HubSpot can be integrated with SAP C4C using various methods, including: * **Third-Party Integration Platforms:** Tools like Zapier, Integromat (now Make), or specialised integration platforms (iPaaS) offer pre-built connectors and workflows to link HubSpot and SAP C4C. These platforms typically allow you to map data fields, set up triggers, and define actions between the two systems without extensive coding. * **Custom API Integrations:** Both HubSpot and SAP C4C provide comprehensive APIs (Application Programming Interfaces) that allow for direct integration. This approach involves developing custom code to facilitate data exchange, synchronisation, and automation between the two platforms. This offers the most flexibility but requires development resources. * **Connectors and Add-ons:** Some third-party vendors offer specific connectors or add-ons designed to integrate HubSpot with SAP C4C. These are often developed with specific use cases in mind and can streamline the integration process. **Common Integration Scenarios:** * **Synchronising Contact and Account Data:** Keeping contact and company information consistent between HubSpot (for marketing and sales) and SAP C4C (as your CRM) is a primary integration goal. * **Lead and Opportunity Flow:** Passing leads from HubSpot marketing campaigns into SAP C4C for sales follow-up, or syncing opportunity stages from C4C back to HubSpot. * **Syncing Product and Sales Order Data:** Integrating product catalogues and sales order information to provide a unified view of customer transactions. * **Marketing Automation and Sales Alignment:** Using HubSpot's marketing automation capabilities to nurture leads that are then passed to SAP C4C for sales engagement. **Key Considerations:** * **Data Mapping:** Carefully plan how data fields will be mapped between the two systems to ensure accuracy and consistency. * **Data Synchronisation Rules:** Define whether the integration will be one-way, two-way, or triggered by specific events. * **Real-time vs. Batch Processing:** Determine if data needs to be synced in real-time or if periodic batch updates are sufficient. * **Error Handling and Monitoring:** Implement robust error handling and monitoring to ensure the integration runs smoothly and to quickly identify and resolve any issues. * **Security:** Ensure that the integration method adheres to your company's security policies and best practices.

The integration between HubSpot and SAP C4C is handled via the SAP Integration Suite on the SAP Business Technology Platform. Qualified leads (SQLs) are automatically transferred from HubSpot to SAP C4C, where a lead object and contact are created. The C4C lead ID and opportunity status flow back to HubSpot bidirectionally, allowing inside sales to track progress without switching systems. At Tyrolit, this integration runs in production as part of the fully automated lead-to-sales process.

What is the difference between SAP Datasphere and SAP Analytics Cloud?

SAP Datasphere is the data layer: it integrates, models, and harmonises data from multiple sources – SAP systems such as S/4HANA and C4C as well as non-SAP systems like HubSpot or Google Ads. SAP Analytics Cloud (SAC) is the visualisation layer: interactive dashboards, reports, and planning applications are built on top of the models in Datasphere. Both products are part of the SAP Business Data Cloud on SAP BTP. At Tyrolit, online marketing data is integrated into Datasphere via Python ETL, combined with CRM and ERP data into a unified lead-to-sales data model, and then visualised in SAC as role-specific dashboards.

Yes, Google Ads and LinkedIn data can be integrated into SAP Datasphere.

Yes. As Google Ads, Meta, and LinkedIn do not offer native SAP connectors, integration is typically done via a Python ETL layer that queries the respective marketing APIs and loads the structured data into SAP Datasphere. There it is merged with CRM data from SAP C4C and transaction data from SAP S/4HANA. Harmonising master data and KPI definitions is essential so that campaign data, lead data, and revenue data can be compared on the same basis. At Tyrolit, this pipeline is running in production.

