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Your Lead to Cash Data Template

SAP Sales Cloud

Lead to Cash Attributes (18) Lead to Cash Activities (15) Extraction Guides (2)

Your Lead to Cash Data Template

This template provides a structured guide to collecting the necessary data for analyzing your Lead to Cash process. It outlines the essential attributes and activities required to build a comprehensive event log. You'll also find practical guidance on extracting this data from your SAP Sales Cloud system, ensuring a smooth start to your process mining journey.

Recommended attributes to collect
Key activities to track
Extraction guidance for SAP Sales Cloud

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

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Lead to Cash Attributes

These are the recommended data fields to include in your event log for a comprehensive analysis of your Lead to Cash process in SAP Sales Cloud.

3 Required 6 Recommended 9 Optional

	Name	Description
	Activity Name ActivityName	The name of the business activity or event that occurred at a specific point in the process.
Description The Activity Name describes a specific step or event within the Lead to Cash process, such as 'Lead Qualified', 'Quote Created', or 'Payment Received'. These events form the sequence of actions that constitute the process flow for each sales opportunity. Analyzing these activities is the core of process mining. It enables the visualization of process maps, the identification of bottlenecks between steps, and the measurement of frequencies for different process paths, revealing how the sales process is actually executed. Why it matters This attribute defines the steps in the process map, which is fundamental for visualizing and analyzing the process flow. Where to get Derived from business object status changes, document creation events, or specific user actions recorded in transaction logs within SAP Sales Cloud. Examples Lead CapturedOpportunity CreatedQuote Sent to CustomerPayment Received
	Event Time EventTime	The timestamp indicating when a specific activity or event occurred.
Description Event Time records the precise date and time that an activity was completed. This temporal data is essential for understanding the timing and duration of process steps. It provides the chronological order needed to build the process flow. In process analysis, timestamps are used to calculate cycle times between activities, measure the overall duration of the sales process, and identify delays or bottlenecks. Analyzing timestamps over time can also reveal trends in process performance. Why it matters The timestamp is critical for ordering events chronologically and for all time-based analysis, including cycle time and bottleneck identification. Where to get Typically found in 'Creation Date' or 'Last Changed Date' fields associated with business objects like Leads, Opportunities, Quotes, and Sales Orders in SAP Sales Cloud. Examples 2023-04-15T10:30:00Z2023-05-02T14:00:00Z2023-06-20T09:15:00Z
	Sales Opportunity SalesOpportunityId	The unique identifier for a sales opportunity, serving as the primary case identifier for the Lead to Cash process.
Description The Sales Opportunity ID uniquely identifies each potential sale from its inception as a lead to its final resolution as a won or lost deal. It acts as the central thread connecting all related activities, such as lead qualification, quote creation, order processing, and payment. In process mining analysis, this ID is crucial for reconstructing the end-to-end journey of each opportunity. It allows for the correlation of all events, enabling the analysis of process variants, cycle times, and conversion rates on a per-opportunity basis. Why it matters This is the essential Case ID that links all activities in the process, making it possible to trace the complete lifecycle of each sales opportunity. Where to get This is the primary key for the Opportunity business object in SAP Sales Cloud. Examples OP-8000123456OP-8000123457OP-8000123458
	Discount Percentage DiscountPercentage	The discount percentage applied to the sales quote or order.
Description This attribute captures the overall discount rate applied to an opportunity, typically at the quote or order stage. It is expressed as a percentage of the list price. This is a critical metric for financial performance and sales discipline analysis. The 'Discount Distribution' dashboard uses this attribute to monitor discounting behavior across sales reps and products. It is also used for the 'Discount Approval Compliance' KPI to ensure that large discounts follow the proper approval workflows. Why it matters Crucial for analyzing pricing strategy, sales team discounting behavior, and its impact on profitability. Where to get Calculated from the pricing elements in the associated Sales Quote or Sales Order business objects in SAP Sales Cloud. Examples 0.050.100.25
	Lead Source LeadSource	The marketing channel or origin of the initial lead.
Description Lead Source specifies how a potential customer was first identified, for example, through a web form, trade show, or partner referral. This information is typically captured when the lead is created. This attribute is critical for the 'Lead Conversion Rate by Source' dashboard. Analyzing this dimension helps marketing and sales teams understand which channels generate the most valuable leads, allowing for better allocation of resources and optimization of marketing campaigns. Why it matters Essential for measuring the effectiveness of different marketing channels and optimizing lead generation strategies. Where to get Found on the Lead and Opportunity business objects in SAP Sales Cloud. Field is often named 'Source' or 'Origin'. Examples WebinarPartner ReferralTrade ShowWebsite Inquiry
	Opportunity Status OpportunityStatus	The current state or outcome of the sales opportunity, such as 'Open', 'Won', or 'Lost'.
Description This attribute reflects the overall status of the sales opportunity at a given point in time. It indicates whether the opportunity is still in progress, has been successfully won, or has been lost to a competitor or for other reasons. Analyzing the final status is fundamental for calculating key KPIs like the Opportunity Win Rate. It also allows for filtering analysis to focus only on successful paths or to investigate the process patterns that most frequently lead to lost deals. Why it matters Defines the outcome of the case, which is crucial for calculating win/loss rates and analyzing the process paths of successful vs. unsuccessful opportunities. Where to get The 'Status' field on the Opportunity business object in SAP Sales Cloud. Examples OpenIn ProcessWonLostStopped
	Product Category ProductCategory	The category of the product or service being sold in the opportunity.
Description Product Category groups the specific items or services in an opportunity into broader classifications, such as 'Hardware', 'Software', or 'Consulting Services'. This simplifies analysis when many individual products are involved. Analyzing by Product Category is essential for understanding how the sales process differs for various types of offerings. It is used in dashboards like 'Discount Distribution' and 'Order Fulfillment Efficiency' to see if certain categories involve higher discounts or longer fulfillment times. Why it matters Allows for analysis of process variations based on the type of product or service being sold, which can have significantly different sales cycles. Where to get Derived from the product master data linked to the line items of a Sales Quote or Sales Order in SAP Sales Cloud. Examples Software LicenseHardwareProfessional ServicesSupport Subscription
	Sales Order Amount SalesOrderAmount	The total monetary value of the sales order associated with the opportunity.
Description This attribute represents the total value of the goods or services being sold once an opportunity is won and a sales order is created. It reflects the revenue generated from the successful completion of the sales process. Sales Order Amount is a critical financial dimension for analysis. It can be used to segment opportunities by size (e.g., small, medium, large deals) to see if process behavior differs. It also provides the value context for KPIs like win rate, showing the total value of won vs. lost opportunities. Why it matters Provides a financial dimension to the process, allowing for value-based analysis of sales cycles and outcomes. Where to get This value would be sourced from the 'Total Net Value' field of the associated Sales Order business object in SAP Sales Cloud. Examples 50000.00125000.507800.00
	Sales Representative SalesRepresentative	The employee responsible for managing the sales opportunity.
