Lead scoring

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Lead scoring is a methodology used to rank prospects against a scale that represents the perceived value each lead represents to the organization. [1] The resulting score is used to determine which leads a receiving function (e.g. sales, partners, teleprospecting) will engage, in order of priority.

Contents

Lead scoring models incorporate both explicit and implicit data. Explicit data is provided by or about the prospect, for example - company size, industry segment, job title or geographic location. [2] Implicit scores are derived from monitoring prospect behavior; examples of these include Web-site visits, whitepaper downloads or e-mail opens and clicks. [3] [4] Additionally, social scores analyze a person's presence and activities on social networks. [5]

Lead Scoring allows a business to customize a prospect's experience based on his or her buying stage and interest level and greatly improves the quality and "readiness" of leads that are delivered to sales organizations for followup.

Key Benefits

When a lead scoring model is effective, the key benefits are:

Lead Scoring Methodologies

Various lead scoring methodologies are employed:

Businesses iterate on existing methodologies and change methodologies in an effort to better prioritize sales engagement. As businesses grow in headcount & the number of products they sell, predictive lead scoring methodologies are generally favored for their ability to ingest new customer data routinely and evolve its predictions. [11]

Predictive Lead Scoring

With machine learning, lead scoring models have evolved to include components of predictive analytics, generating Predictive Lead Scoring models. Predictive Lead Scoring leverage first party data - such as internal marketing, sales & product data - as well as third party data - such as data enrichment & intent data - in order to build a machine learning model of the ideal customer profile. Predictive Lead Scoring models can also be used to identify, qualify & engage product-qualified leads based identifying statistically differentiating elements in historical user behavior which best predicts whether a user will spend above a certain threshold. [12]

Predictive Lead Scoring is particularly beneficial for SaaS businesses, which have a high Customer lifetime value & a plethora of customer data. Predictive lead scoring models enable businesses to identify high-value prospects early in the buyer journey, creating a FastLane experience for prospects predicted to be a good firmographic & behavioral fit.

The success of Predictive Lead Scoring models is measured by their ability to identify a subset of prospective buyers who will account for a significant portion of sales opportunities. This is expressed in the following way:

X% of leads represent Y% of conversions

Optimal performance of a predictive lead scoring model sees X approaching 0, Y approaching 100 & conversions defined as a bottom-of-funnel metric such as opportunity created or opportunity won.

Parameters for Accurate Lead Scoring

Achieving accurate lead scoring is crucial for aligning marketing and sales efforts and maximizing conversion rates. The following parameters are commonly considered to enhance the precision of lead scoring models:

  1. Demographic and Firmographic Data Understanding the characteristics of a lead is essential. Parameters include:
    • Job Title and Role: Identifying decision-makers or influencers within an organization [13] .
    • Company Size: Larger companies may have different needs and purchase behaviors compared to smaller ones.
    • Industry Sector: Tailoring scores based on the industry can improve relevance.
    • Geographic Location: Regional differences can affect product suitability and market dynamics. See also: Demographics, Firmographic
  2. Behavioral Data Tracking and analyzing how leads interact with a company's digital channels provides insights into their level of interest and intent:
    • Website Activity: Page visits, time spent on site, and content viewed.
    • Email Engagement: Opens, clicks, and responses to email campaigns.
    • Content Downloads: Whitepapers, e-books, and case studies downloaded indicate specific interests.
    • Event Participation: Attendance at webinars, seminars, or trade shows. See also: Web analytics, Email marketing
  3. Engagement Level The frequency and recency of interactions can signal a lead's readiness to move forward:
    • Recency: How recently a lead engaged with the company.
    • Frequency: The number of interactions within a specific time frame.
    • Intensity: Depth of engagement, such as watching a full product demo versus a brief website visit [14] .
  4. Fit with Ideal Customer Profile (ICP) Assessing how well a lead matches the company's defined ICP helps prioritize efforts:
    • Alignment with Target Markets: Evaluating if the lead operates within the markets the company serves.
    • Needs and Pain Points: Determining if the lead's challenges align with the solutions offered. See also: Target market
  5. Lead Source The origin of a lead can influence its quality and potential conversion rate:
    • Referral Leads: Often have higher trust and conversion rates.
    • Organic Search: May indicate active searching for solutions.
    • Paid Advertising: Can reflect immediate interest prompted by marketing efforts [15] . See also: Referral marketing, Search engine marketing
  6. Buying Intent Signals Specific actions can indicate a higher likelihood of purchase:
    • Requesting a Demo or Trial: Directly expressing interest in the product.
    • Pricing Page Visits: Reviewing pricing may signal readiness to buy.
    • Product Comparison Activities: Comparing features suggests active evaluation.
  7. Social Media Engagement Interactions on social platforms can provide additional context:
    • Mentions and Shares: Indicate brand awareness and interest.
    • Direct Messages: Show proactive outreach by the lead.
    • Participation in Discussions: Engagement in relevant topics or groups. See also: Social media marketing
  8. Data Quality and Accuracy Ensuring that the data used in lead scoring is accurate and up-to-date is essential:
    • Data Verification: Regularly updating and cleansing data to remove inaccuracies.
    • Integration of Data Sources: Consolidating information from CRM systems, marketing automation platforms, and other tools. [16] See also: Data quality, Customer relationship management
  9. Negative Scoring Factors Identifying behaviors or attributes that decrease lead quality:
    • Unsubscribes: Opting out of communications.
    • Bounced Emails: Invalid contact information.
    • Inactivity: Lack of engagement over an extended period.
  10. Predictive Analytics Variables Incorporating advanced analytics to enhance scoring:
    • Machine Learning Models: Using algorithms to predict lead conversion likelihood.
    • Third-Party Data Enrichment: Augmenting internal data with external sources for a more comprehensive view.
    • Intent Data: Monitoring signals that indicate purchasing intent outside of direct interactions. See also: Predictive analytics, Machine learning

