Guide to B2b purchase Intent



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INDUSTRY INSIGHTS
A Practical Guide to BB Purchase Intent
Seeing Beyond the Hype to Achieve Real Value
By Andrew Briney

A Practical Guide to BB Purchase Intent
|
2 5 Benefits of BB Intent Data
1
Rules-based prospect scoring
2
ABM list building/validation
3
Better response/conversion rates
4
Reduced marketing/sales costs
5
Process efficiencies
5 Gotchas to Watch Out for
1
Inaccurate company identification
2
Misleading intent signals based on too-broad or irrelevant content activity
3
Incomplete information Accounts but no contacts
4
Added complexity Hard to use in your workflow
5
Brochure-ware: Slick-sounding products that can’t perform without better inputs
The hype meter is redlining on the subject of BB purchase intent, and for good reason Finally here’s away to know who’s really in market for your solutions beyond the insights available through traditional inbound and outbound marketing. Intent data promises to give you anew way to rifle- target prospects showing a strong predisposition to buy from you. Its practical applications include prospect scoring, nurturing campaigns, programmatic advertising, ABM and more. Potential outcomes include better conversion rates, faster deal velocity and stronger synergies between marketing and sales. Used effectively, purchase intent brings more effectiveness to your tactics and more efficiency to your processes.
With all this potential, it’s no wonder the intent marketplace has exploded in recent months. Yet as marketers accelerate their experimentation with various providers and platforms, some are struggling to realize practical value. Topline challenges include Misleading signals Real buyers with real intent are hard to find. Automated intent platforms often surface data that looks like intent where none actually exists. Similarly, they may suggest certain companies are in market for your solution when they’re not.

Limited applicability Many solutions are limited to providing only account rankings they can’t provide intent data on a prospector contact level. This limits the usefulness of the information in your marketing practices.

A noisy marketplace So many startups with similar-sounding solutions make it hard to separate value from brochure-ware; fancy GUIs and slick marketing may mask huge performance problems caused by bad data.

A Practical Guide to BB Purchase Intent
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Understanding the Intent Space
Purchase intent information comes in many forms and packages. Getting beyond the hype to achieve practical outcomes requires a clear understanding of the options and a dispassionate assessment of whether they can produce real value for you. The easiest way to think about intent—and to categorize the many solution providers out there is in terms of inputs and outputs. Purchase intent inputs (Table, below) are structured data feeds generated by the digital/
online activities of BB users engaging with relevant content (relevant = specifically focused on topics aligned to your target market or solution. Intent inputs can come from user interactions with both internal and external content.
Internal data refers to your own company’s first- party information drawn from your CRM system, inbound traffic to your website/hosted content, visitors to your trade show booth, etc. Most marketers regard internal data inputs as gold. The problem is they only represent a fraction of the total active market in your target segment—
those who are expressing direct interest in you. Like the tip of an iceberg, internal data exposes you to only a fraction of the addressable market you should be going after.
B2B Purchase Intent Inputs
Intent Source
AKA
Pros
Cons
1st Party Data
Internal intent data
Internal system records and inbound interest from known accounts can provide reliable indicators of interest
A narrow view of the total addressable market
Behavioral Intent
Data
External intent data
Organizes millions of data inputs into a list of account targets
False positives due to inaccurate company identification and mislabeled content not as useful without prospect/
contact data
Predictive Analytics
Modeled lead scoring lookalike targeting
Organizes prospects into a ranking to help prioritize outreach
Often uses company profile matching to expand audiences for lookalike targeting