Description This attribute identifies the user or employee who is the owner of the lead or opportunity. This is often the primary point of contact for the customer and is responsible for moving the deal through the sales pipeline. In analysis, the Sales Representative is a key dimension for performance comparison. It allows for the evaluation of sales cycle times, win rates, and process adherence by individual, team, or region, helping to identify top performers and areas for coaching. Why it matters Allows for performance analysis by employee, helping to identify coaching opportunities and best practices among sales staff. Where to get Linked to the 'Owner' or 'Employee Responsible' party role on the Opportunity or Lead business object in SAP Sales Cloud. Examples John SmithMaria GarciaChen Wei
	Customer Name CustomerName	The name of the company or individual associated with the sales opportunity.
Description This attribute identifies the prospective customer for the sales deal. It links the sales process to a specific account in the system. Analyzing by customer allows for a customer-centric view of the sales process. It can help identify which customers have the longest sales cycles or require the most rework, and is essential for KPIs like the 'Payment Collection Cycle Time' segmented by customer. Why it matters Enables customer-centric analysis to identify patterns in sales cycles or payment behaviors for specific accounts. Where to get Linked to the 'Account' or 'Prospect' party role on the Opportunity business object in SAP Sales Cloud. Examples Global Tech Inc.Innovate Solutions Ltd.Dynamic Corp
	Is Reworked IsReworked	A flag indicating if an activity or case has undergone rework.
Description This boolean flag is set to true if a rework loop is detected in the process, such as a sales order being modified after its initial creation or a quote being revised multiple times. It is derived by analyzing the sequence of activities. This calculated attribute is essential for quantifying process inefficiencies. It directly supports the 'Sales Order Rework Frequency' dashboard and the 'Sales Order Rework Rate' KPI by making it easy to filter for and count instances of rework without complex pattern matching in the analysis tool. Why it matters Directly quantifies process inefficiency by flagging cases with rework loops, simplifying the analysis of rework frequency and its causes. Where to get This attribute is not in the source system. It is calculated during data transformation by detecting repeated sequences of activities (e.g., 'Sales Order Created' followed by 'Sales Order Reworked'). Examples truefalse
	Last Data Update LastDataUpdate	The timestamp when the data was last extracted or refreshed from the source system.
Description This attribute indicates the last time the data for the event log was updated. It provides context for the freshness of the data being analyzed, which is important for understanding the relevance of the findings. For any process analysis, knowing the recency of the data is crucial. It helps stakeholders understand if the insights reflect current operations or a past period, which influences decision-making based on the analysis. Why it matters Provides crucial context on data freshness, ensuring that analysis and decisions are based on up-to-date information. Where to get This is a metadata timestamp added during the data extraction and transformation (ETL) process. Examples 2024-07-20T02:00:00Z2024-07-21T02:00:00Z
	Payment Terms PaymentTerms	The agreed-upon terms for payment, such as 'Net 30' or 'Net 60'.
Description Payment Terms define the conditions under which a customer is expected to pay for the goods or services. This is established during the sales order or contracting phase and dictates the due date for the invoice. This attribute is a key dimension for the 'Payment Collection Cycle Time' dashboard. By segmenting by payment terms, a business can assess whether customers are adhering to the agreed-upon schedules and identify which terms are associated with longer or shorter payment cycles. Why it matters Provides context for payment collection analysis, helping to determine if delays are related to customer behavior or lenient payment terms. Where to get Located in the Sales Order or Customer master data in SAP Sales Cloud. Examples Net 30Net 60Due on Receipt
	Processing Time ProcessingTime	The time spent actively working on a task, excluding wait time.
Description Processing Time, also known as touch time, measures the duration that a resource was actively engaged with a task. It differs from cycle time, which includes the time a case spends waiting between activities. Analyzing processing time helps differentiate between delays caused by inefficient work and delays caused by waiting for resources or information. It provides a more precise measure of the effort required for each activity in the sales process. Why it matters Measures active work duration, helping to distinguish between inefficient tasks and long wait times in the process. Where to get Calculated as the difference between an activity's End Time and Start Time. Requires both timestamps to be available for each event. Examples PT1H30MPT8HP2DT4H
	Region Region	The geographical region where the customer or sales team is located.
Description Region represents the geographic area assigned to the sales opportunity, often based on the customer's location or the sales organization's structure. This could be a country, state, or a custom-defined sales territory. This is a key attribute for segmentation. It's used directly in the 'Opportunity Win Rate Performance' dashboard to compare performance across different regions. This helps identify regional differences in process efficiency, market success, and sales strategies. Why it matters Enables geographical analysis of sales performance, highlighting regional variations in win rates, cycle times, and process execution. Where to get Often derived from the customer's address or the sales organization assigned to the opportunity in SAP Sales Cloud. Examples North AmericaEMEAAPACLATAM
	Sales Organization SalesOrganization	The organizational unit responsible for the sale.
Description The Sales Organization represents a specific unit, division, or team within the company that is responsible for a sales transaction. It is a key element of the enterprise structure in SAP. This attribute allows for analysis to be segmented by different parts of the business. It can be used to compare process performance, win rates, and cycle times across different business units or sales divisions, providing a high-level organizational view of efficiency. Why it matters Enables performance benchmarking and process analysis across different business units or company divisions. Where to get This is a key organizational assignment field on the Opportunity or Sales Order in SAP Sales Cloud. Examples US01DE01Corp Sales North
	Shipping Method ShippingMethod	The method used to deliver the physical product to the customer.
Description This attribute specifies the logistics or delivery method for an order, such as 'Ground Shipping', 'Air Freight', or 'Digital Delivery'. It is relevant for processes involving physical goods. For the 'Order Fulfillment Efficiency' dashboard, this attribute helps to analyze if different shipping methods correlate with different fulfillment times. This can reveal inefficiencies in specific logistics channels or help set customer expectations more accurately. Why it matters Helps diagnose bottlenecks in the order fulfillment part of the process by segmenting delivery performance by logistics channel. Where to get Typically found on the line item or header level of the Sales Order business object in SAP Sales Cloud. Examples GroundNext Day AirDigital DownloadFreight
	Source System SourceSystem	Identifies the system from which the data originates.
Description This attribute specifies the source application where the event data was generated. For this process, it will typically be 'SAP Sales Cloud', but it could include other integrated systems like a marketing automation platform for lead capture. In a multi-system landscape, this attribute is vital for understanding data lineage and troubleshooting data integration issues. It ensures that analysis is based on a clear understanding of where each piece of information comes from. Why it matters Helps differentiate data origins in integrated environments, ensuring data lineage and context are clear during analysis. Where to get This is typically a static value added during the data extraction process to label the origin of the records. Examples SAP Sales CloudSAP C4CSSC_PROD_100

