Lead Scoring: Case Studies and Best Practices
Introduction
Professionals in sales and marketing prioritize turning lists into leads. But sales and marketing professionals know that not all leads become paying clients or return consumers.
How do you identify the leads with the highest likelihood of becoming customers? How can you increase the effectiveness of your marketing to produce more high-quality leads?
Lead scoring is one of the best tools for accelerating the lead-generating process. 68% of seasoned marketers mentioned lead scoring as a significant source of income.
This article provides a concise explanation of lead scoring, along with advantages and examples, enabling even newcomers to implement a productive lead scoring strategy.
What Is Lead Scoring?
Which leads have the highest likelihood of purchasing your offering is determined by lead scoring. Based on their activities and demographics, it rates each inbound lead.
A prospect receiving a lower lead score needs to be further deprioritized, dismissed, or nurtured through marketing. A prospect is more likely to become a qualified lead if they score high. These are potential customers who are knowledgeable about your product and prepared to buy. Sales can then intervene and complete the deal.
There are two primary methods for lead scoring:
• Manual lead scoring: Performing computations by hand to determine which actions impact your conversion rates most. Then you can create a scoring system in that manner.
• Predictive lead scoring: Develops and improves a lead scoring system using machine learning.
The approach that is both simpler and more efficient is predictive lead scoring.
Predictive lead scoring has several advantages to manual lead scoring, including the following:
• Compared to humans manually, predictive technology can include more data from more sources.
• Rather than merely sifting out the unqualified leads, it enables complexity to discover the best leads.
• There is no need to spend time and effort designing, testing, and fixing the ideal system. A predictive scoring tool handles everything for you and updates itself constantly to stay relevant.
Why Is Lead Scoring Important?
Lead scoring improves marketing and sales alignment, lengthens revenue cycles, and boosts return on investment (ROI).
Higher Revenue Cycles
A well-designed lead scoring process helps generate quality leads and speeds up the lead conversion process. This means marketers do not need to spend much time and effort nurturing leads. Identifying early-stage qualified leads could be progressed down the funnel more quickly.
ROI Increase
Even though lead scoring initially appears to be a redundant step in the marketing cycle, 79% of marketing leads never result in sales, according to Hubspot.
Setting up a tested lead scoring model will boost ROI because lead scoring clearly indicates a lead's stage in the marketing cycle.
Aligning the Teams in Sales and Marketing
According to Zoom Info, only 27% of leads sent to sales by marketers are qualified, while 61% of leads are. Over time, this gap between the marketing and sales teams will pressure the system and confuse specific leads.
Setting up a lead score system will boost productivity in all areas, clarify lead stages, and prevent leads from inadvertently being overlooked.
According to Hubspot, 79% of marketing leads never turn into sales because of a lack of lead nurturing.
Different Approaches of Lead Scoring for B2B
There are five critical best practices.
You may now be curious about the methodology used to determine lead scores. However, scoring leads for every organization differs based on the company's marketing goals and work structure. Here, we discuss five common approaches for lead scoring in B2B:
Integrated Client Profiles
Numerous sources, including website inquiries (newsletter registrations, downloads, trial requests, and inquiry forms), sales representatives (calls, meetings, chats), customer support (live chat rooms, calls), and more, can provide information on leads and customers. Data unification encourages an efficient lead-scoring system, which further aids in the expansion of customer profiles.
Pros:
Get data on different customer profiles, behaviors, and pain points
Helps develop relevant marketing material for different levels of the sales journey
Allows you to offer individualized customer experience
Cons:
Consolidating and segmenting data can be a big challenge
Scores for Intelligent Engagement
If you give points for repeat visits to a page on your website but no indication of purpose, the user gets a higher score without becoming a warmer lead. Score degradation may be a good idea to avoid speaking with untrained leads.
Pros:
Easy to generate and score new leads
The data collection process is pretty straightforward
Higher potential for data conversion
Cons:
The chances of scoring dead leads are high
A higher number of visitors does not guarantee engagement
Setup for AI Lead Scoring
AI aids in creating buyer personas that match the ideal client profile. It considers various variables, including seniority, job function, a leader's technological aptitude, talents, and more. Following that, you can order leads and determine which items address the problems of various leads.
Pros:
Automates lead generation process
Simplifies communication with consumers
Efficient and cost-effective
Cons:
May perpetuate biased data
Need a tech-savvy team to optimize results
Scores for Various Types of Content
Visits to product pages indicate a higher level of intent than visits to blog posts. Therefore, the point values of various content items should vary.
Pros:
Helps develop strategized content
Increases the chances of generating hot leads
Cons:
Hard to create engaging, result-driven lead magnets
Distribute the Final Lead Score to All Departments
The sales and marketing teams can better coordinate their efforts by sharing the lead's score. All parties may make more informed decisions and reduce missed opportunities when they have a complete view.
Pros:
Helps to offer a comprehensive solution to customers
Keeps every department on the same page
Provides a 360-degree view of customer problems
Cons:
May compromise the security of confidential data
Best Practices of Lead Scoring
Utilize these lead scoring best practices to facilitate the process now that you have some score instances in mind.
Make Sure You Assign the Right Lead Score
After collecting that behavioral and demographic information, you can now assign your lead scores. To have an effective lead-scoring system, you must understand how to score your leads appropriately.
The most popular way to determine how to assign scores is to review data from your prior marketing campaigns to discover which leads converted into customers.
To determine whether a lead is qualified to become a client, visit your sales team. You can also find out more by asking a few of your top clients what they did to decide to become customers.
By speaking with your reps and consumers, you'll be able to learn more about the behaviors that generally cause one to advance farther down the sales funnel. You might award leads who download a piece of material with a high point value, for example, if your sales team discovers it has a high conversion rate.
Utilize Negative Scoring
Improving your lead scoring model involves deducting points for particular acts or inactions. This will even out your scores and consider any inflation in your model. Unsubscribing from emails, engaging in unfavorable social media behavior, and visiting your job page and job title are some acts or inactions that should be penalized. For instance, students might download resources for academic purposes alone and not to make a purchase.
Calculate Your Sales Qualified Threshold Value
After creating a point system, you must identify the "magic" number distinguishing a nurture-stage lead from a sales-qualified one.
This level will vary depending on the lead scoring model used by each organization.
Getting input from the sales team and current clients will speed up this part of the lead-scoring process, which involves some trial and error.
Automate the Process
Without a marketing automation tool, lead scoring takes time and may result in data errors and lead routing problems because of possible human error.
Furthermore, their score will probably change as a lead moves down the sales funnel. Without an automated application, it is nearly impossible to keep track of even one lead's behavior, much less thousands of leads.
Thankfully, the most well-known marketing automation platforms can handle the entire lead-scoring procedure. You only need to enter your scoring parameters, and the automatic program will assess leads as they come in, changing the scores as necessary. Without investing a lot of time and effort, lead scoring will pay off for you.
The Bottom Line
Lead score data will not only help you align your marketing and sales activities, but it also has the potential to enhance ROI for your business significantly. Lead scoring is a collaborative activity, much like inbound marketing.
A sales team can focus their efforts on the more attractive potential prospects by using a lead score to determine how probable each lead is to buy.
Lead scores, in general, enable more effective marketing initiatives and a smoother sales process. Creating a lead-scoring technique can help sales and marketing organizations convert more leads and uncover unqualified leads. Kohomai enables you to provide individualized experiences at scale while improving conversion rates and lead qualification.
