablet advertising effectiveness in the industry.

This Tablet InsightNorms analysis, which incorporated the 74 individual custom ad executions from The Pool’s tablet lane, explored advertising performance across key brand metrics (unaided awareness, aided awareness, advertising awareness, message association, brand favorability and purchase intent).  The following key findings were revealed:

1. Significant increases in all brand metrics indicate that tablet campaigns are extremely effective, especially when it comes to advertising awareness.
2. To date, tablet campaigns either match or outperform the established mobile channel advertising effectiveness norms.  This pattern was also seen with early mobile campaigns outperforming online.

Tablet Campaigns Drive Strong Brand Metric Performance

As shown in the chart above, significant increases across all brand metrics indicate that tablet campaigns are highly effective.  The striking increase in advertising awareness (41 percentage points) suggests that these ads are both noticeable and memorable to viewers.  The ability of tablet campaigns to drive lower funnel metrics such as message association, brand favorability and purchase intent signifies that these ads are also quite persuasive. However, it is not surprising to see strong results like these for a new advertising channel.

Tablet vs. Mobile Performance

InsightExpress is able to benchmark these tablet norms against the Mobile InsightNorms database, which contains over five years of aggregated mobile campaign effectiveness results.  As shown below, to date tablet campaigns either match or outperform the established mobile channel norms. Tablets’ strong advertising awareness performance illustrates that they are currently far more successful at getting noticed than mobile ads.  Aside from the “novelty factor,” larger displays and enhanced browsing experience likely enable viewers to better notice and absorb tablet creative executions.

The favorability and intent data also show that tablets are also slightly more effective than mobile phones at guiding consumers down the purchase funnel.   As the tablet market matures and becomes more saturated, these strong deltas are likely to stabilize on par with our mobile normative data.

While tablet campaigns are excelling in many areas, others remain on par with mobile.  The tablet campaign brand awareness results do not outperform mobile campaigns yet.  Thanks to a larger screen, tablet ad creative to date can offer more flexibility than just a logo but additional information in the form of images or text may compete with the brand’s logo for the consumer’s attention.

“InsightExpress launched Tablet AdInsights™ in 2011 as the first research solution to measure campaign performance across tablet devices.  Since then we have been compiling tablet normative averages to benchmark the brand communications effectiveness of this quickly growing medium,” said Drew Lipner, Co-Chief Executive Officer at InsightExpress.  “As we have learned through our collaboration with VivaKi, tablets are helping to accelerate share shift across screens and have had a transformative impact on digital advertising.”

“We have a wealth of tablet data and research available at our fingertips that clearly shows the power of the device when it comes to reaching and engaging with consumers,” said Tracey Scheppach, VivaKi EVP, innovations director and founder of The Pool. “As an industry, we need to work towards harnessing the magic of the tablet and move the industry forward.”

For the full Tablet InsightNorms white paper, please email us at info@insightexpress.com.

About The Pool

Part of VivaKi’s Emerging Opportunities pillar, The Pool exists to uncover new and improved advertising solutions – ones supported by consumers, advertisers and media companies. It is designed to bring together the leaders, pioneers, and the futurists to help scale and monetize evolving media platforms. Since launching in 2008, The Pool has expanded its reach across the globe, attracting more than 100 companies to its cause in 14 lanes, or areas of focus, across seven countries. Its efforts have tackled everything from online video to cross-media metrics to tablets and IPTV. www.vivaki.com/the-pool

However, multi-dimensional travel to measure ad effectiveness is not currently an option.  The next best possible alternative would be to randomly assign people to test and control groups.  We could then make sure that everyone in one group was exposed to the ad and nobody in the other group was exposed to the ad.  While this is possible, it is also unreasonable due to factors such as cost, time, feasibility, etc.

When we measure ad effectiveness we are usually not in a position to randomize and force exposure to respondents.  So instead we capture an exposed group and a control group, since we can make sure that exposed people were exposed to a particular ad and control people were not.  But since we did not randomize the exposure, there may be group differences.  That is, the exposed group may have been exposed to the ad because of some difference from the control group, be it behavioral or demographic.  How do we account for these group differences?  Enter the Propensity Model.

