To complete a sale, sales teams need a strategy to ensure they are talking to customers at the right time in the buying process.
As sales and marketing teams align and explore new ways to ensure that leads are more sales-ready, companies are rolling out lead scoring strategies. A sales-ready lead is a prospect that the marketing team pre-qualifies and scores based on qualifying criteria that determine the handoff to the sales team. In order to determine which leads stand out from the others as "hot leads," marketing and sales teams develop criteria based on various parameters or activities to identify when those prospects are likely to make a purchase decision.
Businesses must collect many data points over time and perform significant trial and error to find the perfect lead scoring strategy.
What is lead scoring?
Most lead scoring systems assign point values to leads based on different attributes, such as demographics data that matches an ideal customer profile, or actions a prospective customer takes during the buyer journey, such as website visits, content downloads or email clicks. The point-based lead scoring model assigns a threshold for leads to reach, and when they reach that point total, they qualify as hot leads for the sales team. Knowing when to reach out to a prospect makes sales teams more focused, informed and productive when engaging with prospects.
Longstanding customers or prospects that frequently engage with a brand are more open to individualized attention from a sales representative rather than someone engaging with a brand for the first time. Lead scoring enables teams to focus their efforts on consumers who have already reached a certain step in the sales journey, such as downloading a case study or requesting a quote, and bring that prospect closer to completing a purchase.
Lead scoring best practices
Teams that want to deploy a lead scoring strategy can opt for manual or automated lead scoring that uses the points system. They can also choose predictive lead scoring, which uses machine learning technology to parse through thousands of data points to automatically tell business owners which leads are the best, based on previous conversations from customers.
When defining the lead scoring model, every company has a different approach or scoring process. The best way to come up with a lead scoring strategy is to look at data from past leads to create a value system. This historical data helps pinpoint which leads have more compatibility with a company's product based on their assigned values.
Here are some best practices to help teams reach more qualified leads and higher sales revenue.
1. Align sales and marketing
One of the first steps for rolling out a lead scoring model is to align the sales and marketing teams. Over time, sales teams begin to identify trends in conversations that turn into opportunities for the pipeline. These trends include who the decision-makers are, what problems they are attempting to solve and what pain points prospects have. From these conversations, sales teams are much closer to the leads than marketing teams.
Marketing teams can work with sales to determine which demographics yield positive point assignment and show which content those qualified leads were interested in. Marketing teams can begin to develop a scoring model with this information that incorporates historical patterns to the buyers.
2. Define buyer persona and customer profiles
Developing detailed personas and customer profiles enables marketing teams to flag leads that match that description. Buyer personas are detailed, but fictional, representations of a company's target audience.
Sales and marketing teams apply scores to the target audience, as well as additional scores when those prospects begin engaging with campaigns or content that is tailored for that group, which validates that this is a hot lead for the sales team. Assigning scores based on demographic data is key to identifying marketing qualified leads (MQLs). Some examples of buyer persona and customer profile data to score against include the following:
- company, if a company is looking to target specific companies with account-based marketing (ABM) campaigns;
- annual revenue; and
- location, including state, region or country.
3. Determine a lead scoring threshold
Lead scoring aims to develop sales-ready leads or MQLs. When target customers become MQLs -- based on demographic data or previous actions -- that means they have hit the lead scoring threshold.
The lead scoring threshold can be an arbitrary number, but it may be best to start with an MQL score of 100. Each activity can be weighted appropriately. After nurturing those leads and when their cumulative score equals 100, an internal workflow triggers to notify sales teams that this lead is a sales-ready MQL.
Here is an example of lead scoring points distribution toward a threshold. Lead scoring models and points assignment vary by individual teams.
- 15 points: fills out a form on a landing page;
- 5 points: clicks on last three emails; and
- 30 points: matches the company's ideal buyer persona demographics.
Lead A receives a score of 50 and requires additional nurturing to meet the score threshold of 100.
- 15 points: registers for a webinar;
- 5 points: attends that webinar;
- 5 points: visits the website's homepage; and
- 5 points: clicks on a blog post.
Lead B receives a score of 30 points. If Lead B requests a demo after these engagements, they receive 100 points because that is an immediate MQL action.
Lead B's cumulative score of 130 goes over the threshold, and the CRM system notifies sales that this prospect is a hot lead that it should prioritize. Meanwhile, marketers continue to nurture Lead A.
