Your audience tells you a lot about themselves in their actions and what they pay attention to online. As an ad network or online media company, how you gather and use that behavioral data is critical to your business. But given the sea of data available, what is the right behavioral data to gather and use for targeting?
There are three different types of behavioral data along the "behavioral targeting continuum": category, affinity/interest, and intent data. This post will define and explain the value of each.
Since this is not a short post, let me just start with the key takeaways:
- As with all targeting, reach/scale becomes more limited as you apply narrower targeting.
- Category data provides the shallowest targeting, least ROI yet broadest reach.
- Intent data provides narrow targeting and strong ROI but it's one-dimensional targeting with very limited reach.
- Affinity (interest) data provides the best balance of breadth of reach and depth of targeting. Think about yourself: of all the Web pages you browse to, only a small portion exhibits purchase intent whereas all of it reflects your interests!
- Affinity data offers greatest ability to tune (via the affinity score), multidimensional utility, most flexibility, and therefore the greatest lift across the largest % of your inventory.
Not All Behavioral Data is Created Equal
By definition, category means "a collection sharing a common attribute". Category data allows you to put users into conventional and broadly-defined behavioral "buckets", with labels such as travel, business, entertainment, etc. A user is attributed to a behavioral category if they've been to a site that falls within the particular category, perhaps within some sort of time frame. The value of this approach is breadth in reach and impression volume, but the targeting is quite shallow and definitions can be blind AND arbitrary -- what does an Urban category really mean and what are the sites contributing to that definition? As a result, category-based behavioral targeting (or retargeting) may not perform any better for advertisers than site-based (contextual) category targeting.
By definition, intent means "the planning or desire to perform an act". Intent data results from an explicit indication of purchase intent -- these users are in the market for specific goods/services. A user is indicating "intent" if for example they've added goods to a shopping cart, calculated a mortgage loan, checked airfare prices, etc. Clearly there's value to targeting based on intent data, but the challenge is the limited reach and scale. As a simple demonstration of that point, what % of your online time or page views are YOU indicating purchase intent for big ticket goods and services?
By definition, affinity means "attraction or kinship to something". Affinity/interest data allows you to understand what your audience is interested in -- brands, activities, products, hobbies, places, etc. A user is said to have a high affinity to "iPhone applications" if they've explicitly indicated it (typed-in within a search field, social profile or blog comment) or gone to a number of Web pages about iPhone applications within a short period of time.
There are several attributes to affinity data that differentiate it from category and intent data:
- Affinity data is comprised of keywords and phrases, the lingua franca of SEM and SEO. There isn't a marketer on the Web who doesn't "speak keyword" or know what keywords work best for them, and those definitions are not abritrary. With keywords/phrases at the core, affinity data is then rolled into categories, personas, or custom channels/clusters.
- Each affinity attribute is coupled to an affinity strength score which allows you to target based on a user's level of interest. This score evolves continually based on time and intensity, much like the attention span of users, allowing you to achieve the right balance of scale/reach and ROI.
- Affinity profiling is deeper, since it involves the ongoing collection/analysis of what users are searching for, reading about, and publishing (through their blog, social media, etc.).
- Affinity data therefore has much broader utility, since you may use category ("travel") AND keyword specificity ("aspen, skiing"). Affinity data can also supplement other forms of targeting.
Achieving eCPM Lift and Volume
The average US broadband internet user views 138.1 pages per day, and is online at least 24 days each month. Say each page has 3 ads on it -- that's almost 10K ad impressions for each Web user each month! That's a lot of inventory per unique, especially if your company is a top 50 US ad network.
Let's say you are an ad network. How do you offer better targeting, and optimize both advertiser ROI and your own eCPM rates? Assuming the cost isn't overly prohibitive, leveraging intent data will work but at low volume -- resulting in maybe 100% lift across less than 5% of your reach. You're already offering category targeting (site-based), so it's not a big step to also offer category retargeting across your network. If you do, you'll achieve maybe 5% lift across less than 50% of your reach (you'd only offer category retargeting for your best categories, after all). Both of these approaches are one dimensional approaches however -- targeting single, binary, behavioral attributes.
To get the most lift across the greatest % of your audience, you're best off to layer in affinity data across your entire audience. This provides you with most targetable attributes per user, the greatest flexibility in how you use it (narrow vs. broad targeting, single or multifaceted targeting, etc.), and the ability to tune until you've achieved the right balance of ROI and volume.
Here are a few "for examples" highlighting what you can do with affinity data:
- Tighten your current category targeting ("consumer electronics", for example) with affinity targeting, by filtering to only those who have searched for "iPhone applications" (very narrow), or have shown any interest whatsoever to "iPhone" (broader).
- Reinforce the value of contextual targeting by targeting both current context and previous affinity to the same category. This method of "context disambiguation" can dramatically increase the value of your contextual categories.
- Further segment intent data (such as "in-market auto buyer") with affinity data, narrowing the targeting to hobbies ("skiiing", "climbing") or personas ("young professional").
Of course, the strength of your affinity targeting depends on the breadth and depth of affinity data collection. That's why Others Online offers a shared data model, allowing all participants to pool affinity data collection and scoring so that every participant achieves a wider footprint of touch points than they have access to themselves.
Affinity Data Offers Depth, Breadth and Multifaceted Targeting
Your audience tells you a lot about themselves in their actions and what they pay attention to online. If you're not collecting and using that valuable information, you're leaving money on the table -- especially these days when every advertiser is focused on eliminating wasted impressions.
Category and intent data offer single dimensions that allow you to expand your targeting capabilities at varying levels of resulting eCPM lift and volume. To really move the needle though, both for your advertisers and your business, supplement your targeting with multi-dimensional affinity data to offer the broadest AND deepest level of targeting across your network. You'll be glad you did.