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Your Customer Service Data Template

Freshdesk

Customer Service Attributes (20) Customer Service Activities (13) Extraction Guides (3)

Your Customer Service Data Template

This template provides a comprehensive guide to the essential data attributes and process activities needed for effective customer service analysis. It also includes practical extraction guidance to help you gather your data efficiently. Use this resource to streamline your data collection and prepare for insightful process mining.

Recommended attributes to collect
Key activities to track
Extraction guidance

New to event logs? Learn how to create a process mining event log.

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Customer Service Attributes

These are the recommended data fields to include in your event log for comprehensive customer service analysis and to gain deep insights.

3 Required 5 Recommended 12 Optional

	Name	Description
	Activity Activity	The name of a specific business event or step that occurred within the customer service process.
Description The Activity attribute represents a distinct action or status change in the lifecycle of a service request. It records key business events such as 'Ticket Created', 'Ticket Assigned', 'First Response Sent', and 'Ticket Resolved'. These activities form the nodes in the process map. Analyzing the sequence and frequency of these activities is the core of process mining. It allows for the visualization of process flows, identification of common and rare paths, and detection of deviations from the standard operating procedure. Understanding the activities is crucial for pinpointing inefficiencies, rework loops like 'Ticket Reopened', or compliance issues. Why it matters This attribute defines the steps in the process map, allowing for the visualization and analysis of the process flow from start to finish. Where to get Derived from event types within Freshdesk. This can be a combination of status changes from the 'Tickets' API endpoint and specific events like notes or replies from the 'Conversations' endpoint. Examples Ticket CreatedFirst Response SentStatus Changed to PendingTicket ResolvedTicket Closed
	Event Time EventTime	The timestamp indicating when a specific activity or event occurred.
Description Event Time, or the timestamp, records the exact date and time that an activity took place. It is a critical component for ordering events chronologically and for calculating durations between different steps in the process. Every recorded activity must have a corresponding timestamp. In analysis, this attribute is used to build the timeline of each service request. It is the foundation for calculating all time-related KPIs, such as resolution time, first response time, and the duration of bottlenecks. Accurate timestamps are essential for understanding process performance and identifying delays. Why it matters This timestamp is essential for ordering events chronologically and calculating all duration-based metrics, such as cycle times and SLA compliance. Where to get This corresponds to the 'created_at' or 'updated_at' fields associated with ticket events, replies, or status changes in the Freshdesk API. Examples 2023-10-25T10:00:00Z2023-10-25T10:05:14Z2023-10-26T14:30:00Z
	Service Request ServiceRequest	The unique identifier for a single customer inquiry or issue, commonly known as a ticket or case.
Description The Service Request is the primary case identifier that links all activities related to a single customer interaction. Each new customer inquiry, problem, or request generates a unique Service Request ID. This ID remains constant throughout the lifecycle of the ticket, from creation and assignment to resolution and closure. In process mining, this attribute is fundamental for reconstructing the end-to-end journey of each customer case. By grouping all related events under a single Service Request ID, analysts can visualize process flows, measure cycle times, and identify variations or bottlenecks affecting individual cases. Why it matters This is the essential Case ID that connects all related events into a single process instance, enabling a complete view of each customer service journey. Where to get This is the primary ticket ID in Freshdesk, typically found as the 'id' field in the 'Tickets' API endpoint. Examples SR-2023-10-4831SR-2023-11-0192SR-2023-11-5210
	Assigned Agent AssignedAgent	The name or ID of the customer service agent responsible for the ticket at the time of the event.
Description This attribute identifies the specific agent assigned to handle the service request. The assigned agent can change throughout the ticket's lifecycle due to reassignments or escalations. Analyzing data by Assigned Agent is critical for performance management dashboards. It allows for the measurement of agent-specific KPIs like average resolution time, ticket volume, and re-opening rates. This helps identify top-performing agents, uncover training needs, and analyze the impact of agent transfers on overall resolution time. Why it matters Enables performance analysis by individual agent, helping to identify top performers, training opportunities, and the impact of reassignments. Where to get This corresponds to the 'responder_id' field in the Freshdesk 'Tickets' object, which can then be joined with agent details. Examples Alice JohnsonRobert SmithMaria Garcia
	Priority Priority	The priority level assigned to the service request, such as 'Low', 'Medium', 'High', or 'Urgent'.
Description The Priority attribute reflects the urgency of a service request and often dictates the target response and resolution times. Priority may be set automatically based on rules or manually by an agent during triage. This attribute is essential for analyzing SLA compliance and resource allocation. By filtering the process map by priority, analysts can determine if high-priority tickets are truly being handled faster than low-priority ones. It also helps in understanding if priority changes, a form of rework, are common and what impact they have on the process. Why it matters Essential for SLA analysis and understanding if resources are correctly allocated to handle high-priority issues faster than low-priority ones. Where to get This corresponds to the 'priority' field in the Freshdesk 'Tickets' object. Examples LowMediumHighUrgent
	Resolution Time ResolutionTime	The total time elapsed from when a service request was created to when it was resolved.
Description Resolution Time is a critical case-level KPI that measures the end-to-end duration of the service process. It is calculated as the difference between the timestamp of the 'Ticket Resolved' activity and the 'Ticket Created' activity. This attribute is a primary measure of process efficiency. It is used in nearly all service dashboards to track performance over time, compare performance across different categories, and identify cases that take an unusually long time to resolve. Reducing the average resolution time is often a key goal of process improvement initiatives. Why it matters This is a primary KPI for measuring process efficiency, showing the total time taken to solve a customer's issue from start to finish. Where to get Calculated field. It is the duration between the first event's timestamp (creation) and the timestamp of the 'Ticket Resolved' event for each Service Request. Examples 25920000086400000604800000
