In many organizations, 50% of sales energy is spent on prospects who never convert. What if you could predict which leads are most likely to pay for what you are selling? This is what MadKudu’s customer fit model is here to help with.
Introduction
MadKudu’s customer fit models automatically looks at your leads and identify those who are a good fit to buy from you.
This analysis is based on traits coming from different data sources:
Firmographic data, eg. Company size, industry, location
Demographic data, eg. Job title
Technographic data, eg. Tech stack
MadKudu labels your leads with segments simple to understand and to act upon. The possible values are:
Very Good
Good
Medium
Low
Leads with a “very good” customer fit usually convert about 10 times more than leads with a “low” customer fit.
Main Use Cases
Engaging Leads Quickly: By quickly identifying who are the leads with the most potential (customer fit is “good” or “very good”), your sales team can reach out to these VIPs much faster.
Increasing Sales Efficiency: Keep low quality leads out of your funnel so that reps can spend their time where it matters.
Better Marketing Performance: Instantly know whether your marketing campaigns are bringing good leads or noise.
Improved outbound: You have a list of 25,000 leads? You’re going to an event with 1,000 attendees? Quickly figure out who you should talk to.
Cleaning your CRM: Keep our your junk leads from getting into your CRM.
Initial configuration
For MadKudu Startup plan, the customer fit is an industry-specific model that scores every new lead based on aggregated trends.
For MadKudu Growth and Pro plans, the customer fit model is trained against your existing paying customer database.
It isolates signals that determine the likelihood of a lead to convert based on traits including:
Company Size
Company Industry
Enterprise Technologies used (Marketo, Salesforce, Taleo, Adobe Omniture…)
Retargeting Technologies used
Technologies (amount, type…)
Company Geo-economics (GDP, language…)
Company Tags (B2B, SaaS…)
Company financials (funding, growth, market value…)
Job title (seniority, department)
Using its historical training, the model will then score any new identified lead or user based on those signals.
For MadKudu Pro and Ent API plans, you can also send us a training data set via CSV using the following template.
What are the default signals
personal email address
spam
fortune 500
firmographics profile
potentially spam
education
employee count
number of employees
team email
nonprofit
test email
market cap
capital raised
web traffic volume
based in a country where GDP per capita
enterprise tech found on website
belongs to industry where revenue per employee
number of tech found on website
twitter followers
company founding year