A propensity model can be thought of as a function that looks at a bunch of variables and spits out a propensity score (PS) for each respondent.  The PS is just a probability based on the model, in our case the probability of being exposed to the ad for that particular respondent.  The theory states that if we match control and exposed based off the PS then the two groups should be balanced on every variable included in the model (with some assumptions, of course).  There are variations among specific techniques (PS stratification, PS matching, inverse-propensity weighting, etc.), but any such approach is a Propensity Score Method (PSM).

Here at InsightExpress our Ignite Network offers real advantages when doing PSM.  First, our database contains many demographic variables for respondents which allow us to link all of this anonymous information to the survey responses without worrying about respondent fatigue.  Second, we have anonymous site visitation history for all of our respondents which is also critical because it constitutes the behavioral portion of the respondent’s information.  Since we know what sites each respondent has visited and how often they have visited those sites, we know their web usage and behavior.

Once the survey responses are linked with the demographic and behavioral variables, we start the model build by looking for variables of interest.  Initially we check each variable individually to see if it is a significant predictor of exposure (i.e. Is there is a significant difference between the exposed and control groups?).  At InsightExpress we use both 7 and 30 day site visitation windows.  We choose the more significant of the two to include in the model to avoid any collinearity issues.  (Collinearity:  it is bad to have multiple variables with nearly the same information.  As the 7 and 30 day site visitation variables for a particular site have overlapping information, we only want to use one of these.)  Additionally, we may look at site categories – entertainment, sports, news, etc. – in the 7 and 30 day windows.  This involves extra checking on the collinearity front, but it also can lead to more variable possibilities for the model.

In addition to investigating differences in the exposed/control groups, we also see if any of the variables are significant in predicting metric outcome differences (e.g. Aided Awareness, Message Association, etc.).  Here we look at the variables as a collection rather than individually and use a model selection method.  This approach, along with the previous step, gives us a collection of “interesting” variables.  It is the variable basis from which we can fine tune and tweak the propensity model.

When we put a propensity model together we have two big goals:  (1) a common support and (2) balanced groups.

1. Common support:  We would like to have both groups (control and exposed) with PS values ranging from 0-1.  In particular, we do not want any gaps for either group.  In practice it is difficult to find control respondents with high PS values, thus we recommend over-recruiting for the control group.  Typically we shoot for a 3:1 control to exposed ratio which has allowed us to make comparisons across all ranges of the PS.  (We can only make comparisons when both groups have respondents with similar PS values.  If the control group only had PS values from 0 – 0.6 and the exposed only had PS values from 0.15 – 1, then we would only compare the respondents in the 0.15 – 0.6 PS range.)  Here we may have to do some model tuning to get PS ranges for both groups without gaps.

2.  Balanced groups:  Since balanced groups are the goal of PSM, we’d better ensure that the modeling process is doing what we expect.  Here diagnostics are key.  We check for significant differences between exposed and control groups on all of the “interesting” variables that we collected previously.  If we are using PS stratification, then we want to look within each stratum to ensure that the groups are balanced in these comparison strata.  If we have significant differences, then we go back to tuning and tweaking our propensity model until the groups are balanced (and we still have common support).

So for a good Propensity Model we need three things:

1)      Enough variables to account for any differences between our non-randomized control and exposed groups.

2)      Common support of PS values for the control and exposed groups.  Here over-recruiting in control seems necessary to get a full range of PS values for control (I have not had any issues getting low PS values in the exposed group).

3)      To ensure that the groups are balanced after doing the PS adjustment.  This step is the key point of propensity modeling, so we must look to see if there are still significant differences after adjusting for the PS.  CHECK YOUR MODEL!

Using a PSM we can be assured that any differences, or Δ’s (deltas), we see are a result of the ad exposure and not due to any differences in the groups that also led to the exposure.  This approach is as close as we can get to random assignment to exposed/control groups, the gold standard in evaluating effectiveness of treatment.  By extension, this is also the closest we can currently get to multi-dimensional ad effectiveness measurement.  So until inter-dimensional travel is being used to measure ad effectiveness, why not use a PSM?

Questions about propensity models for Keith?  Contact him directly at kkohrs@insightexpress.com.

As digital media campaigns consume an increasingly large percentage of media buying, marketers are asking, “How can we compare various media channels with different metrics?” There is a distinct need for a consistent, cross-channel gross rating point (GRP) approach and marketers are wondering if media ROI will replace GRP altogether. Attend this session to discuss strategies to identify and implement the best metrics for media planning and buying as well as approaches to increase total media ROI through a consistent cross-channel measurement.

