Sprinklr's Workforce Management (WFM) streamlines contact center operations and offers robust capabilities for Forecasting, Capacity Planning, Scheduling, Time Off management, and Real-time Adherence. Sprinklr’s WFM leverages proprietary AI solutions to accurately predict forecasts, generate optimal capacity plans to gauge staffing requirements, and build employee schedules using the Staffing Report. It ensures that the right agents with the right skills are available at the right time to deliver exceptional customer service.

Persona Apps in Sprinklr WFM

The specific WFM roles and Personas Apps can vary within each contact center depending on several factors, such as the size of the contact center, its operational complexity, the industry it serves, and the specific goals of the organization. Sprinklr’s WFM module has the following Persona Apps:

1. Administrator

2. Workforce Manager

3. Agent

Administrator

Administrators configure and maintain Sprinklr’s Workforce Management (WFM) system. Their responsibilities include creating and managing Shifts and Slots to ensure adequate coverage, implementing Schedule policies/rules according to organizational policies, and overseeing Time Off policies to ensure compliance and proper Time Off management. They establish policies for Shift Trades to facilitate flexibility and coverage, may manage the Shift Bidding process to allow employees to bid for preferred Shifts, and generate reports to provide insights into workforce performance and system usage. Additionally, administrators configure alerts to notify relevant stakeholders of important events or issues within the WFM system.

Workforce Managers

Workforce Managers in Sprinklr WFM (previously known as Workforce Planners) are responsible for forecasting and capacity planning to ensure proper staffing and achieve desired service goals. They develop and implement capacity plans, create and manage schedules, track and analyze performance metrics, and forecast future staffing needs based on historical data and trends. Additionally, they identify training needs and coordinate with training teams to enhance agent skills.

Agents

Agents in Sprinklr WFM are the primary point of contact for customers. They handle various forms of customer interactions, such as phone calls, emails, chats, and social media inquiries. Sprinklr’s WFM module allows agents to view their schedules and raise Time Off requests along with giving them the option to shape their schedules to an extent. Features such as Shift Trade and Shift Bidding aim to provide schedule flexibility and better work-life balance.
Agents can also use Sprinklr’s WFM mobile app to perform these tasks. View features available for agents.

Sprinklr WFM High-Level Capabilities

Sprinklr WFM solution supports a wide range of industry-standard functionalities, which include:

Forecasting

Sprinklr’s Omnichannel Forecasting feature is crucial in helping companies generate their capacity plans by providing insights into the expected volume of cases and determining the optimal number of agents required to handle those cases efficiently. Sprinklr employs a sophisticated forecasting approach, leveraging multiple advanced AI algorithms, like Prophet, ARIMA, and SARIMAX, to estimate future case volumes accurately. The utilization of forecasting algorithms ensures a robust and adaptable approach to predicting future case volumes, providing companies with accurate and actionable insights. Learn how to manage Forecasts.

Capacity Planning

Sprinklr WFM’s Capacity Planning process (also referred to as Staffing) involves identifying the manpower (the right number of agents) needed to maintain the business's service level based on the volume forecast. It also involves generating a report on the staffing needs, which indicates when an organization should start hiring to have enough staff to address the forecasted future case volume. Learn how to manage Capacity Plans.

Capacity Planning Algorithms Supported by Sprinklr

Sprinklr’s WFM module supports the following algorithms for Capacity Planning:

• Erlang C

• Unitary Method.

Erlang C

The Erlang C model calculates the number of agents (also called Full-Time Equivalents (FTEs)) required based on inbound traffic and service level objectives in contact centers. It considers the probability that a customer will have to wait for service and is particularly useful for scenarios with a large number of inbound volumes. Erlang C uses parameters such as Average Handling Time, SLA, Average Speed of Answer, Shrinkage, and Occupancy.

Intended Usage

• For calculating resource attributes like incoming calls offered, emails offered, and text messages offered for a
  vertical.

• Finding the optimal number of agents.