To measure ROAS (Return on Ad Spend) in SAP, you'll typically need to combine data from your advertising platforms (like Google Ads, Facebook Ads, etc.) with your sales and financial data recorded in SAP. Here's a general approach, broken down into steps: 1. **Identify and Export Advertising Spend Data:** * **Platform Reports:** Access your advertising platform's reporting tools. You'll need to extract the total ad spend for a specific period. * **Data Extraction:** Export this spend data, usually in a CSV or Excel format. Ensure you have: * Date / Period * Advertising Platform (e.g., Google Ads, Facebook) * Campaign Name / Ad Group / Ad Creative (if you want to measure at a granular level) * Total Spend 2. **Identify and Export Revenue/Sales Data from SAP:** * **Sales Orders (SD Module):** The most common source of revenue is from sales orders. You'll likely need reports from the Sales and Distribution (SD) module. * **Financial Documents (FI Module):** If your revenue recognition happens through financial postings, you might need data from the Financial Accounting (FI) module. * **Key Data Points:** Extract data that reflects the value attributed to sales. This could include: * Document Number (e.g., Sales Order Number, Invoice Number) * Document Date / Posting Date * Net Value / Revenue * Customer Information (optional, for segmentation) * Material/Product Information (optional, for segmentation) 3. **Attribute Sales Revenue to Advertising Campaigns:** This is the most critical and often the most complex step. You need a way to link a sale back to the marketing effort that influenced it. Common methods include: * **Attribution Models:** * **Last-Click Attribution:** If a customer clicked on an ad as their last interaction before purchasing, attribute the sale to that ad. This requires tracking mechanisms. * **First-Click Attribution:** Attribute the sale to the first ad the customer interacted with. * **Multi-Touch Attribution:** More sophisticated models that distribute credit across multiple touchpoints. * **Tracking Mechanisms:** * **UTM Parameters:** Use UTM parameters in your ad URLs. When a customer clicks, these parameters are passed to your website. Your website/e-commerce platform should capture these and, when a sale is made, associate the UTM source/medium/campaign with the order. * **CRM Integration:** If you have a CRM integrated with SAP and your website, the CRM can track customer journeys and pass attribution information. * **Promotional Codes/Vouchers:** If specific ad campaigns use unique promotional codes, you can link sales using those codes directly to the campaign. * **Brand Search / Direct Traffic:** For campaigns aiming to increase brand awareness, you might need to analyse changes in direct traffic or branded search queries. 4. **Data Consolidation and Calculation:** * **Data Loading:** Load both your advertising spend data and your attributed sales revenue data into a reporting tool or a data warehouse. * **Matching:** Join the data based on common periods (e.g., monthly, weekly) and potentially specific campaigns if your attribution model allows. * **ROAS Formula:** Once you have your attributed revenue and your ad spend for the same period/campaign, calculate ROAS using the standard formula: **ROAS = (Attributed Revenue / Ad Spend) \* 100%** * *Example:* If you spent £1,000 on an ad campaign and it generated £5,000 in attributed sales, your ROAS would be (£5,000 / £1,000) * 100% = 500%. 5. **Reporting within SAP or External Tools:** * **SAP Analytics Cloud (SAC):** A powerful tool for combining data from SAP and external sources, creating dashboards, and performing analysis. You can import your ad spend data and link it to SAP sales data. * **SAP BW/4HANA or SAP Data Warehouse Cloud:** Set up data models to store and process both sets of data, then use reporting tools like SAP BusinessObjects or SAC to visualise ROAS. * **External BI/Analytics Tools:** Tools like Tableau, Power BI, or Qlik Sense can be used to connect to SAP data (via OData services, database connections, etc.) and import/combine with your ad spend data for ROAS reporting. * **Manual Reporting (Less Ideal):** For very small operations, you might export data from SAP and advertising platforms into Excel, do manual lookups and calculations, and generate reports. This is not scalable or efficient for ongoing analysis. **Key Considerations for SAP Integration:** * **Sales Order Types/Item Categories:** Ensure you are selecting the correct sales order types and item categories in SAP that represent actual revenue. * **Revenue Recognition:** Be mindful of SAP's revenue recognition rules. Is the revenue recognised upon delivery, invoicing, or payment? Your ROAS calculation should align with this. * **Currency Conversion:** If advertising platforms report spend in a different currency than your SAP financials, you'll need to perform currency conversions consistently. * **Data Granularity:** Decide at what level you want to measure ROAS (overall, by campaign, by product, by region). This will dictate the data you need to extract and how you link it. * **Partner Solutions:** Some third-party partners offer solutions that integrate advertising platforms directly with SAP data for improved ROAS tracking and reporting. In essence, ROAS in SAP isn't usually a standard, out-of-the-box report. It requires a deliberate setup that combines your marketing cost data with your financially recognised sales revenue, with a robust attribution model in place to link the two.

