How to Navigate the Programmatic Contextual Maze
Ask 100 marketers to describe "contextual advertising," and you are likely to get 400 vague, optimistic answers about the "right message at the right time and place to the right person." Ask 100 startup CEOs and you're likely to get 1,000 black-box answers that will confound you.
There lies the core conundrum. Everybody wants "contextual advertising" but no one knows how to get there with all this techno-babble black-box confusion. This leaves marketers scrambling to figure it out amid a plethora of platforms with their glossy contextual promises that may fall short of what a marketer really needs.
So let's deconstruct the contextual equation recognizing that "contextual tech" covers a wide swath of ad tech. I've confined this highly simplified schema to those technologies that directly drive the "right time/place/message/person" model in the programmatic world for advertisers, leaving out many branches of the ad-tech tree. From this very focused, programmatic perch, we have an excellent view into the highly variable contextual tech landscape:
- Keyword match data for ad placement
- This is a data layer used during the digital ad buying process where ad is placed only when keyword appears on page
- Best used when keyword is very specific with unambiguous meanings
- Buying keywords can misfire because many keywords have vague & multiple meanings which can lead to bad placements
- Native ads/ Content syndication
- These are ad placements in two flavors:
1) Dynamic ad creation to match page design
2) Broad "interest classification" matching – i.e.; shopping
- Quick traffic hit to a page or destination
- 1) Topic classifications are very broad and may not reflect
advertiser real topics of interest
2) Native ad campaign CTR deteriorate quickly due to content fatigue and link bait tactics
- Interest based targeting
- "Interest classification" data is attached to cookie profiles to buy media. Interest data is derived from previous profile digital habits
- Ability to deliver tonnage cost effectively
- Interest classifications are broad, vendor defined and not marketer created resulting in topic gaps and mismatches that waste ad dollars
- GEO based targeting
- Cookie syncing data is used during digital buying to identify users across devices – desktop, tablet, mobile.
- Most typical applications are mobile ads when a clear offer is present
- 1) Low mobile CTR
2) GEO proximity does not predict real time user interest
- Cookie profile targeting
- This is the data backbone of all exchanges so advertisers can target by demographics. Interest and device data can be bundled in or appended to the cookie based targeting.
- Allows advertisers to model alternate profiles to determine optimum ROI
- 1) Data quality
2) Cookie profile data is a rear view mirror not indicative of real time interest
- Presenting an ad to a previous site visitor when they appear in the programmatic exchanges.
- Very effective at getting visitors back to site
- 1) Can't scale beyond known visitors
2) Ads "stalk" users since there is no contextual filter
The contextual moving target
One of the reasons it is so hard to wrestle the contextual equation to the ground is that technologies keep morphing and shifting, undermining most attempts to stabilize technologies. When cookie-based data providers add geo-targeting capabilities or when email goes programmatic, this blurs the lines -- making a clear contextual tech roadmap as easy to calculate as the unifying theory of ad tech.
To transcend the complexity and grasp clarity, let's look at the leading trends in programmatic tech and what they mean for marketers:
1. Growing realization that native advertising/content syndication is just another name for a type of banner ad -- not necessarily more contextually useful than any good banner ad placed on a quality site. Plus, it is potentially laden with FTC disclosure issues.
2. Shift to tech that delivers topic-based context buying (i.e. programmatic content marketing) as that is closest to real-time intent versus cookie-based targeting.
3. Ratcheting down use of "interest classifications" as a targeting parameter because they are often too broad to drive good results. P&G's recent pull back from Facebook's targeting efforts is indicative of this trend.
4. Recognition that keywords work very well in the search paradigm but can struggle to be productive outside of search.
5. New attention to quality, integrated programmatic solutions that cover the sales funnel -- from click to close -- because too many SaaS platforms spoil the contextual soup.
6. Re-engineering retargeting and acquisition marketing from being dominated by platforms that game advertisers to technology that can better align users' real-time interests and behaviors to improve results.
No marketer wakes up and says, "Today I am going run advertising that is NOT contextual," but achieving great contextual marketing has become a caveat emptor market. Great technologies can deliver great contextual campaigns but only if well woven together. Today, this is about as easy as cracking the code on the unifying theory of ad tech. A conundrum indeed.