Required Recommended Optional

Lead to Cash Activities

These are the key process steps and milestones to capture in your event log for accurate discovery and analysis of your Lead to Cash process.

7 Recommended 8 Optional

	Activity	Description
	Opportunity Closed Lost	This activity marks the definitive end of an unsuccessful opportunity where the deal was lost to a competitor or the customer decided not to purchase. It is captured when a user changes the Opportunity status to 'Lost' or 'Closed Lost'.
Why it matters Analyzing when and why opportunities are lost is crucial for improving sales strategy. This failure endpoint allows for analysis of lost deals, such as the average stage where they drop off or common reasons for loss. Where to get Inferred from the status change history of the Opportunity business object. It's the timestamp when the status is updated to 'Lost' or 'Closed Lost'. Capture Identify the timestamp of the status change to 'Lost' on the Opportunity object. Event type inferred
	Opportunity Closed Won	This activity is the final, concluding status of a successful opportunity after all downstream activities like order creation and fulfillment are completed. It is captured when a user changes the Opportunity status to a final 'Closed' state with a reason of 'Won'.
Why it matters This event serves as the successful endpoint for process mining analysis, enabling the calculation of the end-to-end sales cycle duration for all won deals. It provides a clean and definitive process conclusion. Where to get Inferred from the status change history of the Opportunity object, specifically the timestamp when the status is updated to 'Closed' and the reason is 'Won'. Capture Identify timestamp of status change to 'Closed' with a 'Won' reason code. Event type inferred
	Opportunity Created	This is the official start of the sales opportunity, marking the conversion of a qualified lead into a concrete sales pursuit. This event is explicitly captured by the system upon the creation of the Opportunity business object, often carrying over data from the original lead.
Why it matters As the primary starting point for the sales cycle analysis, this activity is fundamental. It enables the measurement of sales cycle duration, opportunity win rates, and the performance of the sales team from the moment a deal becomes tangible. Where to get This event corresponds to the creation timestamp of the Opportunity business object in SAP Sales Cloud. Capture Use the creation date and time of the Opportunity record. Event type explicit
	Opportunity Won	This activity signifies that the deal has been successfully closed and the organization has secured the business. It is captured by a user changing the status of the Opportunity to 'Won', which is a standard, explicit action in the system.
Why it matters This is a crucial milestone for measuring win rates and sales performance. It often serves as the trigger for downstream processes like order creation and fulfillment, making it a key transition point in the Lead to Cash cycle. Where to get Inferred from the status change history of the Opportunity business object. It's the timestamp when the status is updated to 'Won'. Capture Identify the timestamp of the status change to 'Won' on the Opportunity object. Event type inferred
	Payment Received	This activity confirms that the customer's payment has been received and processed, completing the financial transaction. This information is almost always sourced from an external financial or ERP system that updates the invoice or order status in SAP Sales Cloud.
Why it matters This is the final value-realization step in the Lead to Cash process. Measuring the 'Payment Collection Cycle Time' from invoice to payment is a key KPI for managing cash flow and assessing accounts receivable performance. Where to get Inferred from a status change on the Invoice or Sales Order object to a 'Paid' or 'Cleared' status. This data originates from an external financial system. Capture Identify timestamp when an Invoice status changes to 'Paid' or 'Cleared'. Event type inferred
	Quote Sent to Customer	This activity represents the moment a formal quote is officially submitted to the customer for review. This event is typically inferred from a status change on the Sales Quote object, for example, from 'In Preparation' to 'Sent' or 'Proposed'.
Why it matters This is a key milestone that starts the clock on customer negotiation and decision-making time. Analyzing the duration between this event and quote acceptance is critical for understanding deal velocity and negotiation bottlenecks. Where to get Inferred from the status change history of the Sales Quote business object. Look for the timestamp when the status is updated to a value indicating it was sent. Capture Identify the timestamp of the status change to 'Sent' on the Sales Quote object. Event type inferred
	Sales Order Created	This activity marks the creation of a formal sales order, which is the official internal document to kick off fulfillment and billing. It is explicitly logged when a user creates a Sales Order object and links it to the parent opportunity.
Why it matters This is a critical handoff point from sales to operations. Analyzing the time from 'Opportunity Won' to 'Sales Order Created' can reveal internal delays, while tracking rework after this point highlights data quality issues. Where to get This event corresponds to the creation timestamp of the Sales Order business object that is associated with the Opportunity. Capture Use the creation date and time of the associated Sales Order record. Event type explicit
	Invoice Generated	Represents the creation of the customer invoice, initiating the final financial stage of the process. This event may be captured from the creation of an Invoice object within SAP Sales Cloud or, more commonly, from an integrated billing system.
Why it matters This activity is the trigger for the payment collection cycle. Delays in invoice generation directly impact cash flow, so monitoring the time from fulfillment to invoicing is critical for financial health. Where to get Can be the creation timestamp of an Invoice business object linked to the Sales Order or an update from an external ERP. The source depends on system landscape. Capture Use creation date of Invoice record or an activity log entry from an integrated ERP. Event type explicit
	Lead Captured	This activity marks the initial creation of a lead record in SAP Sales Cloud. It represents the first point of contact or interest from a potential customer and is typically captured automatically from a web form, list import, or manual user entry.
Why it matters Tracking this event is crucial for analyzing the effectiveness of different lead sources and measuring the overall lead generation volume. It serves as the starting point for calculating lead conversion rates and understanding the top of the sales funnel. Where to get This event is captured from the creation timestamp of the Lead business object. The source of the lead is typically available as an attribute on the same object. Capture Use the creation date and time of the Lead record. Event type explicit
