Kate Gosselin on Dancing with the Stars Photo Credit: ABC

Can Social Media be used to predict the outcome of Reality TV shows such as American Idol and Dancing with the Stars?  We created Reality Buzz based on our real-time automated web data collection platform to find out.

Jennifer Zaino over at Semantic Web wrote a nice article that captures the essence of Reality Buzz and our process of using real-time social media web data to build intelligence in to predictive analytics:  Taking Sentiment Analysis to Dancing with the Stars and American Idol

Check it out.  And if you have the need to automate the access, collection, harvesting, scrubbing, grabbing or scraping or real-time web data to improve market or competitive analysis to improve your strategic decision making, we’re here to help.

By:  Rick Kawamura

Can oddball metrics from Craigslist apartment listings, Subway ridership tallies, Broadway ticket sales, city parking garage count of empty stalls, cardboard box production, or diesel fuel consumption be better economic predictors than traditional, months old government reports from the labor department?  Absolutely!

The Wall Street Journal just published “New Ways to Read Economy – Experts Scour Oddball Data to Help See Trends Before Official Information Is Available” where author Cari Tuna provides numerous examples of Economists around the country using non-traditional methods to better predict economic trends and direction.

The reason for this trend is that traditional reports and data are out of date and often not very accurate.  Who has six months to wait for a government report to make a decision?

Enter Web Data Services

What would make these oddball metrics more valuable and accurate?  Automating the collection process over multiple sources of data and loading it in to the database or BI tool of your choice.

Imagine you had the ability to automate the monitoring of hundreds of sources of data in real time and could react to changes overnight?  What data would you monitor?

Interest rates?  Gold Prices?  Credit Score reports?  Salesforce data?  Apartment listings?  Competitor’s pricing?  Product Buzz?  Customer complaints?  Financial transactions?  Bank balances?  Twitter?  Facebook?  Google Trends?  Linkedin profiles?  Partner inventory?  Shipment dates?

If you can see it in a web browser, whether on the public web, behind a login screen, or behind your firewall, that data can be accessed with Web Data Services to provide you with improved predictive analytics and strategic decision making.

As a fun example, Reality Buzz uses Kapow’s Web Data Server to monitor popular social media sites to evaluate America’s sentiment towards contestants on American Idol and Dancing with the Stars.  Overnight, data is collected and evaluated, and predictions are made about the fate of the contestants before the elimination show the following night.

Hundreds of businesses incorporate Kapow’s Web Data Server solutions to improve competitiveness, product offerings, and strategic decision making.  You can too.  What are you waiting for?

By:  Rick Kawamura

Together with my product management team, I am currently out speaking with our customers to gather input for future product roadmap priorities.

It’s always great to meet up with our customers, but in particular, what’s been so rewarding this time is hearing one amazing story after another from our customers on the huge time and cost savings they’ve achieved with the Kapow Web Data Server.

Let me share two of their stories.

First, I talked to a large telecommunications company, a long time customer of ours, who has been using Kapow on 80 data automation projects. One project, with expected revenue of $4.9 million over 3 years, included the assembly of 129,000 listings into a directory, all to be finished within 6-8 weeks.

Performed manually, one listing took 15 minutes to complete, so one person was expected to complete 30 listings per day, or 1200 listings in 8 weeks. Basically 100 employees were required to finish the project on time using traditional manual order-entry methodologies.

Using Kapow they successfully built data automation “robots” that took 44 data fields from the source application, transformed it into 69 target fields, and then automatically loaded them into the target system. The Kapow robots completely automated the interaction with the source and target application Web front-ends, as well as automatically performed all the advanced transformations to convert the source data format to the target data format. With robots they were able to complete 5000 automatic listings per day with a 95% success rate and finish the project in only 4 weeks.

This resulted in an astonishing total savings of $1.3 million and at the same time eliminated the risk of the project not getting done in time.

The second company, a business information provider, was extracting energy related data from a dozen government web sites using manual cut-and-paste. With Kapow they built automated data acquisition robots that saved 2868 hours per year of manual labor for each data source. For 12 data sources this resulted in an astonishing savings of 34,416 hours per year.

I love to hear these stories and it’s always very satisfying to hear a customer tell you directly how much they like and benefit from your solutions.

By:  Stefan Andreasen