An integrated engineering consulting and technology services company working with a global clientele was looking to exponentially increase productivity of its business development efforts.
The company’s business development initiatives are targeted towards closing high value clients from targeted industry segments and geographies. The sales and marketing team leans heavily on a huge leads database that is fed through multiple sources like paid and organic search engine activities, email and social media marketing campaigns, events and exhibitions presence, referrals, etc.
Sales representatives navigated through a huge pool of unstructured leads, collected from various sources and mapping to various geographies, industries and services for closures and conversions. The chaotic database made prioritization difficult and a lot of effort was wasted on pursuing unproductive leads or low ticket value clients. Inability to qualify inbound leads substantially impacted their conversion speed.
The company was looking at a solution which would help clean and categorize the leads and then, based on numerous predefined factors, prioritize them in a way that higher ranked leads found their way first to the sales funnel.
- The leads database was unstructured and dirty, which required organizing and cleaning.
- Identifying and applying the right prioritizing factors to rank leads based on ticket-size, industry, services, location etc. Care was needed that important clients were not weeded out of the system as non-priorities because of inaccurate logic or algorithms.
Hitech leads experts developed a key-word based algorithmic model to analyze, score, and rank the leads in a way that high priority leads were first assigned to sales representatives to enhance productivity.
- To prepare the database for running the algorithmic model, Hitech data specialists’ cleansed, validated, updated and standardized leads records collected from several structured and unstructured sources.
- Next, lead scoring experts at Hitech analyzed various prioritization factors like geography, company size, job title, industry, service, etc., and assigned relevant values or weightages to them based on predefined parameters.
- The team analyzed historical data with various scenarios from sources like existing emails, replies, queries and form submissions to understand data correlation trends, their relationship with the outcome, keyword performance patterns etc. For instance, was there a pattern where specifically asking for a quote in a lead query led to higher conversions or was the word “samples” indicative of higher interest. Or did a specific word in an email query indicate higher conversion than its occurrence in a web submission form?
- Developed a predictive algorithmic data prioritization model which was trained to:
- Assign values to each lead based on various factors as per pre-defined criteria
- Prioritize high-value leads, assign scores and funnel them accordingly to sales reps without manual intervention.