	Lead Qualified	Represents the point where a lead has been vetted and meets the criteria to be considered a viable sales prospect. This is typically captured when a user manually changes the status of the Lead to 'Qualified' or a similar state, signifying it is ready for conversion.
Why it matters This activity separates raw leads from serious prospects, allowing for analysis of the qualification process efficiency. It helps determine the quality of leads from various sources and identifies bottlenecks between lead capture and active selling. Where to get Inferred from the status change history of the Lead business object. Look for a timestamp when the status field is updated to 'Qualified'. Capture Identify the timestamp of the status change to 'Qualified' on the Lead object. Event type inferred
	Order Fulfilled	This activity signifies that the products have been shipped or the services have been delivered to the customer. This event is often captured through an integration with an ERP or logistics system, which updates the status of the Sales Order in SAP Sales Cloud.
Why it matters This marks the completion of the delivery part of the process. Measuring the time from 'Sales Order Created' to 'Order Fulfilled' is essential for monitoring order fulfillment efficiency and its impact on customer satisfaction. Where to get Typically inferred from a status change on the Sales Order object, such as 'Fulfilled' or 'Shipped'. This status is often updated via an external system integration. Capture Identify timestamp of Sales Order status change to 'Fulfilled' or 'Delivered'. Event type inferred
	Quote Accepted by Customer	Indicates that the customer has formally or informally agreed to the terms of the quote. This is usually captured via a manual status update on the Sales Quote or Opportunity object by the sales representative.
Why it matters This is a strong buying signal and a critical milestone before contract finalization or order creation. It helps measure the effectiveness of proposals and the length of the customer's decision-making cycle. Where to get Inferred from a status change on the Sales Quote object to 'Accepted' or a similar status change on the linked Opportunity object. Capture Identify timestamp when the Sales Quote status changes to 'Accepted'. Event type inferred
	Quote Created	Marks the creation of a formal sales quote document linked to the opportunity. This is an internal step before the quote is sent to the customer and is captured explicitly when a user generates a new Quote record.
Why it matters This activity separates the internal preparation time from the time the customer has the quote. It helps in analyzing the efficiency of the quote generation process and how long it takes sales reps to prepare pricing. Where to get This event is the creation timestamp of the Sales Quote business object that is associated with the Opportunity. Capture Use the creation date and time of the associated Sales Quote record. Event type explicit
	Sales Order Reworked	Represents a modification or correction to a sales order after it has already been submitted. This event is inferred by detecting loops in the sales order status, such as a status moving from 'Submitted' back to 'In Process' or 'Pending Revision'.
Why it matters Identifying rework is key to uncovering process inefficiencies, data entry errors, and communication gaps between sales and operations. A high frequency of rework indicates quality issues that cause delays and increase costs. Where to get Inferred from the status change history of the Sales Order object. It requires analyzing the sequence of status changes to identify backward movements or loops. Capture Detect status transitions that represent a loop, e.g., 'Submitted' to 'In Process'. Event type inferred
	Solution Proposed	This activity signifies that a potential solution or product configuration has been presented to the customer. It is typically inferred from a sales stage or status change on the Opportunity object, such as moving to a 'Proposal' or 'Solution Design' stage.
Why it matters Analyzing the time spent in this stage and the transition to the quoting phase helps identify bottlenecks in the solutioning process. It provides insight into how long it takes to move from initial contact to a concrete proposal. Where to get Inferred from the change log or status history of the Opportunity object, specifically when the sales stage is updated to a value like 'Solution Proposed' or 'Value Proposition'. Capture Identify timestamp when the Opportunity's sales stage field changes to reflect a proposal. Event type inferred

Recommended Optional

Extraction Guides

How to get your data from SAP Sales Cloud

Steps

Prerequisites: Ensure you have a user in SAP Sales Cloud with access to the Analytics work center, specifically with rights to create and edit data sources and reports.
Create a Combined Data Source: Navigate to the Design Data Sources view within the Business Analytics work center. Create a new Combined Data Source. Join the following standard data sources using a left outer join to ensure all opportunities are captured: Lead Information -> Opportunity Header and Item -> Sales Quote Header and Item -> Sales Order Header and Item. The join condition should typically be based on the Opportunity ID or successor document IDs.
Build a Custom Report: Navigate to the Design Reports view. Create a new report based on the combined data source created in the previous step. Add all necessary characteristics and key figures to the report view, including identifiers (Lead ID, Opportunity ID), all relevant status fields, and all timestamp fields (Creation Date, Status Change Dates, etc.). Also include the required attributes like Sales Representative, Lead Source, and Order Amount.
Expose Report as OData Service: In the Design Reports view, select the newly created report. In the report's configuration or settings, find the option to expose the report as an OData query. This is often a checkbox labeled Enable for OData. Save the changes. The system will generate a unique OData URL for this report.
Identify the OData URL: Go to the OData Services view in the Administrator work center. Find your custom report in the list and copy its service URL. You will need this for the Python script.
Set up Python Environment: Ensure you have Python installed along with the requests and pandas libraries. You can install them using pip: pip install requests pandas.
Configure Python Script: Copy the provided Python script. Replace the placeholder values for C4C_TENANT_URL, OData_SERVICE_PATH, USERNAME, and PASSWORD with your specific SAP Sales Cloud credentials and the OData URL from the previous step.
Execute the Extraction Script: Run the Python script. The script will authenticate with the OData API, fetch the data in pages to handle large volumes, and load it into a pandas DataFrame.
Data Transformation: The script automatically transforms the wide-format data (one row per opportunity) into a long-format event log. It iterates through each potential activity, checking for a valid timestamp. If a timestamp exists, it creates a corresponding event record with the correct ActivityName.
Export the Event Log: Once the script completes, it will save the final event log as a lead_to_cash_event_log.csv file in the same directory. This file is formatted for direct upload into a process mining tool.

