Exploring Your Audience: Targeting Segment Overlap

eXelate segments share users based on similar browsing behavior, and when those segments are targeting people with similar interests, this overlap can be significant. This heat map visualizes 50 of our most popular segments by shared users. Finding unnoticed relationships among users and segments can be invaluable for advertising campaigns.

For example, Auto Buyers: Hybrids and Shopping: Personal Tech draw from unique sources of information yet show an audience with many of the same users. This can be seen in the intersection of the red row and column. In many ways hybrid cars are a form of personal tech, with an emphasis on sophisticated design, interactivity and advanced technology. Other segments with significant overlap include an interest in green living and an interest in overall health shown in green, and an interest in reading as a hobby and having a cat as a pet shown in purple.

This provides ideas for additional marketing. Personal tech shoppers may be ideal targets for hybrid car sales- interested and inclined to like them, yet existing farther up the sales funnel. Conversely, hybrid car shoppers are ideal targets for personal technology, and would be an incredible target for personal technology which integrates with the hybrid car they may soon purchase. While highly targeted segments present the best sales opportunities, it’s important to consider what consumers look like at all points in your sales funnel and how advertising designed to target a different but related audience may benefit your campaign.

exelate Segments

More Data or Better Algorithms? Not So Fast.

As Seen In: All Things D

It’s always a pleasure to follow a stimulating debate between great thinkers within our industry. Recently, Rocket Fuel’s CTO Mark Torrance wrote “Better Algorithms Beat More Data — And Here’s Why” in direct response to BlueKai CEO Omar Tawakol’s piece, “More Data Beats Better Algorithms — Or Does It?” For his part, Mr. Torrance takes Mr. Tawakol to task for asserting that more data trumps algorithms, but that’s not quite Mr. Tawakol’s point. In fact, the bulk of his article explores the importance of having an algorithm that connects disparate data points, giving them enhanced meaning and usefulness through better context.

Still, I think we can all safely agree that both data and algorithms are absolutely necessary to complete any analytical project. But, regardless, the real point of any successful analytics project is to help an organization achieve a specific business goal. In that light, I hope we can also agree that marketing success is actually driven by four primary considerations: business acumen, data, algorithms and operations. Let’s take a look at each.

Business Acumen

By getting caught up in the data and the math, we can easily forget that analytics projects live and die on business knowledge. Analytics projects, therefore, must always begin with a clear business goal. What does this campaign seek to accomplish? What activities or actions does the marketer wish to encourage or measure? What does the organization already know about key prospects? And what pitfalls will marketers need to anticipate and avoid?

The answers to such questions will influence the other three analytics drivers. For example, digital media optimization models require a “dependent” variable (data), which is often expressed in terms of converters and non-converters. Naturally, the stated business goal will drive which users are deemed “converters.” If some arbitrary mismatch exists between goals and definition, the campaign may very well fail.

Data and Algorithms

I linked these two drivers together to emphasis the point that data and algorithms must be used in tandem. In truth, data scientists spend much of their day employing and refining algorithms. Yet I can’t quite accept Mr. Torrance’s example concerning how best to select a marriage partner if the goal is to produce tall and healthy children. The simple algorithm, he says, might be to marry the first suitor who’s over six feet tall. You could add more data, such as a threshold for strength, to get better results, he says. But for best results, a better algorithm is what’s needed. He writes:

“Measure the height of the first third of the people I see, and marry the next person who is taller than all of them. This algorithm improvement has a good chance of delivering a better result than just using more data with a simple algorithm.”

I am not so sure I agree. Without a doubt, a perfect algorithm that considers height alone will select one of the tallest suitors in the community. But is that sufficient to achieve the stated goal of tall and healthy children? What if that marriage partner happens to have a transmittable genetic condition, or is horribly grumpy? Wouldn’t knowing more about the partner (i.e. more data types) lead to a healthier life for all involved?

Of course, we can’t assume that the more data you have, the better off you’ll be. Useful information that you didn’t have before (orthogonal data, in analytics speak) trumps unlimited data for the simple reason that not all data are created equally. Here’s an example of how that’s the case:

Let’s say you’re building a model that will help you find likely prospects for a new luxury sedan. Now let’s say your analytics model begins with one known input: Household income. Given a choice between additional data and a better algorithm, which should you choose? Additional information — purchase intent — say, will tell you something you didn’t know before. It will be useful therefore to know if a user is interested in purchasing high-end vehicles. But, given a choice between a better algorithm and adding new data such as individual assets under management, the better algorithm may be the best approach. Purchase intent represents new information and insight directly relevant to the business goal. But another measure of affluence? Not so much.