Measuring ROAS in SAP means linking advertising spend from marketing platforms to the actual revenue realised in SAP S/4HANA – across all process stages. This requires an end-to-end data model that consolidates impressions, clicks, MQLs, SQLs, opportunities, and orders in a shared data foundation. At Tyrolit, this ROAS is reported daily in SAP Analytics Cloud and is based on booked revenue in SAP S/4HANA – not just clicks or lead counts.

In an SAP CRM context, the difference between MQL and SQL lies in their purpose and application: * **MQL (Marketing Qualified Lead):** This is a **qualitative** term used in marketing and sales to describe a lead that has shown **interest** in your product or service and has been **qualified** by your marketing team as being more likely to become a customer. An MQL typically arises from lead generation activities where a prospect has interacted with your marketing materials (e.g., downloaded an e-book, filled out a contact form, attended a webinar). The focus is on the lead's engagement and expressed intent. It's a stage in the lead nurturing process. * **SQL (Standard Query Language) / Structured Query Language:** This is a **technical** language used for **managing and manipulating data** in relational databases. In SAP CRM, like many other database-driven systems, SQL is the standard language you would use to query, insert, update, and delete data stored within the CRM database. For instance, you might use SQL to extract a list of all customers in a specific region, or to update the contact details of an existing record. **In summary:** * **MQL** refers to a **type of lead** based on their engagement and qualification for sales. * **SQL** refers to a **programming language** used to interact with the underlying database that stores CRM data. You would use **SQL** to retrieve or manage the data that identifies and categorises **MQLs** within the SAP CRM system, but MQL itself is not a query language.

A Marketing Qualified Lead (MQL) is a contact who has indicated initial purchase intent through their behaviour. At Tyrolit, a contact is classified as an MQL as soon as they have actively filled out a contact form. A Sales Qualified Lead (SQL) is an MQL that has been pre-qualified by the inside sales team: mandatory fields are completed and the need is verified. Only once SQL status is reached is the contact transferred via the SAP Integration Suite from HubSpot to SAP C4C and created there as a lead object.