Configuration

Data Source Configuration: The core of this method is a Combined Data Source in SAP Sales Cloud Analytics. It's critical to use Left Outer Joins starting from the Opportunity object to ensure you capture opportunities that do not result in quotes or orders.
Report Characteristics: The custom report must include all date and time fields that correspond to the desired activities. This includes creation timestamps for leads, opportunities, quotes, and orders, as well as status change timestamps.
OData API Filtering: To manage performance and data volume, use the $filter parameter in the OData URL within the Python script. It is highly recommended to filter on a date range, for example, OpportunityCreationDate. A typical date range for initial analysis is 6 to 12 months.
Pagination: The provided script handles OData pagination using the $top and $skip parameters. This is essential for extracting large datasets without timing out the API connection.
Prerequisites: This method requires a dedicated analytics user with permissions to access the OData API and the underlying data sources for leads, opportunities, quotes, and orders.

a Sample Query python

import requests
import pandas as pd
import numpy as np

class=class="hl-string">"hl-comment"># --- Configuration Placeholders ---
C4C_TENANT_URL = class="hl-string">"[Your SAP Sales Cloud Tenant URL, e.g., my123456.crm.ondemand.com]"
class=class="hl-string">"hl-comment"># The service path comes from the OData Services view for your custom report
ODATA_SERVICE_PATH = class="hl-string">"/sap/c4c/odata/analytics/ds/CUST_REPORT_ID.svc"
USERNAME = class="hl-string">"[Your_Username]"
PASSWORD = class="hl-string">"[Your_Password]"

class=class="hl-string">"hl-comment"># --- OData Service Details ---
class=class="hl-string">"hl-comment"># The entity set is usually the report ID followed by class="hl-string">'ResultSet'
ENTITY_SET = class="hl-string">"CUST_REPORT_IDResultSet"
BASE_API_URL = fclass="hl-string">"https://{C4C_TENANT_URL}{ODATA_SERVICE_PATH}/{ENTITY_SET}"

class=class="hl-string">"hl-comment"># --- Data Extraction ---
def get_odata_data(api_url, username, password):
    records = []
    page_size = 1000
    skip = 0
    
    class=class="hl-string">"hl-comment"># It is highly recommended to add a date filter for performance
    class=class="hl-string">"hl-comment"># e.g., class="hl-string">"$filter=OpportunityCreationDate ge datetime'2023-01-01T00:00:00'"
    odata_params = {
        class="hl-string">"$format": class="hl-string">"json",
        class="hl-string">"$top": page_size,
        class="hl-string">"$skip": skip,
        class="hl-string">"$inlinecount": class="hl-string">"allpages"
        class=class="hl-string">"hl-comment"># class="hl-string">"$filter": class="hl-string">"[Your OData Filter String]"
    }

    print(class="hl-string">"Starting data extraction from SAP Sales Cloud...")
    while True:
        odata_params[class="hl-string">'$skip'] = skip
        response = requests.get(api_url, auth=(username, password), params=odata_params)
        
        if response.status_code != 200:
            print(fclass="hl-string">"Error: Failed to fetch data. Status code: {response.status_code}")
            print(response.text)
            break

        data = response.json()[class="hl-string">'d']
        if class="hl-string">'results' in data and data[class="hl-string">'results']:
            records.extend(data[class="hl-string">'results'])
            count = len(data[class="hl-string">'results'])
            print(fclass="hl-string">"Fetched {len(records)} records...")
            if count < page_size:
                break
            skip += page_size
        else:
            break
            
    print(class="hl-string">"Extraction complete.")
    return pd.DataFrame(records)

source_df = get_odata_data(BASE_API_URL, USERNAME, PASSWORD)

class=class="hl-string">"hl-comment"># --- Data Transformation ---
if not source_df.empty:
    print(class="hl-string">"Starting data transformation into event log format...")
    
    class=class="hl-string">"hl-comment"># Rename columns for clarity (adjust these based on your report's field names)
    class=class="hl-string">"hl-comment"># These are example API field names from the OData service.
    class=class="hl-string">"hl-comment"># You MUST inspect your OData metadata to get the correct field names.
    column_mapping = {
        class="hl-string">'C_OpportunityId': class="hl-string">'SalesOpportunityId',
        class="hl-string">'LeadCreationDateTime': class="hl-string">'LeadCaptured_Time',
        class="hl-string">'LeadConversionDateTime': class="hl-string">'LeadQualified_Time',
        class="hl-string">'OpportunityCreationDateTime': class="hl-string">'OpportunityCreated_Time',
        class="hl-string">'OpportunityProposalStageDateTime': class="hl-string">'SolutionProposed_Time',
        class="hl-string">'QuoteCreationDateTime': class="hl-string">'QuoteCreated_Time',
        class="hl-string">'QuoteSentToCustomerDateTime': class="hl-string">'QuoteSentToCustomer_Time',
        class="hl-string">'QuoteAcceptedDateTime': class="hl-string">'QuoteAccepted_Time',
        class="hl-string">'OpportunityWonDateTime': class="hl-string">'OpportunityWon_Time',
        class="hl-string">'SalesOrderCreationDateTime': class="hl-string">'SalesOrderCreated_Time',
        class="hl-string">'SalesOrderReworkedDateTime': class="hl-string">'SalesOrderReworked_Time',
        class="hl-string">'SalesOrderFulfilledDateTime': class="hl-string">'OrderFulfilled_Time',
        class="hl-string">'InvoiceGeneratedDateTime': class="hl-string">'InvoiceGenerated_Time',
        class="hl-string">'PaymentReceivedDateTime': class="hl-string">'PaymentReceived_Time',
        class="hl-string">'OpportunityClosedWonDateTime': class="hl-string">'OpportunityClosedWon_Time',
        class="hl-string">'OpportunityClosedLostDateTime': class="hl-string">'OpportunityClosedLost_Time',
        class="hl-string">'SalesRepresentativeName': class="hl-string">'SalesRepresentative',
        class="hl-string">'LeadSourceName': class="hl-string">'LeadSource',
        class="hl-string">'OpportunityStatusName': class="hl-string">'OpportunityStatus',
        class="hl-string">'SalesOrderAmountValue': class="hl-string">'SalesOrderAmount',
        class="hl-string">'ProductCategoryName': class="hl-string">'ProductCategory',
        class="hl-string">'QuoteDiscountPercentage': class="hl-string">'DiscountPercentage'
    }
    source_df.rename(columns=column_mapping, inplace=True)

