In many organizations, 50% of sales energy is spent on prospects who never convert. What if you could predict which leads are likely to have a need for what you are selling? This is what MadKudu’s customer fit model is here to help with.
MadKudu’s customer fit models automatically research your leads and identify those who are a good fit to buy from you. To do so, we use several data sources including:
- 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
Leads with a “very good” customer fit usually convert about 10 times more than leads with a “low” customer fit.
NB: if your pricing is built upon multiple tiers or contains an Enterprise plan, the customer fit is a prediction of conversion for your highest tier or Enterprise plan.
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.
How is it computed?
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
- fortune 500
- firmographics profile
- potentially spam
- employee count
- number of employees
- team email
- 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