	Service Request Type ServiceRequestType	The classification of the service request, such as 'Question', 'Incident', 'Problem', or 'Feature Request'.
Description Service Request Type is a crucial categorization field that defines the nature of the customer's inquiry. This classification is typically set when the ticket is created or during an initial triage step and helps route the request to the appropriate team or agent. In analysis, this attribute allows for segmenting the process to understand how different types of requests are handled. It helps answer questions like 'Do incidents take longer to resolve than questions?' and 'Which request types are most frequently reopened?'. This segmentation is key to identifying type-specific bottlenecks and optimizing workflows accordingly. Why it matters Allows for process segmentation to compare performance and workflows for different kinds of customer issues, like incidents versus questions. Where to get This is likely the 'type' field available in the Freshdesk 'Tickets' object. Examples QuestionIncidentProblemFeature Request
	Status Status	The current or historical status of the service request, such as 'Open', 'Pending', 'Resolved', or 'Closed'.
Description The Status attribute indicates the state of a service request at a given point in time. Status changes often represent key milestones in the process, for example, moving from 'Open' to 'Pending' when waiting for customer input, or from 'Pending' to 'Open' when the customer replies. Tracking status changes is fundamental to understanding the ticket lifecycle. It helps identify how much time tickets spend in certain states, which is useful for pinpointing bottlenecks. For example, a long duration in the 'Pending' status could indicate delays in receiving information from customers. Why it matters Tracking status changes is key to understanding the ticket lifecycle and identifying how long cases spend in specific states like 'Pending' or 'On Hold'. Where to get This corresponds to the 'status' field in the Freshdesk 'Tickets' object. Historical statuses need to be inferred from activity logs. Examples OpenPendingResolvedClosed
	Agent Transfer Count AgentTransferCount	The total number of times a service request was reassigned from one agent to another.
Description This attribute is a case-level metric that counts the number of times a ticket's assigned agent changed. It is calculated by counting the occurrences of the 'Ticket Reassigned' activity for each service request. Frequent transfers, also known as 'ping-ponging', can lead to significant delays and a frustrating customer experience as the context is lost with each handoff. Analyzing the transfer count helps to identify issues with initial routing, agent skill gaps, or process complexity. The goal is often to minimize unnecessary transfers to improve efficiency and customer satisfaction. Why it matters Measures the frequency of internal handoffs, which can be a major source of delay and customer frustration, helping to improve first-level resolution. Where to get Calculated field. This is the count of 'Ticket Reassigned' activities for each unique Service Request. Examples 0132
	Assigned Group AssignedGroup	The team or department to which the service request is assigned.
Description The Assigned Group represents the team of agents responsible for handling a specific service request. Tickets are often routed to specialized groups based on their type or complexity, such as 'Technical Support' or 'Billing Department'. This attribute is valuable for analyzing inter-departmental handoffs and performance at a team level. It helps identify which groups handle the most tickets, which have the longest resolution times, and how often tickets are transferred between groups. This information is key for optimizing team structures and workflows. Why it matters Allows for performance analysis at the team or department level, highlighting handoffs and identifying group-specific bottlenecks. Where to get This corresponds to the 'group_id' field in the Freshdesk 'Tickets' object. Examples L1 SupportL2 Technical SupportBillingCustomer Success
	Communication Channel CommunicationChannel	The channel through which the service request was initiated, such as 'Email', 'Phone', 'Chat', or 'Web Portal'.
Description This attribute identifies the origin or channel of the customer communication. Different channels can have distinct process flows and resolution times. For example, a request initiated via chat might have a shorter expected resolution time than one submitted by email. Analyzing the process by communication channel helps in optimizing resource allocation and understanding channel efficiency. It can reveal which channels are associated with faster resolutions or higher customer satisfaction, informing strategic decisions about which channels to promote or invest in. Why it matters Helps analyze process performance and efficiency across different customer contact channels like email, phone, or chat. Where to get This corresponds to the 'source' field in the Freshdesk 'Tickets' object. Examples EmailPhoneWeb PortalChat
	Customer Name CustomerName	The name or ID of the customer who initiated the service request.
Description This attribute identifies the customer associated with the service request. It provides a way to analyze the process from a customer-centric view, tracking all interactions for a specific customer over time. Analyzing by customer can reveal patterns such as which customers submit the most tickets or which ones experience the most reopened issues. This can inform customer success initiatives and help in identifying customers who may be at risk of churn due to poor service experiences. It is also crucial for understanding the end-to-end customer journey across multiple service requests. Why it matters Enables a customer-centric process view, helping to identify frequent requesters or customers experiencing repeat issues. Where to get This information is linked via the 'requester_id' in the 'Tickets' object, which connects to the 'Contacts' or 'Users' object in Freshdesk. Examples John DoeJane SmithGlobal Tech Inc.
	First Response Time FirstResponseTime	The time elapsed from ticket creation to the first agent response to the customer.
Description First Response Time measures how quickly a customer receives an initial, non-automated response from a service agent. It is calculated as the difference between the timestamp of the 'First Response Sent' activity and the 'Ticket Created' activity. This KPI is a key indicator of service responsiveness and has a strong impact on customer satisfaction. A short first response time assures the customer that their issue has been acknowledged and is being addressed. Analyzing this metric helps organizations ensure they are meeting initial response SLAs and providing a prompt customer experience. Why it matters Measures service responsiveness, which is a key driver of customer satisfaction and directly addresses the goal of proactive service delivery. Where to get Calculated field. It is the duration between the timestamp of the 'Ticket Created' event and the 'First Response Sent' event. Examples 3000009000001800000