The time it takes to implement a lead-to-sales analytics solution on SAP BTP can vary significantly depending on several factors, but here's a general breakdown and the key considerations: **Typical Timeframes (Estimates):** * **Small/Basic Implementation:** 2-4 weeks * This might involve implementing a pre-built content pack or a very straightforward data model with minimal customisation for a single business area. * **Medium Implementation:** 1-3 months * This typically includes integrating data from a few key sources (e.g., SAP S/4HANA, a CRM), developing custom dashboards, and some level of business logic. * **Large/Complex Implementation:** 3-6 months (or more) * This would involve integrating multiple complex data sources, significant data transformation, advanced analytics, custom workflows, and comprehensive user training across different departments. **Key Factors Influencing Implementation Time:** 1. **Scope and Complexity of Requirements:** * **Number of Data Sources:** Integrating data from SAP S/4HANA, SAP Sales Cloud, third-party CRMs, marketing automation tools, or other systems will increase complexity. * **Data Volume and Quality:** Large datasets and poor data quality require more effort in data cleansing, transformation, and integration. * **Analytic Depth:** Simple reporting versus predictive analytics, forecasting, or AI-driven insights will have different timelines. * **Customisation Needs:** The degree to which standard SAP BTP analytics services (e.g., SAP Analytics Cloud) need to be customised or extended. 2. **Data Integration and Preparation:** * **Data Modelling:** Designing effective data models that support your analytical needs. * **ETL/ELT Processes:** Setting up robust processes for extracting, transforming, and loading data into your BTP environment. * **Data Cleansing:** Addressing inconsistencies, duplicates, and errors in source data. 3. **SAP BTP Services Used:** * **SAP Analytics Cloud (SAC):** This is the primary tool for dashboards, stories, and planning. Its configuration and development time depend on the complexity of the stories and models. * **SAP Data Intelligence / SAP Integration Suite:** Used for data integration and orchestration, especially for complex landscapes. * **SAP HANA Cloud:** For data storage and advanced analytics. * **SAP BTP ABAP Environment:** If custom logic needs to be developed within SAP BTP. 4. **Resource Availability and Expertise:** * **Skilled Team:** Having a team with expertise in SAP BTP, data integration, analytics, and your specific business processes is crucial. * **Internal vs. External Resources:** The availability and efficiency of your internal IT and business teams, or the capabilities of your consulting partner. 5. **Testing and User Acceptance:** * **Thorough Testing:** Ensuring data accuracy, report functionality, and user experience. * **User Training:** Adequate training for end-users is essential for adoption, but it also adds to the overall project duration. 6. **Project Management and Governance:** * **Clear Project Plan:** A well-defined project plan with milestones and deliverables. * **Stakeholder Alignment:** Ensuring buy-in and timely feedback from all stakeholders. * **Change Management:** Managing expectations and user adoption processes. **In summary, while a basic setup might take a few weeks, a comprehensive and robust lead-to-sales analytics solution on SAP BTP is often a multi-month endeavour requiring careful planning, skilled resources, and a clear understanding of business requirements.** It's recommended to conduct a thorough discovery and scoping phase with experienced SAP BTP implementation partners to get a more accurate estimate for your specific situation.

This depends on the complexity of the existing system landscape, data quality within the source systems, and the scope of dashboards required. The Tyrolit project involved integrating five data sources (Google Ads, Meta, LinkedIn, HubSpot, website), native connectivity to SAP C4C and SAP S/4HANA, data model design within SAP Datasphere, and the development of three role-specific dashboards in SAP Analytics Cloud. As a rough benchmark for comparable mid-market projects, expect a timeline of two to four months – provided the SAP BTP foundation infrastructure is already in place.

Contact &
Service

Project Report · Data Analytics

From the first click to the customer order: Lead-to-Sales Analytics at Tyrolit

End-to-End Transparency with SAP Datasphere and SAP Analytics Cloud

Which advertising campaign really brings in revenue? Where in the funnel are qualified leads lost? And what is the actual return on ad spend – not measured by clicks, but by customer orders in SAP S/4HANA? Tyrolit had the data, but it was scattered across different systems. Tycom changed that with SAP Analytics Cloud.

Reference customer Tyrolit AG

World-leading manufacturer of grinding and dressing tools
SAP customer since 1985 with SAP ECC, until Q2/2023: S/4HANA Migration

£ 740 million+

Annual turnover

4.500+

Employees

30+

Production sites

The starting situation

Data in silos – no overall picture possible

The Tyrolit Group operates a fully digitised lead-to-sales process: potential customers are addressed via targeted advertisements on Google, Meta, and LinkedIn, land on Ibexa landing pages, fill out a HubSpot form, and then go through a structured qualification process – right up to the customer order in SAP S/4HANA.

Each system individually provided valuable insights. However, a cross-departmental analysis – which campaign triggered which order, where leads are lost in the funnel, what the real ROI of a LinkedIn campaign is – was only possible with considerable manual effort. Differing KPI definitions, a lack of integration between marketing platforms, CRM and ERP, as well as time-consuming ad-hoc reports, made fact-based control of budgets and measures almost impossible.

The core question

Online marketing and lead generation are becoming an expensive black box and contribute measurably to business success. Without end-to-end analytics, the costs, efficiency, and impact of marketing and sales remain in the dark.

The process

Six phases, many systems

To understand why an analytics solution was necessary here, it is worth taking a look at the complete process. From initial campaign planning to sales realisation, a lead at Tyrolit goes through six clearly defined phases – each with its own systems, responsibilities, and data.

