San Francisco, 94111, CA,
United States
Springboard Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by Springboard and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 3000 Springboard employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that Springboard has purchased the following applications: Stripe Payments for Payment Processing in 2020, Lever by Employ for Applicant Tracking System in 2021, Google BigQuery ML for ML and Data Science Platforms in 2018 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems Springboard is running and its propensity to invest more and deepen its relationship with Stripe , Employ , Greenhouse or identify new suppliers as part of their overall Digital and IT transformation projects to stay competitive, fend off threats from disruptive forces, or comply with internal mandates to improve overall enterprise efficiency.
We have been analyzing Springboard revenues, which have grown to $600.0 million in 2024, plus its IT budget and roadmap, cloud software purchases, aggregating massive amounts of data points that form the basis of our forecast assumptions for Springboard intention to invest in emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database or in cloud-based ERP, HCM, CRM, EPM, Procurement or Treasury applications.
ERP Financial Management
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Stripe | Legacy | Stripe Payments | Payment Processing | ERP Financial Management | n/a | 2020 | 2020 |
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HCM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Employ | Legacy | Lever by Employ | Applicant Tracking System | HCM | n/a | 2021 | 2021 |
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Applicant Tracking System | HCM |
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2021 | 2021 |
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Interview Scheduling | HCM |
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2021 | 2021 |
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AI Development
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Legacy | Google BigQuery ML | ML and Data Science Platforms | AI Development | n/a | 2018 | 2018 |
In 2018, Springboard implemented Google BigQuery ML to perform predictive analysis on the Stack Overflow real-world dataset, deploying the solution as part of its ML and analytics capability set. Google BigQuery ML was used to run in-database model training and predictive queries directly against the Stack Overflow dataset, keeping data and model artifacts inside the BigQuery environment.
The implementation leveraged Google BigQuery ML functionality for SQL-based model creation, iterative model evaluation, and in-place feature engineering, consistent with ML and Data Science Platforms workflows. Teams used BigQuery ML model types and evaluation routines to prototype classification and regression experiments, and they persisted models in BigQuery for repeatable scoring and batch prediction.
Operationally the work centered on the data science and analytics organization, with the Stack Overflow real-world dataset ingested into Google BigQuery as the primary data source. Data ingestion and transformation workflows were implemented as SQL pipelines inside BigQuery, enabling analysts to run predictive analysis without moving large volumes of data out of the platform.
Governance was structured around BigQuery dataset access controls and SQL-based model lifecycle practices, enabling controlled experimentation and model versioning within the Google Cloud environment. The narrative emphasizes Springboard Google BigQuery ML implementation within the ML and Data Science Platforms category, and highlights architecture choices that kept modeling, feature engineering, and prediction tightly coupled to the BigQuery data warehouse.
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AI-Powered Application
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Chatbots and Conversational AI | AI-Powered Application |
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2020 | 2020 |
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Collaboration
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Collaboration | Collaboration |
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2020 | 2020 |
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Collaboration | Collaboration |
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2011 | 2011 |
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Survey and Questionnaire | Collaboration |
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2018 | 2018 |
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Content Management
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Digital Signing | Content Management |
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2022 | 2022 |
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CRM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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CRM | CRM |
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2018 | 2018 |
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Customer Data Platform | CRM |
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2018 | 2018 |
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Customer Experience | CRM |
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2018 | 2018 |
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Customer Support | CRM |
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2020 | 2020 |
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Digital Advertising Platform | CRM |
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2015 | 2015 |
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Digital Advertising Platform | CRM |
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2017 | 2017 |
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Digital Advertising Platform | CRM |
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2020 | 2020 |
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Marketing Analytics | CRM |
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2015 | 2015 |
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Marketing Analytics | CRM |
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2015 | 2015 |
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Marketing Analytics | CRM |
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2016 | 2016 |
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Marketing Analytics | CRM |
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2016 | 2016 |
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Marketing Analytics | CRM |
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2018 | 2018 |
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Marketing Automation | CRM |
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2016 | 2016 |
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Marketing Automation | CRM |
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2021 | 2021 |
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Marketing Automation | CRM |
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2020 | 2020 |
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Marketing Automation | CRM |
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2020 | 2020 |
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Sales Automation, CRM, Sales Engagement | CRM |
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2021 | 2021 |
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ITSM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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IT Service Management | ITSM |
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2020 | 2020 |
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PaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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API Management, iPaaS (Integration Platform as a Service) | PaaS |
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2022 | 2022 |
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Transactional Email | PaaS |
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2015 | 2015 |
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Transactional Email | PaaS |
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2015 | 2015 |
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Transactional Email | PaaS |
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2020 | 2020 |
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IaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Application Hosting and Computing Services | IaaS |
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2012 | 2012 |
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Content Delivery Network | IaaS |
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2020 | 2020 |
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Content Delivery Network | IaaS |
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2015 | 2015 |
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| First Name | Last Name | Title | Function | Department | Phone | |
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| Date | Company | Status | Vendor | Product | Category | Market |
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