Case Study: The Disconnected Customer Data Challenge - Google Cloud Marketing Data Store
- Andy Ryan
- Feb 4
- 4 min read
From my experience working with a diverse range clients, smaller businesses find it more challenging to establish foundational customer data capabilities now than five years ago. High entry costs for major Enterprise Marketing Clouds prevent them from accessing these capabilities, often leaving them with outdated, disconnected CRM and MarTech systems. They will certainly have a blinkered view of their customers; many of the processes to generate insight and make decisions are manual, and in the worst-case scenario, they don't know what data they are sitting on nor if they have the legal basis for doing so.
It doesn't have to be this way!
Do these challenges sound familiar?
Your Customer data across Lead-gen, CRM, Call Centre, Website, Outbound Marketing is disconnected and siloed
You cannot orchestrate experience across channels
Your data is stagnating and represents a compliance risk
Insight and measurement is patchy and is channel-limited
You have limited MarTech budget, and can't justify the cost of an Enterprise Marketing Cloud implementation.
If the answer is yes, read on to hear how the delivery of a modern cloud Marketing Data Store hosted in Google Cloud can provide an affordable solution. We even managed to demonstrate some cool AI innovations on the resulting data.
The Challenge...
My client was in a situation very familiar to many SMEs. A selection of MarTech platforms, each with their own capabilities and tasks to perform in the conversion funnel, and almost entirely disconnected from each other...
Their challenge to me was straightforward...
We need to be able to measure the effectiveness of our Marketing spend
We need to be able to understand and report on the FULL customer journey
We want to move away from manual Sales & Marketing reports in MS Excel
How do we start to add value to our data and processes with AI?
We want to be able to orchestrate experience across ALL channels in the future
The Solution...
The good news was: there was no shortage of high-value data, I was confident we wouldn't have to replace any systems in the short-term, and there were mature and capable APIs available so integration should be possible.
Step 1 - A Fit-for-Purpose assessment of the current MarTech stack analysing capabilities, strengths & weaknesses, and cost of each platform against a target maturity model. The objective was to place Marketing spend and capability within the broader transformation roadmap.
Step 2 - Identify gaps in capability which, when closed will begin to meet the challenges. Enter Google Cloud. Since GA4 is able to natively export it's session and user data to BigQuery, this offers a cost-efficient and scalable Google Cloud Marketing Data Store.
Step 3 - Integrate with an IPaaS platform. Zapier provides a quick and easy low-code solution to getting data flowing between systems. In this case it provides the plumbing into Google Cloud on a real-time and batch basis.
Step 4 - Bring it all together. Using identity matching logic across all customer touchpoints, it was possible to build up a Single Customer View (SCV). Every inbound and outbound event stored against a global definition of "Customer". A standard data model which applies to every customer, regardless of where they came from and how much is known about them. This forms the data foundation upon which to build.
Step 5 - Provide self-serve analytics capability. Power BI was already in the early stages of adoption with the client, and it is an easy choice for this capability. With the creation of a range of reporting views in Google BigQuery and standard semantic models, users were able to easily create, publish, and share their own reports and analysis in either Power BI or MS Excel.
The Result...
Strategic Direction
A scalable and methodical approach to measuring current and future state capability and data maturity. This forms the basis for Marketing Technology Strategy and roadmap when applied alongside the broader CRM/Marketing Strategy.
Customer Data Foundations
The value of data is greater than the sum of it's parts. In this case, bringing together the individual sources and signals of customer engagement provides a rich Single Customer View as well as the granular detail of each engagement event.
Data Intelligence
Providing tooling to self-serve and share reports, dashboards and analyses is a significant step in helping and organisation become data-driven. Having access to an up-to-date view of every customer, from the earliest touchpoint through to conversion to purchase, without having to manually wrangle data in MS Excel is a huge time saver.
Bonus Innovation
With the Single Customer View in place it was possible to experiment with taking a call recording and enriching the Salesforce Lead record with greater context and depth of data. Using AI models (Speech-to-text & LLMs) we were able to identify the purpose of the call, the product of interested, how likely they are to convert, and general sentiment.
How I can help...
With over 25 years experience in providing Technology and Data strategic thinking, solutions and nurturing technical teams, I can help you with these challenges.
I have developed and deployed proprietary MarTech capability in the Customer Data Platform (CDP), Content Management (CMS), Customer Relationship (CRM) and Orchestration/Execution spaces, as well as partner cloud technologies from Microsoft, Google, Salesforce and various smaller third-parties.
If this engagement resonated with you, or if you have your own challenge you'd like to discuss, feel free to reach out through the usual channels, and we can get the conversation started.
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