By carefully considering and integrating these parameters, organizations can develop more accurate lead scoring models that effectively prioritize leads and improve conversion rates [17] .

See also

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References

  1. ↑ "What is Lead Scoring?". TechTarget.
  2. ↑ "Introduction to Lead Scoring". act-on.
  3. ↑ DM News, “Lead Scoring by the Numbers”
  4. ↑ "A 10 Step Framework to Accurate Lead Scores". CRM Search. 30 March 2023.
  5. ↑ "A Quick How to Guide on Social Media Lead Scoring". TargetMarketing.
  6. ↑ "Basic Idea of Lead Scoring". Marketing Automation Insider. 22 June 2015.
  7. ↑ "Hacking Hubspot's Lead Scoring System". Matthew Deal. 2020-12-09. Retrieved 2019-03-01.
  8. ↑ Boogar, Liam (2019-01-29). "The Three Stages of Lead Scoring: Lambs, Ducks & Kudus". All others bring data. Retrieved 2019-03-01.
  9. ↑ "Predictive Lead Scoring: Why, How & Where". DigiTech-Media. 2023-12-01. Retrieved 2023-12-01.
  10. ↑ "Lead scoring solution for Salesforce Sales Cloud". SalesWings. 2019-10-10. Retrieved 2019-10-11.
  11. ↑ Boogar, Liam (2019-01-29). "The Three Stages of Lead Scoring: Lambs, Ducks & Kudus". All others bring data. Retrieved 2019-03-01.
  12. ↑ Brero, Francis. "Beyond MQLs & SQLs: How to Use Your Product to Qualify Leads". www.appcues.com. Retrieved 2019-03-01.
  13. ↑ Zabin, Jeff; Brebach, Gresh (February 2004). Precision marketing : the new rules for attracting, retaining, and leveraging profitable customers. Wiley. ISBN   978-0-471-64822-2.{{cite book}}: CS1 maint: date and year (link)
  14. ↑ Kotler, Philip; Keller, Kevin Lane (May 28, 2015). Marketing Management, 15th edition (15th ed.). Pearson. ISBN   9780134236933.{{cite book}}: CS1 maint: year (link)
  15. ↑ Järvinen, Joel; Taiminen, Heini (2016-04-01). "Harnessing marketing automation for B2B content marketing". Industrial Marketing Management. 54: 164–175. doi:10.1016/j.indmarman.2015.07.002. ISSN   0019-8501.
  16. ↑ "Bad Data Costs the U.S. $3 Trillion Per Year". Harvard Business Review. 2016-09-22. ISSN   0017-8012 . Retrieved 2024-10-30.
  17. ↑ Huang, Ming-Hui; Rust, Roland T. (2021-01-01). "A strategic framework for artificial intelligence in marketing". Journal of the Academy of Marketing Science. 49 (1): 30–50. doi:10.1007/s11747-020-00749-9. ISSN   1552-7824.
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