Cultivate Labs is expanding Cultivate Ignite, an internal crowdfunding platform for enterprises, to include a self-service package for small and medium sized businesses to find and fund the best ideas in their companies.
A multinational energy company uses Cultivate Forecasts predict economic and geopolitical events that impact oil and gas prices. We find out why they decided to incorporate internal crowdsourcing within their business and how they have been so successful at engaging employee participation.
We've all participated in group brainstorming sessions in both academic and business settings at some point complete with whiteboards, Post-it notes, and afternoons around a conference table. Though it has been proven that brainstorming, especially in group settings, doesn't work, people continue to look to it as the go-to technique for stimulating creativity. When the afternoon session is over, someone takes a picture of the whiteboard and promises to follow up with the team. And that's as fa
if companies, especially the large ones, want lasting innovation - the kind that permeates everything they do, the kind where “innovation” never has to be spoken about, i.e. “let’s be innovative,” they just are by design, there are two things that have to be fundamentally re-thought...
As a small bootstrapped company, we're constantly worrying about our next stage of growth. And of course start up orthodoxy says any size business should have a few key performance metrics they track to be aware of how you're doing in the things that influence revenue, profit, and the culture you want to cultivate. But as we've learned, it's not enough to just track your KPI's, you need to forecast how you're going to do on them weeks and months ahead of time.
With an election coming next month in the Netherlands, Marco van Schaardenburgh has successfully launched Prendid, a “voorspellingsmarkt” (prediction market) where players are predicting the outcome of the parliamentary elections. Joining is free and available to anyone globally.
Broadspectrum, whose parent organization is Ferrovial, is an Australian company that operates in the Defence, Property, Social, Infrastructure, Resources and Industrial sectors, and provide Logistics, Facilities Management, Consulting, Construction, Care, Welfare, Operations, Maintenance, Well Servicing, and Business Support services. We recently completed a pilot project in their Defence line of business...
Thanks to the Illinois Technology Association (of which we are a member) for publishing our guest blog post on toxic corporate culture. We make the argument that before you can heavily invest in shiny new technology as you prepare for the #futureofwork, you should look to make positive changes to your culture first to take maximum advantage of your investment.
We recently had an article run in the Huffington Post about the future of work and what skills will be the most valuable given the new ways organizations will be structured and their desires to be more agile...
Using crowdsourcing only at the front-stages of the product development cycle means organizations are missing out on a big opportunity to further tap the wisdom and knowledge of the organization. Here's how you can use internal crowdsourcing across your entire development lifecycle.
For years, companies of all shapes and sizes have utilized the power of the crowd to research, test, and drum up support for their products or service offerings. It makes sense — tapping into the external crowd can not only power idea generation at scale and in real time, but it can also drive engagement among your most important brand ambassadors.Traditionally, market research has dictated that customers (or people like them) are always the best sources of information. But this is limiting...
One of our developers left Cultivate recently to go work for a much larger company - an experience he has never had before. They are throwing more money at him than we ever could, and he will work on a team larger than our entire company...
It is incredible that a company of Uber's size, with the experience they have entering new markets is still having the kinds of colossal failures they are having in Germany. But Uber is certainly not alone in costly missteps like this and billions are being lost every year. Yet a solution already exists: your employees.
I enjoyed John Horgan's piece on Bayes Theorem for Scientific American. Bayes Theorem and Bayesian reasoning are highly applicable when thinking about forecasting and prediction markets; indeed, one prediction market built a Bayes Net into its platform. In this post I'll explain what Bayesian reasoning is, why it matters to prediction markets, and give a concrete (but semi-fictitious) example of how it's applied.
Our sites use a popular prediction market algorithm called LMSR to determine how markets adjust when someone makes a forecast, and how user scores are affected by making correct and incorrect forecasts.
I've been trying to pick NFL game winners. I'm not using any complex analytical model; rather, I'm making decisions the way most sports bettors do--I watch some games, read the news, and use my judgment. I make each of my picks on SportsCast, which allows me to track my performance, interact with other forecasters, and track the performance of the prediction market--that is, the collective performance of all the forecasters on SportsCast.
One of the first and most important questions we get from clients, forecasters, and consumers of our data is: “How accurate are these forecasts?”. In order to answer this question, we have utilized and built upon a widely accepted proper scoring rule, i.e. a way to measure accuracy for a probabilistic forecast.
Joining a prediction market can be confusing and anxiety-inducing. It's easy to be overwhelmed by all the questions, to not understand the forecasting interface, or to have trouble forming opinions to base forecasts on. All of this is pretty natural--as a now-experienced forecaster, I can remember these feelings the first time I joined a prediction market. In this post I'll address a few specific emotional barriers that make it difficult to start forecasting.