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How a customer data strategy comes to life

After investing in martech solutions — often layering in new platforms and software over time — many organizations find themselves stuck. Whether the root issue is technology, processes, or capabilities, teams get frustrated when their tools don’t deliver. 

If you’re in a similar position, the best plan is often to step back and review your customer data strategy.

  • It might be time to re-evaluate in light of changing circumstances and shifting organizational goals.
  • You might need a new roadmap to accommodate new privacy regulations.
  • Or you might need a fresh take on how your martech stack fits into your enterprise architecture.

Customer data strategies come to life in different ways, but smart implementations always start with well-aligned use cases and clear expectations. In this article, we’ll look at three real-life examples of how organizations we work with got unstuck by creating or refreshing their customer data strategies.

Transformation 1: From scattered data to always on marketing

Our client managed customer data across multiple platforms, with no connectivity between digital and on-premises touchpoints. Lacking a unified view of customer behavior, the client defaulted to scatter-shot marketing, with disappointing results.

As part of a customer data strategy engagement, Fusion helped this client:

  • Define what wasn’t working and identify root causes 
  • Align business objectives, technical requirements, and key use cases
  • Recommend near-term remediation and future-state strategies
  • Establish a roadmap with incremental steps toward the solution

Then, we worked with the client to implement, test, and refine the customer data strategy, bringing the new solution to life in a way that fit the company’s culture and environment, including:

  • Developing a Master Data Platform 
  • Customizing multiple platform APIs to unify customer engagement data
  • Integrating multiple digital platforms
  • Implementing PowerBI for data visualization

As a result, the client now has a consolidated view of real-time customer behavior and multi-channel marketing activities, which enables an “always on” approach to customer engagement.

Transformation 2: From customer churn to customer retention

Another client was experiencing high rates of customer turnover but because they couldn’t discover the cause, they couldn’t develop a strategic plan for turning the trend around.

Our team suspected that the key was in the client’s customer data. To identify root causes for the customer churn, we:

  • Assessed the client’s customer data, which was housed in various locations and at different levels of quality across the organization
  • Implemented a centralized data platform to reconcile and unify customer data from different systems of record
  • Consolidated and cleansed the customer data, making it easier to use and analyze
  • Designed machine learning models to test high-value use cases like identifying warning signs of customer churn, flagging high-risk customers that fit the indicators
We utilized 5 steps in our machine learning approach: innovation cycle, data capture, model development, integration, and optimization.

As a result of centralizing and standardizing customer data, and using machine learning to quickly analyze significant current and historical information, our team helped the client flag customers likely to leave and put retention strategies into action to reduce the churn rate.

 Transformation 3: From disconnect to martech maturity

Another client we worked with had invested in powerhouse martech tools but wasn’t seeing the return they had expected. Overwhelmed by the disconnect between expectations and results, the organization asked us to help sort out what had gone wrong.

Our team helped the client re-evaluate their customer data strategy to determine the best path forward. Some of our work included:

  • In-depth analysis of existing technology platforms, software, and services
  • Clarifying the customer journey and identifying friction points both for internal and external users
  • Optimizing technology configuration and integrations, including key architectural changes
  • Cleansing data to remove duplicate information and give the client greater confidence in the quality and reliability of the data they collected
  • Implementing process and governance improvements

As a result, the client’s marketing team now works faster and more independently of IT, confidently using customer data to automate and personalize marketing touchpoints, and speeding up time to execution for their outreach and campaigns.

Get your transformation back on track

Ready to do more with your customer data and martech solutions? Defining a customer data strategy and bringing it to life doesn’t have to be so daunting. Whether you need a quick consultation or an in-depth engagement, our team can help you identify opportunities, outline a path forward, and put you on track to optimize the ways you collect, store, and use your customer data.

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Get the Ultimate Guide to Creating a Customer Data Strategy >>

About the author

Amy Brown

Amy Brown is a Digital Solutions Director at Fusion Alliance. With more than a decade of expertise, she finds her passion in crafting experiences that make brands and customers more valuable to one another. Amy has created numerous omnichannel strategies that are backed by executives of global Fortune 10 companies. Her strength lies in bridging the technical and the practical. With a unique background that blends both hard and soft skillsets, she finds joy in tackling business challenges that require both technology and empathy.

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