Accurate forecasting has become more crucial in managing digital and social strategy as firms use social media more and more for marketing and consumer involvement. A variety of forecasting algorithms are supported by Sprinklr, a social media management platform, to assist organizations in predicting trends and making data-driven choices. Some of the forecasting algorithms that Sprinklr is compatible with:

1. Prophet:

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The Core Data Science team at Facebook created Prophet with time-series forecasting in mind. Its capacity to manage datasets with prominent seasonal trends, holidays, and other temporal abnormalities is what sets it apart from other AI algorithms.

Key Features:

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Seasonality Handling: - Prophet is appropriate for datasets containing recurrent patterns since it automatically recognizes and accounts for seasonality in its projections. In WFM, where call volumes may show daily, weekly, or seasonal trends, it can precisely forecast when case volumes would peak, which aids in scheduling and resource planning.

Flexibility: - Users have a great deal of flexibility in collecting distinctive aspects of social media trends since they may add personalised seasonality components and special events. WFM forecasting involves considering various factors, such as public holidays and promotions, which can impact case volumes. Prophet's flexibility to add custom seasonality components allows for a tailored approach to incorporate external factors.

Handling of Missing Data: - Prophet is robust in handling missing data and outliers, ensuring accurate predictions even in the presence of irregularities and abnormalities.

Algorithm Performance and Robustness:

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Prophet algorithm excels in scenarios where datasets exhibit multiple seasonality patterns such as daily, weekly or yearly, demonstrating superior performance as compared to other alternative models when a substantial and coherent dataset is available. However, its effectiveness diminishes in the absence of clear trends or seasonality, making it less suitable for datasets lacking discernible patterns or insufficient data to capture meaningful seasonal variations.

Prophet algorithm possesses the capability of handling missing data through estimation and approximation, however, its efficacy is expected to diminish in the presence of datasets featuring abundance of zeros or extreme values and its prone to fail when confronted with limited data, i.e., less than a month's data.

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Use Cases:

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Prophet can be applied in WFM for time series forecasting of case volumes, where effects of both seasonality and holidays are vital.

2. ARIMA

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Auto-Regressive Integrated Moving Average (ARIMA) is a traditional forecasting technique which combines elements of Differencing, Moving Average and Auto-Regression. In time-series data, it works well for capturing seasonality and linear patterns.

Key Features:

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Trend Analysis: - ARIMA incorporates differencing to make the data stationary, aiding in the identification of trends. Capturing linear trends in case volumes is a useful application of ARIMA. It aids in identifying and predicting gradual shifts in demand, enabling WFM teams to schedule resources.

Short-term Forecasting: - As ARIMA is ideally suited for short-term forecasting, WFM can leverage it to anticipate changes in case volumes in real time. It can also prove to be beneficial when adding manual modifications or adjustments.

Algorithm Performance and Robustness:

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ARIMA algorithm excels in scenarios where data is stationary, and the relationship is linear, demonstrating optimal performance whne making prediction in proximity to the tail of the training data. However, it faces challenges when encoutering structural breaks in the data, as well as situations where capturing seasonality or handling numerous anomalies proves to be intricate.

ARIMA is ideally not suitable for managing missing data or extreme outliers, but it exhibits some capability in handling situations with limited data.

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Use Cases:

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ARIMA algorithm in WFM is used for forecasting case volumes, especially when the historical data has been made stationary by removing trends and seasonality.

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3. SARIMAX

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SARIMAX extends the capabilities of the ARIMA model by incorporating exogenous variables, making it a versatile choice for forecasting. Exogenous variables represent external factors that can influence case volumes, such as holidays, marketing campaigns, or special events.

Key Features:

Exogenous Factors: - SARIMAX permits the inclusion of outside variables that affect the need for labour. For instance, a marketing initiative or an introduction of new product may cause a brief rise in the number of calls received by a call centre. SARIMAX takes this into account for a more precise prediction.

Seasonality Handling: - SARIMAX retains the capability of handling seasonality, which is crucial in WFM where case volumes may exhibit recurring patterns based on the time of day, day of the week, or season.

Algorithm Performance and Robustness:

SARIMAX, enhanced by exogenous variables influencing the series and effective in presence of clear seasonality, excels when making predictions near the tail of the training data. However, it encounters challenges in scenarios with unclear seasonality, marked by highly non-linear relationships or abrupt changes and when the forecasted date range is significantly distant from the training time period.

Use Case:

SARIMAX is beneficial in WFM for cases when external factors play a significant role in workforce demand. By incorporating exogenous variables, it allows for a more comprehensive and accurate forecasting model that reflects both internal and external influences on case volumes.