This document discusses understanding intent data and provides information about its promise, reality, basics, vendors, quality issues, execution issues, applications in acquisition, nurture, and other areas. It recommends performing viability checks and performance tests on intent data for specific applications to evaluate results before fully adopting its use. While intent data is not snake oil and has many potential uses, careful testing is needed as its quality and predictive ability can vary.
4. Reality
“Third-party intent data often isn’t
conclusive enough to actually be
predictive. It may be helpful to compare
people with no activity to people with
lots of activity, but keep in mind that
you’re likely to find a lot of false
positives.”
- Infer Inc., What’s the Deal with Intent Data?
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6. Intent Data Basics
• Sources: page views & ad networks, also: downloads,
Webinars, social posts, site search, registrations, reviews, …
• 1st Party vs 3rd Party (big difference!)
• Anonymous vs Known
• Processing Required
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7. Intent Data Vendors
• Web Sites: IDG, TechTarget, Demandbase, Bombora, MRP,
Madison Logic
• Ad Exchanges: The Big Willow, Everstring
• Predictive: 6Sense, Infer, Leadspace, Lattice Engines, Mintigo,…
• B2C: Nielsen eXelate, Oracle DataLogix,
Magnetic, Neustar, …
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8. Under the Hood
Quality Issues
• Coverage
• Timeliness
• Accuracy
• Precision
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9. Under the Hood
Execution Issues
• Data load
• Account vs Person
• Contact details
(email, phone, etc.)
• Identity match
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14. Getting Started
Performance Tests
• Select Application
• Define Success
• Define Test Process
• Execute Test
• Evaluate Results
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15. Acquisition Tests
Net New Names
• Measure list quality
• Find near-term metric
Prospecting
• Run trial promotion
• Measure cost per response
• Consider response quality
Display Retargeting
• Use intent-based content
• Measure results by account
• Compare vs control accounts
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16. Nurture Tests
Lead Scoring
• Compare scores with and without
intent data
• Use intent-based scores to change
treatment of test group
• Compare test vs control results
Personalization
• Send intent-based content to test
group
• Send standard content to control
• Compare test vs control results
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17. Other Tests
Reactivation
• Match intent list against inactive leads
and accounts; find names with activity
• Promote to test group using intent-
based content
• Also promote to random sample
• Compare results
Content creation
• Acquire intent category trend data
• Identify gaps and create new content
• Test performance of new content
against all leads (vs control)
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18. Intent Data
… is NOT snake oil
… comes in many varieties
… has many different uses
… needs careful testing
… is probably worth the effort
(your mileage may vary)
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19. Thank You
David Raab
Raab Associates Inc.
www.raabassociatesinc.com
draab@raabassociatesinc.com
Twitter @draab
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Editor's Notes
Promise: intent data will give you bushels of people ready to buy your product. Just hand them off to sales and take the afternoon off.
Reality: not quite so sunny
Infer did detailed study – found that external intent data by itself wasn’t very useful. Found that fit and internal intent were effective.
So, is intent data just a bunch of snake oil? I would argue it’s not. But you do have to know what you’re doing to get the real benefits.
sources of intent data for business and consumer marketers
- where it comes from: web sites, ad networks, internal (for retargeting), search terms; consumers also get from surveys, commerce, comparison/review sites, loyalty programs
1st party vs 3rd party
Anonymous (e.g. cookie) vs known (have contact info)
- vendors (aggregators: Bombora, Big Willow; publishers: IDB, Tech Target; predictive vendors (who mostly buy it); Magnetic
- Processing required – e.g. identify surge
vendors
- Web sites: based on who reads which content; have networks of sites they monitor
- Ad Exchanges: (aggregators: Bombora, Big Willow; publishers: IDB, Tech Target; predictive vendors (who mostly buy it); Magnetic
…not listing actual white paper download/content syndicators e.g. NetLine, TrueInfluence, etc.
limitations - quality
- coverage: match rate vs own data, % of market captured
- Mintigo: Over 99% of web visitation events are anonymous and only 30% to 40% are resolved at the company level. (http://www.mintigo.com/blog/demystifying-the-intent-behind-intent-data/)
- timliness: how quickly data reaches you; speed is critical but vendors accumulate from many sources
- accuracy: are they really in-market? Must look at behavior (surge), who person is. Requires intelligent processing; can’t just look at a few page views
- precision: are they in-market for my product. Vendors have taxonomies but they don’t necessarily match your business categories
- execution: identity matching, loading, account vs individual, group buying, contact info
limitations – execution
- data load (format presented, ability of own systems to import
- account vs person (which level?
- contact details: email, phone, cookie, etc.;
- identity match (ability to match to own data, match 1st vs 3rd party; depends on data quality; many people provide false info
applications: - name acquisition:
net new (doesn’t usually work by itself; won’t be sales ready or close; might have long-term utility
Prospecting (even if name isn’t a lead, can use list for prospecting e.g. send acquisition offer, etc.; is basically same as nurture
Display retargeting - big advantage is these are people who didn’t give you their name, which is most of them
- works on 1st party (own site visitors) or 3rd party (other sites)
applications: - nurture:
lead scoring/nurture (use intent data to help set lead score, where to put in nurture
- applies to names already on file as active leads well as net new name (=prospecting),
Personalization (use intent topics to define what content to send; can give insight into interests
- applies to both 1st and 3rd party intent sources; is only way to get at 3rd party
- can apply to ad personalization too
applications: - other:
Reactivation (inactive leads or customers; find names showing new interest, esp. on 3rd party sites
- has been particularly successful application
content creation (look at popular topics and make sure have enough content; test effectiveness)
getting started: how to incorporate intent data into your customer data systems
- viability checks
- business terms (can you get permission to do what you want, is the pricing reasonable)
- adequate quantities available (for your interests, meeting your other criteria e.g. company size, contact info if needed, etc.)
- data elements (is data with the elements you need, in a format you can use)
- match rates (if you’ll load it)
-quality (check for completeness, validate some records, etc.)
getting started: how to incorporate intent data into your customer data systems
- viability checks
- business terms (can you get permission to do what you want, is the pricing reasonable)
- adequate quantities available (for your interests, meeting your other criteria e.g. company size, contact info if needed, etc.)
- data elements (is data with the elements you need, in a format you can use)
- match rates (if you’ll load it)
-quality (check for completeness, validate some records, etc.)
ways to test intent data for practical value
- generally, try programs with and without and see if is lift
- specifically:
- net new: buy a list and see how it works; look for near-term event e.g. Sales Acceptance; exclude existing names
- prospecting: run a trial promotion and measure cost/name, /SAL; exclude leads from other sources
- retargeting: with and without (inc. intent-based content)
- lead scoring/nurture: does it make scores more accurate? does it push people down different paths in nurture programs? Are those paths more effective?
- personalization: standard content vs. intent-based content
Other Tests
- reactivation: value from reactivated name, esp. vs. other reactivation criteria (inc. intent-based content)
- content creation: intent-based content vs control
- can use content on all leads, not just those identified as having intent