	Is Reopened IsReopened	A boolean flag indicating whether a resolved service request was ever reopened.
Description This case-level attribute is a flag that is set to true if a service request experienced a 'Ticket Reopened' activity at any point in its lifecycle. It provides a simple way to identify and analyze cases that required rework. A high rate of reopened tickets suggests that the initial resolution was not effective or complete, leading to inefficiency and customer frustration. By filtering for reopened tickets, analysts can investigate the root causes, such as common reasons for reopening, which agents or teams have higher rates, and which ticket types are most prone to being reopened. Why it matters Identifies cases that required rework, a key indicator of resolution quality and process inefficiency. Analyzing these cases helps improve first-contact resolution. Where to get Calculated field. Set to true for a Service Request if an event with Activity = 'Ticket Reopened' exists for that case. Examples truefalse
	Is SLA Breached IsSlaBreached	A boolean flag indicating whether the service request resolution time exceeded the SLA target.
Description This calculated attribute provides a clear, binary indicator of SLA compliance for each service request. It is derived by comparing the actual 'ResolutionTime' with the 'SlaTargetResolutionTime'. If the actual time is greater than the target, the flag is true. This attribute simplifies analysis and dashboarding for SLA compliance. It allows for easy counting and filtering of breached tickets, enabling analysts to quickly identify the scale of SLA non-compliance and drill down into the process patterns of breached tickets to find root causes. It directly supports the SLA Compliance Overview dashboard and KPI. Why it matters Simplifies SLA compliance analysis by providing a clear flag for each case that failed to meet its target, enabling root cause analysis of breaches. Where to get This is a calculated field, derived by comparing the actual resolution timestamp with the 'SlaTargetResolutionTime' or 'due_by' field from Freshdesk. Examples truefalse
	Last Data Update LastDataUpdate	The timestamp indicating the last time the data was refreshed from the source system.
Description This attribute records the date and time when the dataset was last extracted or updated from Freshdesk. It provides transparency into the freshness of the data being analyzed, which is critical for making timely and relevant business decisions. Analysts use this information to understand the time window covered by the data and to confirm they are working with the most current information available. It is a key piece of metadata for any process mining dashboard or report, ensuring that stakeholders are aware of the data's recency. Why it matters Indicates the freshness of the data, ensuring that analyses and decisions are based on up-to-date information. Where to get This is a metadata field generated during the data extraction process, capturing the timestamp of the data pull. Examples 2023-12-01T08:00:00Z
	Product Product	The product or service that the customer's request is related to.
Description This attribute specifies the product or service line that is the subject of the service request. This is often a custom field configured in Freshdesk to help categorize tickets for routing and reporting. By filtering the process by product, organizations can uncover product-specific issues. For instance, it can highlight if a certain product generates a disproportionate number of support tickets or if a new product launch is causing a spike in inquiries. This analysis provides valuable feedback to product development and management teams. Why it matters Allows for filtering the process by product area, providing insights into which products generate the most support requests or have the longest resolution times. Where to get This is typically a custom field. The exact location depends on the Freshdesk configuration, likely found in the 'custom_fields' part of the 'Tickets' API response. Examples Alpha PlatformBeta Mobile AppGamma Subscription
	Satisfaction Rating SatisfactionRating	The satisfaction score provided by the customer after the ticket was resolved.
Description The Satisfaction Rating is a key outcome metric, typically collected via a survey sent after a ticket is resolved. It usually consists of a numerical score or a categorical rating like 'Satisfied', 'Neutral', or 'Unsatisfied'. This attribute allows for correlating process patterns with customer outcomes. By analyzing the process variants that lead to low satisfaction scores, organizations can identify specific behaviors or delays that negatively impact the customer experience. This provides a direct link between process efficiency and customer happiness. Why it matters Links process execution to customer outcomes, helping to identify which process behaviors lead to high or low customer satisfaction. Where to get This data is part of the satisfaction ratings feature in Freshdesk and can be retrieved via the 'Surveys' or 'Satisfaction Ratings' API endpoint. Examples 5314
	SLA Target Resolution Time SlaTargetResolutionTime	The contractually agreed upon or targeted time for resolving the service request.
Description This attribute defines the target duration within which a service request should be resolved according to the Service Level Agreement (SLA). This target is often dependent on the ticket's priority or type. This is a critical input for calculating SLA compliance. By comparing the actual resolution time to this target, a determination of 'Met' or 'Breached' can be made. Analyzing SLA performance across different request types, teams, or agents is a core part of service management. Why it matters Provides the baseline for measuring SLA compliance, a key performance indicator for any customer service organization. Where to get This may be available as a field like 'fr_due_by' (first response) or 'due_by' (resolution) in the Freshdesk 'Tickets' object, or it may need to be derived based on SLA policy rules. Examples 2023-10-25T14:00:00Z2023-10-27T09:00:00Z2023-11-01T17:00:00Z
	Source System SourceSystem	The system from which the data was extracted, which is 'Freshdesk' in this case.
Description This attribute identifies the source application where the process data originated. For this analysis, the value will be static, for example, 'Freshdesk', indicating that all events are from the Freshdesk customer service platform. While it may seem simple, this attribute is important for data governance and in scenarios where data from multiple systems is merged. It provides clear lineage and context, ensuring that analysts understand the origin of the data they are examining. This is crucial for maintaining data integrity and building trust in the analysis. Why it matters Provides essential context about data origin, which is crucial for data governance and when combining data from multiple source systems. Where to get This is a static value, 'Freshdesk', added during the data transformation process to identify the data source. Examples Freshdesk

