Calls May be Recorded for Quality or Training Purposes

Introduction

The all too familiar notification that your phone call to almost any call center in the world is being recorded has been heard by enough people enough times that it is probably forgotten almost as quickly as it is processed. But that has not stopped data analysts who work for the companies that operate these call centers from improving customer outcomes with these and other data that they record about client experiences.

One of the first obstacles to maintaining a successful call center is the ability to connect clients to an operator in a timely manner, never mind the companies now trying to use personality metrics to connect clients to a similar operator or one they predict meshes with the customer. A low call response time is essential to starting client interactions off on the right foot and sets the tone for the entire interaction.

But what exactly is a low response time? How long is the average person willing to wait on hold before speaking to someone on the other line?

Methodology

Using public call center data from the San Francisco Health Service System, which is an insurance provider to city employees, elected officials, and their families, call abandonment rates can be compared to call response times to establish baselines for call center performance and customer patience thresholds. These data are visualized in ChartLab and dissected with SQL Console in ATSD to calculate concrete wait times that are likely to result in an abandoned call.

Data

View in ChartLab

The visualization above shows the total number of calls received by the Health Service System over the entire observed period, which began in January 2011 and continues until June 2017. Abandoned calls are shown in orange. Retrieve data using the query below. See the complete dataset in the Appendix.

SELECT datetime AS "Date", ibc.value AS "Inbound Calls", abc.value AS "Abandoned Calls", car.value AS "Abandonment Rate"
  FROM "inbound_calls" AS ibc JOIN "abandoned_calls" AS abc JOIN "call_abandonment_rate" AS car
| Date       | Inbound Calls | Abandoned Calls | Abandonment Rate |
|------------|---------------|-----------------|------------------|
| 2017-01-01 | 5438          | 105             | 1.9              |
| 2017-02-01 | 4122          | 53              | 1.3              |
| 2017-03-01 | 4521          | 33              | 0.7              |
| 2017-04-01 | 3084          | 29              | 0.9              |
| 2017-05-01 | 3402          | 28              | 0.8              |
| 2017-06-01 | 3425          | 65              | 1.9              |

When scaled appropriately, the relationships between the three values contained in the above visualization are noticeable. Although tempting to conclude that an increased call volume immediately results in an increased call abandonment rate, which is simply the calculated percent of total calls that are abandoned, is an invalid oversimplification.

View in ChartLab

Scale Modifications:

  • inbound_calls = value/10
  • call abandonment rate = value*100

To learn more about scale modification in ChartLab, see Charts Documentation.

Although an increased call volume results in increased abandonment rates several times throughout the observed period, several local maxima in call volume are not matched by a corresponding maxima in abandoned calls. The visualization below compares call wait time to call abandonment rate.

View in ChartLab

The scale of this visualization has been intentionally modified to exclude the 2013 absolute maxima to avoid distorting the rest of the data.

call_abandonment_rate = value*100

There is a visible relationship between call wait time and call abandonment rate. The associated SQL query is below:

SELECT datetime AS "Date", car.value AS "Call Abandonment Rate", ROUND(cwt.value, 0) AS "Call Wait Time"
  FROM "call_abandonment_rate" AS car JOIN "average_speed_of_answer_in_secs" AS cwt
| Date       | Call Abandonment Rate | Call Wait Time |
|------------|-----------------------|----------------|
| 2016-12-01 | 5.6                   | 77             |
| 2017-01-01 | 1.9                   | 34             |
| 2017-02-01 | 1.3                   | 16             |
| 2017-03-01 | 0.7                   | 11             |
| 2017-04-01 | 0.9                   | 14             |
| 2017-05-01 | 0.8                   | 16             |
| 2017-06-01 | 1.9                   | 32             |

View the complete result set in the Appendix

Analysis

The figures below visualize the information as a distribution table to highlight median values of the above data. The first visualization applies the same scale modification as above, while the second has no scale modification.

View in ChartLab

Scale Modification:

  • call_abandonment rate = value*10

View in ChartLab

The relatively small deviation in wait times is highlighted by the tight concentration of bars on the lower graph. That data, along with call abandonment rate, are further analyzed below.

SELECT date_format(time, 'yyyy') AS 'Date', AVG(value) AS 'Call Wait Time by Year'
  FROM 'average_speed_of_answer_in_secs'
GROUP BY date_format(time, 'yyyy')
Date Call Wait Time by Year
2011 32.4
2012 31.6
2013 47.9
2014 12.1
2015 12.1
2016 27.7
2017 20.5

Overall average wait time: 26.3 seconds

SELECT date_format(time, 'yyyy') AS "Date", AVG(value) AS "Average Call Abandonment Rate"
  FROM "call_abandonment_rate"
GROUP BY date_format(time, 'yyyy')
Date Average Call Abandonment Rate
2011 2.2
2012 1.4
2013 2.6
2014 0.9
2015 0.9
2016 1.9
2017 1.2

Overall average call abandonment rate: 1.6

The values are ranked, shown outside the table.

