LeadSift Develops a Score to Calculate Social Leads

LeadSift today launched a cloud-based software application& that scans millions of conversations across social media channels to find and deliver potential leads to businesses while also giving each lead a metric that classifies intent.

Designed to save companies time and money by streamlining the social lead discovery process, each lead is assigned a LeadScore, which provides companies with a unique way to sift though the noise and target the best opportunities to help them grow their businesses by finding and engaging with both new and existing customers. The LeadSift technology has already been integrated into HootSuite's App Directory and the Salesforce Marketing Cloud Social Insights Ecosystem to assist their customers in finding and classifying leads.

"Signal is better than noise," said Tapajyoti Das, CEO of LeadSift, in a statement. "We cut through the noise of static keyword searches to track and find relevant opportunities for brands to have targeted engagement with customers. After months of caffeine, coding, and feedback from beta testers, we have created a unique tool that allows brands to engage in helpful ways with the people online that matter to them."

The LeadSift natural language processing software currently uses more than 50 signals to identify and score leads from social data and identifies them as hot, warm, or cold. Similar to a Klout score, the LeadSift LeadScore is calculated by a combination of semantic analysis of users post and domain knowledge. For example, someone saying "I need a car" is scored lower than a specific mention of a car model, "looking for a Honda Civic," because they are more mature in their thoughts and thus further down the buying process.

For every lead identified, LeadSift analyzes historical conversations from a user's public social profile to further qualify each potential lead. Based on an individual past posts and other available data, such as LinkedIn profiles, LeadSift extracts demographic information and predicts the buying power of a consumer, ultimately affecting each individual's LeadScore. For example, if a person is looking for a new car and currently has a job and owns a house, he will have a higher LeadScore than someone who is unemployed or underage.

"Most companies are already looking for leads via social media and may even have a specific employee that spends timeless hours everyday searching for these leads," said Sreejata Chatterjee, chief operating officer at LeadSift, in the statement. "The algorithm we developed will take the pain out of finding leads and will allow companies to put those hours in actually building relationships and growing their businesses. Through our work with our beta testers, we were able to create a platform that allows companies to find leads globally or right in their backyard."

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