Required Recommended Optional

Customer Service Activities

These are the key process steps and significant milestones to capture in your event log for accurate process discovery and bottleneck identification.

5 Recommended 8 Optional

	Activity	Description
	First Response Sent	Marks the first public reply sent by an agent to the customer after the ticket was created. Freshdesk explicitly captures this event to measure 'First Response Time' for SLA tracking.
Why it matters This is a critical milestone for measuring customer responsiveness and SLA compliance. Analyzing the time to this activity helps identify delays in initial engagement with customers. Where to get This is a specific event that Freshdesk tracks for SLA purposes. It corresponds to the timestamp of the first public comment added by an agent. Capture Identified by the first public agent reply in the ticket's conversation history. Event type explicit
	Ticket Assigned	Represents the assignment of a ticket to a specific agent or group for handling. This event is explicitly logged in the ticket's history whenever the assigned agent or group field is populated or changed.
Why it matters Tracking assignments is crucial for analyzing agent workload, identifying routing inefficiencies, and measuring time-to-assignment KPIs. It helps understand how work is distributed and where delays occur before work begins. Where to get Captured from the 'Ticket Activities' log where changes to the 'Agent' or 'Group' fields are recorded with a timestamp. Capture Event logged when the 'Assigned to' field is updated for an agent or group. Event type explicit
	Ticket Closed	This is the final activity, representing the permanent closure of the ticket. This is often performed automatically by the system after a set period of time in the 'Resolved' state without any new customer replies.
Why it matters This activity marks the definitive end of the service request lifecycle. It provides the final endpoint for accurate end-to-end cycle time calculations. Where to get Captured from the 'Ticket Activities' log, which records the final status change to 'Closed'. This is often triggered by a system automation. Capture Event logged when the ticket's 'Status' field is updated to 'Closed'. Event type explicit
	Ticket Created	This is the first event in the customer service lifecycle, representing the moment a customer's request is formally logged in Freshdesk. This activity is captured explicitly when a new ticket is generated, either through email, a portal, phone, or API integration.
Why it matters This activity serves as the starting point for every case, making it essential for calculating overall resolution times and analyzing ticket volume trends by channel or type. Where to get This is an explicit event in the Freshdesk 'Ticket Activities' log. It is generated automatically upon the creation of a new ticket record. Capture Directly logged in the ticket's activity stream upon creation. Event type explicit
	Ticket Resolved	Represents the key milestone where the agent has provided a solution and changed the ticket status to 'Resolved'. This is an explicit status change recorded in the ticket's history.
Why it matters This activity marks the end of the active work on a ticket and is the basis for measuring resolution time. It is a critical event for analyzing agent performance and overall process efficiency. Where to get Captured from the 'Ticket Activities' log, which records the specific status change to 'Resolved' along with a timestamp. Capture Event logged when the ticket's 'Status' field is updated to 'Resolved'. Event type explicit
	Customer Responded	Represents a new reply or communication received from the customer. This is an explicit event in the ticket's conversation thread and typically triggers a status change from 'Pending' back to 'Open'.
Why it matters This activity is key to understanding the back-and-forth nature of customer interactions and measuring customer response times. It also restarts any paused SLA timers, impacting compliance metrics. Where to get Logged as a new entry in the ticket's conversation thread. The event is associated with the customer's contact record. Capture A new public note added to the ticket by the customer contact. Event type explicit
	Internal Note Added	An agent adds a private note to the ticket, intended for internal collaboration with other agents. This is an explicit event captured in the ticket's activity stream, visible only to agents.
Why it matters Tracking internal notes helps analyze collaboration patterns and identify issues that require significant internal discussion. High frequency of internal notes before resolution may indicate complex issues or knowledge gaps. Where to get Recorded as a 'Private Note' in the ticket's conversation history, distinguishable from public replies to the customer. Capture Event logged when an agent adds a note marked as 'Private'. Event type explicit
	Satisfaction Survey Sent	Represents the sending of a customer satisfaction survey, typically triggered by an automation rule after a ticket is resolved. This event may be captured if the automation action is logged in the ticket's history.
Why it matters Marks the beginning of the feedback collection process. Correlating survey responses with process variants can provide deep insights into how process performance impacts customer satisfaction. Where to get This is typically triggered by an 'Automation Rule'. Its visibility as a discrete event in the ticket activity log depends on Freshdesk's logging configuration for automations. Capture Event logged by an automation rule execution after ticket resolution. Event type explicit
	SLA Breached	A calculated event that occurs when the time taken to respond or resolve a ticket exceeds the defined SLA policy target. Freshdesk tracks SLA status and marks tickets as 'violated', which can be used to derive this activity.
Why it matters This activity directly supports SLA compliance analysis by pinpointing exactly when and where service level commitments are not met. It is critical for identifying systemic causes of delays. Where to get This event is inferred or calculated by observing the 'SLA' status of a ticket. An activity can be generated when the ticket's SLA status changes to 'Violated' or by comparing response/resolution timestamps against SLA targets. Capture Derive from ticket data when 'Time to Resolve' exceeds the 'SLA Target Resolution Time'. Event type calculated
	Status Changed to Pending	This activity occurs when an agent is waiting for information from the customer and changes the ticket status to 'Pending'. This event is explicitly logged as a status change in the ticket's activity history.
Why it matters Identifies periods where the process is paused awaiting external input. Analyzing time spent in this status helps quantify customer-side delays and supports SLA compliance analysis, as SLA timers are often paused in this state. Where to get Captured from the 'Ticket Activities' log, which records all status changes, including the transition to 'Pending'. Capture Event logged when the ticket's 'Status' field is updated to 'Pending'. Event type explicit
	Ticket Priority Changed	This event occurs when an agent or an automation rule changes the priority level of a ticket, such as from 'Low' to 'High'. This is recorded as an explicit update in the ticket's activity log.
Why it matters Changes in priority can signal escalations or a re-evaluation of the issue's urgency. Analyzing these changes helps understand escalation drivers and their impact on resolution time. Where to get Captured from the 'Ticket Activities' log, which records all changes to ticket properties, including the 'Priority' field. Capture Event logged when the 'Priority' field value is updated. Event type explicit
	Ticket Reassigned	Occurs when a ticket is transferred from one agent or group to another after the initial assignment. This is an explicit event captured in the ticket's activity log as a change to the owner.
Why it matters Frequent reassignments, or a high agent-to-agent transfer rate, often indicate incorrect initial routing or siloed knowledge. This analysis helps pinpoint opportunities to improve first-contact resolution. Where to get Tracked via changes to the 'Agent' or 'Group' fields in the 'Ticket Activities' log after the first assignment. Capture A subsequent update to the 'Assigned to' field after an initial assignment was made. Event type explicit
	Ticket Reopened	This activity occurs when a customer replies to a ticket that is already in a 'Resolved' state, which automatically changes its status back to 'Open'. This is an explicit event logged by the system.
Why it matters A high reopen rate indicates that initial solutions are not effective, leading to rework and customer dissatisfaction. Analyzing this activity is essential for the 'Ticket Re-opening Analysis' and improving first-contact resolution. Where to get This event is captured when a customer reply triggers an automatic status change from 'Resolved' back to 'Open'. This status change is recorded in the 'Ticket Activities' log. Capture Status change from 'Resolved' to 'Open' triggered by a customer interaction. Event type explicit

Recommended Optional

Extraction Guides

How to get your data from Freshdesk

Steps

Log in to your Freshdesk instance with an Administrator account that has permissions to view and export tickets.
Navigate to the main Tickets tab from the left-hand navigation menu to view the ticket list.
Use the Filter Tickets panel on the right to define the scope of your data extraction. At a minimum, set a Created date range, for example, the last 6 months. You can add other filters like Group, Agent, or Source to refine the dataset.
Once the filters are applied, click the Export icon, which looks like a downward arrow into a tray, located at the top right of the ticket list.
In the Export Tickets pop-up window, select the fields you want to include. Ensure you select all relevant default and custom fields needed for the event log, including: Ticket ID, Created at, Resolved at, Closed at, First responded at, Agent, Group, Status, Priority, Type, and any other custom fields that might be useful.
Confirm your selection. Freshdesk will begin processing the export in the background. You will receive an email containing a link to download the data as a CSV file. This may take some time for large datasets.
Download the CSV file from the link in the email.
The exported file contains one row per ticket, representing a snapshot of the ticket's current state and key timestamps. To create an event log, you must transform this data.
Use a script, like the Python script provided in the Query section, or a data preparation tool to unpivot the timestamp columns (Created at, First responded at, Resolved at, Closed at) into separate rows. Each new row will represent a single activity.
Map the original timestamp columns to specific Activity names, for example, map the 'Created at' column to the 'Ticket Created' activity.
Generate the final event log CSV with the required columns: ServiceRequest, Activity, and EventTime, along with any other recommended attributes.
Save the transformed file in a CSV format suitable for upload into your process mining tool.