Operations

All the best data and algorithms will be for naught if analytics isn’t fully embedded and widely distributed within appropriate marketing systems, so that the analytics can be directly leveraged whenever and wherever it’s most needed. At eXelate, we recognize that platform flexibility is a critical component for realizing digital media success via cross channel marketing execution.

One last point I’d like to make has to do with the value of contextual and behavioral data. In his article, Mr. Torrance writes, “At Rocket Fuel, we’re big believers in the power of algorithms. This is because data, no matter how rich or augmented, is still a mostly static representation of customer interest and intent.” As far as I can tell, Rocket Fuel therefore sees all data as contextual data, limited to the time of the marketing event.

While it’s of course possible to ignore the time component of data, doing so throws away vital information that an appropriate algorithm could easily digest. In a very simple example, treating someone as “auto intender” or not is far less powerful that tracking a user’s behavior over time to better understand how their intents and interests evolve. That is, unlike contextual data, behavioral data is by its very nature directly linked to longer-term patterns of user behavior. As a result, behavioral data are far better at driving long-term value across a variety of campaigns, especially branding efforts that address personal aspirations.

In a 2011 article entitled “Consumers Are People Too…,” I argued that when asking which data are better, behavioral or contextual, the only right answer is both. And therefore, I reject any absolutes, because marketers clearly will benefit from a variety of data types.

In conclusion, if you’re asked to pick between more data or a new and better algorithm, which should you choose? The proper response is that business acumen, varied information, great algorithms and operations are all critical to the success of an audience model.

Dollar Data Club – Brought to you by eXelate

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Get high quality data for life with Dollar Data Club, brought to you by eXelate. Stop paying for fancy ad tech that’s just not getting the job done. For a few bucks a month, we’ll provide you with best of breed data to help you target your campaigns – soon enough, you’ll have so much extra brand loot you won’t even know what to do with it. See what else our CRO Damian has to say about it:

 

Be sure to check out Dollar Data Club to learn more about how we can make Big Data work for you. Contact eXelate to get started today.

Special thanks to:

Meerkat Media

Damian Garbaccio

Vlada Kabatyanskaya

Jackson Coakley

Big Data Myths: Why All Data is NOT Created Equal

At Digiday Exchange in NYC in August, eXelate CEO eXelate CEO Mark Zagorski took to the stage to discuss Big Data, how companies should approach it, and some of the most common myths surrounding it. Take a look at the full interview below, or scroll down to read some of the discussion highlights.

 

On Big Data: Big Data isn’t about having a big database – it’s about having many pieces of unstructured data in which you look for signals that create patterns of consumer behavior upon which you can act. It’s not CRM data, but it’s about seeing traffic in and out of stores, knowing sales, and making connections between the two. Most companies think they need an all-encompassing solution for data. But in reality, there are a lot of small things brands and advertisers can do to claw away at each pile of data, and these small things can prove incremental results – for example, bringing your offline data online. The trick is to look at the specific thing you want to do – drive higher conversion rates or acquire more customers – and apply a specific data set to your problem.

Data Myth #1 – All data is created equal. There is good data, and there is bad data. There are valuable and non-valuable signals. When you look at the Lumascape (another myth!) there are a lot of different data companies. Every logo is the same size but in reality, every data set is not the same size nor do they have the same caliber of data. Take online data versus offline data; a modeled offline user is different than a registration-based online user when it comes to something like demographic information.

Data Myth #2 – First party data is more reliable than third party data. First party data is great – for the first 50 people that you’ll reach with it. The issue with first party data is that it doesn’t scale, and using it is essentially talking to the same customers that you’re already in contact with. That’s great for upselling and sales retention, but what about acquiring new customers? The best use for first party data is as a seed to model against – you want the best first party data to model a top-of-the-funnel audience.

Data Myth #3 – The Lumascape is an accurate depiction of the complexity of the ad tech world. All due respect to Terry Kawaja & Co, but the chart was created for one reason – to sell banking services. There are two key issues with the amount of logos on the slide – (1) there are a lot of opportunities for consolidation and (2) they create a lot of confusion, which in turn creates a market for services that will clear up that confusion. An advertiser or a publisher does not need to work with every single company on the Lumascape chain to make a connection – many times they’re not involved with any major decisions. The Lumascape overcomplicates areas that are simple, and oversimplifies some areas that are complicated.

Join the conversation – follow Mark and Digiday on Twitter.

Measuring Campaign Accuracy– There’s No Escaping the Math

Over the summer, there’s a natural tendency to let the mind wander from some of the more rigorous and demanding parts of the digital advertising world. Whether on vacation or at our desks, when the weather is warm the last thing some of us want to focus on is math.

