Behavior scoring web-0208
- 1. Surfacing Gold from Nurture Leads with Behavior Scoring Predictive Playbook
- 2. As companies start experiencing the benefits of fit-based predictive scores, they typically seek out more places where predictive can add value. One logical step is to add predictive behavior scoring. Infer mines the full spectrum of activity data being collected by your marketing automation platform including every email click, website visit, etc. Machine learning is then used to weigh each signal appropriately in order to predict the likelihood of an outcome (e.g. conversion) within a set time period (e.g. next three weeks). This predictive playbook will describe how companies can supplement fit-based predictive lead scoring with an Infer behavioral scoring model. There are several ways to operationalize this -- from advanced sales prioritization and activity-based triggers, to measuring account-based marketing engagement and campaign effectiveness. For the purposes of this playbook, we’ll focus on the winning strategy of resurfacing gold out of nurture by identifying previously disqualified prospects that have re-engaged and are worth another look. Many Infer customers enjoy a healthy inbound lead flow that fuels sales and marketing, and use Infer’s fit-based predictive scores to easily qualify which leads are the most well-matched for the company’s products. Infer’s custom-built fit models put inbound leads into categories from Infer A-Leads (best fit) to Infer D-Leads (worst fit), and can automatically route all A and B-Leads to the sales queue. Infer C and D-Leads – along with the good leads that are disqualified by sales – are typically moved into a nurture database for marketing follow up. But as these archived leads accumulate, it becomes increasingly difficult for the marketing team to monitor all of the older leads and find accounts that have re-engaged and are likely to buy. Introduction Getting Started
- 3. Once that process is demonstrating results, most companies ask Infer to build a second predictive model, this time pulling in key activity signals from a marketing automation platform like Marketo. Unlike traditional marketing automation lead scoring that requires users to manually add points for given actions, Infer’s behavior scoring model uses powerful machine learning to mine the full spectrum of a lead’s activity data – including concentration (or decay) of activity and breadth of engagement – to predict which prospects will convert in the next three weeks. Prioritizing “Older” Leads – True Nurture vs. Neglect By closely observing behavior scores to find key moments in a lead’s buying cycle, you can pinpoint the right time(s) to reach out again and trigger the appropriate actions therefore empowering sales to own the moment in a first-conversation or second-chance engagement. Website Sales Queue Good Fit Leads Bad Fit Leads Qualified Archived Marketing Nurture Database High Behavior Score
- 4. Closing New Deals from Nurture Once a new behavioral model is in place, you should start monitoring these predictive scores and establish a manageable way to work high-scoring leads from the nurture bucket. Some marketing teams start by placing unqualified leads into an unassigned “Up-for-Grabs” queue where sales development reps can find additional leads to work when they have time. Leads taken from this queue can be easily prioritized based on their behavior, with the highest scores indicating that the contact or account is showing fresh activity indicative of purchase intent. This also gives reps increased confidence during outreach because the scores are driven entirely by a prospect’s interaction with your company. This approach brings the best nurture leads back to sales without fully opening the floodgates to thousands of archived leads. And as more and more reps start working these leads, the team’s confidence in both the fit and behavior scores grows because it becomes easy to see how much better higher-scoring leads convert. If you break leads into behavior buckets, from Infer 1-Leads (those showing the most buying behavior) to Infer 4-Leads (least buying behavior), it might play out something like this: By renewing sales’ focus on select older leads, one Infer customer closed nearly a half-million dollars in deals with prospects that otherwise might have languished in nurture. Behavioral Score Buckets Leads Grabbed from “Up-for-Grabs” Queue Converted Opps Lead to Opp Rate Conv. to Opp Rate Infer 1-Leads (scored 70+) Infer 2-Leads Infer 3-Leads Infer 4-Leads 407 335 269 682 115 94 72 240 53 30 18 40 13.02% 8.96% 6.69% 5.87% 46.09% 31.91% 25.00% 16.67%
- 5. While predictive scoring is most often used to prioritize new leads, there are clear benefits to pulling in behavioral data and monitoring aging leads over time. Several Infer customers have proven that predictive can deliver a material return on investment when scores are operationalized effectively across sales and marketing teams. Want to learn more about how your business can adopt predictive to drive success? Get in touch with Infer today. When looking at the full spectrum of your leads, it’s helpful to combine your fit and behavior scoring buckets into a simple 4x4 matrix so that you can optimize programs around various segments. For example, conversion multipliers across different lead classifications often look similar to this: There are many ways that companies are leveraging this insight to increase sales and marketing productivity. For example, by sending Infer A1-Leads (which represent a disproportionate percentage of your overall conversions) directly to your best reps, you can close those key deals faster. Leads that typically represent a lower return but are showing a high level of buying behavior, like Infer C1, D1, C2 and C3-Leads, can be worth investigating if your team has the bandwidth – perhaps using an “up-for-grabs” queue as described above. In addition, companies use this grid to create service level agreements (SLAs) that are applicable for different stages of the buying journey, personalize communications, and measure the impact of marketing programs. Walking Your Predictive Scoring Grid Look for opportunities to use “OR” logic in your filters and triggers as you operationalize fit and behavior scores. This will cover your bases and keep opportunities from slipping through the cracks. http://www.infer.com/contact-us A 1 2 4.2x 1.7x 0.9x 0.8x 0.7x 1.2x 0.4x 0.5x 0.0x 0.0x 0.0x 0.3x 15.2x 3.2x 2.1x 1.5x 3 4 B C D Behavior Score Likely to convert within the next 3 weeks Fit Score Good fit to buy your product http://go.infer.com/predictive-playbook/prioritization
- 6. About Infer Infer delivers predictive business applications that help companies win more customers. It leverages proven data science to rapidly model the untapped data sitting in enterprises, along with thousands of external signals from the web. Customers include high growth companies like AdRoll, Cloudera, Concur, New Relic, Nitro, Tableau, Xactly and Zendesk. Headquartered in Palo Alto, California, Infer is funded by leading investors, including Redpoint Ventures, Andreessen Horowitz, Social+Capital Partnership, Sutter Hill Ventures and Nexus Venture Partners. Mountain View, CA www.infer.com