Configuration

Permissions: Requires Administrator or a custom role with permissions to view and export tickets from the account.
Date Range Filter: It is crucial to set a date range using the 'Created' or 'Closed' time fields to avoid exporting the entire history, which can be very slow. A period of 3 to 6 months is typically recommended for an initial analysis.
Ticket Fields: In the export dialog, you have the option to select which fields to include. Be sure to select all timestamp fields available, such as Created at, First responded at, Resolved at, and Closed at, as these form the basis of the event log. Also include identifier fields like Ticket ID and context fields like Agent, Group, Priority, and Type.
Data Volume: Exports are limited in the number of tickets that can be exported at one time, typically 30,000 tickets. For larger datasets, you may need to perform multiple exports by breaking down the time range into smaller chunks, for example, month by month.
Delivery: The export is an asynchronous process. The CSV file is not downloaded directly but is sent as a download link to the email address of the user who initiated the export.

a Sample Query config

### Part 1: Freshdesk Export Configuration

This process uses the user interface for extraction. In the **Export Tickets** dialog, configure the following settings:

```json
{
  "filter_by": {
    "created": "[Specify Date Range, e.g., Last 6 Months]",
    "status": ["Open", "Pending", "Resolved", "Closed"],
    "// additional_filters": "Optionally filter by Group, Agent, Type, etc."
  },
  "export_fields": [
    "Ticket ID",
    "Created at",
    "First responded at",
    "Resolved at",
    "Closed at",
    "Group",
    "Agent",
    "Status",
    "Priority",
    "Type",
    "// include_custom_fields": "[Select any relevant custom fields]"
  ]
}
```

### Part 2: Data Transformation Script (Python with Pandas)

The exported CSV is a snapshot and must be transformed into an event log. The following script reshapes the data to create a row for each key activity.

```python
import pandas as pd

# --- Configuration ---
# Replace with the actual path to your downloaded Freshdesk CSV export
input_csv_path = 'freshdesk_export.csv'
output_csv_path = 'freshdesk_event_log.csv'

# --- Load Data ---
def transform_freshdesk_export(file_path):
    """Loads the Freshdesk export and transforms it into an event log."""
    try:
        df_raw = pd.read_csv(file_path)
    except FileNotFoundError:
        print(f"Error: The file {file_path} was not found.")
        return None

    # --- Rename and Select Columns ---
    # Adjust column names based on your specific export file
    column_mapping = {
        'Ticket ID': 'ServiceRequest',
        'Agent': 'AssignedAgent',
        'Type': 'ServiceRequestType',
        'Status': 'Status',
        'Priority': 'Priority',
        'Created at': 'Time_Ticket_Created',
        'First responded at': 'Time_First_Response_Sent',
        'Resolved at': 'Time_Ticket_Resolved',
        'Closed at': 'Time_Ticket_Closed'
    }
    df = df_raw.rename(columns=column_mapping)

    # Define the columns that are common to all events for a given ticket
    base_columns = ['ServiceRequest', 'AssignedAgent', 'ServiceRequestType', 'Status', 'Priority']
    # Ensure all required base columns exist
    for col in base_columns:
        if col not in df.columns:
            df[col] = None # Add missing columns as null

    # --- Unpivot Timestamp Columns to Create Events ---
    events = []
    activity_map = {
        'Time_Ticket_Created': 'Ticket Created',
        'Time_First_Response_Sent': 'First Response Sent',
        'Time_Ticket_Resolved': 'Ticket Resolved',
        'Time_Ticket_Closed': 'Ticket Closed'
    }

    for time_col, activity_name in activity_map.items():
        if time_col in df.columns:
            # Select rows where the timestamp exists
            df_event = df[df[time_col].notna()].copy()
            if not df_event.empty:
                df_event['Activity'] = activity_name
                df_event['EventTime'] = pd.to_datetime(df_event[time_col])
                events.append(df_event[base_columns + ['Activity', 'EventTime']])

    if not events:
        print("No events could be generated. Please check your column names and data.")
        return None

    # --- Combine, Sort, and Finalize ---
    df_event_log = pd.concat(events, ignore_index=True)

    # Calculate Resolution Time (optional, as an attribute)
    if 'Time_Ticket_Created' in df.columns and 'Time_Ticket_Resolved' in df.columns:
        df_res_time = df[['ServiceRequest', 'Time_Ticket_Created', 'Time_Ticket_Resolved']].dropna()
        df_res_time['ResolutionTime'] = (pd.to_datetime(df_res_time['Time_Ticket_Resolved']) - pd.to_datetime(df_res_time['Time_Ticket_Created'])).dt.total_seconds() / 3600.0 # In hours
        df_event_log = df_event_log.merge(df_res_time[['ServiceRequest', 'ResolutionTime']], on='ServiceRequest', how='left')

    # Sort the event log for proper ordering
    df_event_log = df_event_log.sort_values(by=['ServiceRequest', 'EventTime'], ascending=[True, True])

    return df_event_log

# --- Execute Transformation ---
if __name__ == "__main__":
    event_log = transform_freshdesk_export(input_csv_path)
    if event_log is not None:
        event_log.to_csv(output_csv_path, index=False)
        print(f"Event log successfully created at: {output_csv_path}")
```

Steps

Prerequisites: Ensure you have Python 3.6 or newer installed on your system. Install the required Python libraries, requests for making API calls and pandas for data manipulation, by running the command: pip install requests pandas.
Obtain Freshdesk API Key: Log in to your Freshdesk instance as an Administrator. Click on your profile icon in the top right corner and select 'Profile Settings'. Your API key will be displayed on the right side. Copy this key securely.
Set Up the Python Script: Create a new Python file (e.g., extract_freshdesk.py). Copy the complete Python script provided in the 'query' section into this file.
Configure Script Parameters: Open the script and update the placeholder variables at the top. Set DOMAIN to your Freshdesk domain (e.g., yourcompany.freshdesk.com), API_KEY to the key you obtained, and START_DATE_STR to the desired start date for the data extraction in 'YYYY-MM-DD' format.
Execute the Script: Run the script from your terminal using the command: python extract_freshdesk.py. The script will begin fetching ticket data from the Freshdesk API.
Monitor the Extraction: The script will print progress updates to the console, showing which page of tickets it is currently processing. Depending on the date range and number of tickets, this process can take a significant amount of time due to API rate limiting.
Handle API Responses: The script includes logic to handle API pagination by fetching tickets page by page. It also respects API rate limits by pausing execution if a 429 Too Many Requests error is received, retrying after the duration specified by Freshdesk.
Data Transformation: For each ticket, the script fetches its associated conversations and activities. It then parses these raw JSON responses to identify and create explicit records for each of the 13 required activities, such as 'Ticket Created', 'Status Changed to Pending', and 'Ticket Resolved'.
Review the Output: Once the script completes, it will generate a file named freshdesk_event_log.csv in the same directory. This file contains the structured event log with all extracted activities and attributes.
Prepare for Upload: Open the generated CSV file to ensure it contains the expected data. Verify that the columns ServiceRequest, Activity, and EventTime are present and correctly populated. The file is now ready to be uploaded to ProcessMind.

