New Methods and Data that Improves Contact Center Forecasting. Ric Kosiba and Bayu Wicaksono

Transcription

1 New Methods and Data that Improves Contact Center Forecasting Ric Kosiba and Bayu Wicaksono

2 What we are discussing today Purpose of forecasting (and which important metrics) Humans versus (Learning) Machines A solid and standard forecasting process New journey data Thoughts Unauthorized disclosure is prohibited. 2

3 Why do we forecast? Forecast Requirements Simulation Staff & Capacity Plan Optimization Budget Forecasting is just one part of the planning process We forecast to make resource decisions! What types of contacts to service? How do we best match channels to segments of customers? What standards are right for each contact types? Where (which centers and staff types) and when (which weeks) do we hire? Hiring versus overtime? How many centers are optimal? Budgets and budget priorities? and so much more Unauthorized disclosure is prohibited. 3

4 What should you forecast? Everything! Predicting shrink is as important as volumes! Unauthorized disclosure is prohibited. 4

5 Man Vs. Machines There are many things that humans do intuitively, that is difficult to code Pattern recognition Anomaly detection Intuition Error detection (we can see when something is off) Machines are great at Searches Algorithms Number crunching But machines (really algorithms) are getting better at human stuff! Unauthorized disclosure is prohibited. 5

6 There is a standard forecasting process Source, organize, and clean data Evaluate and add judgment (or scenarios) Get data Look for anomalies Smooth outliers Describe data Does the forecast make sense? Are there possible scenarios that history isn t aware of? Model and prove Choose appropriate statistical method(s) Choose model parameters Understand error on holdout sample What is the seasonality? Is it growing, shrinking, or stationary? Unauthorized disclosure is prohibited. 6

7 Source and Clean Data 1. Data needs to be accessible 2. Must find anomalous data Outliers unexplained change Data we know is wrong. Data hiccup? Missing data? Flagged? Non-repeating, one-off events 3. Smooth or correct anomalous data Unauthorized disclosure is prohibited. 7

8 Can we get a computer to do this? Yes! Three odd data points. How can a computer find these? Unauthorized disclosure is prohibited. 8

9 Anomaly/outlier detection: a simple process Interesting method: 1. Build a rough forecast, using a standard method, and keep track of the forecast error 2. Assume the forecast is correct 3. The data point farthest away from the forecast (say 3 standard deviations from the mean) is considered an outlier and should be replaced by the forecast 4. Repeat until no more outliers Unauthorized disclosure is prohibited. 9

10 More iterations When finished iterating, you have cleaned and removed outliers. Question does a human need to check the machine?? Unauthorized disclosure is prohibited. 10

11 Special events/calendar effect Trading/working day effect: Leap year effect: every 4 years, February Number of working days in a given month varies Volumes affected by 1 st and 15 th pay cycles Holiday effect: fixed holidays are accounted for in seasonal component; moving holidays may occur in different weeks or months Easter (can fall on different day/week every year) Lunar vs. Lunisolar vs. Gregorian (i.e. Solar) Calendar Impact surrounding the holiday itself (i.e. build-up to Christmas) Unauthorized disclosure is prohibited. 11

12 Describe the data: Trend and/or Seasonal or Stationary Time series data can be described mathematically as a combination of various components: trend, seasonal, and irregular (random/stationary) components Trend: An upward or downward variance in time series data. It represents a general linear or nonlinear component that changes over time and does not repeat Seasonal: The systematic, calendar related variance in time series data. It repeats itself in regular intervals over time Irregular: The unsystematic, unpredictable, short-term fluctuations around a mean value Unauthorized disclosure is prohibited. 12

13 How to spot seasonality (if you are a computer) First, if you are not a computer, much of the seasonality (for contact center data) is already known. We have weekly and annual seasonality (we can often just look at the data) But sometimes, we can t see the seasonality! Computers can help You can transform your data to determine periodicity: see Fast Fourier Transform ( and e ve a new white paper coming out soon!) There is a prominent spike at frequency , indicating that it is the dominant frequency of the time series. The corresponding dominant period is 1/ = 52, which is the number of weeks in a year Unauthorized disclosure is prohibited. 13