The session will be moderated by Srishti Gupta, Executive Vice President and General Manager, New Media Solutions, SymphonyIRI Group.  Additional panelists include David Algranati, PhD, Senior Vice President of Product Innovation and Custom Research, Rentrak, and Abhay Patel, AVP, Consumer & Market Intelligence (CMI), L’Oreal USA.

If you are in Vegas for the Summit, please join us for what is sure to be an informative session.  Thanks to SymphonyIRI for inviting InsightExpress to contribute to this great event.

Don’t get me wrong, this is an absolutely reasonable request to get from a client. However, the one thing that’s happened with the digital revolution is the creation of a global caste system based on access to technology.  Starting with the US market, it is easy to go crazy with analytics because we live in a data rich system.  Ad spend figures are high, impressions are significant, we have a large population, and are undergoing a data availability explosion.  Given all of those factors, we’re in a market perfectly tuned to media measurement innovation.  We can do all kinds of things other countries can only dream of.

On the other hand, while we’re doing fantastic things in the US, we’re behind on mobile which happens to be something that’s readily available in Japanese and Korean markets because of the significant penetration and use of smartphones. Then at the other end of the spectrum there are markets such as Germany with incredibly strict privacy laws that prevent all but the most basic forms of campaign performance measurement.

Access to technology and data makes for a great analytical environment, but the other big factor that should be taken into account is the sheer size of the markets being measured.  Your average European country has around 60 million residents. That’s the equivalent to restricting your typical analysis of a campaign to only those who saw it in California and Texas. Granted it’s still a big group of people and you can do a lot with that population, but you certainly can’t do the same as you would with 300 million.

Given the different smoke points of technology across countries, it only stands to reason that the measurement of campaign performance will have to differ from market to market.  Yet, incredibly, I have lengthy conversations with clients who tell me they need the same measurement standard across all markets.  On the one hand I get it, you want to have the same measuring stick so you can identify which markets need the most help. On the other hand I find that these clients are willing to suppress valuable insights in the interest of global measurement standardization.

I haven’t quite decided where I stand on this issue. I realize the need to identify weaknesses in a global strategy.  Yet, digitally speaking, we live in an unequal world, one populated by the technologically rich and technologically poor, and I believe we need to extract the greatest insights on a market level because that provides the best chance to tune a specific market for success.  At the end of the day that’s the goal, right?

We regularly engage in global research on behalf of our clients and fully understand that the name of the game is forking your research strategy to gain the best insights from each market.  I do find though that the number of researchers who understand that strategy – who understand the benefits of research as optimization, not as a KPI – is still low.

At the most recent ARF Re:Think conference one of the main topics of conversation surrounded the coming changes in research; the shift from survey to analytics, from research to statistics.  It was a great topic to discuss and one we need to continue because I don’t think the message has broken through yet: having a central global tracking KPI is less important than findings that allow for local markets to deliver the greatest insights and ability to improve advertising return on investment.

Imagine being able to tell when your consumers are interested in what you’re communicating, open to your message, distracted by other things in your ad, or even completely bored.  That kind of power in a conversation is rarely seen (except by your mom who always knows what you did). If you had insight into the habitual body language and accompanying facial expressions that people use, you too could interpret the secret language of nonverbal communication.

Research on facial expressions has revealed that consumers will sometimes say “yes” with their words, and unconsciously shake their head “no” at the exact same time. Which leads one to ask “What message are they trying to send me?” Research suggests that while one message may be communicated consciously, there is always another more subtle message that may enhance, emphasize, or contradict the spoken statement.

It has been said that the eyes are the windows to our soul. No other aspect of your facial expression is more important when it comes to communicating sincerity and credibility. Making eye contact is one of the most important elements of a genuinely honest conversation, but how do we measure that nonverbal communication?

At InsightExpress we partner with Affectiva, the global leader in emotion measurement technology!  With this exciting alliance, Affectiva will provide emotional-response insight to CreativeImpact, our copy testing solution.  By seamlessly integrating Affectiva’s Affdex™ Automated Facial Analysis technology, brands now have the opportunity to further optimize their advertising campaigns with Affectiva’s market-leading, quantitative neuromarketing technique.

Affdex Automated Facial Coding is an award-winning neuromarketing tool that reads emotional states from facial expressions using a webcam, providing fast, accurate insight into consumer response to advertising and media.