Configuration

Prerequisites: A Freshdesk account with administrator privileges is required to obtain an API key with full access to ticket data. You must have Python 3.6+ and the requests and pandas libraries installed.
API Credentials: The script requires your specific Freshdesk DOMAIN and a valid API_KEY. These must be configured as variables within the script.
Date Range: The START_DATE_STR variable determines the starting point for the extraction. It is recommended to start with a smaller range, such as 3 to 6 months, to manage execution time and API usage. The script uses the updated_since filter for efficiency.
API Endpoint Version: The script is built for the Freshdesk v2 API. Ensure your account is compatible with this version.
Performance Considerations: The script is subject to Freshdesk's API rate limits. A built-in delay mechanism prevents exceeding these limits, but this means extracting large volumes of data will be slow. For full historical loads covering several years, consider running the script in batches or during off-peak hours.
Included Data: The script is configured to retrieve tickets and their associated conversations. The include=stats parameter is used to efficiently fetch metadata like resolution and response times.

a Sample Query python

import requests
import pandas as pd
import time
import datetime
import re

class=class="hl-string">"hl-comment"># --- Configuration ---
class=class="hl-string">"hl-comment"># Replace with your Freshdesk domain
DOMAIN = class="hl-string">'[your_domain].freshdesk.com'
class=class="hl-string">"hl-comment"># Replace with your Freshdesk API Key
API_KEY = class="hl-string">'[your_api_key]'
class=class="hl-string">"hl-comment"># Set the start date for the extraction in class="hl-string">'YYYY-MM-DD' format.
START_DATE_STR = class="hl-string">'2023-01-01'

BASE_URL = fclass="hl-string">'https://{DOMAIN}/api/v2'
AUTH = (API_KEY, class="hl-string">'X')

def make_api_request(url, params=None):
    class="hl-string">"""Makes a GET request to the Freshdesk API, handling rate limits."""
    while True:
        try:
            response = requests.get(url, auth=AUTH, params=params)
            response.raise_for_status()
            return response
        except requests.exceptions.HTTPError as err:
            if err.response.status_code == 429:
                retry_after = int(err.response.headers.get(class="hl-string">'Retry-After', 60))
                print(fclass="hl-string">"Rate limit exceeded. Retrying after {retry_after} seconds...")
                time.sleep(retry_after)
            else:
                print(fclass="hl-string">"HTTP Error: {err}")
                return None
        except requests.exceptions.RequestException as err:
            print(fclass="hl-string">"Request Error: {err}")
            return None

def parse_iso_timestamp(ts):
    class="hl-string">"""Parses Freshdesk's ISO 8601 timestamp string."""
    if not ts or not isinstance(ts, str):
        return None
    return datetime.datetime.strptime(ts, class="hl-string">'%Y-%m-%dT%H:%M:%SZ')

def get_freshdesk_event_log(start_date):
    class="hl-string">"""Fetches and processes Freshdesk tickets to create an event log."""
    events = []
    page = 1
    
    class=class="hl-string">"hl-comment"># Convert start_date to the required format for the API query
    start_datetime = datetime.datetime.strptime(start_date, class="hl-string">'%Y-%m-%d')
    query_date = start_datetime.strftime(class="hl-string">'%Y-%m-%dT%H:%M:%SZ')

    while True:
        print(fclass="hl-string">"Fetching tickets page {page}...")
        tickets_url = fclass="hl-string">'{BASE_URL}/tickets'
        class=class="hl-string">"hl-comment"># Use class="hl-string">'updated_since' for incremental loads. For a full load, consider filtering by class="hl-string">'created_at'.
        params = {class="hl-string">'page': page, class="hl-string">'per_page': 100, class="hl-string">'order_by': class="hl-string">'updated_at', class="hl-string">'order_type': class="hl-string">'asc', class="hl-string">'updated_since': query_date, class="hl-string">'include': class="hl-string">'stats,requester'}
        
        response = make_api_request(tickets_url, params=params)
        if not response:
            break
            
        tickets_data = response.json()
        if not tickets_data:
            print(class="hl-string">"No more tickets found.")
            break

        for ticket in tickets_data:
            ticket_id = ticket[class="hl-string">'id']
            created_at = parse_iso_timestamp(ticket[class="hl-string">'created_at'])
            resolved_at = parse_iso_timestamp(ticket.get(class="hl-string">'stats', {}).get(class="hl-string">'resolved_at'))
            
            resolution_time_hours = (resolved_at - created_at).total_seconds() / 3600 if resolved_at and created_at else None

            common_attrs = {
                class="hl-string">'ServiceRequest': ticket_id,
                class="hl-string">'ServiceRequestType': ticket.get(class="hl-string">'type'),
                class="hl-string">'Priority': ticket.get(class="hl-string">'priority'),
                class="hl-string">'AssignedAgent': ticket.get(class="hl-string">'responder_id'),
                class="hl-string">'ResolutionTime': resolution_time_hours
            }
            
            class=class="hl-string">"hl-comment"># 1. Ticket Created
            events.append({
                **common_attrs,
                class="hl-string">'Activity': class="hl-string">'Ticket Created',
                class="hl-string">'EventTime': created_at.strftime(class="hl-string">'%Y-%m-%d %H:%M:%S'),
                class="hl-string">'Status': class="hl-string">'Open' 
            })
            
            class=class="hl-string">"hl-comment"># 2. First Response Sent
            first_responded_at = ticket.get(class="hl-string">'stats', {}).get(class="hl-string">'first_responded_at')
            if first_responded_at:
                events.append({
                    **common_attrs,
                    class="hl-string">'Activity': class="hl-string">'First Response Sent',
                    class="hl-string">'EventTime': parse_iso_timestamp(first_responded_at).strftime(class="hl-string">'%Y-%m-%d %H:%M:%S'),
                    class="hl-string">'Status': ticket.get(class="hl-string">'status')
                })

            class=class="hl-string">"hl-comment"># Fetch conversations for detailed activities
            conv_url = fclass="hl-string">'{BASE_URL}/tickets/{ticket_id}/conversations'
            conv_response = make_api_request(conv_url)
            if conv_response:
                conversations = conv_response.json()
                for conv in conversations:
                    event_time = parse_iso_timestamp(conv[class="hl-string">'created_at']).strftime(class="hl-string">'%Y-%m-%d %H:%M:%S')
                    