To preserve 2016 and 2017 data as holdout data, the values used for modeling exclude 2016 and 2017 data. The new baseline values are shown below:

Metric Baseline
Wait Time 32.1 seconds
Abandonment Rate 1.56%

Reviewing the training data, the first three instances of above 32 second wait times show below average abandonment rates, indicating a need to adjust the threshold up from the average. In practical terms that means that on average, a person is willing to wait at least the average wait time before hanging up. However such an adjustment would be passively accepting a 1.56% call abandonment rate as normal and rarely are successful businesses content with the average. The alternative method is to adjust the abandonment rate down and establish a less-than-average call wait time that meets the selected target abandonment rate criteria. Both methods are pursued below.

Model 1: Maintain Abandonment Rate <= 1.56%

Suppose a company is content to maintain the 1.56% call abandonment rate and is simply interested in finding the typical wait limit of the average client before their call is fielded or abandoned. By averaging the wait time for only months which demonstrated a call abandonment rate of greater than 1.56% with this query:

SELECT datetime AS "Date", car.value AS "Call Abandonment Rate", cat.value AS "Call Wait Time"
  FROM "call_abandonment_rate" AS car JOIN "average_speed_of_answer_in_secs" AS cat WHERE car.value > 1.56 AND datetime < '2016-01-01 00:00:00'
| Date       | Call Abandonment Rate | Call Wait Time |
|------------|-----------------------|----------------|
| 2011-03-01 | 2.1                   | 29             |
| 2011-04-01 | 1.8                   | 29             |
| 2011-07-01 | 1.8                   | 43             |
| 2011-09-01 | 1.7                   | 29             |
| 2011-10-01 | 1.7                   | 18             |
| 2011-11-01 | 4.7                   | 21             |
| 2011-12-01 | 5.7                   | 22             |
| 2012-03-01 | 2                     | 29             |
| 2012-04-01 | 1.7                   | 21             |
| 2012-12-01 | 3.8                   | 80*            |
| 2013-01-01 | 9                     | 224*           |
| 2013-02-01 | 7                     | 106*           |
| 2013-03-01 | 2                     | 37             |
| 2013-12-01 | 3.8                   | 49             |
| 2014-01-01 | 1.6                   | 13             |
| 2015-01-01 | 2.4                   | 13             |
| 2015-10-01 | 1.8                   | 28             |

*Values greater than twice the average wait time are be counted as 64.2 seconds, twice the average.

Adjusted average wait time: 33.7 seconds

The training data is visualized below with an alert-expression included to highlight those months where the call abandonment rate exceeded 1.56%.

View in ChartLab

The adjusted average wait time value acts as the threshold by which the 1.56% call abandonment rate may be successfully maintained and be tested below in the Validation section.

Model 2: Achieve Abandonment Rate <= 1.00%

Suppose the goal of the Health Service System is to have a call abandonment rate no higher than 1%. What is the longest tolerable wait time before one can be reasonably sure the call is abandoned? The following SQL query summons all instances in the training data of a call abandonment rate less than or equal to 1%:

SELECT datetime AS "Date", car.value AS "Call Abandonment Rate", cat.value AS "Call Wait Time"
  FROM "call_abandonment_rate" AS car JOIN "average_speed_of_answer_in_secs" AS cat WHERE car.value <= 1 AND datetime < '2016-01-01 00:00:00'
| Date       | Call Abandonment Rate | Call Wait Time |
|------------|-----------------------|----------------|
| 2012-01-01 | 1                     | 17             |
| 2012-06-01 | 1                     | 14             |
| 2012-08-01 | 0.9                   | 24             |
| 2012-10-01 | 0.9                   | 28             |
| 2012-11-01 | 1                     | 83*            |
| 2013-06-01 | 1                     | 20             |
| 2013-08-01 | 0.9                   | 15             |
| 2013-10-01 | 0.9                   | 18             |
| 2013-11-01 | 1                     | 20             |
| 2014-02-01 | 0.8                   | 16             |
| 2014-04-01 | 0.6                   | 12             |
| 2014-06-01 | 0.5                   | 12             |
| 2014-08-01 | 0.7                   | 12             |
| 2014-09-01 | 0.3                   | 9              |
| 2014-10-01 | 0.8                   | 10             |
| 2014-11-01 | 0.7                   | 10             |
| 2014-12-01 | 0.8                   | 14             |
| 2015-02-01 | 0.7                   | 10             |
| 2015-03-01 | 0.6                   | 9              |
| 2015-04-01 | 0.6                   | 8              |
| 2015-05-01 | 0.3                   | 8              |
| 2015-06-01 | 0.8                   | 9              |
| 2015-07-01 | 0.5                   | 7              |
| 2015-08-01 | 0.7                   | 10             |
| 2015-09-01 | 0.8                   | 10             |

*Values greater than twice the average wait time are counted as 64.2, twice the average.

