Leads Scoring

Real-time lead scoring model drives higher conversion rates for USA-based tech company

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Real-time lead scoring model drives higher conversion rates for USA-based tech company
Business Outcomes
increase in the sales reps’ productivity
improvement in MQL to SQL lead conversion
of the sorted emails match the keyword models

The client is a USA-based integrated technology services company delivering industrial engineering, construction and manufacturing solutions across the globe.

Business Need

The company’s business development efforts are targeted towards building a customer database of high value clients from targeted industry segments. Their marketing efforts were sharply focused on the leads funnel. This funnel contained data flowing in through multiple sources like paid and organic search engine activities, email and social media marketing campaigns, events and exhibitions presence, referrals, etc.

Sales representatives needed to navigate this sea of unstructured leads collected from various sources in different formats. The chaotic database made prioritization difficult and a lot of effort was wasted on pursuing unproductive leads. They were unable to qualify inbound leads and substantially lost their speed-to-lead.

The company partnered with Hitech Analytics to develop a leads scoring model to categorize, rank and prioritize leads in the right way.

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Hitech Analytics leads experts developed a key-word based algorithmic model to analyze and rank the leads based on location, size and nature to accelerate closures and improve conversions.


  • To make database usable for prioritization, Hitech Analytics data specialists’ cleansed, validated, updated and standardized leads records collected from multiple structured and unstructured sources
  • Evaluated different leads factors like geography, company size, job title, industry, service, etc., and determine relevant value or weighted volumes based on pre-set parameters.
  • Analyzed historical trends with various scenarios from sources like existing emails, replies, queries, form submissions, etc. to understand and document patterns and model keywords.
  • Developed a predictive algorithmic data prioritization model which was trained to:
    • Classify data for geography, company type and size, industry, services etc. using keywords.
    • Understand data correlation trends, prioritization of the variables and its relationship with the outcome
    • Assign value to each lead based on various factors as per pre-defined criteria
    • Prioritize high-value leads and assign it to sales reps without manual intervention to expedite lead time.


  • Enhanced conversions ratio of targeted customer segment
  • Reduced sales cycle time
  • Strengthened business revenue pipeline

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