LeadSift Consumer Insights API Provides Actionable Insights from Social Data

LeadSift, a leading social media intelligence solutions provider, has announced the official launch of the LeadSift Consumer Insights API, which allows the LeadSift platform to be integrated with third-party business applications. The LeadSift platform uses machine learning algorithms and natural language processing (NLP) techniques to classify social media users as well as to detect demographic and consumer data about them.

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Image Credit: LeadSift

The LeadSift platform crawls through social media networks every day and indexes over 175 million of the most active social media users. The platform classifies social media users based on who they are, their conversations, and at what point in the purchasing process they are. The data collected is then sorted into categories such as customers, prospects, industry, and affinities and then extracted into specific attributes. There are over 100 attributes, including demographic information (gender, location, age, marital status, etc.), in-depth psychographic traits, and behavior patterns. Users can explore visual representations of the highly refined data in real time to discover trends and gain actionable business insights.

The LeadSift Consumer Insights API provides programmatic access to the platform’s real-time social media insights. API endpoints include Twitter user insights, Instagram user insights, and generic user insights. Developers of data-driven marketing platforms can use the LeadSift Consumer Insights API to add refined social media user data to contact or user profile records. The enhanced platform user data can help marketing companies provide highly personalized content.

According to Jonathan Seller, VP Product and Technology at InNetwork, “LeadSift is a great partner in providing the data we need to make our product more robust. The Consumer Insights API is unparalleled in its ease of use, quality of support, and the rich insights it provides. It has helped InNetwork customers find partners that better match their brand.”

ProgrammableWeb reached out to Tukan Das, CEO of LeadSift, who explained that the entire LeadSift system was developed in-house and that the company built and trained the machine learning algorithms powering the platform from the ground up. “The data for the machine learning algorithms were collected by leveraging crowd-sourced platforms for labeling of the training and test data sets,” said Das.

Das also told ProgrammableWeb that in addition to machine learning algorithms, the LeadSift platform uses convolutional neural networks (also known as deep learning) for predicting people’s buying stages (i.e., what products/services consumers are going to buy next).

The LeadSift Consumer Insights API includes Twitter user insights and Instagram user insights, which are generated from several sources. Das explained to ProgrammableWeb that “we collect the raw data (public posts and friend network data) using a mix of publicly available API endpoints from Twitter and Instagram and also by consuming the data via Gnip streams. The raw data is then processed by LeadSift’s algorithms to build models for inferring/extracting the user insights.”

The information and insights provided by the LeadSift Consumer Insights API is particularly useful for enterprise marketing and sales platforms as well as ad networks, the primary audiences for the API.

“Consumers today are all engaging with brands on multiple platforms across different channels. It is absolutely imperative for marketers to have a single consolidated view of every existing customer and prospect,” Das told ProgrammableWeb. “With the LeadSift Consumer Insights API, marketers can now have a 360-degree view of consumers in their platforms to engage with them in a personalized and relevant manner across every channel.”

For more information about the LeadSift platform and Consumer Insights API, visit LeadSift.com.

 

Janet Wagner is a technical writer and contributor to ProgrammableWeb who covers breaking news, in-depth analysis, and product reviews. She specializes in creating well-researched, in-depth content about APIs, machine learning, deep learning, computer vision, analytics, and other advanced technologies.
 

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