    class=class="hl-string">"hl-comment"># Convert date strings to datetime objects, coercing errors to NaT (Not a Time)
    date_cols = [col for col in source_df.columns if class="hl-string">'_Time' in col]
    for col in date_cols:
        class=class="hl-string">"hl-comment"># OData date format is /Date(timestamp)/. We need to extract the timestamp.
        source_df[col] = source_df[col].str.extract(rclass="hl-string">'\/Date\((\d+)\)\/').astype(float)
        source_df[col] = pd.to_datetime(source_df[col], unit=class="hl-string">'ms', errors=class="hl-string">'coerce')

    class=class="hl-string">"hl-comment"># Define the activities and their corresponding timestamp columns
    activity_mapping = {
        class="hl-string">'Lead Captured': class="hl-string">'LeadCaptured_Time',
        class="hl-string">'Lead Qualified': class="hl-string">'LeadQualified_Time',
        class="hl-string">'Opportunity Created': class="hl-string">'OpportunityCreated_Time',
        class="hl-string">'Solution Proposed': class="hl-string">'SolutionProposed_Time',
        class="hl-string">'Quote Created': class="hl-string">'QuoteCreated_Time',
        class="hl-string">'Quote Sent to Customer': class="hl-string">'QuoteSentToCustomer_Time',
        class="hl-string">'Quote Accepted by Customer': class="hl-string">'QuoteAccepted_Time',
        class="hl-string">'Opportunity Won': class="hl-string">'OpportunityWon_Time',
        class="hl-string">'Sales Order Created': class="hl-string">'SalesOrderCreated_Time',
        class="hl-string">'Sales Order Reworked': class="hl-string">'SalesOrderReworked_Time',
        class="hl-string">'Order Fulfilled': class="hl-string">'OrderFulfilled_Time',
        class="hl-string">'Invoice Generated': class="hl-string">'InvoiceGenerated_Time',
        class="hl-string">'Payment Received': class="hl-string">'PaymentReceived_Time',
        class="hl-string">'Opportunity Closed Won': class="hl-string">'OpportunityClosedWon_Time',
        class="hl-string">'Opportunity Closed Lost': class="hl-string">'OpportunityClosedLost_Time'
    }

    class=class="hl-string">"hl-comment"># Unpivot the DataFrame into an event log format
    event_log_df = pd.DataFrame()
    for activity, time_col in activity_mapping.items():
        if time_col in source_df.columns:
            temp_df = source_df[[class="hl-string">'SalesOpportunityId'] + [col for col in column_mapping.values() if col in source_df.columns]].copy()
            temp_df = temp_df.dropna(subset=[time_col])
            if not temp_df.empty:
                temp_df[class="hl-string">'ActivityName'] = activity
                temp_df.rename(columns={time_col: class="hl-string">'EventTime'}, inplace=True)
                event_log_df = pd.concat([event_log_df, temp_df])

    class=class="hl-string">"hl-comment"># Select and order final columns
    required_cols = [class="hl-string">'SalesOpportunityId', class="hl-string">'ActivityName', class="hl-string">'EventTime']
    recommended_cols = [class="hl-string">'SalesRepresentative', class="hl-string">'LeadSource', class="hl-string">'OpportunityStatus', class="hl-string">'SalesOrderAmount', class="hl-string">'ProductCategory', class="hl-string">'DiscountPercentage']
    final_cols = required_cols + [col for col in recommended_cols if col in event_log_df.columns]
    
    event_log_df = event_log_df[final_cols]
    event_log_df.sort_values(by=[class="hl-string">'SalesOpportunityId', class="hl-string">'EventTime'], inplace=True)

    class=class="hl-string">"hl-comment"># --- Export to CSV ---
    event_log_df.to_csv(class="hl-string">'lead_to_cash_event_log.csv', index=False)
    print(class="hl-string">"Event log saved to 'lead_to_cash_event_log.csv'.")
else:
    print(class="hl-string">"No data extracted. Skipping transformation and export.")

Steps

Prerequisites: Ensure you have a user in SAP Sales Cloud with access to the Analytics work center, specifically with rights to create and edit data sources and reports.
Create a Combined Data Source: Navigate to the Design Data Sources view within the Business Analytics work center. Create a new Combined Data Source. Join the following standard data sources using a left outer join to ensure all opportunities are captured: Lead Information -> Opportunity Header and Item -> Sales Quote Header and Item -> Sales Order Header and Item. The join condition should typically be based on the Opportunity ID or successor document IDs.
Build a Custom Report: Navigate to the Design Reports view. Create a new report based on the combined data source created in the previous step. Add all necessary characteristics and key figures to the report view, including identifiers (Lead ID, Opportunity ID), all relevant status fields, and all timestamp fields (Creation Date, Status Change Dates, etc.). Also include the required attributes like Sales Representative, Lead Source, and Order Amount.
Expose Report as OData Service: In the Design Reports view, select the newly created report. In the report's configuration or settings, find the option to expose the report as an OData query. This is often a checkbox labeled Enable for OData. Save the changes. The system will generate a unique OData URL for this report.
Identify the OData URL: Go to the OData Services view in the Administrator work center. Find your custom report in the list and copy its service URL. You will need this for the Python script.
Set up Python Environment: Ensure you have Python installed along with the requests and pandas libraries. You can install them using pip: pip install requests pandas.
Configure Python Script: Copy the provided Python script. Replace the placeholder values for C4C_TENANT_URL, OData_SERVICE_PATH, USERNAME, and PASSWORD with your specific SAP Sales Cloud credentials and the OData URL from the previous step.
Execute the Extraction Script: Run the Python script. The script will authenticate with the OData API, fetch the data in pages to handle large volumes, and load it into a pandas DataFrame.
Data Transformation: The script automatically transforms the wide-format data (one row per opportunity) into a long-format event log. It iterates through each potential activity, checking for a valid timestamp. If a timestamp exists, it creates a corresponding event record with the correct ActivityName.
Export the Event Log: Once the script completes, it will save the final event log as a lead_to_cash_event_log.csv file in the same directory. This file is formatted for direct upload into a process mining tool.