4. Assign positive scores for customer actions
Now that the company established a lead threshold and certain persona demographics give points toward the overall score, assigning points based on online behavior will help push those prospects closer to their MQL designation. The previous example shows how customer actions receive points. These actions can include the following:
- frequent visits to a website over a time period;
- multiple content downloads or event registrations;
- visits to a pricing page;
- opened emails;
- contact form submissions or requests for a demo or free trial;
- blog views;
- call-to-action clicks;
- social media account follows; and
- clicks on paid ads.
5. Include negative attributes
Assigning positive point totals to leads pushes them closer toward an MQL, but negative scores are also important to recognize. A lead score threshold should consider a variety of attributes, including negative interactions with a brand, to determine if a lead is a good fit. By having negative scores, sales and marketing teams can see who are hot leads and when is the right time to engage with them. Negative score examples include the following:
- customer unsubscribes from email communications;
- email bounces;
- customer doesn't match the title or role of the target audience;
- customer's annual revenue is below target company profile; and
- person fills out a job application, which implies they are not a lead who could turn into a customer.
6. Integrate marketing automation and CRM tools
The company's marketing automation system tracks and scores a prospect's behavior, but sales teams do most of their work in the company's CRM database. When these two tools integrate, a sales representative can view leads' activity on their contact record, and understand leads' actions or campaigns that they engaged with. The data that flows between these two systems is key when a sales representative is reaching out to an MQL.
When a lead becomes an MQL, the integration between marketing automation and CRM tools enables administrators to create automation rules to alert or assign these leads to specific reps. From the previous example, where Lead B scored above the threshold, the marketing automation system tells the CRM system that this lead is an MQL, and the CRM system notifies the lead owner that they can reach out to the customer and begin sales qualifying discussions, hopefully resulting in an opportunity in the pipeline.
7. Determine point decay
The less engagement leads have with the brand, the more it illustrates they may not be interested in a company's products or services. In contrast, leads that engage frequently are likely to become MQLs. If a lead doesn't visit the website as frequently anymore, doesn't open email communications or doesn't act on any active campaigns, points should begin to drop off. While every company's points decay model varies, a good rule of thumb is to subtract points over time:
- After 30 days of no action, reduce 10 points.
- After 60 days of no action, reduce 25 points.
- After 90 days of no action, reduce 50 points.
8. Track MQL conversion rates
In order to determine if a lead scoring model is successful or not, sales and marketing teams need to look at the MQL conversion rates. The MQL conversion rates refer to how many of the MQLs are turning into sales qualified leads (SQLs) or opportunities. The output of this evaluation may require a marketing team to rework its lead scoring model.
Measuring the percentage of MQLs that turn into SQLs gives teams an idea of the quality or readiness of these leads going through the lead scoring model. If one out of every 20 MQLs turns into an SQL, the sales and marketing teams may be qualifying leads too soon, and they need to adjust the points threshold or score assignments.
9. Revisit lead scoring models frequently
If qualified leads aren't turning into opportunities or a marketing team is constantly adding on new campaigns that require their own scoring model, sales and marketing teams should schedule regular check-ins to evaluate the performance of the existing model.
Holding a meeting between the sales and marketing teams once a quarter gives the opportunity to discuss if the lead scoring mechanism works, where there is room for improvement or what scores they need to adjust based on performance.
10. Talk to customers
Marketing teams should regularly send out Net Promoter Score surveys but should also interview or survey customers beyond that. Customers often have a different experience than the way sales or support would describe it, so it's important to account for all angles of their experience.
When speaking with customers, the best information to collect -- outside of their demographics data, which assists with company and persona profiles -- are specifics around what drove them to make their purchase. Between short sales cycles or long sales cycles, teams want to know what causes customers to convert quickly or what activities assisted in their decision-making process. This helps marketers develop more content or campaigns that align with those customers' experiences.
Lead scoring technologies
It is difficult to execute a lead scoring strategy without the right technology. Most organizations use a CRM system to manage leads, contacts and accounts. However, CRM systems on their own don't offer the best lead scoring capabilities. Marketing automation tools, such as HubSpot, Pardot and Marketo, include lead scoring abilities and can integrate with a company's CRM system to display a lead score. The marketing automation tools offer the mechanisms to set up a scoring model and send that information to the CRM system.
There is also lead scoring software, such as ActiveCampaign, 6sense and VanillaSoft, that focuses solely on setting up lead scoring models for automatic and predictive scoring of prospects. These tools offer more functionality than what comes out of the box with marketing automation tools or CRM systems.
Editor's note: TechTarget offers ABM and project intelligence data, tools and services.