Marketing Data Integration
Connect all your data — whether from marketing, customers, social networks, apps, web analytics, or more — for real-time, data-driven analysis.
Marketing data integration, for the full customer journey
Capture all interactions
Access deeper insights
Achieve hyper personalization
Get real ROI insight
Integrate the entire stack
Enjoy an open and flexible pipeline
Learn more about marketing data integration
Why data integration is important for marketers
Data integration involves assembling all your marketing data into a single "source of truth" for easy access and broad insights.
A department’s marketing data may be found in different silos, scattered far and wide. A typical department may draw from more than 50 sources, including, say, Salesforce data in one silo, Marketo in another, spreadsheets on an individual share, Google analytics, ad metrics from social networks like LinkedIn or Facebook, and of course, custom application or web events stored on backends like MongoDB, MySQL, or Google Big Query.
By integrating your marketing data from different campaigns, analytics, and many of the above-mentioned silos into a single data store, you can create a single source of truth and a unified view of the customer journey.
Common benefits of integrating your marketing data
With a single source of truth, it’s far easier to visualize insights, KPIs, and metrics that do not exist in scattered individual data stores. Marketing has greater control, when, across all data sources, it’s easy to collect and compile:
- aggregated metrics, like average time on page, content downloads, conversions, click through rate, and impressions;
- efficiency metrics like ROI, customer lifetime value, customer acquisition cost, and cost per action;
- various other miscellaneous metrics like sales, marketing, SEO, devops, etc.
The downsides of not integrating your marketing data
Without a unified view of all their data, a marketing team might get momentarily lucky with a few quick metric wins, but they will not be able to sustain this success over time. This lack of success may manifest as inaccurate ROI measurements, "whack-a-mole" maintenance efforts, time wasted by data scientists on massaging and integrating data (rather than creating advanced analytics), and discontinuous multi-screen experiences for customers, among other anti-patterns.