14 What method(s) should you use to forecast? Unauthorized disclosure is prohibited. 14

15 Forecasting methods Stationary Simple moving average Point estimate single exponential smoothing Point estimate weighted average Single exponential smoothing Seasonal Holt Winters (many flavors) Additive decomposition Multiplicative decomposition ARIMA Trends Simple moving average Point estimate (many flavors) Linear weighted moving average Double Moving Average Double exponential smoothing Damped linear exponential smoothing Unauthorized disclosure is prohibited. 15

16 Validating models (what humans can do) Data Train Test Use this part of history to build statistical forecasting models. This data trains your model Is the model accurate? Well if it passes a statistical accuracy test on the test data, then it is our best guess as to whether the model will work in the future Use this part of history to test whether your model works on parts of the history that were not used to build the model. This is test or hold out data Unauthorized disclosure is prohibited. 16

17 Cross-validating (what computers can do (better)) When evaluating different forecasting methodologies against a time series data, the question then becomes which of these methodologies is best used for forecasting activity. Cross-validation is the most robust process to use in order to select the best-of-the-best forecasting methodologies. Unauthorized disclosure is prohibited. 17

18 Finding the best parameters for each model Each method has a set of parameters that describe the slope, periodicity, etc The goal for the forecaster is to find the set of parameters that develop forecasts that reduce forecast error on the holdout data Humans can guess and test but computers (cloud) can test every combination of every parameter of every method, and fairly quickly! Unauthorized disclosure is prohibited. 18

19 Measuring error Mean Absolute Percentage Error (MAPE) Expresses accuracy as a percentage of the error. Because this number is a percentage, it can be easier to understand than the other statistics. Percentage errors have the advantage of being scale-independent, and so are frequently used to compare forecast performance between different data sets. For example, if the MAPE is 5, on average, the forecast is off by 5%. Mean Absolute Deviation (MAD) MAD expresses accuracy in the same units as the data, which helps conceptualize the amount of error. MAD is scale-dependent and therefore cannot be used to make comparisons between series of different scales. Also, outliers have less of an effect on MAD than on MSE. Mean Squared Error (MSE) A commonly-used measure of accuracy of fitted time series values. MSE is also scale-dependent and cannot be used between series of different scales. Unauthorized disclosure is prohibited. 19

20 Cool New Data: Journey Mapping The below journey map outlines the overall process a customer may travel when interacting with a company. Customers move between journey stages following a path either determined by their own choice, or driven by the organization s processes. Some of these interactions have agent involvement, others do not. Interactions without human involvement are critical to identifying which the customer stage. Journey Stages Stage 2a Stage N+1 Agent Activity Non Agent Activity Stage 4a Stage N+1 Stage 1 Stage N+1 Customer N+1 Stage 3a Stage N+1 Stage N+1 Stage 2b Stage 3b Stage N+1 Stage N+1 Stage 3c Stage N+1 Unauthorized disclosure is prohibited. 20

21 Tracking customer journeys provides great data Activity A 20% Activity Z 70% 10% 20% Activity C Stage 3b Tracking of the customer journey is important and cool; understanding the distribution of time among activities allows for great analytics including forecasting. Activity O Activity to Activity Distribution Pattern Activity to Activity Distribution Pattern Activity to Activity Distribution Pattern Volume Volume Volume Time Time Time 1000 Interactions 200 Interactions 40 Interactions Activity A Activity Z Activity O Unauthorized disclosure is prohibited. 21

22 Final Thoughts Forecast Everything: Volume forecasts are important, but so, too, are shrinkage, handle times, attrition, Automate: So much of forecasting drudgery can be removed by taking time to code Embrace the Future: Cloud computing will bring opportunities to improve every stage of Customer Experiences even forecasting and planning Embrace the Future (2): New data sources will improve our resource planning and WFM Unauthorized disclosure is prohibited. 22

23 What is Decisions? Decisions is a long-term contact center strategic planning and what-if analysis system. Forecast Requirements Simulation Staff & Capacity Plan Optimization Budget Because it is fast and accurate: Perform risk and sensitivity analysis of your contact center Evaluate center what-ifs: investments, consolidation, and growth opportunities Decisions complements traditional workforce management software by focusing on strategic decision making and long-term planning Unauthorized disclosure is prohibited. 23

24 Thank you, and (please) sign up for the rest of the webinar series! Ric Kosiba, Vice President, Genesys Decisions office mobile