## Matchmaking maths holds the key to online love

Despite my good intentions as an early career teacher, my tiered assignments were communicating messages around the students worth as a thinker. Today, in second grade, we returned to the Inaba Place Value Puzzles , at the end of our unit on place value. The set of problems gets increasingly more difficult more regrouping, more digits, etc. It works up to problems like this. We launched the lesson with 3 sample problems — lots of turn and talks. Anticipation built when I revealed the third problem: Liora raised her hand to add on. I recorded it under the 9 tens. We then talked about what other ways we could record the 3 hundreds.

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• Matchmaking With Math: How Analytics Beats Intuition to Win Customers.
• No More Mathematical Matchmaking: The Return of the Inaba Place Value Puzzles?
• Matchmaking With Math: How Analytics Beats Intuition to Win Customers?
• .

Could we write it as 10s? He started to pick up 9 blocks, but shook his head as he held only 2. I only want I watched to see how students combined the different values. Some added the first two numbers e. Others combined all the numbers in the same place value e. In some conferences, I gently nudged students to attend to place value. In others, we talked about the challenges of adding three numbers, and how we can keep track of the sum of the first two in order to count on. Jaxon tried to explain a pattern he noticed. The 8 and the 2 make another He was starting to notice a pattern.

Lila is on an IEP, and the special educator frequently takes her from class for individualized instruction, missing out on the experience and content the whole class is exploring. Today, Lila stayed with us the entire time. At no point, did we decide for kids where they should start, or put them off on a different path. We did not cut students off from opportunities based on a single assessment. Instead, we listened to students, and then used what we knew about them to nudge them to new understandings. Some of the students surprised us!

While everyone was working on the same task, with the same paper in front of them, the thinking varied. Some modeled directly, and others employed a wide range of additive strategies. Listening to the student gave us some ideas about where to go next with this task, or future learning experiences we would like to create. The classroom teacher and I met briefly afterwards. Maybe this is a better structure for me. Instead, we gathered as a community, and everyone pushed their thinking in new directions. We opened opportunities instead of directing students down a path of my choosing.

The students are excited to continue tomorrow.

I enjoyed reading this post. Its both a topic I often think think about and a structure I often use in my math circle. I had some followup questions for you that I still grapple with. How do you manage this? For me this automatically means groups will split by at least gender.

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How does group discussion fit into this especially when the room has made very different choices on which problems to work through. But this use of analytics was fresh for us. What they found surprised them. In a sense, it was simple: They found that technology could assist the company in retaining customers by leveraging the fact that some customer service reps are extremely successful at dealing with certain types of customers.

Matching each specific in-calling customer to a specific CSR made a difference. Not just an incremental difference.

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8. In the interview that follows, Hurst explains how Assurant Solutions figured out the right questions to ask, used analytics to focus on new ways to match customers with reps and figured out the best ways to solve the problem of conflicting goals. What triggered the epiphany that caused you to start looking at things differently? The epiphany occurred because we knew we wanted more. We wanted to retain more customers, and we wanted to get more wallet share by up-selling them. And so we put the problem to a different group. So I think the first important step was to have a different set of eyes looking at the problem, and looking at it from a completely different discipline.

Success and failure are very easy things to establish in our business. If you retained them, you did it by either a cross-sell, up-sell or down-sell. So this is what they started asking: What was true when we retained a customer? What was true when we lost a customer? What was false when we retained a customer? And what was false when we lost a customer?

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For example, we learned that certain CSRs generally performed better with customers in higher premium categories while others did not. These are a few of the discoveries we made, but there were more. Putting these many independent variables together into scoring models gave us the basis for our affinity-based routing. We had information about our customers that seemed, from the perspective of call center routing, totally irrelevant.

They took the whole data set and started crunching it through our statistical modeling tools. The approach they took was to break down our customers into very discrete groups. Any bank or insurance company or financial services company that sells products to customers is tempted to cluster their customers into discrete groups. That was the first process, to find out all the different kinds of customers that we have: The more variables that go into the creation of a cluster, obviously the more clusters you can have; so, not just customers with high balances who tend to pay off early, but customers with those characteristics who also have low credit scores.

How did we do in that one? Looking at all of these interactions let the team see patterns that establish that this CSR tends to do well, historically and evidentially, with customers in these specific sets of clusters. What they also discovered was that the results were completely different from the existing paradigms in the contact center. Let me stop you. We say that CSRs have expertise in an area. The problem is that expertise is a subjective term. They took a test. Or they grade out well in the QA tools.

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## Matchmaking maths holds the key to online love

What the evidence showed us is that the carbon-based intelligence tends to judge incorrectly. The silicon never does. What happens on the customer side? Are you looking at them in a different way? There are obvious characteristics that we can study in our core systems.