But at eXelate we don’t have that luxury, and now that summer has reached its symbolic end with Labor Day it’s time for everyone to face some hard truths that aren’t changing anytime soon. In case you missed it, Nielsen Online Campaign Ratings released their latest set of Benchmarks or “norms” in August.

These norms help marketers evaluate their campaign effectiveness by benchmarking their campaign accuracy against campaigns with similar targeting criteria.  Nielsen Online Campaign Ratings benchmarks take results from prior campaigns that used Nielsen Online Campaign Ratings and creates an average result for campaigns within various “zones” or audience parameters. This helps provide guidance as to accuracy expectations for a broad campaign targeting, say “all Women” versus a more narrow campaign targeting “Men 18-24.”

What’s interesting about the norms is that they provide a good measure of targeting efficiency between varying types of campaign targeting.The latest norms break down as follows:

Zone Zone Description Gender Target Span Avg of % on target
Zone 1 Broad, All Genders Persons >30 year span 84%
Zone 2 Broad, Single Gender Males, Female >30 year span 54%
Zone 3 Medium, All Genders Persons 15-29 year span 58%
Zone 4 Medium, Single Gender Males, Female 15-29 year span 41%
Zone 5 Narrow, All Genders Persons <15 year span 44%
Zone 6 Narrow, Single Gender Males, Female <15 year span 32%

One key takeaway is that there is no “one-size-fits-all” when it comes to gauging campaign effectiveness. Any marketer that is spending on digital advertising expects perfection, and our goal at eXelate is nothing less. At the same time it’s interesting to see how elusive perfection can be, even with the broadest of campaign targets. How you target, who you target, what data you use to fuel your campaigns: all of these factors are going to determine how successful your campaigns are in reaching their audience goals.

Another takeaway is that marketers can do better. As an industry, the results above should concern us because there’s simply too much inefficiency here. While the targeting methodologies that make up these norms surely differ, there’s no escaping the fact that marketers won’t put up with campaigns that fail to hit their targets over and over. A key promise of digital is accuracy of measurement and delivery, so it’s time for us to make good on that promise, or we’ll be doing a lot more make goods.

We take campaign ratings very seriously: quality data is at the core of our business. That’s why we spend so much time measuring our data quality with the same tools marketers use, like Nielsen Online Campaign Ratings. We validate our own data so that when marketers use our data to reach their intended audience, they are successful. Summer, fall, whatever – there’s no avoiding the math.

Combining Data to Create Powerful Modeling

The Netflix Tech Blog recently posted an entry that discusses their recommendation algorithms and outlined the Netflix Prize, a machine learning and data mining competition to predict movie ratings. The 2009 winner of the contest improved Netflix’s ratings prediction system by more than 10% with a new algorithm. For a company owing 75% of viewership to recommendations, this would seem to be a huge step for Netflix. They didn’t adopt the winner of the contest, however – and the reasoning is perfectly logical.

The winning algorithm focused mainly on predicting ratings. Ratings are an important source of data for Netflix, but new types of analyses & inputs beyond ratings alone have emerged, all of which can help Netflix create even better recommendations. These include context, movie title popularity, novelty, diversity and freshness.

If you’ve been reading this blog or our Twitter handle for the last 2 months, then you are probably familiar with maX™ – or Modeled Audience eXtension, our custom modeling product that helps advertisers build a scaled audience with broad reach that is 3-5x more likely to respond to their campaigns.

We make it work by focusing on all the data – the advertiser’s first party data as well our eXelate premium marketplace data. And we look at as many data points as possible, including demographic, lifestyle, intent, behavior, brand affinity information and other proprietary data sets many advertisers may not have access to. We’ve learned, as Netflix seems to have, that combining multiple forms of data can prove to be the most powerful way to reach an audience. If you were Netflix, what would you have done?

Ad Age Digital: Connecting Madison Avenue with Silicon Valley

eXelate is proud to be a sponsor of the 2012 Ad Age Digital Conference, taking place over the next two days at the Metropolitan Pavilion in the Chelsea neighborhood of NYC. Ad Age always puts on a good conference, and after already hearing from the CEO of Hulu and Gap’s CMO, this one is quickly proving to be a premiere event in the digital marketing space. Going on year 6 of holding the Digital conference, Ad Age boasts over 700 high-level attendees and speakers as well as jam-packed workshops and plenty of networking opportunities.

In her opening remarks, Ad Age Editor Abbey Klaassen pointed out that in the past, the conference centered around how marketers reacted to the digital space. These days, it’s about how marketers are engaging with the digital space – and that’s what we’re all about. We will be here all day to answer any and all questions. Come by to talk to one of our sales or marketing team members and learn how we’re helping digital marketers make the best advertising decisions with our premium, syndicated, and maX modeled data sets.

