With major shifts in consumer behavior and privacy-driven policies, it’s now more important than ever for you to architect (or re-architect) your Customer Data Solution. This new Customer Data Solution should create a view of the customer that is consistent across their journey regardless of the device they are on and the erosion of the reliability of the third-party identifiers.
In order to maintain an understanding of your customer and their actions across the user journey – despite tracking restrictions imposed by cookies not being supported – consented first-party data is paramount. You need data collection and activation strategies that enable a bigger picture view of the user for both reporting and targeted advertising. So where’s a good place to start? Read on to find out.
The Function of Customer Data Solutions
Identity resolution is used to stitch together data across devices and channels in order to build a more comprehensive customer profile for better-targeted advertising. There are two major methodologies for identity resolution in both built and bought customer data solutions (a homegrown solution vs a Customer Data Platform (CDP)).
- Probabilistic modeling – this method is more scalable and often used for building profiles of prospective customers for whom you’ve collected less first-party data.
- Deterministic matching – this method relies on the collection of first-party data that can be used to accurately identify users that would otherwise be anonymous in their customer journey within specific data sets.
Requiring or giving the option for user authentication for your website increases the opportunity for deterministic matching. Having first-party data collected in several contexts allows for truly understanding if data coming from different platforms, devices or identifiers are from the same user.
Customer Data Activation
After data is stitched and consolidated, another goal of a Customer Data Solution is making the holistic customer database available across platforms for use in reporting and targeted advertising. There are several different ways this can be done, from a homegrown built solution using analytics and cloud platforms to a bought CDP that can provide some automation for data stitching, porting data back into platforms and a user interface to interact with the resulting data.
Not only can this data be used in your databases to resolve customer identities, but this first-party data is now being used more often within the ad-serving platforms themselves for customer match services that let brands build targetable audiences that can be used in campaign activation.
Your Customer Data Solution Options
- Use a CDP (often in conjunction with LiveRamp or similar identity resolution services)
- Use your own customer data and tools from the Google Marketing Platform (GMP)/ Google Cloud Platform (GCP) stack to build your own homegrown solution
- Use a data connectivity tool like LiveRamp as an add-on tool for identity resolution
Let’s break these down in more detail. First up, CDPs.
1) Customer Data Platforms (CDP)
There are several options on the market for CDPs. They aim to meet a variety of customer data needs, including:
- Customer identification
- Customer data consolidation
- Analysis and insights
- Intelligent audience building/segmentation
- Publishing audiences back into the media platforms.
CDPs often have robust user interfaces and promise “plug-and-play” type capabilities for many of the above-listed challenges. It’s important to choose a CDP carefully based on your specific use case and to ensure there are resources available to get the data into those platforms. It’s also important to take into consideration any additional vendors that might be required to actually build out all CDP functionality.
Some popular CDPs include Segment, mParticle, and Amperity.
Next, let’s look at homegrown solutions.
2) Built Homegrown Solution
Building out a homegrown Customer Data Solution using Google Analytics and Google Cloud Platform can give you exactly the tools and pipelines you need to identify customers, consolidate that data, provide analysis and insights on that data, build intelligent audiences and segmentation and publish those audiences back into media platforms. CDPs are only as valuable as they are utilized and they can at times be expensive platforms that aren’t fully taken advantage of. Homegrown solutions using the GMP and GCP stack are nice because you have to build them intentionally based on how you plan to use them, so the up-front planning needed to meet business objectives and make sure the investment is worth it is built into the process.
A DIY custom-built customer data solution using Google tools could look like the following:
- GA4 + Server Side GTM for durable cookies and reliable data collection and customer identification
- BigQuery exports for campaign and customer clickstream data sources to consolidate data
- Using Data Studio, Looker or Tableau for analysis and insights
- Machine Learning (ML) Modeling to help build out intelligent audience segmentation
- API GA Data Import, GMP Tentacles, etc. for porting audiences back into marketing platforms for retargeting and campaign optimization
And finally, there are tools like LiveRamp.
LiveRamp’s identity graph associates anonymous device IDs, cookie IDs and other online customer IDs from premium publishers, platforms or data providers around an IdentityLink — a single person-based identifier. LiveRamp can be used in conjunction with a CDP or as a homegrown solution to support data stitching.
No matter what Customer Data Solution you use, it’s important to have one in place. If you have questions about which option is right for your organization, contact us and we’ll help you figure it out!