By utilizing Affectiva’s  technology we can track visible changes on a person’s face as they watch an ad and respond to amusing content or disagree with negative content. The software detects the slightest deviations of facial movement in the eyes, cheeks, and mouth, tracking whether a person smiles, frowns, grins, furrows their brow, or breaks out in laughter. Continuously measuring facial cues allows us to determine a person’s emotional interest in the ad.

These important observations can help companies refine ads by balancing their efforts to capture attention using a moderate amount of engagement or emotion with delivery of the message in a relevant and credible way.  Advertisers should pay attention to that balance, and monitor not only how far into the ad their brand is presented, but also how the branding aligns with the creative element that is garnering the engagement. When emotional engagement follows the brand cues a synergy effect is created, which increases both interest in the brand and intent to buy. Think of Pavlov’s theory of classical conditioning: if you show somebody a stimulus (the brand) and then you show engaging qualities (fun, positive emotions) right after, people should associate the two elements.

With facial tracking we are able to determine if the creative elements of an ad are indeed invoking positive emotions and creating the synergy between those emotions and the brand, or if the creative elements are working against the ad’s intended goal.  Facial tracking is another tool, yet a very sophisticated one, to understand the subconscious complexities of how a consumer processes the message and content of your TV ad.

Affdex Automated Facial Coding is an award-winning neuromarketing tool that reads emotional states from facial expressions using a webcam, providing fast, accurate insight into consumer response to advertising and media.

InsightExpress’ CreativeImpact copy testing solution features a unique methodology that blindly exposes respondents to ads embedded within actual TV show content for an authentic viewing experience. While the viewers’ are watching the ads, Affdex automatically captures their facial expressions via webcam. By simulating a real TV watching environment and testing in a true blinded exposure, the combination of InsightExpress’ methodology with Affdex technology delivers powerful insights, as the test ad is being evaluated without cognitive bias.

“Our ad testing and media measurement work with world-class brands and media owners has enabled us to further enhance our facial analysis science and technology,” stated Nick Langeveld, VP of Business Development at Affectiva. “By partnering with InsightExpress, we continue to solidify our position as the global leader in delivering emotion insights.”

Marc Ryan, Co-CEO of InsightExpress added, “We are extremely pleased to integrate Affectiva’s technology into CreativeImpact, InsightExpress’ copy testing solution.  The powerful combination of InsightExpress and Affectiva provides our clients with an invaluable window into emotional insight to enhance the strategic refinement of their advertising and branding efforts.”

About Affectiva
Affectiva, an MIT Media Lab spin-off, is a global leader and industry expert in emotion measurement technology. Through Affdex™ Facial Coding software, Affectiva delivers cost-effective, scalable emotion analytics to Fortune 500 companies and leading market research agencies. Based on the world’s largest repository of naturally occurring emotional response, their technology has become the global standard for real-world accuracy and relevance in emotion analytics.

This morning MediaPost’s Online Media Daily publication covered the announcement here.  Stay tuned for more posts on this exciting topic later in the week!

InsightExpress is proud to be working closely with the IAB on this project. As the research partner powering these important Cross-Screen Optimization Studies, or “XSOS,” InsightExpress will collaborate with Joe’s team to test cross-media impact of actual, in-flight ad campaigns that include mobile. This relationship is a natural fit, since InsightExpress was a key participant in the IAB’s original XMOS (Cross-Media Optimization Studies) project from the early 2000s which provided the industry with landmark insights and, as Joe notes, offered “a groundbreaking model of cross-media research.”

His ClickZ article goes on to conclude with “three things to emphasize about this [the XSOS] project:

• Synergy focus. Our design for XSOS will show how mobile works together with PC digital and traditional media to have a holistic, synergistic impact on consumer awareness and attitudes. We believe that a cross-media plan is worth more than the sum of its parts; XSOS will help prove that.
• State-of-the-art methodology. Much has changed in the research world since the days of XMOS, but the core principles of good research design remain constant. XSOS takes advantage of the methodological advances while maintaining a high standard for research excellence.
• We need it. XSOS will fill a vital industry need. The IAB commissioned a piece called “The State of Mobile Measurement” back in 2011. That report referred to cross-media measurement as the ‘Holy Grail’ of the mobile world, a statement that remains true today.”

We look forward to playing a role in this effort, and are excited to share what we learn with the industry. We also encourage you to read Joe’s entire ClickZ piece, and send any questions or thoughts you may have to us at info@insightexpress.com.