Credit: MarketingProfs
Enterprises using lead scoring benefit from a 77% increase in lead generation ROI compared with organizations that don’t use lead scoring, according to research.
And implementing lead scoring is one of the most effective ways to improve sales.
To better identify sales-ready customers who should be prioritized for engagement, many businesses now employ lead scoring to effectively and efficiently measure prospects’ level of interest in the company and product/service fit. However, a simple lead-scoring solution is not enough. Marketers need a solution that enables companies to see the full picture—on the level of individual leads as well as accounts—to accurately pinpoint and select their target audiences and personalize campaigns to reach them.
RingCentral, a provider of Cloud-based communications and collaboration solutions for businesses, recognized it was missing out on many potentially valuable opportunities because leads weren’t being aligned to the right accounts with the right information. That meant, in turn, that sales and marketing activities weren’t aligned to be relevant to potential customers. To improve results and boost its pipeline, the company implemented an account-based marketing (ABM) strategy. Three core components of this strategy were as follows:
- Accurate lead-to-account matching
- Accurate and actionable account and lead insights
- Improved routing and prioritization of leads
The company found that more than 30% of inbound leads had missing or inaccurate data in key fields, such as company size (by number of employees). As a result, the leads couldn’t be automatically routed to the right sales team and were ignored. Equally troublesome from an ABM perspective: 20% of inbound leads couldn’t be matched to accounts. Company information was often missing or in a nonstandard format.
Do you have similar issues with your lead-scoring model? You can find out by ticking off the following checklist:
Yes[ ] No[ ] Do you have the right data—and is all the information accurate? You may have the most sophisticated lead-scoring model, but it won’t work without enough of the right data. That means looking beyond superficial insights (job titles, type of business, etc.) to more relevant info that actually tells you whether your lead is compatible to your offering (e.g., installed technologies).
That data needs to be constantly refreshed to avoid data degeneration (“decay” or inaccuracy) that stems from people’s changing jobs or titles and companies’ changing their status or situation.
Yes[ ] No[ ] Do you use both persona and predictive scoring together? You need persona scoring to pinpoint your key buyer personas. Then you need predictive scoring to dig deeper (via AI) to know who really your ideal customer is, based on any number of criteria (e.g., propensity to buy, likelihood to buy over X amount, least likely to churn…).
Yes[ ] No[ ] Do you also consider company-level data? Today’s “lead generation” is much more sophisticated than just “Let’s get as many great-looking leads as possible.” Both Sales and Marketing know that what they’re actually looking for is “buying centers,” that is, company + key leads/individuals within the company with buying power and/or influence. They know that by scoring leads without any context on their respective accounts could well mean barking up the wrong tree (e.g., the lead could look perfect, but their company is the wrong size/industry or is using a competing or incompatible technology.) (Find out more about that here.)
Yes[ ] No[ ] Do you go beyond demographics and behavioral data to capture as much customer information and insights as you can via additional services that can provide other singular identifiers, such as IP addresses? Such data enrichment can help enhance personalization and give you an idea of what content they see, products they buy, or social platforms they frequent.
Yes[ ] No[ ] Do you identify your target segments’ scoring thresholds—the point score that distinguishes sales-ready prospects from need-more-nurturing leads? Doing so enables you to peg prospective customers in the “hot,” “warm,” and “cold” categories and allows you to adjust your outreach and engagement with those leads accordingly.
Yes[ ] No[ ] Does your scoring system follow a methodology? Besides having a standard 1-10 scale, if you’re in B2B you can also classify leads as small, medium-sized, and large businesses or categorize them by region or industry; assign each a number; and score them accordingly. Though the scoring scale must be straightforward, it must also be flexible enough to allow for unexpected issues.
Yes[ ] No[ ] Do you score on impulse? It’s best not to assign extra points to a lead scoring rule that you “guess” is more important than others, thus risking imbalance and inaccurate scores. Start with a balanced point distribution by breaking down points among categories of rules prior to doing the same among the rules themselves. You can also use the advanced modeling technologies out there that use AI to score more accurately—and consider far more factors—than any manual method could.
Yes[ ] No[ ] Do you avoid effort-derailing lead scoring errors such as using one scoring model for different product lines? You should not rank all activities the same, even if some actions provide more evidence of interest or buying behavior. You also need to consider the use of negative scoring to avoid inflated scores and resulting bias in your lead-scoring model.
So how did you score? The more YES boxes ticked, the better your lead scoring model is.
As a B2B marketer, you understand that although traditional lead scoring has its benefits, you may need a more sophisticated scoring model that helps you identify truly high-value leads. And, in RingCentral’s case, the company more than tripled its lead-to opportunity conversions by using real-time data enrichment to more effectively route leads to the right sales teams.
Whether you’re focused on account-based marketing or specific individuals and roles across companies, they need to score high enough to be worthy of your efforts.
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