CATEGORY: machine-learning

  • How Machine Learning Can Help With Sales

    June 8, 2017 | Author Girish Reddy

    This post is a summary of a webinar we recently hosted, “Boosting Sales Operations Efficiency with Machine Learning”. The 30 minute on-demand version can be viewed (no form fill required) on our Vimeo channel. Artificial intelligence has been posed as a solution to everything from self-driving cars to the Internet… [Read More.]

  • Revolutionizing Sales With A Poker Playing AI

    May 16, 2017 | Author Alana Kaselitz

    Artificial Intelligence & Sales Forecasting Here's a scenario that might be familiar to you: You're on the phone with the purchasing rep at a large vendor. They're making buying signs, but the process is starting to drag on. Are you seriously in contention, or would your time be better spend… [Read More.]

  • Our take on Google Next Conference

    March 15, 2017 | Author Prabhu Palanisamy

    Google has been providing effective services for so long that we don’t remember how we lived without them. For me, no Google search or Gmail is as good as no power. But Google isn’t resting on its laurels. One of Google’s newest platforms – Google Cloud – is poised to… [Read More.]

  • Machine Learning is Easy, then what is difficult?

    February 13, 2017 | Author Prabhu Palanisamy

    In the whole Machine Learning development, model development is the easy and least time-consuming effort. Operationalization is the hardest and that's where all projects are either failing or struggling to justify investment. It's not a one time effort. Most Machine Learning projects start with access to existing data and particular use case… [Read More.]

  • On the Trail at Dreamforce – Data Science Goes Mainstream

    October 6, 2016 | Author Charles Landry

    It’s Dreamforce week, which is always a great time to connect with customers, meet new prospects and hear what’s on their minds. This week everyone is talking about how to become a data driven enterprise. Every company knows that data is a competitive advantage for their organization, but how they… [Read More.]

  • Make Decisions Based on Data Change Insights

    September 27, 2016 | Author Girish Reddy

    As a business leader in revenue, sales or customer operations you may be familiar with these situations. Last month your monthly recurring revenue was $50,000 and this month it's only $35,000.  What happened?  Were there specific accounts that contributed to this shortfall?  Was it a specific region?  Were there too… [Read More.]

  • Customer segmentation – combining RFM and predictive algorithms

    June 10, 2015 | Author SpringML

    The Recency-Frequency-Monetary value segmentation has been around for a while now and provides a pretty simple but effective way to segment customers.  An RFM model can be used in conjunction with certain predictive models to gain even further insight into customer behavior.  In this post we’ll discuss three predictive models… [Read More.]

  • AzureML model for customer segmentation

    May 31, 2015 | Author SpringML

    As mentioned previously, we are approaching the customer segmentation problem holistically with a view to provide an end to end solution.  This end to end solution comprises of three components. Data preparation and enrichment. Any complex enterprise landscape comprises of multiple systems, each performing a specific function.  There could in fact be… [Read More.]

  • Customer segmentation

    May 31, 2015 | Author SpringML

    Consumers, customers, clients or users.  Call them what you will, but businesses exist and grow only when they can serve them well and attract more of them.  While there are several business strategies that one employs, one key methodology is to group or segment similar customers based on past purchasing… [Read More.]

  • Loan data analysis using Azure ML

    May 17, 2015 | Author SpringML

    Here’s my first AzureML model: http://gallery.azureml.net/Details/6f058343b2224057a8ae259311f7ed31.  The model uses a two class logistic regression algorithm for binary classification. This is based on Python based sample from learnds.com. It leverages various AzureML Studio components as well as custom R code for data cleansing and feature engineering. Here are the various steps… [Read More.]