- Rarely to companies rely on one single vendor for B2B leads
- Sales leaders need seamless integrations with their existing CRM and Martech solutions
- Service should ‘learn’ from your successes and improve over time
What’s in Your B2B Data?
B2B data is complicated! We would like to take on the challenge and try to simplify the concepts. First, let’s define what you want, and need, to obtain from your B2B data. There are four basic pillars for B2B data, which are:
- Firmographic data. Firmographic data vendors help filter companies based on multiple search criteria and provide B2B data vendors, such as D&B Hoovers, DiscoverOrg, InsideView, and ZoomInfo.
- Demographic data. Demographic data is data related to socio-economic data and includes features like age groups, geography, employment, and education. Demographic data is important to marketers because it allows them to create market segments based on demographics and in turn create targeted messages. I
- Technographic data. Technographic data is used by vendors that wish to target certain companies based on what technologies they have installed. For example, a company may choose to target companies that have HubSpot because they have a cool new add-on for their CRM.
- Behavior data. In our opinion, behavior data is where most of the innovation is taking place among data vendors, simply because there is so much data involved and because the models that predict propensities to convert based on said data are also sophisticated. Brand sentiment within social media and sudden increases in queries for a certain solution from key company employees are two examples of how behavior data can impact a propensity model.
Integration is Golden
As an end-user, if you can’t use the tools you know and love to get your work done then it’s downright annoying. Who wants to spend time plowing through user guides or chatting with support staff to complete the most basic tasks? As a vendor if you can’t easily integrate with third party tools and services you are toast. So offering clear integration guides, templates, and customer service engineers are important to guarantee that the data can live within existing CRM and Marketing Technology systems.
Emphasis on Learn in Machine Learning and Deep Learning
Artificial intelligence (A.I.) is all about learning. Machine Learning and Deep Learning are pre trained statistical models that predict results based on unseen data. For example: a machine learning model may predict the Customer Lifetime Value (CLTV) for an end-user when they visit a marketing site. Another example is a deep learning model that automatically ‘reads’ your text messages with natural language processing (NLP) which helps improve customer satisfaction (assuming that it works well!).
Have you ever used a Nest thermostat? If so, you may recall a one or two week ‘training’ period where the thermostat ‘learned’ your patterns and adjusted the model accordingly to adjust your temperature. Sales and marketing ML and DL models should do the same: as you close more deals, the models should ‘learn’ and get better over time. The ideal combination of firmographic, demographic, techographic, and behavior data points to recommend the best possible B2B lead for you.