Also, be sure to follow the conversation on twitter – look for #aadigital.

March Madness Sees Spike in Travel Intent

March Madness has come and gone, leaving the Kentucky Wildcats the victors of the esteemed college basketball tourney. Kentucky took on the Kansas Jayhawks on April 2 to determine the national title following their defeat of the Louisville Cardinals and Kansas’ defeat of the Ohio State Buckeyes. The Final Four duked it out in New Orleans, so we decided to watch for spikes in travel to Louisiana. Here’s what we found:

Travel from Kentucky and Kansas to Louisiana spiked between March 24 and 30, peaking on the 26th.

We also saw a spike in college basketball-related behavior at the beginning of March as well as the beginning of April:

College basketball interest spikes in the beginning of March and April, then quickly recedes.

Interest recedes through the month of March then picks up again at the beginning of April, right before the championship game. Following the game on April 2, college basketball interest again dipped down.

Are you glad that all the madness of March is over? Or are you enjoying your bracket winnings?

Q&A with V12 Group

This week, eXelate announced our partnership with V12 Group, a leading provider of offline premium data and data technology. Our alliance with V12 Group allows us to continue to help digital marketers make the best marketing decisions through actionable data. We sat down with Kelly Leger, VP Digital Solutions, V12 Group, to learn more about V12 Group, their data, and their thoughts on the new partnership.

  • What is V12 Group hoping to see from their partnership with eXelate?

We are so excited to partner with eXelate.  Strong strategic partners such as eXelate help elevate awareness of the offline to online data boom happening in the advertising display marketplace. We hope to see a strong partnership that exposes V12 Group’s unique data to eXelate’s clients, thereby creating success for both of our businesses.  We value eXelate’s forward thinking stance on marrying media and data, as well as your strong privacy standards.

  • Tell us about V12 Group’s data and why it is unique.

V12 Group owns our data as well as a data warehouse facility. We scrub and rebuild our file quarterly – so our data is highly accurate. We strive to make our audience segments easy to understand at every level of the process: explaining to clients, filling out an RFP, etc.  We believe that accurate data will win out every time, in every campaign.

  • eXelate is excited to bring V12 Group’s automotive and PYCO personality profiles online. Where does V12 Group’s automotive data come from?

V12 Group’s auto data is a subset of our vast consumer database of over 200 million consumers covering 110 million households, which is compiled from over 40 data sources. Our automotive data is comprised of over 130 million offline vehicle records, all matched at the household level.  The 100% privacy compliant file is compiled from a proprietary collection of auto services, including insurance, warranties, vehicle maintenance, driver statistics, manufacturer’s promotions, title services, government sources, online sources, parts providers and public records. We are seeing great results with our auto data and continue to gain market share in this space. The ability to have this data available in eXelate’s platform is one of the main reasons we formed a strategic partnership between the two companies.

  • V12 Group’s PYCO Personality Profiles are based on the Myer’s-Briggs Personality Type Indicator and use 320 different data points to accurately assign a personality type to 85% of American consumers aged 18 and above. How can this data being brought online benefit digital marketers?

We are seeing a great demand for our PYCO data set.  The PYCO data set is something the display marketplace can use in multiple ways. We are seeing clients apply this data to creative messaging – showing different sets of creative to extroverts versus introverts;  PYCO is an answer to that intangible request that comes through in RFPs; we’ve seen it used in Auto, Travel, and Finance verticals, to name a few.

We’re looking forward to providing V12 Group data as a vital tool for digital marketers leveraging data via the eXelate platform. Click here to learn more about V12 Group.

maX Data is Here! Custom-Made Audiences for Specific Campaign Goals

As a digital marketer, some major concerns you face are reaching your target audiences and being sure they are driving advertising results. It’s a constant struggle to find your optimal audience every time, and more often than not, impressions are wasted. With the introduction of eXelate’s Modeled Audience eXtension – maX – marketers will be able to reach more people who improve their campaign results.

  • It all starts with YOUR data. Your first party data is critical to maX. eXelate analyzes your data to define attributes that distinguish converters from non-converters. Then, we use the attributes of your best performers and match them to our premium data marketplace to build a custom audience. That audience can then be distributed on any media you choose.
  • Custom audience based on your specific campaign goals. Each audience we create is unique. Our data scientists customize the audience to achieve your campaign goals.
  • Performance at scale. maX delivers 3-5x better campaign results as compared to traditional data targeting.
  • Flexibility. We’re building custom data segments that you can make actionable across any platform you choose. 

maX is an exciting endeavor that allows us to continue to push the bounds of traditional targeting. Let us know your thoughts! To learn more, visit our site.