Strategic planning

Website · HubSpot

The marketing team defines buyer personas, develops lead magnets (whitepapers, webinars, demo requests) and creates landing pages with integrated HubSpot forms. Campaign control only begins once this strategic basis has been established.

Advertising & Traffic Generation

Google Ads · Meta Ads · LinkedIn Ads

Targeted ads appeal to B2B audiences by industry, position, and company size. A potential customer clicks on an ad and is redirected to the landing page.

(3) Lead Generation & Qualification (MQL → SQL)

HubSpot · Inside Sales

The prospect completes the form and is recorded as a HubSpot contact. At Tyrolit, a contact is considered an MQL as soon as they have actively completed the form. The Inside Sales team takes over the pre-qualification, and only when all qualification criteria have been met is the lead marked as an SQL and released for handover to sales.

(4) Handover to Sales

HubSpot · SAP Integration Suite · SAP C4C

The qualified SQL is automatically transferred via SAP Integration Suite to SAP C4C: Lead objects and contact objects are created in the CRM. The C4C Lead ID flows back to HubSpot, allowing Inside Sales to track further progress.

Sales Processing & Opportunity

SAP C4C · SAP S/4HANA · SAP Integration Suite

The sales representative contacts the prospect, documents all interactions in the C4C Lead object, and converts promising leads into an Opportunity. From the Opportunity, they create a quotation or order directly in SAP C4C — the creation is delegated to SAP S/4HANA via the Integration Suite.

Order processing & revenue recognition

SAP S/4HANA

Availability checks, picking, goods issue, delivery, and invoicing are all fully managed within SAP S/4HANA. Upon creation of the invoice, revenue is posted in finance. The sales process is therefore fully completed, from the initial marketing advertisement to the final realisation of revenue.

Example calculation for the lead-to-sales funnel

Example calculation for the lead-to-sales funnel

The solution

End-to-End Analytics on SAP Business Data Cloud

Tycom, in collaboration with Tyrolit, has developed an integrated lead-to-sales analytics solution based on SAP Datasphere and SAP Analytics Cloud — natively on the SAP Business Data Cloud and SAP BTP. At its core is an analytical data model that maps the complete funnel into a central database: from the first impression through MQL, SQL, and Opportunity to Order and Invoice.

Data integration in SAP Datasphere

Online marketing platforms (Google Ads, Meta, LinkedIn) are integrated into SAP Datasphere via a Python ETL layer. SAP C4C and SAP S/4HANA are connected using native connectors from SAP Integration Suite. Master data is harmonised, and KPI definitions are uniformly established – enabling Marketing, Sales, and Management to speak the same language for the first time. Conversion rates and lead nurturing times between funnel stages are calculated automatically, updated daily, without manual preparation.

Architecture and Data Flow: Technical Implementation in SAP Business Data Cloud

Three dashboards – three perspectives

Role-specific dashboards in SAP Analytics Cloud

Using the shared data repository, we developed three role-specific dashboards in SAP Analytics Cloud that answer different questions.

Management

Executive Dashboard

Is customer acquisition profitable and is the business growing?

ROAS, CAC vs. CLV, Total Revenue from Lead Gen Customers — At a Glance

Sales & Marketing Leadership

Lead-to-Sales process

Where do we lose leads and which sources generate revenue?

Funnel, Conversion rates, Nurturing times, Pipeline development with drill-down

Marketing Management

Campaign Performance

Which campaigns are providing the best leads for our budget?

Cost per MQL/SQL, ROAS by Campaign, Individual comparison by channel & period

The results

Fact-based control now possible

Our solution finally provides end-to-end, up-to-date transparency across the entire lead-to-sales process, without manual data preparation. Marketing, sales, and management work on a common database with uniform key figures, and budgets can be guided data-drivenly to where they demonstrably generate revenue. Lead nurturing times between funnel stages become visible, making it clear where leads get stuck and where measures are effective.

