A great B2B lead nurturing program is one that moves leads from initial inbound marketing programs through to sales. This requires a strong strategy, engaging content, discipline, creativity and a strong analytics program that can utilize your automated marketing system. Most importantly, a truly great lead nurturing program quickly and accurately identifies when it is time to move a lead to your sales force, when to keep it in the nurture program and when to release it completely. Traditionally, standard BANT criteria are set up based on either past experience/data or requirements from the Sales group. Smart marketers also will use implicit scoring that tracks the individual’s engagement through the buyer’s journey and move them to Sales after a predetermined amount of engagement. This is all based solely on the individual contact’s information and behavior.
But what about the behavior of the company the prospect is representing? Today’s savvy marketer will need to take that into account as well. Predictive analytics now allows you to score your leads beyond typical BANT and engagement scores at an individual lead level. Using a predictive score that incorporates data from both account level and contact level will help classify your lead in the funnel immediately and more accurately.
Why Account-Based Data Is Useful
While your contact is telling you a great deal about what he/she is searching for, companies also exhibit behavior that indicates purchase intent. Firmographic data is the basis for indicating if your lead is the right industry profile or size, as well as additional information that may tell you simple time frames around need. But what about the account-level buying signals such as hiring patterns, growth trends, government contracts, patent filings, technology usage and other ongoing changes at the company? These account-level activities are often the earliest buying signals, possibly preceding contact-level activities by weeks or months.
How to Use Account-Based Information
- First, you need to track your contact’s company’s buying signals, as well as the ongoing business and technology changes. Lattice (www.lattice-engines.com) has a great system that uses the power of the cloud to track account-level data from a multitude of data sources and digital footprints. Then, simply connect it to your contact information (registration information, digital engagement implicit scores, etc.), using your automated marketing or CRM platform.
- Next, you need to generate a model comparing this data to a set of your previous leads that converted into quality customers. Remember, this current customer information needs to include account-level information as well. This analysis will show you the set of conditions that precede customer purchase. You’ll then use this model to score your incoming and current prospects based on what intelligence you learned from the analysis.
Now you have a blended score based on both account- and contact-level information.
Testing the Model
This score will allow you to more accurately determine where the lead is in the buyer’s journey. In some cases, the contact’s limited initial lead information, combined with the company-level indicators, is enough to drive directly to a sales call. Other times, lead nurturing is required. But how can you be sure which is needed? Test this new model. Take a portion of your incoming leads and run them through your model. Then, assign those leads based on their scores. Take the remaining leads and assign them as you normally would (BAU). Track all the leads closely using analytic platforms or your automated marketing platform/Sales CRM. After a period of time, depending upon your sales cycle, do a comparison analysis and see which methodology is driving more efficient sales.
There are many more uses for predictive modeling to accelerate leads through the funnel. Account-based data inclusion is one that tops the list and can provide huge lifts in sales and decreases in cost per sale.