                    class=class="hl-string">"hl-comment"># 3. Customer Responded vs 4. Internal Note Added
                    if conv[class="hl-string">'source_type'] == class="hl-string">'note':
                        if conv.get(class="hl-string">'private', False):
                            events.append({
                                **common_attrs,
                                class="hl-string">'Activity': class="hl-string">'Internal Note Added',
                                class="hl-string">'EventTime': event_time,
                                class="hl-string">'Status': ticket.get(class="hl-string">'status')
                            })
                        else: class=class="hl-string">"hl-comment"># System generated notes for status changes etc.
                            body_text = conv.get(class="hl-string">'body_text', class="hl-string">'')
                            class=class="hl-string">"hl-comment"># 5. Status Changed to Pending
                            if class="hl-string">'Status changed from' in body_text and class="hl-string">'to Pending' in body_text:
                                events.append({**common_attrs, class="hl-string">'Activity': class="hl-string">'Status Changed to Pending', class="hl-string">'EventTime': event_time, class="hl-string">'Status': class="hl-string">'Pending'})
                            class=class="hl-string">"hl-comment"># 6. Ticket Assigned / Reassigned
                            if class="hl-string">'Assigned to' in body_text:
                                activity_name = class="hl-string">'Ticket Assigned' if any(e[class="hl-string">'Activity'] == class="hl-string">'Ticket Assigned' for e in events if e[class="hl-string">'ServiceRequest'] == ticket_id) else class="hl-string">'Ticket Reassigned'
                                events.append({**common_attrs, class="hl-string">'Activity': activity_name, class="hl-string">'EventTime': event_time, class="hl-string">'Status': ticket.get(class="hl-string">'status')})
                            class=class="hl-string">"hl-comment"># 7. Ticket Priority Changed
                            if class="hl-string">'Priority changed from' in body_text:
                                events.append({**common_attrs, class="hl-string">'Activity': class="hl-string">'Ticket Priority Changed', class="hl-string">'EventTime': event_time, class="hl-string">'Status': ticket.get(class="hl-string">'status')})
                            class=class="hl-string">"hl-comment"># 8. Satisfaction Survey Sent
                            if class="hl-string">'Satisfaction Survey was sent to the contact' in body_text:
                                events.append({**common_attrs, class="hl-string">'Activity': class="hl-string">'Satisfaction Survey Sent', class="hl-string">'EventTime': event_time, class="hl-string">'Status': ticket.get(class="hl-string">'status')})
                    else: class=class="hl-string">"hl-comment"># Not a note, so it's a reply
                        class=class="hl-string">"hl-comment"># 9. Customer Responded
                        if conv.get(class="hl-string">'incoming', False):
                            events.append({
                                **common_attrs,
                                class="hl-string">'Activity': class="hl-string">'Customer Responded',
                                class="hl-string">'EventTime': event_time,
                                class="hl-string">'Status': class="hl-string">'Open' class=class="hl-string">"hl-comment"># Typically a customer response re-opens the ticket
                            })
            
            class=class="hl-string">"hl-comment"># 10. SLA Breached
            if ticket.get(class="hl-string">'fr_escalated', False) or ticket.get(class="hl-string">'resolution_escalated', False):
                 breach_time = ticket.get(class="hl-string">'stats', {}).get(class="hl-string">'status_updated_at', ticket.get(class="hl-string">'updated_at'))
                 events.append({
                    **common_attrs,
                    class="hl-string">'Activity': class="hl-string">'SLA Breached',
                    class="hl-string">'EventTime': parse_iso_timestamp(breach_time).strftime(class="hl-string">'%Y-%m-%d %H:%M:%S'),
                    class="hl-string">'Status': ticket.get(class="hl-string">'status')
                })

            class=class="hl-string">"hl-comment"># 11. Ticket Resolved
            if resolved_at:
                events.append({
                    **common_attrs,
                    class="hl-string">'Activity': class="hl-string">'Ticket Resolved',
                    class="hl-string">'EventTime': resolved_at.strftime(class="hl-string">'%Y-%m-%d %H:%M:%S'),
                    class="hl-string">'Status': class="hl-string">'Resolved'
                })

            class=class="hl-string">"hl-comment"># 12. Ticket Reopened
            reopened_at = ticket.get(class="hl-string">'stats', {}).get(class="hl-string">'reopened_at')
            if reopened_at:
                events.append({
                    **common_attrs,
                    class="hl-string">'Activity': class="hl-string">'Ticket Reopened',
                    class="hl-string">'EventTime': parse_iso_timestamp(reopened_at).strftime(class="hl-string">'%Y-%m-%d %H:%M:%S'),
                    class="hl-string">'Status': class="hl-string">'Open'
                })
                
            class=class="hl-string">"hl-comment"># 13. Ticket Closed
            closed_at = ticket.get(class="hl-string">'stats', {}).get(class="hl-string">'closed_at')
            if closed_at:
                events.append({
                    **common_attrs,
                    class="hl-string">'Activity': class="hl-string">'Ticket Closed',
                    class="hl-string">'EventTime': parse_iso_timestamp(closed_at).strftime(class="hl-string">'%Y-%m-%d %H:%M:%S'),
                    class="hl-string">'Status': class="hl-string">'Closed'
                })

        page += 1

    class=class="hl-string">"hl-comment"># Create DataFrame and sort by time
    df = pd.DataFrame(events)
    df[class="hl-string">'EventTime'] = pd.to_datetime(df[class="hl-string">'EventTime'])
    df = df.sort_values(by=[class="hl-string">'ServiceRequest', class="hl-string">'EventTime']).reset_index(drop=True)
    df[class="hl-string">'EventTime'] = df[class="hl-string">'EventTime'].dt.strftime(class="hl-string">'%Y-%m-%d %H:%M:%S')

    return df

if __name__ == class="hl-string">'__main__':
    print(class="hl-string">"Starting Freshdesk data extraction...")
    event_log_df = get_freshdesk_event_log(START_DATE_STR)
    if not event_log_df.empty:
        output_filename = class="hl-string">'freshdesk_event_log.csv'
        event_log_df.to_csv(output_filename, index=False)
        print(fclass="hl-string">"Extraction complete. Event log saved to {output_filename}")
    else:
        print(class="hl-string">"No data extracted.")

Steps

Log in to your Freshdesk instance with an Administrator account that has permissions to view and export tickets.
Navigate to the main Tickets tab from the left-hand navigation menu to view the ticket list.
Use the Filter Tickets panel on the right to define the scope of your data extraction. At a minimum, set a Created date range, for example, the last 6 months. You can add other filters like Group, Agent, or Source to refine the dataset.
Once the filters are applied, click the Export icon, which looks like a downward arrow into a tray, located at the top right of the ticket list.
In the Export Tickets pop-up window, select the fields you want to include. Ensure you select all relevant default and custom fields needed for the event log, including: Ticket ID, Created at, Resolved at, Closed at, First responded at, Agent, Group, Status, Priority, Type, and any other custom fields that might be useful.
Confirm your selection. Freshdesk will begin processing the export in the background. You will receive an email containing a link to download the data as a CSV file. This may take some time for large datasets.
Download the CSV file from the link in the email.
The exported file contains one row per ticket, representing a snapshot of the ticket's current state and key timestamps. To create an event log, you must transform this data.
Use a script, like the Python script provided in the Query section, or a data preparation tool to unpivot the timestamp columns (Created at, First responded at, Resolved at, Closed at) into separate rows. Each new row will represent a single activity.
Map the original timestamp columns to specific Activity names, for example, map the 'Created at' column to the 'Ticket Created' activity.
Generate the final event log CSV with the required columns: ServiceRequest, Activity, and EventTime, along with any other recommended attributes.
Save the transformed file in a CSV format suitable for upload into your process mining tool.

