I got a briefing from Quantivo recently. This is a company focused on behavioral analytics – uncovering patterns within the mountains of customer data that companies have – web analytics and point of sale data for instance. They help companies find these patterns, find the insight that they are not seeing with their current tools, and then help them make better decisions. Quantivo started with Retail (market basket analysis and promotion analysis) then expanded beyond that, including web analytics, and acquired new customers throughout 2009. The focus now is on their latest product release, Quantivo 4, and a strategic partnership with Webtrends (who now resell Quantivo). Quantivo has signed some good retail, B2B, marketing and insurance customers, including OSH and Cisco WebEx. Quantivo describe themselves as offering advanced analytics at scale in the cloud.
Quantivo sees companies trying to find who did what, when and why so they can target customers and promote more effectively. Companies want access to their data and effective answers without having to go through the IT department. The need is to democratize access to data and the answers hidden in data. People have a thirst for answers that is not being met by the BI infrastructure IT departments have implemented. In particular there is a gap between analytics and action – data is too far from decision makers who anyway can’t use the analytic tools that are available. Quantivo has tried to re-think the current data/ETL/Data Warehouse/BI/Data mining tool stack and do this re-thinking in the cloud to take advantage of the flexibility and elastic computing power available that way.
Their target user is a business analyst who wants to know things like who purchased movies and games together or what coupon users bought the 10 days following their use of the coupon, which campaign drove high-value repeat customers etc. A marketing analyst, for instance, trying to figure out what works and what does not. These are “advanced” analytics not because the questions are conceptually difficult to ask or because the representation of the answer is complex but because they are hard to answer using classic OLAP/reporting tools.
Quantivo 4 has focused in a few key areas:
- Dynamic Behavioral Segmentation
Context filtering and context-specific queries (over a web session, lifetime of a customer, product range etc), multi-attribute segmentation and segmentation comparison
- Drag and drop web UI to make the solution accessible to business analysts
- Instant export so can load into some tool to take action using downstream applications
The web environment allows business analysts to create and manage worksheets (which can be shared between users). These worksheets can be built using drag and drop feature from lists of dimensions and measures in an OLAP-like way. Performance is good, with large numbers of records being processed quickly and filters can be easily added to restrict the data and see results. Within the results users can start to select elements (one department, say) and make them a comparison target. This allows them to see, for instance, what else people who bought from a specific department purchased at the same time. Or what people bought in the week following a purchase from that department.
Users can drill down, navigate around etc in an easy to use and pretty responsive interface. This is the kind of analysis most people would do in data mining or high-end analytic tools but made available in a very easy to use end-user analyst interface. These worksheets are live and updated when new data is uploaded and they can be shared across users. Customers’ data is uploaded to Quantivo, which is hosted on Amazon EC2.
At any point the user can take the population (of people who might be a good target for instance for an offer) and export to a marketing application etc. Quantivo makes it easy to access the result of a worksheet programmatically and they are working on more advanced APIs also.