• Calls and other digital modes (Email, WhatsApp messages) detection at a particular time interval using trained model.

The Erlang C model uses the following parameters:

• Average Handling Time: The average duration taken to handle a customer call, from initiation to closure.

• Service Level Adherence: A quantitative metric used in call centers to evaluate whether agents are adhering meticulously to their scheduled work hours and maintaining the pre-established service quality standards. In Sprinklr, it is the percentage of calls answered within a specified duration.

• Average Speed of Answer: The average time it takes for a customer's call to be answered by an agent.

• Shrinkage: The percentage of time agents are unavailable to handle customer calls due to breaks, training, meetings, and so on.

• Occupancy: The percentage of time that call center agents spend on active communication with customers, excluding idle time.

Service Level Calculation

​

In this equation, the term [P_w×e^(-[(N-A)×((Target Time)/AHT)])] provides the average wait time of a customer.

The term e^(-[(N-A)×((Target Time)/AHT)]) describes the expected average queue waiting time of customers (not the exact time), under an exponential distribution of waiting times assumption. It is exponential because the assumption is mostly based on queuing theory, which means in practical situations, including queuing systems like call centers, the distribution of waiting times can often be well-approximated by what is called an exponential distribution. As exponential distribution possesses the memoryless property, the time until the next event (for example, the next customer arriving) does not depend on how much time has passed since the last event.

Sample Calculation of Service Level

Assume the following values:

• Wait Probability (P_w): 0.3

• Number of agents (N): 50

• Number of agents actually available (A): 45

• Target Time: 20 seconds

• Average Handling Time (AHT): 300 seconds

Plugging these values into the above mathematical expression gives a Service Level of approximately 0.7849, or 78.49%. This means that about 78.49% of the calls are expected to be answered within the target time of 20 seconds.

Wait Probability Calculation

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This equation can be understood like any probabilistic equation:

• The numerator denotes the probability of all servers being busy (and thus, someone must wait) adjusted by the remaining capacity.

• The denominator denotes the total probability, which includes all possible states (from 0 customers up to N-1 customers) plus the state where all servers are busy.

Understanding the Wait Probability (P_w) Formula

In this section, we will understand the components of the mathematical expression for Wait Probability.

Explaining the Terms in the Numerator

• (A^N/N!): The probability that all servers are busy in a system with Poisson arrivals and exponential service times. The formula explains that, under the intensity A, the chance that we have exactly N customers being served simultaneously. Hence, it captures the combinatorial ways the system's load A distributes among exactly N workers.

• (N/(N-A)): This term reflects the impact of having customers arrive at a rate A into a system with N servers. As A approaches N, the system's servers become highly utilized, increasing the likelihood that any arriving customer will have to wait.

Thus, these two terms together denote the probability of all servers being busy (and thus, someone must wait) adjusted by the remaining capacity, when multiplied together.

Explaining the Terms in the Denominator

• The term (N/(N-A)) is present in both the numerator and the denominator, which denotes the probability of all workers being busy.

• The term () is the combined probabilities of all states where fewer than N workers are busy. This term ensures that every possible scenario of partial server utilization is included in determining the overall system's behavior.

Sample Calculation of Wait Probability

Assume the following values:

• Traffic intensity (A): 10

• Number of agents (N): 5

Plugging these values in the above mathematical expression for Wait Probability gives a Wait Probability of approximately 0.00155 or 0.155%. This means there is a probability of 0.155% that a call will have to wait before being answered.

Occupancy Calculation

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• The percentage of time call center agents spend on customer interactions. The aim is to keep the agent's maximum occupancy below the specified threshold.

• The number of agents is not increased to adjust the occupancy involved.

• By calculating Service Level and Occupancy, you can get the agents that suffice the service level for the given number of transactions (or call volume).

• The equation is tuned to give precise values in float places by updating the internal mathematics to improve the waiting time calculations of a Queue.