Configuration

Data Source Configuration: The core of this method is a Combined Data Source in SAP Sales Cloud Analytics. It's critical to use Left Outer Joins starting from the Opportunity object to ensure you capture opportunities that do not result in quotes or orders.
Report Characteristics: The custom report must include all date and time fields that correspond to the desired activities. This includes creation timestamps for leads, opportunities, quotes, and orders, as well as status change timestamps.
OData API Filtering: To manage performance and data volume, use the $filter parameter in the OData URL within the Python script. It is highly recommended to filter on a date range, for example, OpportunityCreationDate. A typical date range for initial analysis is 6 to 12 months.
Pagination: The provided script handles OData pagination using the $top and $skip parameters. This is essential for extracting large datasets without timing out the API connection.
Prerequisites: This method requires a dedicated analytics user with permissions to access the OData API and the underlying data sources for leads, opportunities, quotes, and orders.

a Sample Query python

import requests
import pandas as pd
import numpy as np

class=class="hl-string">"hl-comment"># --- Configuration Placeholders ---
C4C_TENANT_URL = class="hl-string">"[Your SAP Sales Cloud Tenant URL, e.g., my123456.crm.ondemand.com]"
class=class="hl-string">"hl-comment"># The service path comes from the OData Services view for your custom report
ODATA_SERVICE_PATH = class="hl-string">"/sap/c4c/odata/analytics/ds/CUST_REPORT_ID.svc"
USERNAME = class="hl-string">"[Your_Username]"
PASSWORD = class="hl-string">"[Your_Password]"

class=class="hl-string">"hl-comment"># --- OData Service Details ---
class=class="hl-string">"hl-comment"># The entity set is usually the report ID followed by class="hl-string">'ResultSet'
ENTITY_SET = class="hl-string">"CUST_REPORT_IDResultSet"
BASE_API_URL = fclass="hl-string">"https://{C4C_TENANT_URL}{ODATA_SERVICE_PATH}/{ENTITY_SET}"

class=class="hl-string">"hl-comment"># --- Data Extraction ---
def get_odata_data(api_url, username, password):
    records = []
    page_size = 1000
    skip = 0
    
    class=class="hl-string">"hl-comment"># It is highly recommended to add a date filter for performance
    class=class="hl-string">"hl-comment"># e.g., class="hl-string">"$filter=OpportunityCreationDate ge datetime'2023-01-01T00:00:00'"
    odata_params = {
        class="hl-string">"$format": class="hl-string">"json",
        class="hl-string">"$top": page_size,
        class="hl-string">"$skip": skip,
        class="hl-string">"$inlinecount": class="hl-string">"allpages"
        class=class="hl-string">"hl-comment"># class="hl-string">"$filter": class="hl-string">"[Your OData Filter String]"
    }

    print(class="hl-string">"Starting data extraction from SAP Sales Cloud...")
    while True:
        odata_params[class="hl-string">'$skip'] = skip
        response = requests.get(api_url, auth=(username, password), params=odata_params)
        
        if response.status_code != 200:
            print(fclass="hl-string">"Error: Failed to fetch data. Status code: {response.status_code}")
            print(response.text)
            break

        data = response.json()[class="hl-string">'d']
        if class="hl-string">'results' in data and data[class="hl-string">'results']:
            records.extend(data[class="hl-string">'results'])
            count = len(data[class="hl-string">'results'])
            print(fclass="hl-string">"Fetched {len(records)} records...")
            if count < page_size:
                break
            skip += page_size
        else:
            break
            
    print(class="hl-string">"Extraction complete.")
    return pd.DataFrame(records)

source_df = get_odata_data(BASE_API_URL, USERNAME, PASSWORD)

class=class="hl-string">"hl-comment"># --- Data Transformation ---
if not source_df.empty:
    print(class="hl-string">"Starting data transformation into event log format...")
    
