1010data Advanced Analytics
The 1010data platform includes a powerful array of advanced, built-in analytic functions including:
- Statistics (distribution analysis, correlation, variance),
- Predictive modeling and forecasting (linear and multivariate regression, logistic regression)
- Machine learning (clustering analysis, Markov chains for Monte Carlo simulations, principal component analysis)
- And hundreds more.
These functions are integrated directly into the system, so they run incredibly quickly on large volumes of data.
For example, in the area of telecommunications, a user created a churn model on 8 billion rows of customer interaction data for over 1.5 million subscribers in just 8 minutes. The model took into account the individual characteristics of all 1.5 million subscribers and did not use aggregates or sampling.
Working with customers on real-world problems, analysts at 1010data have built several applications atop the 1010data platform that wide applicability for analytical analysis and are optimized to work in Big Data environments. Examples inlcude:
- Trade-Area Analysis
- Out-of-Stock Analysis
- Affinity Analysis
- Auto-Modeling (Micro-Segmentation Wizardtm)
Segment data and perform advanced predictive analytics and modeling on billions of rows of data in minutes. Retailers, CPGs and Telecom companies can use this tool to model individual customers' actions and perform many other analyses.
In retail or banking, create visualizations showing the trade area for individual stores. This can be mashed up with competitors' locations to optimize site selection, promotions, and marketing.
Designed for the retail and CPG industries, this tool allows store operations and supply chain/logistics analysts to identify out-of-stock items. It statistically looks at the velocity of sales and other factors to create a probabilistic guess at what items may be out of stock. A recent trial at a major retailer saw a 3% lift in sales in items where restocks were optimized using this application.
Affinity analysis detals the likelihood of various products being bought together. If a customer buys a box of Cheerios, what is the likelihood that that customer buys certain other products? What is the most likely other thing that he or she buys? This is Affinity Analysis, and 1010data lets you easily complete these analyses quickly and easily. Doing so, requires that you use granular data on each purchase, you can't use summary data. For each shopping cart, you need to know what items were in it and then do comparisons of them.