Sample Calculation of Occupancy

Assume the following values:

• Traffic Intensity: 30 Erlangs

• Number of Raw Agents: 40

Plugging these values in the above mathematical expression for calculating Occupancy gives a 75%. This means that each agent is occupied with calls 75% of the time.

ErlangCCustom

For Fractional Support, we have added support for ErlangCCustom, which does the following things differently:

1. Calculation of agents on a fractional level: The calculation of clients is done on a more granular level, unlike the standard “ErlangC” class, which yields only an integral number of agents.

2. Improvement on calculation of Service Level: Sometimes "ErlangC" tends to predict very small Service Level, when the original Service Level on the same set parameter by intuition is much higher.

3. Improvement on Occupancy: The calculation of "ErlangC" assumes every request was catered to and makes this calculation in occupancy, which we improved with the number of requests that were fulfilled.

Scenario

A contact center receives 600 calls per hour. The Average Handling Time (AHT) is 5 minutes per call. The business goal is to achieve a Service Level Agreement (SLA) of answering 80% of calls within 20 seconds while maintaining an agent occupancy of 85%.

Step-by-Step Application

1. Calculate Traffic Intensity (Erlangs), which is a measure of the workload being placed on a service system, such as a call center.

   Traffic Intensity = Calls Per Hour * AHT (in hours) = 600 * 5/60 = 50

2. Estimate Initial Number of Agents Required: Using the Erlang C formula and assumptions:

   1. SLA: 80% of calls answered within 20 seconds.

   2. Occupancy target: 85%.

3. Verify SLA Compliance: Apply the Erlang C formula to check whether the initialized agents can maintain the service level:

   1. Wait Probability: Calculate the likelihood that incoming calls will wait in the queue.

   2. Average Speed of Answer (ASA): Use Wait Probability to determine how quickly customers are served.

   3. If the current number of agents cannot achieve the SLA, then they should increase the number of agents.

      

4. Validate Occupancy: Check if agent occupancy of agents remains below the 85% target, calculated as:

   1. Occupancy = Erlangs/Raw Agent * 100.

      

5. Adjust for Shrinkage: Incorporate shrinkage (for example, breaks, training, absenteeism). Assuming a 30% shrinkage rate:

   Required Agents = (Calculated Agents (or Raw Agents)/(1 - Shrinkage)) = 58.82/(1-0.3) = 84.02.

   A simulation would require 84.02 agents (the exact number is slightly larger, 84.03361344537817, as rounded off for simplicity of calculation), according to ErlangCCustom implemented by Sprinklr.

Conclusion

To handle 600 calls per hour with a 5-minute AHT, an SLA of 80/20, and 30% shrinkage, the contact center requires 84.033 agents. This ensures that service levels and agent occupancy targets are met.

Unitary Method

The Unitary Method is a mathematical approach used to estimate the number of contact center agents required. It assumes linear scaling, which is a constant relationship between the inbound traffic and the amount of work performed by each contact center agent. In the Unitary Method, we assume that a call lasts M minutes, and we receive C calls in an interval of I minutes. In this scenario, the minimum number of agents that will be required for this interval will be ((C * M)/I), where the numerator is the total incoming traffic time, and the denominator is the interval size. This method gives us the minimum number of agents required to handle the incoming traffic for an interval.

Use Case

The Unitary method is employed in WFM Capacity Planning for quick and initial estimations, especially when a basic linear relationship between inbound volume and the task is sufficient. This method provides a rapid assessment of staffing needs.

Scheduling

Sprinklr WFM’s Scheduling process involves allocating human resources to tasks, shifts, or activities within a contact center. It goes beyond simple timekeeping and considers factors such as employee availability, skills, preferences, and adherence to labor laws.

Sprinklr WFM’s Scheduling integrates essential elements such as business hours, upcoming holidays, and agents’ time off into its decision-making process. The objective is to achieve 100% contact handling while meeting target SLAs and adhering to service level constraints like shrinkage and occupancy. Schedules are designed to align with business hours, shifts, and defined activities, following scheduling guidelines such as maximum weekly working hours, preferred shifts, week-offs, and shift rotation rules. Agent assignments are considered to ensure schedules match their expertise and assigned skills.