„Previously, we only had isolated analyses per channel and campaign – a complete picture across the entire funnel wasn't possible. With the SAC dashboards, we now have, for the first time, a daily, continuous view from the first click to revenue. This allows us to immediately see which campaigns are successful, how the pipeline is developing, and whether we are meeting our revenue targets. This creates transparency, saves time, and enables fact-based decisions.“

Digital Commerce & E-Operations, Tyrolit AG

Example: Lead-to-Sales Dashboard for Senior Management

Example: Lead-to-Sales Process Dashboard

Alexander Lieb

Data Analyst

Interested in a comparable solution?

We'll show you what Lead-to-Sales Analytics can look like in your SAP environment.

Lead-to-Sales Analytics with SAP – what companies want to know

Die Integration von HubSpot mit SAP C4C lässt sich durch verschiedene Methoden erreichen, darunter: * Drittanbieter-Integrationsplattformen: Dienste wie Zapier, Make (ehemals Integromat) oder Boomi bieten vorgefertigte Konnektoren und Workflows, um Daten zwischen HubSpot und SAP C4C zu synchronisieren. * APIs (Application Programming Interfaces): Beide Plattformen verfügen über APIs, die es Entwicklern ermöglichen, benutzerdefinierte Integrationen zu erstellen. Dies erfordert fortgeschrittene technische Kenntnisse. * Spezialisierte Integrationslösungen: Es gibt Unternehmen, die sich auf die Integration von CRM- und ERP-Systemen spezialisiert haben und maßgeschneiderte Lösungen für HubSpot und SAP C4C anbieten können. Die Wahl der Methode hängt von Faktoren wie Budget, technischem Know-how und den spezifischen Integrationsanforderungen ab.

The integration between HubSpot and SAP C4C is carried out via the SAP Integration Suite on the SAP Business Technology Platform. Qualified leads (SQLs) are automatically transferred from HubSpot to SAP C4C, where the lead object and contact are created. C4C lead IDs and statuses flow back to HubSpot bidirectionally, allowing Inside Sales to view sales progress directly without switching between systems. At Tyrolit, this integration is operational and forms part of a fully automated lead-to-sales process.

Der Hauptunterschied liegt in ihrem Zweck: SAP Datasphere ist eine umfassende Datenintegrations- und Bereitstellungsplattform, während SAP Analytics Cloud ein Tool zur Business-Intelligence- und Planungsanalyse ist.

SAP Datasphere is the data layer: it integrates, models, and harmonises data from various sources: SAP systems such as S/4HANA and C4C, but also non-SAP systems like HubSpot or Google Ads. SAP Analytics Cloud (SAC) is the visualisation layer: interactive dashboards, reports, and planning applications are built on the data modelled in Datasphere.

Both products are part of the SAP Business Data Cloud on SAP BTP. At Tyrolit, online marketing data (Google Ads, Meta, LinkedIn) is integrated into Datasphere via Python ETL, merged there with CRM and ERP data to form a unified lead-to-sales data model, and then visualised in SAC as role-specific dashboards.

Können Google Ads- und LinkedIn-Daten in SAP Datasphere integriert werden?

Yes. Since Google Ads, Meta and LinkedIn do not offer native SAP connectors, the integration typically takes place via a Python ETL layer which queries the respective marketing APIs and loads the structured data into SAP Datasphere. There, it is merged with the CRM data from SAP C4C and the transactional data from SAP S/4HANA. Harmonisation of master data and KPI definitions is important here so that campaign data, lead data and revenue data can be compared on the same basis.

How long does the implementation of a Lead-to-Sales Analytics solution on SAP BTP take?

This depends significantly on the complexity of the existing system landscape, the data quality in the source systems, and the scope of the desired dashboards. For Tyrolit, the project involved integrating five data sources (Google Ads, Meta, LinkedIn, HubSpot, Website/Landing Pages), native connection to SAP C4C and SAP S/4HANA, modelling the end-to-end data model in SAP Datasphere, and developing three role-specific dashboards in SAP Analytics Cloud. As a rough guideline for comparable projects in the medium-sized business sector, a project duration of two to four months is considered—provided that the SAP BTP basic infrastructure is already in place.