{"id":4046,"date":"2013-09-19T18:10:42","date_gmt":"2013-09-19T12:40:42","guid":{"rendered":"http:\/\/blog.fusioncharts.com\/?p=4046"},"modified":"2026-01-20T14:41:34","modified_gmt":"2026-01-20T09:11:34","slug":"4-more-businesses-on-the-frontier-of-predictive-analytics-part-3-in-the-series","status":"publish","type":"post","link":"https:\/\/www.fusioncharts.com\/blog\/4-more-businesses-on-the-frontier-of-predictive-analytics-part-3-in-the-series\/","title":{"rendered":"4 More Businesses Leading the Charge in Predictive Analytics 2026"},"content":{"rendered":"Last week, we took at look at 5 startups<\/a> doing cutting edge work in predictive analytics. But there really are so many examples of innovation in predictive analytics, that we couldn\u2019t include them all in a single post, and have spread them out over to this post too. Here are 4 more startups that are paving the way for a future in predictive analytics.\r\n
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  1. Lattice Engines – Letting Sales Do What They Do Best – Sell<\/strong>\"Lattice\r\nLattice Engines\u2019 product SalesPRISM use machine learning to predict the size of a deal, and likelihood of a sale converting, in the process making for a smarter, more productive Sales Team. SalesPRISM does this by analyzing large amounts of data to determine a customer\u2019s intent. This data is sourced from many sources both internally, like CRM, Marketing Automation, and legacy databases, and even externally from websites, and social media. It then provides Sales with suggestions for the right talking points to use when calling customers, what to cross-sell, how big a deal to expect and what products the customer is most likely to purchase. This kind of intelligence allows Sales Teams to spend more time selling than preparing for a sales calls. It enables more personalized interactions, and even shortens the ramp up time for new Sales Reps on the team.In this video, Lattice Engines<\/a> CEO, and Co-founder, Shashi Upadhyay discusses the shortcomings of CRM and MAS systems, and how Lattice Engines fills this gap.\r\n\r\n[youtube=https:\/\/www.youtube.com\/watch?v=3y4gXYLu-ic&w=560&h=315]\r\n\r\n<\/li>\r\n \t
  2. Allazo Health – Just What the Doctor Ordered<\/strong>\r\n\"Allazo\r\nIn technical terms, Allazo Health is a solution for medication non-adherence. In simple words, it uses predictive analytics to both anticipate which patients will not take their medications and to determine the most effective interventions to influence each of those patients to take their medication. This intelligence is sought after by health insurance providers, pharmacy benefit management companies who want to reduce costs, have fewer readmissions, and an overall improvement in patient health.In the video below, Allazo Health CEO, Clifford Jones discuss how their system analyzes not just prior medical history, but also tracks a patient\u2019s engagement with online websites to make predictions. It looks at the records of the patient, and compares it with other individuals to end up with a well-informed, accurate predictive model.<\/li>\r\n \t
  3. Context Relevant – Teaching an Old Dog New Tricks<\/strong>\"Context\r\nIn the first post<\/a> in this series, we discussed how predictive analytics is becoming more available to businesses, and is no more the way of the analytics ninjas. Context Relevant is one company that\u2019s breaking new ground in this direction. It offers predictive analytics solutions to businesses without the need for extensive teams of data scientists. Context Relevant uses real-time data from HDFS, SQL, web logs, CRM systems, market data and social media to output analyses and projections on how a business might be impacted in the future. There are currently three solutions in the startup\u2019s behavioral analytics library \u2014 banking and finance, web context personalization, and online travel. In an interview, Stephen Purpura, CEO & Co-founder, cites an example of how Context Relevant takes weather data, government data, and demographic data for a particular area, and analyzes it so a company like BestBuy can predict how many video games they\u2019ll sell based on these conditions.Context Relevant claims to be \u201cthe world\u2019s fastest predictive analytics application.\u201d The opportunity for them seems ripe given the disparity between supply and demand for data scientists today.\r\n\r\n\"Context<\/li>\r\n \t
  4. Parse.ly – A Healthy Data Diet for Publishers<\/strong>\"Parse.ly\"Parse.ly\u2019s product Dash is a content optimization platform for publishers which enables them decide what types of content to publish. It does this by analyzing billions of pages across the Web, allowing publishers to track the performance of authors, articles, and topics in real-time. It gives publishers recommendations for what topics to cover, and allows them to capitalize on trends before they take off. All of this comes together to inform the content strategy of a publisher.\r\n\r\nWe saw in a previous post that media companies are among the frontrunners in big data adoption, and Parsel.ly<\/a> is one of the companies accellerating this growth. Here\u2019s a video showing how Dash works:<\/li>\r\n<\/ol>\r\nBonus section – Predictive Analytics Startups Being Acquired by Enterprises<\/strong>\r\n\r\nWhile all the companies mentioned here are just starting up, the enterprises too want in on the action in predictive analytics. This has resulted in a slew of acquisitions in the past year, a few of which we\u2019ve listed below.\r\n