- Lead scoring is a methodology used to rank marketing leads based on their perceived value. It helps sales and marketing prioritize which leads to engage with.
- Traditional lead scoring relied on limited data and rules-based scoring by contact centers. Modern approaches use machine learning on digital user behavior data from websites and apps combined with CRM data.
- The presentation provides an example of a company that saw a 22% increase in conversion rates and 18x higher return on ad spend by implementing a lead scoring solution combining online behavior data with ML models.
2. ● Importance of Lead Scoring & Industry Stats
● Lead Scoring - Traditional Vs Modern Approach
● Dataiku-Tatvic Differentiation
● Our Lead Scoring Model - A Quick Walkthrough
● Digital Maturity Assessment
2
Our Agenda
3. 3
Speakers
Head - Solutions Development
Tatvic Analytics
Bismayy Mohapatra
Lead Partner Solution Architect
Dataiku
Tarun Lalwani Kaushal Bhatt
Head - Marketing & Partnerships
Tatvic Analytics
Abhishek
Pathak
Lead Data Scientist
Tatvic Analytics
5. Lead Scoring
5
“Lead scoring is a methodology used to rank prospects
(or leads) against a scale that represents the perceived
value each prospect represents to the enterprise.The
resulting score is used to determine which leads [sales
and marketing teams] will engage, in order of priority”
Defined By: Sirius Decisions, a research firm
6. 6
Interesting Stats to share!
70%
According to a Garner study, 70%
of leads are lost because of poor
follow-up practices.
77%
Marketing Sherpa study states companies
who use lead scoring regularly see a 77%
higher lead generation ROI over
companies who do not
7. 7
Another Stat!
A Kentico study found 38% of businesses surveyed experienced higher
lead to sale conversion rates thanks to lead scoring
38%
8. The Traditional Way of Lead Scoring
8
Limited Data access to
profile/ score Leads
Rule based scoring by
Contact Center team
Lack of continuous
optimization process
User Profiling Data from
CRM tools
9. The New Way of Lead Scoring
9
Digital User Behavior from
Analytics tools
User Profiling Data from
CRM tools
Machine Learning Algorithms
Real-time scoring High degree of accuracy Automated & Scalable
10. 10
Why User Behavior data is a must in Lead Scoring?
74% 16% 10%
Site/ App Behavior Traffic Source User Profile
Sessions, page visits, time
spent, tenure, day of week,
time of day, micro events
Channel, source, medium,
campaigns (for all sessions
and lead session)
Age, gender, location (city /
region),Device Category,
Browser, Operating System
11. 11
Lead Scoring Solution
80
60
ML Models
Logistic
Regression
Random
Forest
Dense Neural
Network
95
40
25 10
Leads with high Propensity to
convert
Leads with medium
Propensity to convert
Leads with low Propensity to
convert
Contact Center
Activation
Remarketing &
Prospecting
Campaigns
Leads and
Conversions data
Online Behavior
data from
Website and App
Email/ SMS
Activation
14. 14
Success Story
● Contact Center Team wanted to
differentiate between unqualified and
qualified leads quickly and efficiently
for prioritizing their calling efforts
● Marketing team wanted to make use of
rich online behavior data of qualified
leads for customer segmentation and
targeted paid marketing campaigns
15. 15
22%
Higher Conversion Rate
18x
Higher ROAS from high
propensity audiences
32%
Higher Leads
61%
Lower Cost Per Lead
Contact Center Activation Prospecting and
Remarketing Campaigns
Outcome
17. Code your way
• Code freely with Jupyter, R, Python IDEs,
any package, isolated environments,
and full Git integration
• Have full programmatic control with full-
fledged API for models, pipelines,
automation
Leverage advanced tech
• No need to master the underlying
infrastructure
• Connect, explore and navigate
• Combine point and click with R, SQL,
Python
and leverage code snippets
• Packaging of cloud services
Point & click
• Search, connect to, and explore data
from preconnected or local systems
• Data wrangling, (auto) machine learning
directly through visual and click interfaces
• Dashboarding, advanced statistics
and data visualization
Don’t get distracted
• Self-provision of compute resources
including cloud-based elastic processing
for large volumes of data, users or
services
• Expedite wrangling with facilitated
connections to SQL, HDFS, cloud storage,
NoSQL, HDFS, APIs,...
