A state university wanted to increase the success rate of incoming students in their “Gateway” courses. Gateway courses are pre-college level Math and English courses that students must pass before moving on to credit courses if they do not have the qualifications to place them directly into college level courses. There were eight Math and five English Gateway courses. Most of these were sequenced so that a student who needed to start with the first in the sequence may need to take as many as 3-4 courses before their first college credit course. For example, the courses a student needed to complete in Math depended on whether they were in the Science, Education, Social Sciences or Business track.
A student’s work in a course was considered a success if they achieved at least a B-. Our objective was to predict success based on many of the factors that could be related to whether a student would be successful in a specific (Gateway or first college credit) course. We used data sources such as
Placement testing results
High school coursework and grades
High school attended
Date of last course in Math or English (and grade)
If English is the student’s first language
GED completion if no high school degree
Previous Gateway courses
Courses started, completed and grades
Time since last course
Next Gateway or college credit course
Student status (e.g. PT/FT, number of concurrent courses)
Student employment status (number of hours per week)
Number of classroom sessions per week
Classroom hours per week
The resulting model worked quite well in predicting the success of each student in the next Gateway or college credit course. The school is using this model to make recommendations to students as to which Gateway course they should take next as well as advising students on how to be successful in their next course. Administrators and faculty may use these results to improve the sequence of gateway courses for all students. Finally, there was one accelerated Gateway Math course that could be taken in one quarter or taken at a slower pace as two courses over two quarters. The study helped the school understand who should not be taking this accelerated course.
Call centers are a natural place to run experiments because all the elements are present to give good experimental results. (For a list of these elements, see White Paper.) In a typical call center there are potentially hundreds of agents handling many calls per day. Each call can have several quantifiable outcomes related to the objective of interest. For example, the length of each call can be measured if the objective is to improve efficiency. If the objective is to increase revenue, sales per call is measured. Other metrics may include customer satisfaction, return call rate, customer retention, etc.
In some cases several call centers from the same organization will be part of the same experiment which will ensure the results are applicable to all call centers. Industries that use experimentation in call centers include credit card companies, banks, service, retail and online. Experimentation works equally well whether the calls are in-bound or out-bound.
Case Study –Improving Call Center Sales
This organization wanted to improve net sales in their call centers. They had eight call centers where they received calls regarding their credit cards and chose to use three in this improvement effort. In addition to the primary objective of increasing sales they wanted to decrease time on call and improve employee satisfaction since they were experiencing high employee turnover. After conducting a number of brainstorming sessions with customer service representatives (CSRs), team leads and managers the list of ideas was narrowed down to those that were actually tested. The experiment was in three call centers, included hundreds of CSRs, 24 team leads and tested 10 ideas.
The ideas they tested were:
Sales coach availability (coach was ready to coach after any sales call or not)
Unit manager monitoring calls (or not)
Use of lead associates as coaches (instead of dedicated sales coaches)
Operations manager available on the floor (or not)
Use of unit managers as coaches
Increase the time off the phone for call center associates
Increased training to access customer and product information
New hire coaching (or not)
Self-paced training for call center associates (via taped calls, or not)
Self-paced training for call center associates (via Web, or not)
Five of these factors were identified as improving at least one of the key metrics (increasing sales, decreasing call time, improving employee satisfaction). The increase in net sales was approximately four times what Citibank management had hoped the experiment would achieve and resulted in an additional millions of dollars in sales per year!
In addition to improvement in sales, the implemented factors also had a notable positive impact on employee morale and engagement.
Although we would hope it weren’t the case, sometimes our customers have a problem. It may be with the product/service itself, or it may be with the licensing process or other issue. This often leads to what is known as the “moment of truth” when the response to the customer determines whether that customer will abandon you, remain as an unhappy customer or become a loyal advocate. These interactions should be anticipated and a planned response ready for the occasion. Some of the responses are quick and inexpensive and others may be prolonged and expensive. Are the expensive solutions worth it? Can we implement inexpensive solutions that have higher ROI than the more expensive ones? An experiment can help determine how we should respond.
Case Study – Insurance Company
This insurance company had done several process improvement projects, some with designed experiments, to improve cost structure and customer satisfaction in the call center, insurance application and claims processing processes but still had a significant problem. There was an alarming trend toward more claimants hiring attorneys where there was bodily injury (BI) after an auto accident. When they looked at the data for a large number of claims they found that for claims with an attorney involvement 1) the cost to the company was significantly higher, 2) the claims process took significantly longer and 3) the claimant received less than $100 on average. Therefore, it seemed it would be in everyone’s best interest to reduce attorney involvement in the claims process (except for the attorneys 😉 ).
At the beginning of this effort 40% of the claims with BI had attorney involvement. Initial process improvement efforts targeting the “low-hanging fruit” lowered that to 36%. Each reduction of 1% equated to a savings of more than $5,000,000 to the company.
They held brainstorming sessions with many employees and did several surveys to get a long list of potential ideas they could test to reduce the percentage of claims where an attorney was retained in the first 60 days after the accident. They narrowed the list of ideas to be tested down to 15.
The screening test showed four ideas that had a beneficial effect (in order of benefit):
Pay more than blue book value when the value of the automobile was debatably higher. (This was a controversial finding which went against the philosophy of the CEO and the industry – to pay more than necessary for property damage claims in order to reduce the overall cost of bodily injury claims.)
When appropriate (and legal) give the claimant an open-ended BI release form, i.e. if the claimant came back later with additional medical expenses than originally anticipated, the company would pay those expenses.
Increase the number of in-person contacts with the claimant.
Increase the range and discretion by agents in settling BI claims.
These ideas were all put into a second (refining) experiment and not only were they validated, they got a more precise estimate of the cost and benefit of each idea. The final, verified improvement in percentage of claims with attorney involvement was a reduction of an additional 8%, from 36% to 28%.