Configuration

Permissions: Requires Administrator or a custom role with permissions to view and export tickets from the account.
Date Range Filter: It is crucial to set a date range using the 'Created' or 'Closed' time fields to avoid exporting the entire history, which can be very slow. A period of 3 to 6 months is typically recommended for an initial analysis.
Ticket Fields: In the export dialog, you have the option to select which fields to include. Be sure to select all timestamp fields available, such as Created at, First responded at, Resolved at, and Closed at, as these form the basis of the event log. Also include identifier fields like Ticket ID and context fields like Agent, Group, Priority, and Type.
Data Volume: Exports are limited in the number of tickets that can be exported at one time, typically 30,000 tickets. For larger datasets, you may need to perform multiple exports by breaking down the time range into smaller chunks, for example, month by month.
Delivery: The export is an asynchronous process. The CSV file is not downloaded directly but is sent as a download link to the email address of the user who initiated the export.

a Sample Query config

### Part 1: Freshdesk Export Configuration

This process uses the user interface for extraction. In the **Export Tickets** dialog, configure the following settings:

```json
{
  "filter_by": {
    "created": "[Specify Date Range, e.g., Last 6 Months]",
    "status": ["Open", "Pending", "Resolved", "Closed"],
    "// additional_filters": "Optionally filter by Group, Agent, Type, etc."
  },
  "export_fields": [
    "Ticket ID",
    "Created at",
    "First responded at",
    "Resolved at",
    "Closed at",
    "Group",
    "Agent",
    "Status",
    "Priority",
    "Type",
    "// include_custom_fields": "[Select any relevant custom fields]"
  ]
}
```

### Part 2: Data Transformation Script (Python with Pandas)

The exported CSV is a snapshot and must be transformed into an event log. The following script reshapes the data to create a row for each key activity.

```python
import pandas as pd

# --- Configuration ---
# Replace with the actual path to your downloaded Freshdesk CSV export
input_csv_path = 'freshdesk_export.csv'
output_csv_path = 'freshdesk_event_log.csv'

# --- Load Data ---
def transform_freshdesk_export(file_path):
    """Loads the Freshdesk export and transforms it into an event log."""
    try:
        df_raw = pd.read_csv(file_path)
    except FileNotFoundError:
        print(f"Error: The file {file_path} was not found.")
        return None

    # --- Rename and Select Columns ---
    # Adjust column names based on your specific export file
    column_mapping = {
        'Ticket ID': 'ServiceRequest',
        'Agent': 'AssignedAgent',
        'Type': 'ServiceRequestType',
        'Status': 'Status',
        'Priority': 'Priority',
        'Created at': 'Time_Ticket_Created',
        'First responded at': 'Time_First_Response_Sent',
        'Resolved at': 'Time_Ticket_Resolved',
        'Closed at': 'Time_Ticket_Closed'
    }
    df = df_raw.rename(columns=column_mapping)

    # Define the columns that are common to all events for a given ticket
    base_columns = ['ServiceRequest', 'AssignedAgent', 'ServiceRequestType', 'Status', 'Priority']
    # Ensure all required base columns exist
    for col in base_columns:
        if col not in df.columns:
            df[col] = None # Add missing columns as null

    # --- Unpivot Timestamp Columns to Create Events ---
    events = []
    activity_map = {
        'Time_Ticket_Created': 'Ticket Created',
        'Time_First_Response_Sent': 'First Response Sent',
        'Time_Ticket_Resolved': 'Ticket Resolved',
        'Time_Ticket_Closed': 'Ticket Closed'
    }

    for time_col, activity_name in activity_map.items():
        if time_col in df.columns:
            # Select rows where the timestamp exists
            df_event = df[df[time_col].notna()].copy()
            if not df_event.empty:
                df_event['Activity'] = activity_name
                df_event['EventTime'] = pd.to_datetime(df_event[time_col])
                events.append(df_event[base_columns + ['Activity', 'EventTime']])

    if not events:
        print("No events could be generated. Please check your column names and data.")
        return None

    # --- Combine, Sort, and Finalize ---
    df_event_log = pd.concat(events, ignore_index=True)

    # Calculate Resolution Time (optional, as an attribute)
    if 'Time_Ticket_Created' in df.columns and 'Time_Ticket_Resolved' in df.columns:
        df_res_time = df[['ServiceRequest', 'Time_Ticket_Created', 'Time_Ticket_Resolved']].dropna()
        df_res_time['ResolutionTime'] = (pd.to_datetime(df_res_time['Time_Ticket_Resolved']) - pd.to_datetime(df_res_time['Time_Ticket_Created'])).dt.total_seconds() / 3600.0 # In hours
        df_event_log = df_event_log.merge(df_res_time[['ServiceRequest', 'ResolutionTime']], on='ServiceRequest', how='left')

    # Sort the event log for proper ordering
    df_event_log = df_event_log.sort_values(by=['ServiceRequest', 'EventTime'], ascending=[True, True])

    return df_event_log

# --- Execute Transformation ---
if __name__ == "__main__":
    event_log = transform_freshdesk_export(input_csv_path)
    if event_log is not None:
        event_log.to_csv(output_csv_path, index=False)
        print(f"Event log successfully created at: {output_csv_path}")
```

Your Customer Service Data Template

Your Customer Service Data Template

Customer Service Attributes

Customer Service Activities

Extraction Guides

Freshdesk Native Ticket Data Export to CSV

Steps

Configuration

a Sample Query config

Python Script Extraction via Freshdesk REST API

Steps

Configuration

a Sample Query python

Third-Party ETL Tool with a Freshdesk Connector

Steps

Configuration

a Sample Query config

Ready to Get Started?

Optimize Freshdesk Customer Service Today

Your Customer Service Data Template

Your Customer Service Data Template

Customer Service Attributes

Customer Service Activities

Extraction Guides

Freshdesk Native Ticket Data Export to CSV

Steps

Configuration

a Sample Query config

Python Script Extraction via Freshdesk REST API

Steps

Configuration

a Sample Query python

Third-Party ETL Tool with a Freshdesk Connector

Steps

Configuration

a Sample Query config

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Ready to Get Started?

Optimize Freshdesk Customer Service Today