Orchestrate advanced flows
• Build check, metrics, and warnings
on top of your data flows
• Orchestrate simple and complex
data flows in minutes for continuous
value generation
Ensure impact
• Low effort CI / CD through orchestrated
pipelines with optional automatic checks
• Create deployment artifacts
• Deploy your models as containerized APIs
• Showcase insights with webapps (Shiny,
Flask, Bokeh) and deploy in Kubernetes
package for reuse by the target population
Upskill
• Gain exposure to more advanced
usage of data within a controlled
environment
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Spreadsheet
wizards
Python
expert
Statistician
Data Scientist
Analyst
High
code
Low
code
No
code
Build on shoulders of giants
• Use packaged plug-ins and full apps
to deliver data scientist-grade projects
with business expert insights
• Copy and paste past projects, data
and models for reuse
Human and readable
• Understand projects, from data
connection to trained models
Empower Everyone From Low to Full Code
18. GE Aviation’s analytics projects can be
moved to production in <2 minutes
A Pre-Integrated Framework to Drive
Large-Scale Impact in Minutes vs. Days
One global investment bank scaled to 500+
users and 2000+ projects
Aviva became x5 more efficient in
developing AI products
Business
Analysts
Business
Owner
Data
Scientist
Data
Engineer
Ideation Validation
Business rules
monitoring
Data Preparation Auto Modeling
Data Preparation Modeling
Refactoring Deployment Operation
(Value)
19. 19
Benefits & A Quick Recap
● Faster Time to Market for deployment
○ From 4+ weeks to less than 10 days for a Lead Scoring use case
○ Visual EDA to Quick Interactive Dashboards to Multiple Algos to Retraining Pipelines
○ Time savings on coding transformation recipes from scratch and pipeline deployment
● AI Democratization within multiple teams
○ No Black Box, Fully Customizable
○ Data Science Team
○ Data Engineering Team
○ Data Analytics & BI Team
● Access and exposure to conduct variety of DS use cases via DSS
○ Visitor/ Lead Scoring
○ Lifetime Value Modelling
○ Video Classification
○ Sentiment Analysis
○ Recommendation Engine
21. Which Digital Maturity Stage do you Fall in?
The Digital Maturity Framework is a critical tool to realize the full potential of data-driven marketing
strategies across your business. It has been designed by BCG in collaboration with Google.
Campaigns are executed
mainly using external data
and direct buys with
limited link to sales
Some use of owned or 1P data
in automated buying with single
channel optimization and
testing
Dynamic execution
across multiple channels,
optimized towards individual
customer business outcomes
Data integrated or activated
across channels with
demonstrated link to ROI or
sales
Nascent Emerging Connected Multi-Moment
Find more details at : BCG's Digital Maturity Framework
21
Media Centric Tech Centric Data Centric Automation Centric
22. How Does Organizations Benefit
Nascent
Efficiency
Effectiveness
1
2
3
Multi-moment
Emerging
Connected
Up to
+20%
revenue
Up to
−30%
cost
As one advances
Digital Maturity to a
Multi-Moment Stage,
Customers have
reported gains in
Profitability and Cost
Savings
Ref: Google + BCG Study
23. Overall score
The overall score is an aggregate of the 5 dimensions we measured
using equal weighting. Both qualitative questions and a quantitative
account analysis were considered. The individual scores below can
guide future areas of focus. Emerging
(1.9 out of 4)
Emerging Emerging Emerging Nascent Connected
Audience Assets Access Attribution Automation Organization
Connected
Maturity Assessment Scorecard