Data Accelerator is a cloud-based data management solution that is quickly onboarded to allow for analysis, insights, and visualization of your data.
- Match individuals across disconnected data sources
- Comply with CCPA and GDPR regulations
- Build better customer experiences and prospect audiences
- Accelerate into cloud-based data management
- Generate analytics and reporting
- See ROI on marketing and CRM data
- Power leading marketing platforms, like Salesforce Marketing Cloud and Adobe Marketing Cloud
- Quickly stand-up industry-specific reporting with built in dashboard templates (abandoned cart, fundraising, and more)
Built on the cloud platform of choice
With a modular approach to solving your marketing needs today and as they evolve, our Data Accelerators are pre-configured to get your data up and running from online and offline channels on the following platforms:
Amazon Web Services (AWS)
A leader in the cloud space, with a suite of tools (S3 Storage, RedShift Database, etc.) perfect for Data Accelerator in either a Merkle-hosted or client-hosted environment.
Google Cloud Platform (GCP)
Data Accelerator integrates with Google Cloud Platform and Google Marketing Platform, leveraging our serious strength in the ad tech space. Big Query is friendly with multiple tools (Looker, Tableau, Python, R, etc.) to further enhance, analyze and visualize your data.
Easily expand Data Accelerator’s functionality with our pre-built extensions
Get more out of your Accelerator with additional tools, services, and/or integrations to further enhance your existing data.
Salesforce Interaction Studio (IS)
Salesforce Interaction Studio (IS) is a powerful customer data platform and Data Accelerator was built to tightly integrate with it, acting as a rapid data ingestion tool that identifies and dedupes disparate data.
Adobe Experience Platform (AEP)
A leading customer data platform that not only has full integration with the other Adobe Cloud products but can also harness the power of the Data Accelerator.
Data privacy laws require compliance and preference management. Look up and view individual customers while managing personal data privacy requests.
With over 2,000 data attributes across sociographic, demographic, and purchase history, DataSource provides a robust foundation for the all-channel marketer seeking to influence the entire customer journey.
Built with Nonprofit marketers and fundraisers in mind, our Nonprofit extension stitches together disparate data sources to quickly enable campaign and reporting uses cases that accelerate ROI. Learn more here.
View your data loaded into the Data Accelerator solution. The dashboards provide both an aggregated view of data loading trends, job status, and a detailed view for proactive analysis.
Audience Profile and Retail Behavior Dashboards
Understand your customer and prospect profile and explore their purchase and engagement behavior using an interactive set of dashboards and self-service tools across socio-demographic, geographic, and recency, frequency, and monetary (RFM) attributes)
Leverage the power of the Merkle ID that supplies terrestrial identities for the individual, address, and household, and helps users understand the growth and quality of your contactable universe.
Built with retail industry marketers in mind, our Retail extension is a set of standard tables and fields built on Merkle best practices to enable standard reporting, analytics and campaign management.
Meet Our Team
Frequently Asked Questions
- Include a column header
- Column headers should not have any spaces or special characters. All lowercase characters is highly preferred.
- Do not use column names that can also be considered SQL commands (Ex: 'date', 'order', 'trim', etc.)
- When sending a date field, these should be sent as YYYY-MM-DD.
- If sending date / time stamp values, the following formats are accepted YYYY-MM-DD HH:MM:SS or YYYY-MM-DD HH:MM:SS.MMM
- Field delimiters can be either pipe, tabs, semi-colons or commas. Be aware of your data values and if some text values have commas in them, choose a different delimiter
- Record delimiters can be either Carriage Returns and/or Line Feeds
- It is strongly recommended that you use text qualifiers around your data values, especially if they potentially contain a value that is also serving as the delimiter (Ex: comma, pipe, etc.)
- NaN and NaT values are not also acceptable as NULL values.
- A single column file MUST have a header record