Adjusted average wait time: 15.5 seconds

The training data is visualized below with an alert-expression included to highlight those months where the call abandonment rate did not exceed 1.00%.

View in ChartLab

The adjusted average wait time value acts as the threshold by which the 1.00% call abandonment rate may be successfully achieved and tested in the Validation section.

Validation

Model Number and Goal Wait Time (t) Threshold
Model 1: Maintain 1.56% CAR t <= 34 seconds
Model 2: Achieve 1.00% CAR t <= 16 seconds

The following SQL query returns the holdout data to test the above thresholds:

SELECT datetime AS "Date", car.value AS "Call Abandonment Rate", cat.value AS "Call Wait Time"
  FROM "call_abandonment_rate" AS car JOIN "average_speed_of_answer_in_secs" AS cat WHERE datetime >= '2016-01-01 00:00:00'
| Date       | Call Abandonment Rate | Call Wait Time |
|------------|-----------------------|----------------|
| 2016-01-01 | 3.3                   | 39             |
| 2016-02-01 | 1.2                   | 15             |
| 2016-03-01 | 0.8                   | 14             |
| 2016-04-01 | 1.1                   | 14             |
| 2016-05-01 | 0.9                   | 15             |
| 2016-06-01 | 2.1                   | 31             |
| 2016-07-01 | 1.3                   | 25             |
| 2016-08-01 | 0.9                   | 16             |
| 2016-09-01 | 1.8                   | 27             |
| 2016-10-01 | 2.2                   | 31             |
| 2016-11-01 | 1.4                   | 28             |
| 2016-12-01 | 5.6                   | 77             |
| 2017-01-01 | 1.9                   | 34             |
| 2017-02-01 | 1.3                   | 16             |
| 2017-03-01 | 0.7                   | 11             |
| 2017-04-01 | 0.9                   | 14             |
| 2017-05-01 | 0.8                   | 16             |
| 2017-06-01 | 1.9                   | 32             |

To create confusion matrices, this table details true positives (p,t), true negatives (n,t), false positives (p,t), and false negatives (n,f): Confusion Matrices

Model (p,t) (n,t) (p,f) (n,f)
1 3 11 0 4
2 9 7 2 0

Error Rates

Model Probability (p,t) Probability (n,t) Probability (p,f) Probability (n,f) Total Error Probability
1 .167 .611 0 .222 .222
2 .5 .389 .111 0 .111

Accuracy

Model Accuracy
1 77.78%
2 88.89%

Conclusions

Wait time threshold analysis is a common practice among businesses that operate call centers, and has become standard practice for determining staffing and infrastructure requirements. With additional data such as average call time for example, an analyst could determine the amount of time that any given call is likely to take, and then cross reference call volume numbers to staff numbers to determine the capacity of the call center at any given time and make informed decisions about times to increase or decrease operator numbers based on the results. Determining thresholds of caller patience is an essential first step in streamlining a workflow and demonstrates the intrinsic value of maintaining detailed operations data.

Seeing the different kinds of business optimization possible from easy to collect data such as these, corporate motivations to employ analysts and implement data solutions capable of increasing overall profitability are not simply understandable, but required to maintain a competitive edge.