By analyzing this comprehensive dataset, the AI system generates schedules optimized for efficiency while considering individual agents’ shift pattern preferences. Agents benefit from schedules that align with their preferences and personal commitments, leading to improved job satisfaction and a more engaged workforce. Learn how to manage Schedules.

Time Off Management

Sprinklr WFM’s Time Off Management (previously known as Leave Management) contributes to a more organized, compliant, and employee-friendly work environment, leading to improved overall workforce management and operational efficiency. It enables contact centers to efficiently track and manage employee time off while maintaining operational continuity and compliance with company policies and labor regulations. Sprinklr’s Time Off Management is dynamic, efficient, and user-friendly, and the AI-driven model helps completely automate the process of Time Off management.

Advantages of Sprinklr Time Off Management include:

• Streamlined Processes: Automates and streamlines Time Off requests, approvals, and tracking, minimizing manual administrative tasks and enhancing communication between employees, managers, and HR about employee time off.

• Minimizes OverlappingTime Off: Helps supervisors and HR anticipate and manage staffing levels by preventing multiple employees on leave simultaneously. It also enables better workforce planning by providing insights into planned Time Off patterns.

• Optimal Resource Planning: Assists in forecasting and planning for periods of high leave activity, enabling organizations to adjust staffing levels accordingly. It improves overall resource allocation and ensures smooth business operations.

Shift and Activity

Sprinklr’s WFM allows users to construct unlimited Shifts and Activities. Users can also define the Activity window, and Sprinklr’s AI will efficiently manage the schedule. In addition, Sprinklr’s WFM system provides the functionality to create overtime Shifts, offering flexibility and adaptability in managing your workforce.

Shift Bidding

Sprinklr WFM’s Shift Bidding functionality is a dynamic scheduling process that enables agents to actively participate in selecting their work shifts. It allocates work shifts or schedules based on a transparent and competitive framework. Agents can actively engage in the scheduling process, which helps improve work-life balance and job satisfaction.

Once a supervisor creates a shift bid for agents to submit their preferences, the AI factors in several agent ranking criteria to distribute shifts efficiently according to their preferences. On the Sprinklr platform, the supervisor can view and analyze these preferences through a user-friendly interface, comparing them across all agents. Additionally, the
supervisor can request agents to re-submit their shift preferences if needed.

From the agent’s perspective, they receive a notification when the bids go live. Agents can then drag and move their preferences and press submit. Once the supervisor finalizes the bids, the updated schedule automatically reflects on the agent’s schedule page.

Shift Trade

Sprinklr WFM’s Shift Trade functionality (previously known as Shift Swap) enables agents to swap their shifts with other agents who share the same skills and are part of the same work queue. This enables agents to have more flexibility and control over their work schedules, enabling them to adjust their shifts to fit their personal preferences. Sprinklr’s Shift Trading feature supports intraday and multiday trades.

Slot Management

Sprinklr WFM’s Slot Management allows Administrators to create and manage different types of slots (Volunteer Time Off, Overtime, Flexible Working) that agents can select from their calendars. Supervisors can define slot details such as date range, time off policies, and slot capacities and manage agent requests through a dashboard.

Shift Patterns (Upcoming)

Sprinklr WFM’s Shift Patterns allow Administrators and Workforce Managers to create and manage structured weekly Shift templates for agents. Patterns involve scheduling employees for different Shifts at varying times throughout the week, which ensures continuous operations, prevents employee burnout, and balances work-life demands.

WFM Reporting

Sprinklr’s WFM solution provides in-depth reporting and analytics using Sprinklr’s Care Reporting. Workforce managers and administrators can analyze multiple KPIs, including forecast adherence, schedule adherence, conformance, and Time Off reports.