We at springML have published a Spark package that connects to Salesforce Wave to push data. This package is available here. Code is published on GitHub here. The advantage of using this package is that once data is analyzed in the Spark environment, the resulting results dataset can be written into Wave for further visualization on …
Iteration and convergence is a key requirement for machine learning and Spark does this well and fast because it can load data in memory and do in memory computation. In addition its support for languages like Python and R helps data scientists who are at home with these languages. SparkR is an R package that provides …
Databricks is a company founded by the creators of Apache Spark. As you can tell from our other blogs, we believe Apache Spark is a revolutionary big data technology and springML provides expert consulting services in Spark implementations. We have been working on the Databricks platform and love the great work they’ve done to simplify Spark …
Introduction to Spark Over the last few years, Hadoop has emerged as the standard platform for big data processing. At a high level, Hadoop consists of distributed storage (HDFS) and distributed computing (MapReduce). However MapReduce has a couple of limitations. Programming in MapReduce is difficult and needs chaining multiple MapReduce jobs in a sequence for complex …
Understanding the impact of advertising spend and knowing how various parts of an ad campaign contribute to sales is key to measuring impact on overall advertisement strategy. Traditional solutions that provide such analytics includes an ETL tool to transform and transfer data from one system to another, a data warehousing system that can store large amounts …
Ad Analytics using Google Cloud Dataflow and Salesforce Wave Read More »
We got the opportunity to collaborate with Google Cloud Platform this year soon after the relationship between Salesforce and Google Cloud Platform was announced. We worked with both Google Cloud Platform and Salesforce product managers to outline a use case that involved Salesforce CRM, Wave and Google Cloud Dataflow. Here’s a more detailed description of the …
In this previous post we discussed how lead scoring can be done by consuming data from various systems. We have implemented this as an AzureML model and published it as a web service. However since this uses a custom module (described here), we’re unable to publish this experiment to the gallery . Here are a few …
We present a comprehensive lead scoring system that will help take guess work out and help your sales machinery focus on the right leads. What makes this a comprehensive system is the fact that it takes into account thousands of data points across the following systems. CRM – Salesforce Marketing Automation – Marketo, Pardot Social – …
Lead Scoring with With CRM, Marketing, Finance and Social Data Read More »
The RFM customer segmentation model is a simple way to segment customers. The resulting segments are easy to understand and helps marketers target campaigns better. R, F, and M stand for (from Wikipeida): Recency – How recently did the customer purchase? Frequency – How often do they purchase? Monetary Value – How much do they spend …
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 – K-means clustering, Logistic Regression and …
Customer segmentation – combining RFM and predictive algorithms Read More »
