How would your hiring process change if you only focused on the candidates best suited for the job?

Predictive analytics takes data from every applicant and employee — then identifies the patterns in your company’s collective experience to create a hiring formula that helps you select the candidates best suited for success at your organization.

For instance, CAN was approached by a large trucking company that — due to competitive industry standards and long days away from home — had a problem with driver retention. After analyzing their data, we made three important hiring recommendations to help them reduce driver turnover by 10%.

Read more…


What if you knew about — and could fix — breakdowns before they even happened?

CAN helps companies prevent breakdowns by letting them to listen to their assets. Using sensors and actuators, we collect data on heavy machinery, buildings, and products. With this data, we build models to predict breakdowns and mechanical failures.

Learn how we helped a Fortune 500 Telecommunications company predict and prevent critical roof failures — saving millions in downtime, replacement hardware, and customer refunds. In addition, learn how we helped a Fortune 500 Construction company predict and manage breakdowns of their fleet of large haul trucks. Read more…


What if you knew which prospects to focus on for the best results?

Working with a Top 10 Online University, CAN used predictive analytics and data science to find patterns in their admissions data to help them make better decisions and focus their efforts.

We developed a model showing which prospects were most likely to convert, which needed extra attention, and which were unlikely to enroll at all. Armed with these insights, they are able put their most valuable resources — time and money — towards building relationships with the prospects that mattered, instead of wasting their efforts trying to engage uninterested individuals. Read more…


What if you could determine — in advance — your most beneficial business relationships?

Our predictive models help you sort relationship opportunities to determine which are beneficial, and which are distractions. You will be able to focus your resources on the Requests for Proposals (RFPs) that will have the most impact on your organization.

To make business relationship decisions, companies and organizations often rely on solicited bids and RFPs. The problem is that responding takes time and money — often more than 20 hours for a basic RFP and weeks for a more complex RFP. This investment of resources makes it very important to select and respond to the RFPs you are most likely to win.

Using your existing data, and the knowledge and intuition of your team, we build a model that helps focus your efforts. You will be able to select the RFPs that you have the greatest chance of winning, allowing you to use your resources more effectively — and close more bids.  Read more…


What if you knew who was going to vote, who they would vote for, and if their vote could be changed?

We recently were hired by a campaign where there was no incumbent but their competitor was well funded. They needed a smarter way to campaign.

Contemporary Analysis combining Predictive Analytics, Political Campaigns and Big Data to help the campaign be better at understanding what the vote count was going to be, how to turnout the voter, and what issues were significant to the voting population.

The campaign’s competitors were using large budgets to follow a traditional strategy. But predictive analytics levels the playing field, allowing candidates with smaller budgets to target the right potential voters.  We helped our client focus their marketing efforts and avoid wasting time, energy, and money trying to appeal to everyone. Read more…


CAN helped a regional healthcare system prepare for the 2009 H1N1 Swine Flu Pandemic. From April 2009 to April 2010 the demand for healthcare spiked putting pressure on nursing staff.

Using predictive analytics CAN developed a staffing plan for a large regional healthcare system with 10 hospitals. Hospitals across the country were hiring as many nurses as they could. A shortage of nurses was quickly developing. Our manager needed to know how many nurses to hirer. He need to optimize his budget to meet the demand without purchasing and holding excess capacity.

Learn how CAN used data from previous flu seasons, nurse schedules, hourly requirements per nurse, training and orientation requirements, and number of active nurse recruiters to forecast demand.

Read more…


What if you knew your target audience well enough to market to them exactly?

Your organization may serve a variety of different demographics — and that’s a good thing! But the factors that motivate one type of customer can be completely different from another customer choosing the same product or service. The key is determining your market segments, and learning what messages will have the most impact for each demographic.

By micro-targeting your customer base, you are able to reach the people that need or want your service, with marketing campaigns tailored to what matters most to them. Instead of sending a generic message to everyone on your list, you are able to send specific messages to targeted groups — increasing your likelihood of response, and ultimately, your overall conversion rate. Read more…


What if you could increase loyalty — and revenue — by selling smarter to your existing customers?

By failing to recognize cross-, re-, and up-sell opportunities within your existing customer base, your organization can experience decreased share of wallet, decreased customer loyalty, and increased customer churn.

Using predictive analytics, we were able to increase the share of wallet and customer loyalty of a 12,000 member credit union.  We identified which members were most likely to need a home or auto loan — and which were most likely to leave. These insights allowed them to create a proactive sales approach targeting their most valuable existing customers. Read more…


CAN has been fortunate to work with several cities on urban planning. In this case study we highlight our work researching public transportation decisions. We were approached by the Transportation Authority Director of one of the 10 largest cities in the United States. The city had experience rapid social and demographic changes and was struggling to keep up.

From 1960 to 2000, the population declined 20%. The city was supporting a metropolitan area of 5.5M people, while only 500,000 people were living inside the city. In addition, urban renewal projects had changed the income and racial mix of the city. Young urban professionals are moving into the city center, while lower income residents are moving to the cheaper suburbs. Also, the city is losing Black residents while gaining White, Asian and Latino residents.

The Transportation Authority was receiving complaints that the system was under-serving specific demographic groups, and needed to determine whether this was true, and how routes, rails and road could be redesigned to ensure access without discrimination.

Learn how we helped the city make smart public transportation decisions.

Read more…



Looking for something?