I have called this Micro Targeting because my marketing clients target beyond filters such as: market, industry and in the Account Based Marketing (ABM) era by account. They target using filters which only previously existed in advertisers wish list – by specific job function or title.
Desirable? Sure, but really how possible is this to achieve?
I came across an interesting article, written by Jarrod Wright, well worth the read for an introduction to ABM, called: Account Based Hype – How ABM Makes Outbound sales Cool Again for B2b. He posed a similar question and asked many of the ABM tech providers he met to explain how they were able to achieve this kind of targeting. While he felt the answers were not entirely convincing it did confirm that there were probably a few main ways to target in a B2B environment.
i) Reverse IP tracking would enable targeting of company domains and locations of prospects that had visited yours or other websites. Additional information such as key word searched, last page visited, while not confirming a job function or title will provide more information on the prospect.
ii) Using cookies to collect behavioural data, such as titles or topics of articles read and/or downloaded on your site or others which would suggest possible job functions or titles. However analysing online content consumption without context could lead to some very misleading results. Visiting a medical site wouldn't necessarily make me a Brain Surgeon!
iii) From a database where B2B buyers have registered their information and agreed to share (Premium content/news/trade sites) or a social platform, (LinkedIn) where data is provided and shared specifically for networking/promotion purposes.
LinkedIn is often mentioned in the context of targeting and micro targeting and clearly forms a key part of many ABM campaigns through its stellar ability to micro target. The only question about suitability for many ABM campaigns is when you need to scale significantly and the cost then of using LinkedIn.
Many ABM commentators suggest anyway that job functions and job titles may not be the most effective way of targeting buyers and buying committees as they may not be in the market to buy. Demandbase – one of the leading providers of ABM technology, say they have over 500 different job titles in their database of buyers illustrating how difficult it may be to narrowly define the right job title for purchasing decisions.
Far better then to target not by title but by those showing purchasing intent as you only target the most relevant accounts that are in the market to buy. So how would one go about targeting this way?
Acquiring first party data from your own website visitors would be a start, capturing onsite behaviour and plugging that into your marketing automation software, Oracle Eloqua or Marketo for example. While valuable to see what content prospects are consuming on your site, if they are actively looking at case studies for examples that may indicate a certain level of buyer readiness. Though it should be noted a lot of buyer research may take place on third party sites.
Many businesses are going further and using or planning to use third party intent data - 60% of B2B businesses according to a Demand Gen report.
Aberdeen, who provide intent based marketing and sales solutions, have published a report that serves as a good introduction to this area called “Demystifying B2B Purchasing Intent Data.” They identify three primary ways to capture data:
Analyses key words used by vendors or advertisers to assess intent. The Big Willow, now part of Aberdeen, are one such provider however other programmatic / specialist ABM technology providers such as Demandbase are another example. Demandbase’s programmatic database has approximately 150 billion B2B transactions per month and 100,000 intent keys words tracked. The platform identifies keywords that buyers look for early the buying stage and then, using AI, pinpoints people within your targeted accounts and calculates a score of their buying intent level. By taking into consideration how recent the activity was, the frequency, how it is trending over time a more relevant score can be provided. As it is an ABM programmatic advertising platform it can also offer options for bidding for advertisements against users that show the most intent
2. Third party websites
Aggregated websites that use tags to track topics that visitors are interested in. Bombora are one of the best known in this category. They use a data co-op, a collection of 4,000 websites where consumption of content is measured over different business topics, aggregated and then shared with users of their service. Publishers such as Forbes are part of the co-op as are agencies, technology providers, research and event firms. The service can tell you which enterprises are researching, on what topics, the intensity – a score of between 1 and 100 tells you when interest is peaking on a topic.
3. Registered sites
Where users exchange data in return for content. For sites like G2 that provide buying information and service comparison to users you have potentially very relevant purchasing intent data. Using G2 you can identify who has viewed your product page, comparison page or competitor page so any downloading of content may be viewed as quite strong buying intent. Some sites like G2 integrate with LinkedIn (and Marketo, Eloqua, Hubspot etc) so that you receive a customised alert when prospect enterprises are shopping on G2
Purchasing intent data - usage in Asia
Given its potential its surprising that intent data is not used more widely in B2B. Perhaps in Asia its relatively new, as is ABM, and it has been used mainly by tech businesses. Many companies that do use it perhaps use it as one of many variables, and therefore have not given sufficient time to test or understand it. While some marketers may be reluctant to use or attach too much importance due to the lack of transparency from some providers. With providers like Aberdeen though claiming up to 91% accuracy in identifying companies that are in-market to purchase solutions intent data is certainly worth an investment of time to evaluate.
Whilst I am certainly no expert in this field, I have researched and read widely on this topic so please feel free to reach out to me with questions or comments: