As illustrated across our website, time series are everywhere. Each industry vertical, each domain, each company comes into contact with time series in one way or another. In the use case library below, you can explore the extensive applications of time series, or look for applications in your specific field of interest.
Michal Bezak - Tangent Works
Contact centers typically operate with a pool of resources. For contact centers, predicting volume of incoming requests at specific times is critical to make proper resource scheduling. In such case, forecasts are expected for very short, and short-term, horizon (e.g. a week ahead).
High-quality short-term forecast brings confidence that FTEs (full time equivalent) planned for the next week are just right for delivering on SLAs, not to mention other benefits, such as higher confidence when planning absence, or improving morale of employees who would not face overload from “sudden” volume peaks.
Predicting volume of requests for mid-term horizons, e.g. 3-months ahead for weekly data, is important input to resource management. It takes time (weeks if not longer) moving people around, hiring, upskilling, or down-sizing pool of resources. Because of this, forecasts for longer horizon are needed, starting from one to more months.
The picture below depicts how contact centers are typically linked to WFM (work force management), internal departments and other factors. This provides intuition which factors should be included in data used for building models and forecasting.
Big contact centers would support not one, but multiple regions, cultures, and languages. Very likely forecast by language, or country would be beneficial.
Having forecast for just one perspective, one or two prediction horizons is not sufficient, also dynamics of ever-changing business means that using model built one month ago is suboptimal. This means that having capability to build models and make new predictions instantly is necessary for successful management of resources.
TIM can forecast for any prediction horizon, from intra-day to days or weeks ahead, and thus can be used for short term, midterm as well as long term forecasting. It can build new model in faction of time, building models from the latest data which helps to achieve better accuracy.
It’s automated, thus your analysts and data scientists have free capacity to focus on other agenda. You’d gain new capability, more frequent forecasting or forecasting per various perspectives with minimal additional effort is possible.
Effort and time required to set up forecasting is reduced to fraction of what would be typically required. TIM, by design, automates most of the steps required for set up and operations, and offers robust ML solution capable to quickly adapt to structural changes.
High-quality forecasts would deliver following benefits:
Explanatory variables should include historical actual volume values, meteo predictors, holiday information, marketing activity (campaign) information, factors describing customer base, planned outages, and/or other relevant data with as low latency as possible.
TIM’s output consists of forecasted volume of requests per each hour/day/week in selected prediction horizon.
