What is B2B Intent data?
B2B intent data, also called buyer intent data, is the information about products and services that prospective buyers are searching online. You can measure the buyer intent signals from leads or existing customers through social media, product review and comparison websites, and focused surveys. It is useful to help design marketing and advertising campaigns, and target the right segments.
Where does the data come from?
Intent data comes from a number of online sources, where machine learning models rate data of each user indicating intent to buy a product or service.
First-party intent data refers to records of the pages that people visit on your website, the subsequent links they clicked, and the time they spent on your site. This data includes personal information only when the potential prospects fill the contact forms.
It can also come from your enterprise applications such as CRM, subscription-based emails, surveys you conduct, engagement on your social media pages, and customer feedback.
First-party buyer intent data is helpful to improve the content, approach, and performance of the website or platform.
Third-party intent data comes from similar data from other websites, from news portals, blogs, product review sites, social media, and competitor websites. This data helps in planning your response when interest in a product or service that you offer spikes.
What types of attributes should I expect when working with this type of data?
You should expect data about the prospect’s online activity, including searching, browsing, interactions, downloads, and clicks, among others.
Keywords and search terms offer great insights. Each keyword or long-tail keyword phrase provides a strong indication of the prospect’s intent, motivations, interests, and behavior. Tracking the patterns and trends of information consumption on your website, you can segment and target the prospects with relevant content.
The demographic data about prospects is helpful to identify the population segment interested in your product or service so that you can target them with relevant offers.
How should I test the quality of the data?
Considering that each industry and organization has its specific requirements, you should test the quality of the data in the context of your own goals.
In general, the data must be complete and accurate. You can also assess:
- data consistency
- data recency or how timely the data is
- frequency in a dataset
- conformity to the established standards and formats
- conformity to the values within a specified range
- how the data is collected
Experts in data security recommend the following additional points for data quality:
- Compliance with regulatory standards
- Protection measures for data security
- Transparency in documentation
- Accountability of individuals responsible for maintaining data quality
Who uses B2B Intent data?
The B2B intent data powers uncovering the search patterns and behavior of the prospective buyers. Typically marketing and sales teams use these insights to:
- plan marketing strategy
- design advertising campaigns
- track leads
- convert leads to customers
- grow the customer base
- identify industry trends
- compare with competitors
You can also learn about the channels prospects prefer, how they access the content, and what type of content interests them the most. Based on this information, you can decide on:
- channels of communication
- website design and content
- marketing collateral type and content
- targeted messages
What are the common challenges when buying B2B Intent data?
B2B intent data is used to derive actionable insights for marketing and sales, and to plan the marketing and advertising campaigns. Ensuring the quality of B2B intent data is critical. Only high-quality data can unlock its full potential and deliver a high ROI.
Some of the common challenges when buying B2B intent data include source credibility, data timeliness and scale, customer fit issues, and integration concerns.
- Source credibility: The biggest challenge when buying B2B intent data is vetting its sources. Verify with all your prospective B2B intent data vendors that their data is regularly tested for quality and their sources are trusted.
- Compliance: B2B intent data may contain personally identifiable information. Data vendors need to comply with data privacy regulations such as GDPR and CCPA. Data protection and privacy regulations differ across the globe, and it is essential that this data is compliant in the region you want to operate.
- Data timeliness: The buyer intent is fluid and can change quickly. Reacting to that intent in time is critical. The market also changes with multiple factors, digital as well as physical, which can influence the buyer’s willingness to buy. Your sales decisions are based on B2B intent data, and the data needs to be up-to-date.
- Scale issues: B2B intent data does not account for all potential buyers and businesses. It often does not capture some businesses that may be interested. Some prospects may conduct their research on out-of-network sites. Enquire with your vendor about the scale of their data, and how wide their network is.
- Customer fit issues: If the buyer intent is not clear, verify with your vendor how the buyer intent signal was triggered. At times the intent can be clear but not accurate. In such cases, the data may be outdated and you need to confirm with your vendor if the data is regularly updated. If the intent is clear and accurate but does not provide correct customer fit, it may indicate a lack of contextual information. Ensure that your vendor has a wide variety of sources.
- No immediate returns: B2B intent data presents intent triggers and does not guarantee any immediate returns. It may take some time to arrive at the right solution to avoid false positives and reach the defined goals. You can begin with a B2B intent data sample, see the results over time, and then decide on the vendor.
- Integration issues: Larger companies with internal predictive analytics teams have their predictive scoring models based on discrete attributes and regression modeling. B2B intent data on the other hand is a near-continuous variable. It needs to be modeled against the behavior across the internet, along with the internal datasets. Integrating B2B intent data is challenging as you need to incorporate it into existing predictive models. Confirm with your vendor that their B2B intent data can be integrated rapidly.
Research and evaluation of vendors will help you overcome these challenges and unlock the potential of the B2B intent data.
What are similar data types to B2B Intent data?
You can find a variety of examples of B2B and company data in the Explorium Data Gallery
Sign-up for Explorium’s free-trial to access the data available on the platform.
What are the most common use cases of B2B Intent data?
The most common use cases for B2B intent data are for marketing and lead generation. Some key use cases are listed below.
- Account-Based Marketing (ABM): Also called Key Account Marketing, ABM uses B2B intent data to improve B2B marketing. For ABM, the account or the target company data also needs crucial information on the key decision-makers within the company besides the purchase executives.
- Lead Scoring: As you generate more leads, identifying the most promising ones becomes difficult. You want to focus the time and effort spent in converting the top-ranked leads. The lead scoring models help you score and rank your leads automatically, based on the perceived value each one represents for your company. Lead scoring ensures that marketing and sales efforts are distributed by the priority of leads determined by the model.
- Campaign Strategy: Companies use a marketing campaign strategy to optimize the process of realizing a marketing goal. While it typically focuses on sales and lead conversions, it can also help create awareness of a new product or service and collect customer feedback.
- B2B Fraud: Risks of fraud in B2B payments interrupt the flow of business and affect reputations. Identifying fraud and stopping criminals from committing fraud is becoming increasingly difficult due to the sophisticated techniques fraudsters use. Machine learning technology leverages data patterns to detect possible fraud risks before they can happen.
- Supplier Risk: Modern supply chains span the globe with numerous services and sourcing managed across several partners in different jurisdictions. Companies are increasingly using third-party suppliers in executing the key strategic imperatives. While third-party operations are increasing, they are also becoming more complex. Companies need to identify supplier risks and upgrade their risk management framework to avoid potential losses.