Matchmaking with math

Students chose their own starting places. Some students even started midway down the page. Because students were encouraged to choose their own working space around the room, this means that some students did cluster and work on similar problems. The classroom teacher led this particular close, and she opted to have two kids come up and explain their strategies for solving two problems. She wanted them to describe their thinking clearly enough so that students who had not worked on those particular problems would be have at least one access point. There was some light discourse around it, although the close was short that day.

Every students we conferred with had pushed their thinking, and hit a productive challenge. I agree that, depending on the problem s involved, it can be tricky to manage the various paths as the kids branch out! It depends on my mathematical goal. I also find this particular dilemma difficult to discuss in the abstract. You are commenting using your WordPress.

You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. November 26, Author: Jaxon shook his head. From across the rug, Miles thought aloud: Then it was time to explore. Access to the same problem set. Access to manipulatives that could support and push thinking, e.

Access to teachers during conferences.


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Students making choices about where to start in the problem set Students using fingers to keep track of some problems Students looking upwards at the ceiling, mouthing the names of some numbers — deep in thought Students using base 10 blocks to support their thinking, e. Pairs of students huddled to discuss a problem. I love listening to students talk about their thinking. Gio was working on this problem: I pushed Jaxon to explain again.

Matchmaking maths holds the key to online love

Playful Math Education Carnival Maybe you say that you don't want to date someone who has been married before, but you consistently email divorced men as long as they want to have children. In addition to tuning into your behavior to decide who you might like, Match. The site looks for other users whose behavior mirrors your own i. It considers people who your behavior twins have interacted with to be more likely matches for you.

In 16 years of business, Match. It uses that data to make some guesses about who you like before you even rate your first match.

No More Mathematical Matchmaking: The Return of the Inaba Place Value Puzzles

Can an algorithm figure out what we really want in a partner better than we can articulate ourselves? If our likelihood for compatibility is so betrayed by our patterns, where's the magic? And what is it that really makes people click? Neither the matchmaking company nor its algorithms claim to have the answer to any of these questions.

1. What You Say

Image courtesy of iStockphoto , adventtr. We're using cookies to improve your experience. Click Here to find out more. Nor do we need to. And therein lies the difference. So do you see the difference? I would describe it like this: The science does not explain why an affinity will be likely to exist, but it does show that an affinity will be likely to exist. Is that what drives your models? So if your goal is long-term revenue, you can use these economic predictors to determine which customers we should be focusing on.

[CS:GO] Math vs Matchmaking

In other words, if we have 50 callers in queue and 1, CSRs on the floor, we can create 50, different solutions, and we make those calculations 10, 15 times a second. We know how long we keep customers in queue. If the optimal match is too far away, maybe 45 seconds or three minutes away, then the score for that optimal match becomes dampened and someone else might look more attractive to us. When you became more rigorously evidence-based, what did you discover about what might have been wrong in your old assumptions?

What we learned is that satisfaction has almost nothing to do with that. Obviously the faster you answer, the better, over a larger body of interactions. But we found most customers are willing to wait much, much longer, on the order of 39 to 49 seconds, before annoyance affects outcome.


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At some point there is a negative effect on abandon rates. But what we were surprised to learn is that there is no negative effect on abandon rates until you start approaching 60 seconds. It leads to a very, very direct impact on revenue. A direct correlation between time and revenue. This relates back to what you said earlier about having to make choices about which customer to serve during busy periods? We use those predicted economic availability models to help us focus on the more valuable customers. But our focus is on revenue, so saved fee rate is more important to us.

Effectively that means that 58 cents of every dollar that was at risk has been saved. Those are very substantial numbers for us in our business. Just so we can do the apples-to-apples comparison, what was the saved fee rate before? In my own space, in the contact center world, I still am amazed when I come across very, very large Fortune 50 organizations that are still running very, very old technology. There are early adopters and there are adopters. I think our creativity is in how we deployed it.

What do you see in organizations that makes it hard to apply analytics in this kind of an effective way? Another objection is the perception that this is just a skills-based routing solution and that we already have skills-based routing. Those are legitimate objections. What do you say to get someone started down the path that could enable them to get results like yours?

Well, we have proof that it works. So we say, let us prove it to you by giving us some teaser data. We are taking randomness and chaos and making order out of it. I foresee some innovative new services in the area of human resource selection consultation based on this article. Not all the sales personnel are equally effective in utilizing these appointments and, like in your article, the outcome may be related to currently undefined factors.

Based on this article, the mix or composition of auto sales personnel could radically change. David Short — I concur. This is exactly the kind of work my company does. We focus exclusively on human capital management for the call center industry. In recruiting and screening call center agents, we look to match the skills, personality types, and aptitude scores of successful agents to those in the applicant pool. Our aim is to change that!