    class=class="hl-string">"hl-comment"># Rename columns for clarity (adjust these based on your report's field names)
    class=class="hl-string">"hl-comment"># These are example API field names from the OData service.
    class=class="hl-string">"hl-comment"># You MUST inspect your OData metadata to get the correct field names.
    column_mapping = {
        class="hl-string">'C_OpportunityId': class="hl-string">'SalesOpportunityId',
        class="hl-string">'LeadCreationDateTime': class="hl-string">'LeadCaptured_Time',
        class="hl-string">'LeadConversionDateTime': class="hl-string">'LeadQualified_Time',
        class="hl-string">'OpportunityCreationDateTime': class="hl-string">'OpportunityCreated_Time',
        class="hl-string">'OpportunityProposalStageDateTime': class="hl-string">'SolutionProposed_Time',
        class="hl-string">'QuoteCreationDateTime': class="hl-string">'QuoteCreated_Time',
        class="hl-string">'QuoteSentToCustomerDateTime': class="hl-string">'QuoteSentToCustomer_Time',
        class="hl-string">'QuoteAcceptedDateTime': class="hl-string">'QuoteAccepted_Time',
        class="hl-string">'OpportunityWonDateTime': class="hl-string">'OpportunityWon_Time',
        class="hl-string">'SalesOrderCreationDateTime': class="hl-string">'SalesOrderCreated_Time',
        class="hl-string">'SalesOrderReworkedDateTime': class="hl-string">'SalesOrderReworked_Time',
        class="hl-string">'SalesOrderFulfilledDateTime': class="hl-string">'OrderFulfilled_Time',
        class="hl-string">'InvoiceGeneratedDateTime': class="hl-string">'InvoiceGenerated_Time',
        class="hl-string">'PaymentReceivedDateTime': class="hl-string">'PaymentReceived_Time',
        class="hl-string">'OpportunityClosedWonDateTime': class="hl-string">'OpportunityClosedWon_Time',
        class="hl-string">'OpportunityClosedLostDateTime': class="hl-string">'OpportunityClosedLost_Time',
        class="hl-string">'SalesRepresentativeName': class="hl-string">'SalesRepresentative',
        class="hl-string">'LeadSourceName': class="hl-string">'LeadSource',
        class="hl-string">'OpportunityStatusName': class="hl-string">'OpportunityStatus',
        class="hl-string">'SalesOrderAmountValue': class="hl-string">'SalesOrderAmount',
        class="hl-string">'ProductCategoryName': class="hl-string">'ProductCategory',
        class="hl-string">'QuoteDiscountPercentage': class="hl-string">'DiscountPercentage'
    }
    source_df.rename(columns=column_mapping, inplace=True)

    class=class="hl-string">"hl-comment"># Convert date strings to datetime objects, coercing errors to NaT (Not a Time)
    date_cols = [col for col in source_df.columns if class="hl-string">'_Time' in col]
    for col in date_cols:
        class=class="hl-string">"hl-comment"># OData date format is /Date(timestamp)/. We need to extract the timestamp.
        source_df[col] = source_df[col].str.extract(rclass="hl-string">'\/Date\((\d+)\)\/').astype(float)
        source_df[col] = pd.to_datetime(source_df[col], unit=class="hl-string">'ms', errors=class="hl-string">'coerce')

    class=class="hl-string">"hl-comment"># Define the activities and their corresponding timestamp columns
    activity_mapping = {
        class="hl-string">'Lead Captured': class="hl-string">'LeadCaptured_Time',
        class="hl-string">'Lead Qualified': class="hl-string">'LeadQualified_Time',
        class="hl-string">'Opportunity Created': class="hl-string">'OpportunityCreated_Time',
        class="hl-string">'Solution Proposed': class="hl-string">'SolutionProposed_Time',
        class="hl-string">'Quote Created': class="hl-string">'QuoteCreated_Time',
        class="hl-string">'Quote Sent to Customer': class="hl-string">'QuoteSentToCustomer_Time',
        class="hl-string">'Quote Accepted by Customer': class="hl-string">'QuoteAccepted_Time',
        class="hl-string">'Opportunity Won': class="hl-string">'OpportunityWon_Time',
        class="hl-string">'Sales Order Created': class="hl-string">'SalesOrderCreated_Time',
        class="hl-string">'Sales Order Reworked': class="hl-string">'SalesOrderReworked_Time',
        class="hl-string">'Order Fulfilled': class="hl-string">'OrderFulfilled_Time',
        class="hl-string">'Invoice Generated': class="hl-string">'InvoiceGenerated_Time',
        class="hl-string">'Payment Received': class="hl-string">'PaymentReceived_Time',
        class="hl-string">'Opportunity Closed Won': class="hl-string">'OpportunityClosedWon_Time',
        class="hl-string">'Opportunity Closed Lost': class="hl-string">'OpportunityClosedLost_Time'
    }

    class=class="hl-string">"hl-comment"># Unpivot the DataFrame into an event log format
    event_log_df = pd.DataFrame()
    for activity, time_col in activity_mapping.items():
        if time_col in source_df.columns:
            temp_df = source_df[[class="hl-string">'SalesOpportunityId'] + [col for col in column_mapping.values() if col in source_df.columns]].copy()
            temp_df = temp_df.dropna(subset=[time_col])
            if not temp_df.empty:
                temp_df[class="hl-string">'ActivityName'] = activity
                temp_df.rename(columns={time_col: class="hl-string">'EventTime'}, inplace=True)
                event_log_df = pd.concat([event_log_df, temp_df])

    class=class="hl-string">"hl-comment"># Select and order final columns
    required_cols = [class="hl-string">'SalesOpportunityId', class="hl-string">'ActivityName', class="hl-string">'EventTime']
    recommended_cols = [class="hl-string">'SalesRepresentative', class="hl-string">'LeadSource', class="hl-string">'OpportunityStatus', class="hl-string">'SalesOrderAmount', class="hl-string">'ProductCategory', class="hl-string">'DiscountPercentage']
    final_cols = required_cols + [col for col in recommended_cols if col in event_log_df.columns]
    
    event_log_df = event_log_df[final_cols]
    event_log_df.sort_values(by=[class="hl-string">'SalesOpportunityId', class="hl-string">'EventTime'], inplace=True)

    class=class="hl-string">"hl-comment"># --- Export to CSV ---
    event_log_df.to_csv(class="hl-string">'lead_to_cash_event_log.csv', index=False)
    print(class="hl-string">"Event log saved to 'lead_to_cash_event_log.csv'.")
else:
    print(class="hl-string">"No data extracted. Skipping transformation and export.")

Your Lead to Cash Data Template

Your Lead to Cash Data Template

Lead to Cash Attributes

Lead to Cash Activities

Extraction Guides

SAP Sales Cloud Analytics OData API Extraction

Steps

Configuration

a Sample Query python

SAP Sales Cloud Standard OData Business Object Query

Steps

Configuration

a Sample Query python

Ready to Get Started?

Optimize Your Lead to Cash Now, Increase Win Rates!

Your Lead to Cash Data Template

Your Lead to Cash Data Template

Lead to Cash Attributes

Lead to Cash Activities

Extraction Guides

SAP Sales Cloud Analytics OData API Extraction

Steps

Configuration

a Sample Query python

SAP Sales Cloud Standard OData Business Object Query

Steps

Configuration

a Sample Query python

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