Leads are the lifeblood of any sales effort. But not all leads are created equal. Some have a high value for an organization and represent a realistic opportunity to win business. Others are early-stage engagements that take months or years of development.

Because of this disparity, the question “What is a lead?” puzzles many organizations. Sales and marketing groups have worked for years to formalize the definition of a lead and what it means within an existing business model. Regardless of your definition, one thing is consistent – marketing has to adapt its strategies to bring in more, better, or just different mixes of leads. The key question is: “How do you get there?”

The challenge

Over the years, the SAS marketing organization built a complex method of passing leads from marketing to sales. The process was similar to what other companies have in place, that is, leads that met a set of rules were qualified and then sent to a salesperson to follow up. The system was effective but difficult to manage, especially when business needs changed.

To build a new model to score and qualify leads, the marketing team looked at existing data and then conferred with their counterparts in sales to reorient the lead management process to accomplish two main goals:

• Increase the number and percentage of leads that convert to opportunities. This meant identifying the best leads and finding a faster way to pass more high-qualified leads to sales.
• Improve the outcomes from the lead conversion process. Obviously, high-quality leads are essential to creating a larger pipeline of deals. The team needed a better way to score, and then prioritize, leads.

An added wrinkle was that the project had to be global. For example, a lead in Australia would have the same meaning as a lead in Germany. That way, the company could compare lead performance across geographies and fuel global decisions about what strategies would be more effective.

The approach

While the previous rules-based model was geared more toward quantity, the team opted for a model-based approach to lead scoring that emphasized quality based on likely outcomes. The team developed an analytics-driven model that could evaluate the range of customer behaviors (registrations, website page views, e-mail clicks, and so on) to identify the best leads.

Beyond the quality-versus-quantity discussion, the sales and marketing teams agreed that the timing of the lead handoff to sales was also important. To accomplish this effectively, the model evaluated many behaviors, and once certain criteria were met, the information was added to the customer relationship management (CRM) system. To improve the lead conversion process, the team also focused on converting more sales-ready leads. Not only did the new scoring model evaluate more behavioral data, but that information was passed on as a “digital footprint” for each lead. The salesperson can see interactions for the lead from within the CRM system, giving her important information to guide her initial outreach.

Additionally, the team decided not to send all leads to the CRM system. Because the model does a better job of classifying better leads, those that aren’t routed to sales go to a lead-nurturing pro- gram, where the contact receives a cadence of relevant e-mails. The contact’s behavior when receiving those e-mails (click-thrus, registrations, website visits, etc.) are all fed into the model.

The results

When the lead-scoring model was still in the early stages, the initial feedback was positive. Salespeople appreciated that the leads were more qualified and reliable. Rather than sifting through dozens of contacts, they know that leads indicate an interest in SAS and its solutions. That was once a luxury for a salesperson. Now, it’s an everyday reality.

To fine tune the model, analysts track the total number of leads passed to sales and the number of leads that convert to opportunities. The marketing team wants to make sure rates continue to rise for both numbers. If there is a plateau or a decline, the analysts receive rapid feedback and can adjust programs as necessary.

SAS marketing analysts can also fine tune the model as sales requirements change or the market evolves. The model is more flexible than the rules-based approach, allowing the team to rapidly adjust strategies. The team can adjust the lead conversion rate if there is a shift in internal focus or if a sales group an increase or decrease in capacity.

How SAS can help

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Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.