FAQs: Account Identification

  • Updated
  • The objective of Demandbase identification is to identify employees of potential customers when they are browsing your site, before they provide their contact information to you. Our machine learning techniques allow us to identify the thousands of visitors who have not clicked on an email, filled out a form, or provided their corporate email address.

    Many solutions, such as Pardot, Marketo, or HubSpot, identify leads via a form-fill or email click with a corporate email address. While this can be a useful means of identification and we access this data as well, it's late in the customer's journey and can result in many missed opportunities. Because Demandbase identification takes advantage of probabilities, it comes with the risk of some inaccuracies; however, it ultimately identifies many companies with high intent earlier in the customer's journey than companies who don't use these techniques. This practice results in less spend and more conversions.


    A form fill is already half-way through the customer's journey. Demandbase identifies potential customers while they are still anonymous.

  • Demandbase uses probabilities throughout the customer journey from identification through, attraction, engagement, conversion, closing, retention, upsell, and cross-sell. The following plots are for illustrative purposes only. Your mileage will vary depending upon your learning stage and business strategy.


    one.png Traditional Volume Marketing with volume leads wastes a lot of spend. Even worse, it takes away your time and focus from real opportunities and can result in lost sales due to missed opportunities.

    two.png The opposite extreme would be to wait for hand raises from customers who really want to and can buy your product. You wouldn't have any wasted spend, but waiting passively can waste time and result in lost sales due to missed opportunities.

    three.png Consider instead, Account-Based Marketing with Artificial Intelligence, which takes advantage of probabilities. These educated guesses early on use objective criteria, so they focus your spend on accounts with more potential and can uncover the highest success rate. According to McKinsey and Company, "It can reduce acquisition costs by as much as 50%, lift revenues by 5-15%, and increase the efficiency of marketing spend by 10-30%.”

  • Demandbase uses the power of probabilities, so we expect some discrepancies. Please report them to support@demandbase.com, so we can add this information to our data and take corrective action. Probabilities are much more powerful than human guesses and well worth the tradeoff. Here's how it works, so you know what to expect:

    To identify visitors, we start by using as many facts as possible, such as known cookies, IP addresses, device identifications, and VPN identifications. When these aren’t available, we use machine learning to make very educated guesses based on changes in visitor behavior, such as volume of visitor activity and keywords in the content they consume. We then apply an algorithm that includes probabilities, so we expect some deviations from the facts; however, continuous improvement is built into the algorithm by enabling it to continually learn from new information. 

    Our algorithm has a high degree of accuracy because it has been learning and updating for over a decade with hundreds of billions of data signals to determine the strongest probabilities. In addition, we’re always learning new techniques. For example, during COVID lockdown, with so many people working from home, we learned to adjust identification based on whether the same company uses an IP address (confirming the association) or a mix of different companies uses it (showing that they are too loosely associated).

    With the recent integration of the Engagio platform, our overall identification accuracy has increased even more. We added Engagio's first-party data sources to ours, so we have access to even more facts and make even fewer “guesses.” What we offer is a narrowing down of your funnel from identifying a very wide set of random candidates to a much smaller set of quality candidates that makes better use of your money and your sales team’s time.

    Please continue to inform us of inaccuracies. The more you allow us to review them in your day-to-day work, the better we can sort out different types of mistakes, and the faster our algorithm can learn even more.

    In the meantime, we continue to work on the accuracy of our derived data and make significant progress.

  • We have data from third-party sources as well as a crawler that we use to map domains to each other, based on activity and redirects. We intentionally do not map all subdomains to top-level domains, because subdomains (especially internationally) often map to different brands that have been acquired under the parent. However, in some cases, it could be that the domain was valid when we published it and has since become invalid. We can correct it when it’s from a valid record in our database.

  • ABM is still a rapidly developing technology, much of the data we use for our product in the US and Europe is not available in other regions. Unfortunately, we still have a high degree of misidentification issues in India, because one IP is used for many people in APAC as opposed to fewer people in North America. Other ABM providers face similar issues in India, APAC, and the Middle East. We try to mitigate this issue as much as possible by complimenting our IP identification with Cookie identification.

    That being said, we are constantly examining the available data sources and trying new proprietary techniques. While it is still too early to estimate a timeline for better identification, rest assured this initiative is a high, long-term priority and, as data improves, we will be able to improve identification in India.

  • ABM is still a rapidly developing technology. Much of the data we use for our product in the US and Europe is not available in other regions. To name a few, government company records, private data providers, IP registries, ad networks, and B2B websites all vary drastically in availability, cost, and technological integration needs. While we can examine a company’s website and Wikipedia page and create a record in our system, this record would be useless without the full set of signal that makes our product work. Other ABM providers face similar issues in these regions.

    That being said, we are constantly examining the available data sources and trying new proprietary techniques to expand our coverage in the Middle East and APAC (about 48 countries, including China, India, Indonesia, Pakistan, Russia, Japan, Philippines). While it is still too early to estimate a timeline for fuller coverage, rest assured this initiative is a high, long-term priority and as data and signal availability improves, we will be able to expand coverage in these regions.

  • Many large, global companies have multiple different domains and brands for each market or region they operate in. In these cases, most of ABM signal we receive will be grouped at the parent domain level, for example, traffic and and signal from “.mx”, “.fr”, and “.uk” domains will all be rolled up into the “.com” domain. This is because much of the signal we receive from various third-party and proprietary sources does not map reliably to a single, specific domain. This is why some features, such as advertising, allow you to filter to specific states or countries, so that you can target the right market or region you are interested in.

    We recognize this kind of geographic detail is important to our clients are we are working on making it available across the platform.

  • In these cases, we are not always able to distinguish between traffic from the parent company and the subsidiary company or brand. Many times the subsidiary team uses parent company IT services, meaning all of their traffic comes from the same IPs as does traffic from the parent company. That being said, our algorithms and underlying data are constantly improving, so we are able to identify this traffic separately in some cases.

  • On average we currently identify about 20% of B2B traffic by cookie only, but it can vary widely by client. Keep in mind this would be on average about 10% of overall traffic since much of the overall traffic we see is not B2B. This again, can vary widely by client.

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