Appendix

Call Abandonment Data

Date Inbound Calls Abandoned Calls Abandonment Rate
2011-01-01 3171 45 1.4
2011-02-01 2520 30 1.4
2011-03-01 4379 72 2.1
2011-04-01 6009 110 1.8
2011-05-01 3559 130 1.3
2011-06-01 3790 164 1.2
2011-07-01 3685 82 1.8
2011-08-01 3472 100 1.3
2011-09-01 3101 62 1.7
2011-10-01 2752 40 1.7
2011-11-01 2763 45 4.7
2011-12-01 2584 37 5.7
2012-01-01 2970 41 1
2012-02-01 3117 43 1.3
2012-03-01 3893 83 2
2012-04-01 5637 102 1.7
2012-05-01 3325 44 1.1
2012-06-01 3242 38 1
2012-07-01 3682 67 1.3
2012-08-01 3585 46 0.9
2012-09-01 3153 54 1.4
2012-10-01 6771 120 0.9
2012-11-01 4455 219 1
2012-12-01 3864 233 3.8
2013-01-01 6481 969 9
2013-02-01 3566 271 7
2013-03-01 3926 80 2
2013-04-01 3331 45 1.3
2013-05-01 3317 37 1.1
2013-06-01 3034 30 1
2013-07-01 3898 51 1.3
2013-08-01 3138 27 0.9
2013-09-01 4108 57 1.4
2013-10-01 6452 60 0.9
2013-11-01 4340 44 1
2013-12-01 5274 210 3.8
2014-01-01 4150 66 1.6
2014-02-01 3187 25 0.8
2014-03-01 3332 39 1.2
2014-04-01 3535 21 0.6
2014-05-01 2897 34 1.2
2014-06-01 3288 17 0.5
2014-07-01 4563 49 1.1
2014-08-01 3104 23 0.7
2014-09-01 3725 13 0.3
2014-10-01 6674 56 0.8
2014-11-01 3153 21 0.7
2014-12-01 4403 34 0.8
2015-01-01 5070 127 2.4
2015-02-01 3367 23 0.7
2015-03-01 3298 21 0.6
2015-04-01 3957 22 0.6
2015-05-01 2716 9 0.3
2015-06-01 3361 27 0.8
2015-07-01 3611 18 0.5
2015-08-01 3073 21 0.7
2015-09-01 3715 29 0.8
2015-10-01 7625 142 1.8
2015-11-01 3288 36 1.1
2015-12-01 4676 52 1.1
2016-01-01 4133 140 3.3
2016-02-01 3695 46 1.2
2016-03-01 3786 32 0.8
2016-04-01 3184 36 1.1
2016-05-01 2740 25 0.9
2016-06-01 3552 77 2.1
2016-07-01 3153 43 1.3
2016-08-01 3607 34 0.9
2016-09-01 5012 91 1.8
2016-10-01 9943 225 2.2
2016-11-01 4052 58 1.4
2016-12-01 5705 340 5.6
2017-01-01 5438 105 1.9
2017-02-01 4122 53 1.3
2017-03-01 4521 33 0.7
2017-04-01 3084 29 0.9
2017-05-01 3402 28 0.8
2017-06-01 3425 65 1.9

Call Wait Time and Call Abandonment Rate

Date Call Abandonment Rate Call Wait Time
2011-01-01 1.4 25
2011-02-01 1.4 18
2011-03-01 2.1 29
2011-04-01 1.8 29
2011-05-01 1.3 49
2011-06-01 1.2 62
2011-07-01 1.8 43
2011-08-01 1.3 44
2011-09-01 1.7 29
2011-10-01 1.7 18
2011-11-01 4.7 21
2011-12-01 5.7 22
2012-01-01 1 17
2012-02-01 1.3 21
2012-03-01 2 29
2012-04-01 1.7 21
2012-05-01 1.1 15
2012-06-01 1 14
2012-07-01 1.3 24
2012-08-01 0.9 24
2012-09-01 1.4 23
2012-10-01 0.9 28
2012-11-01 1 83
2012-12-01 3.8 80
2013-01-01 9 224
2013-02-01 7 106
2013-03-01 2 37
2013-04-01 1.3 20
2013-05-01 1.1 20
2013-06-01 1 20
2013-07-01 1.3 20
2013-08-01 0.9 15
2013-09-01 1.4 26
2013-10-01 0.9 18
2013-11-01 1 20
2013-12-01 3.8 49
2014-01-01 1.6 13
2014-02-01 0.8 16
2014-03-01 1.2 13
2014-04-01 0.6 12
2014-05-01 1.2 10
2014-06-01 0.5 12
2014-07-01 1.1 14
2014-08-01 0.7 12
2014-09-01 0.3 9
2014-10-01 0.8 10
2014-11-01 0.7 10
2014-12-01 0.8 14
2015-01-01 2.4 13
2015-02-01 0.7 10
2015-03-01 0.6 9
2015-04-01 0.6 8
2015-05-01 0.3 8
2015-06-01 0.8 9
2015-07-01 0.5 7
2015-08-01 0.7 10
2015-09-01 0.8 10
2015-10-01 1.8 28
2015-11-01 1.1 13
2015-12-01 1.1 20
2016-01-01 3.3 39
2016-02-01 1.2 15
2016-03-01 0.8 14
2016-04-01 1.1 14
2016-05-01 0.9 15
2016-06-01 2.1 31
2016-07-01 1.3 25
2016-08-01 0.9 16
2016-09-01 1.8 27
2016-10-01 2.2 31
2016-11-01 1.4 28
2016-12-01 5.6 77
2017-01-01 1.9 34
2017-02-01 1.3 16
2017-03-01 0.7 11
2017-04-01 0.9 14
2017-05-01 0.8 16
2017-06-01 1.9 32