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Big data simplified. 
Big data individualized.

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What do you need?

(Click the + sign to learn the Kanri Distance Calculator steps for each industry.)

Clinical Researcher

+ "I need to understand why some patients respond to treatments and others don't."

  1. Define your target group as all patients that have responded as desired to clinical treatment.
  2. Gather all available data related to the target group.
  3. Gather the same data for all the patients that have not responded as hoped to clinical treatment. This is your off-target group.
  4. Load the target and off-target files in Kanri Distance Calculator™, and receive detailed output for each off-target patient quantifying their distance from the target group and what percentage each variable contributed. You will also receive a matrix identifying all the patients that share significant variable drivers.

Sales Manager

+ “I want more sales people to drive double digit growth."

  1. Define your target group as all salespeople that have successfully achieved double-digit organic growth.
  2. Gather all available data related to the target group.
  3. Gather the same data for all the salespeople that have not achieved double-digit organic growth.
  4. Load the target and off-target files in Kanri Distance Calculator™, and receive detailed output for each salesperson failing to grow revenue at a double-digit pace, quantifying their distance from the target group and what percentage each variable contributed. You will also receive a matrix identifying all the salespeople that share significant variable drivers so that you can customize action plans for these clusters of individuals.

Customer Service Manager

+ "I want more customers to give us a 9 or 10 on the customer satisfaction survey."

  1. Define your target group as all customer’s who took a satisfaction survey and gave you an overall score of 9 or 10. This is your target group.
  2. Gather all available data related to the target group.
  3. Gather the same data for all the customers that either gave you a score lower than 9 or 10, or who failed to take the survey altogether. This is your off-target group.
  4. Load the target and off-target files in Kanri Distance Calculator™, and receive detailed output for each off-target customer quantifying their distance from the target group. For off-target customers who failed to take a survey, you can use the overall distance from target to approximate the survey scores they would most likely have provided. This solves the age-old question of “what about the people who didn’t take the survey?”

KDC also provides you with a diagnostic for each and every off-target customer, detailing what percentage each variable contributed to the overall distance.

You will also receive a matrix identifying all the customers that share significant variable drivers. This will allow you to create service protocols at a micro-segment or even an individual level, depending upon the nature of the root cause drivers.

Click here to see a case study for this example.

Financial Services

+ "I need to understand why some loans default, while others loans don't."

  1. Define your target group as all loans in the portfolio that have not defaulted. This is your target group.
  2. Gather all available data related to the target group.
  3. Gather the same data for all loans that have defaulted or are in some other state of non-performance or delinquency. This is your off-target group.
  4. Load the target and off-target files in Kanri Distance Calculator™, and receive detailed output for each off-target loan which quanties their distance from the target group. This indicates the degree of default risk associated with each loan.

KDC also provides you with a diagnostic for each and every off-target loan, detailing what percentage each variable contributed to the overall distance.

You will also receive a matrix identifying all the customers that share significant variable drivers. This will allow you to create decision and action protocols for the loans within the portfolio at a micro-segment or even an individual loan level.

Depending upon the nature of the root cause drivers, you might affect countermeasures for individual loans or clusters of loans, modify your underwriting process to account for new risk insights, or even decide to sell off parts of your loan portfolio at a very surgical level.


Retail District Manager

+ "I need more stores to meet their profit goals."

  1. Define your target group as all retail branches that have successfully achieved double-digit year-over-year growth.
  2. Gather all available data related to the target group.
  3. Gather the same data for all the branches that have not met the double-digit growth goal.
  4. Load the target and non-target files in Kanri Distance Calculator™, and receive detailed output for each branch failing to grow revenue at a double-digit pace, quantifying their distance from the target group and what percentage each variable contributed.

You will also receive a matrix identifying all the branches that share significant variable drivers so that you can customize action plans for these clusters of stores.

Six Sigma Engineer

+ "How do I explain why 2 production groups outperform the other 5, and then provide countermeasures to reduce differences in performance?"

Throughput and accuracy for 2 of the company’s 7 warehouses consistently outperform. Management has been trying a series of ad hoc changes to improve the 5 low performers, but has had very little success.

Traditional six sigma methods of variable by variable testing approach has not been able to progress quickly enough to satisfy senior management, and executives have begun to lose patience, threatening the overall effort. Utilizing Kanri Distance Calculator™:

  1. Define your target group as the two high-performing warehouses.
  2. Gather all available data related to the target group.
  3. Gather the same data for the 5 lower performing warehouses (off-target group).
  4. Load the target and off-target files in Kanri Distance Calculator™ and receive detailed output for each off-target warehouse, quantifying their distance from the 2 high-performing target warehouses, and also what percentage each variable contributed to this distance.

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