As generative AI permeates the business landscape and brands look to deliver more personalized customer experiences, effectively leveraging customer data has become essential to staying competitive. Recognizing that imperative, businesses are investing in data transformation to fully realize the power of their data.
Yet, many organizations underestimate the complexity of data transformation, often treating it as a buzzword rather than a foundational element of their digital strategy. A common misconception I hear from marketers is that merely investing in a customer data platform (CDP) or similar tool will be sufficient to launch and scale targeted omnichannel customer journeys. In practice, standing up one of these tools often reveals deeper issues that must be addressed before true data transformation can take place.
In this post I will explain data transformation — what it is and why it matters. In part two, I’ll lay out a roadmap for accomplishing effective data transformation.
What is data transformation?
The most common challenge businesses face in activating customer data is siloed people, data, and systems. Data transformation is the process of breaking down these silos for a more unified approach. Ultimately, this approach can unlock the true value of your data and teams.
At GrowthLoop, we’ve seen first-hand how unification can lead to better team coordination and drive better outcomes. Through effective data transformation, we’ve helped clients achieve a 15% increase in retention and a 20% lift in revenue for targeted audiences. That’s why helping teams build this critical foundation is central to our mission.
Siloing: The biggest hurdle to better customer experience
Why is siloing a problem? Imagine trying to build a house when the carpenters, electricians, and plumbers don’t know what each other is doing. To add to the challenge, only the general contractor has the blueprints, and each team must continually ask to see the plans whenever they want to start on a new room.
In business, the blueprints are your customer data, and siloing occurs when only the data team can access it. Teams across your organization must request specific data to do their jobs, which slows productivity. In addition, multiple copies of this data could be spread across various SaaS platforms, martech tools, CRMs, etc.
The costs of siloing
When one hand doesn’t know what the other is doing, businesses can see far-reaching, negative outcomes. Siloed data and teams leads to:
- Poor and inconsistent customer experience
- Disjointed objectives across teams
- Stifled revenue growth due to a lack of data to iterate on campaign performance
- Duplicate technologies that lead to expensive tech debt
- Inadequate competitive advantage as competitors take a new, unified approach
Organizations with successful data transformation strategies recognized the need to break down silos in both their data and their teams. They approached the challenge with a two-pronged strategy: choose the right platform (i.e., a composable CDP) and implement proper change management.
I will explain how to do both of those in my next post. For now, let’s explore why data transformation is essential to thriving in today’s business landscape.
Why data transformation matters: The imperative of digital engagement
The digital age demands that businesses not only collect but also thoughtfully and quickly leverage data to engage customers. This approach involves three key elements:
- Targeting – Identifying and reaching the right audience
- Personalization – Crafting tailored experiences that resonate with individual customers
- Orchestration – Seamlessly managing interactions across multiple channels
In the crowded media landscape, targeted, personalized, and coordinated interactions are critical for capturing and retaining customers. McKinsey estimates that 75% of customers use multiple channels in their ongoing experience. A similar number say they expect personalized interactions with brands and will switch to a competing brand to get that experience.
Failure to embrace data transformation that supports those outcomes can leave companies lagging. It can also lead to challenges addressing other factors, like privacy compliance and leveraging AI for growth.
AI and data transformation
Generative AI is ushering in a new era of generative marketing where marketers can rapidly create customer experiences through AI-driven suggestions for targeting, personalization, and channel strategies. But that can only happen with a solid data strategy in place. AI solutions rely on accurate, robust customer data to be effective — otherwise, the recommendations from these tools can be outdated, unsuited for the audience, or even nonsensical, which hurts customer trust.
An additional challenge facing organizations is the added privacy tools implemented by large tech providers. While Google reversed its decision to phase out third-party cookies, its Privacy Sandbox will likely greatly reduce opt-in for tracking, similar to what happened on iOS devices in 2020.
This shift means businesses can no longer rely on third-party data for customer insights. Marketers should prioritize owned first-party data (data collected directly from customers through interactions on their websites, apps, and other owned channels). This data is often more reliable, complete, and therefore more valuable for crafting targeted, personalized campaigns.
And again, this is where data transformation comes in — to be useful, this first-party data can’t be siloed.
Maintaining a competitive edge in the evolving digital landscape requires that data be unified, accessible, and actionable. Siloed teams relying on siloed data will find it difficult to launch and scale personalized customer experiences and compete with organizations that have achieved successful data transformation.
The power of data transformation
At GrowthLoop, we’ve partnered with numerous clients on the front lines of data transformation and have seen it lead to impactful business outcomes. By pairing Growthloop with a centralized cloud data warehouse, we’ve helped clients embrace a democratized view of data where teams self-serve their data needs with accessible tools that draw from one central source of truth.
By unifying data and efforts, marketing and data teams experience increased productivity and velocity. But there are other benefits, too:
- Campaigns thrive with personalization that increases sales impact.
- Marketers make rapid incremental improvements through faster closed-loop reporting.
- Performance is measured apples-to-apples across all campaigns and in the context of your business.
- Data teams spend more time on vital BI analysis and data science that impact all business teams.
All this adds up to a better customer experience and business growth. It’s made possible through data transformation.
In my next blog post, I will outline strategies for achieving effective data transformation. Watch this space!
