Next Caller provides Advanced Caller ID™ to businesses. Across industries, inefficient exchanges between sales/support reps and customers cost $14B annually. With the Next Caller API, unknown inbound phone numbers get matched with relevant background data for the caller, giving reps near-instant access to all relevant data on a customer.
Winning in real estate is about better information, with data sources like Redfin and MLS as table stakes. The most successful real estate players win by having an information edge. Kimono is a smart web-scraper – letting innovative real estate players like Michael Tomko find leads that others can’t. Here’s how Michael identifies high value properties and unlocks the best deals on the market.
We’re excited to announce two new features to help you scrape more data, faster.
|Fox News on 1/29/15||
CNN on 1/29/15
Turning the mountains of unstructured text scattered across the web into insights can be a daunting prospect. At kimono, we are working to make this much easier. Take news for example – New York Times alone publishes 350+ pieces of content per day; The Huffington Post releases 1,200. With just a few kimono APIs, we can create a structured corpus of text that we can mine to understand trends, biases and patterns across sources. We’ll make our first scratch on the surface here by setting up APIs for CNN and Fox News to ‘read’ every article on each site’s respective front page and compare what meaningful words are being said.
&kimmodify=1) to get back your transformed data.
Tutorial: Map your own location data
Data is more accessible, tangible and interesting when you can visualize and interact with the figures on a page. That’s why we love teaming up with our friends over at CartoDB! Kimono is a smart web scraper that let’s you turn data on a website into an API – a structured feed of updating data. CartoDB let’s you take that data set and create beautiful interactive maps. In this post, we will use kimono to get over a year’s worth of bike trip data from New York City’s citibike bike sharing program. We’ll then use cartoDB to plot our friend Andrew’s movements on a map. A big thanks to Andrew for riding his bike a lot and sharing his data with us!
Contributed by Raúl Garreta, co-founder of MonkeyLearn
New tools have enabled businesses of all sizes to understand how their customers are reacting to them – do customers like the location, hate the menu, would they come back? This increased volume of data is incredibly valuable but larger than any mere mortal can assess, understand and turn into action. Several technologies have emerged to help businesses unlock the meaning behind this data.