A Practical Guide to BB Purchase Intent
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A growing number of marketers are looking to external data sources to supplement internal audience pools by identifying accounts that are highly active against relevant third-party content, with the assumption that this activity is a good predictor of future buying. These so- called behavioral intent signals are harvested from external BB content communities, Websites or social networks based on browsing, content downloads, video/streaming media, social interactions and the like. Vendors offering behavioral intent data include
TechTarget,
Bombora, The Big Willow and MRP/Prelytix, among others.
Other external signals include inferred intent gleaned from publicly posted information about the account, including M&As, competitors, budgets, IT installs or IT job postings. This type of data might suggest disposition, interest, need or maybe even an active project in a particular technology area. Companies such as
Spiderbook (recently acquired by
Demandbase), Owler and HG Data offer these services.
Another method marketers use to increase target audience pools is based on analytical models, commonly referred to as predictive analytics. This approach has been used for decades in BC and, with the rise of better systems, there’s hope of benefits for BB as well. These tools and services, offered by vendors such as sense, Everstring,
Mintigo and Lattice Engines, create a data model to help you identify and rank accounts based on their predicted likelihood of buying from you. The model’s output is a prioritized list of accounts built out of your first-party historical information either accounts that have purchased from you in the past and could be strong upsell/cross-sell candidates, or net-new accounts showing an increase in inbound visits to your Website that suggest purchase interest. Some predictive platforms take the additional step of supplementing modeled internal lists with lookalike accounts that share the firmographic or behavioral characteristics of your first-party target pool. Companies such as Demandbase work with Data Management Platforms (DMPs) to help you build a programmatically targetable cookie pool covering both known and lookalike account targets. This pool of cookies is then used for banner advertising and promoting content or landing pages that can be personalized by account type when they click through. Challenges with External Intent Data The biggest challenge with external intent data is ensuring its relevance to you and your business. Many suppliers require you to take a huge leap of faith when you invest in a list of accounts they claim are heating up. That’s because some external intent providers are in the business of selling data volume if they can’t locate a high-enough volume of account activity against a specific market topic say, Flash storage, they maybe incentivized to include ancillary content/topic activity associations say, general-purpose IT storage) to pad the account pool. This problem is made worse by the fact that many external data providers don’t actually own any of the content they are analyzing, so they can’t vouch for its relevance. If a vendor cantor won’t reveal the content sources they’ve used to derive intent, you should be skeptical about its usefulness.

A Practical Guide to BB Purchase Intent
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For all these reasons, you should push for maximum transparency when dealing with external intent providers. Since the primary output you’re looking for is better insight into purchase intent, your success depends on getting clear answers to questions about exactly how those accounts are identified, aggregated and prioritized into any list.
Applying Intent Data in Practice
Whether used alone or in combination, internal owned) and external (purchased) intent data has many practical marketing applications. The keys to success are getting the data into a format that can be ingested into your marketing workflow; applying the data to as many marketing and sales tactics as possible setting up AB tests to optimize content and followup and adjusting and improving your list/campaign flows based on intent- driven results. The output you get from an intent data source maybe a simple list of accounts or domain names it may also include contact names and firmographic information. Some lists come ranked or prioritized truly advanced outputs may include specific user-level activity information. The most common application of BB intent data today is BB programmatic ad targeting. This involves adapting well-known methodologies used in BC advertising using a different type of data.
In the consumer market, BC advertisers commonly use demographic and behavioral data to try to drive efficiency across large ad buys. They look to new data sources to narrow down audiences and better focus their targeting. To achieve this, they’ll create targetable cookie pools based on demographics such as gender, age group, economic status and zip code and add in online (consumer) activity such as point-of-sale purchases. This helps improve direct response KPIs.
But in BB this type of information is virtually useless for ad targeting—it really does nothing to help identify BB buyers. Recently, some BB advertisers have seen improved click-thrus and landing page engagement by leveraging account- based intent insights into their programmatic audience pools. The risk in this approach is serving ads to, say, the janitor at a target account vs. someone with a role in IT purchasing. To avoid that, your DMP can filter the total cookie pool using third-party title overlays, though doing so adds costs and significantly reduces your cookie pool.
Continued on p. 8


The biggest challenge with
All intent signals are not



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