How to approach your data strategy: Insights from an industry expert

Customer Data is a hot topic. And with good reason. Data-driven businesses have a proven advantage over their competitors. And data is the lifeblood of AI. So, that advantage is only going to increase.

But most of the businesses I talk with have no comprehensive strategy when it comes to their Customer Data. It’s an inherently complex area that spans the entire org structure.

I spoke with Customer Data strategist John Coniglio to de-mystify the approach to putting customer data to use, and provide practical advice for marketers, technologists, and business leaders.

Here’s what John had to say.

John...you’ve been helping businesses get a handle on their customer data for more than a decade. What’s the biggest misconception you hear about customer data that you wish you could clear up once and for all?

One of the recurring themes I hear from clients is their reluctance to invest in tools to leverage customer data more effectively, because they feel the quality of their data is poor. Many companies have siloed data, inconsistent data collection practices, and outdated information, so the hesitancy is understandable.

But here's the thing: investing in data tools can be the key to improving your data quality. For example, a Customer Data Platform (CDP) can be used to centralize data, identify inconsistencies, and help companies cleanse, unify, and enrich customer data.

You’re not suggesting investing in technology without a plan, are you? It sounds like you’re saying that some semblance of a plan needs to be in place, but don’t over-invest in cleaning up your data before you start investing in tech and tools.

Absolutely! Planning is critical, and I always recommend developing a data strategy focusing on people, process, and technology. The strategy can cover a lot of areas, and there are opportunities to make incremental improvements along the way.

Companies typically start with data quality, which is the foundation of any successful customer data initiative. That doesn’t mean everything needs to be perfect before implementing new tools, however.

For example, I led a customer data platform (CDP) selection project for a NYC fashion apparel brand. During the RFP process, the team learned valuable insights about how to properly prepare data before it is ingested into a CDP. I worked with the company’s IT team to map improvements to data collection and the management of customer data in the data lake. Some of the improvements were completed before the CDP implementation began.

By taking a tactical, progressive approach, you can unlock the value of your customer data while you simultaneously improve its quality. It's an investment that pays off in the long run.

What is the most common issue that you see holding companies back from maximizing the value of their customer data?

One of the most common barriers to maximizing the value of their data is the lack of a comprehensive data strategy. The sources of customer data are varied, and the data stores are typically siloed, making it challenging to compile a true 360-degree view of the customer. Different departments like Sales, Marketing, and Customer Service may have their own data sets that are not shared or integrated, leading to disjointed customer experiences and missed opportunities for insights.

Creating a customer data strategy brings business and technology leadership together to align on a unified vision, ensuring that data-driven insights are integrated into business operations to enhance decision-making, improve customer engagement, and drive competitive advantage.

It sounds like the process of thinking through a Customer Data strategy – bringing together business and technology leadership around a unified vision – can create an impact beyond using data. Can it help change the way a business operates?

One of the goals of a good customer data strategy is, in fact, to change the way businesses engage and communicate with their customers. The shared vision I spoke about earlier should establish a well-defined customer-centric model, which is foundational because it puts the customer at the heart of all process changes. A shared understanding of customer needs becomes the driving force behind a customer-centric model. By analyzing data and fostering a data-driven culture, the entire organization becomes equipped to anticipate and fulfill customer needs proactively.

In essence, the customer data strategy is the blueprint. It defines how you'll gather customer insights, break down silos, and leverage data to understand and serve your customers better. By following this blueprint, you naturally pave the way for a successful customer-centric business model that will foster process and technology improvements.

What are the top 3 uses cases for customer data that you see in the businesses you work with?

• Customer Segmentation and Targeted Marketing: The ability to segment customers based on various criteria such as demographics, behavior, brand engagement, and purchase history enables marketers to create audiences for one-to-many targeted campaigns that will resonate with specific customer groups and produce better results than generic campaigns sent to larger audiences.
• Personalized 1:1 Customer Engagement: The next level of engagement after one-to-many campaigns is one-to-one customer interactions, crafting hyper-personalized experiences that resonate on an individual level and deliver greater value through tailored recommendations and services.
• Predictive Analytics and Decision Making: Utilizing customer data for predictive analytics helps companies anticipate customer needs, forecast trends, and make informed strategic decisions. This can range from predicting future buying behaviors to identifying potential churn risks, allowing for proactive measures to enhance retention.

What are the key things businesses need to address when they set out to create a customer data strategy?

• Business Goals Alignment: The strategy should be built around achieving specific business goals, not just collecting data for the sake of it. Determine how investing in customer data will help to increase sales, improve customer retention and improve brand loyalty.
• Data Governance and Compliance: Establish clear rules and procedures for how customer data is collected, stored, accessed, and used, to ensure data privacy, security, and compliance with regulations.
• Data Quality and Integration: Address the challenge of siloed data by creating a centralized source of unified customer data, using all sources to expand customer profiles that can be used for audience building and activating 1:1 personalization.
• Technology and Talent: Choose the right technology stack to acquire, manage, analyze, and activate your data. Invest in building an analytics team with the skills to turn data into insights and action.
• Culture of Data-Driven Decision Making and Change Management: Foster a company culture where data is valued and informs decision-making at all levels. Train employees on how to interpret data and translate insights into actionable strategies. Manage the change process effectively to ensure buy-in and adoption of the data strategy across all levels of the business.

How important Is it to address these in this order? I get that business goals and alignment should probably be addressed before anything else. But can some of the other items run in parallel?

A good customer data strategy should not only determine what needs to be done, it should also provide a roadmap for implementing changes in the proper sequence. This might involve some changes happening concurrently, like centralizing data while simultaneously tackling critical data quality issues to ensure a smooth transition and continuous improvement.

So, where should a business start?

Businesses should start by defining clear business objectives, developing customer journeys, and conducting a thorough assessment of current data capabilities.

• Focus on data that supports specific objectives, like improving customer acquisition or boosting customer lifetime value.
• Identify customer journeys that can be implemented in the short term and refined later as the quality of data improves.
• Plan on creating business intelligence dashboards and reports to analyze the performance of new marketing and personalization campaigns.

What else should retailers know?

I am usually asked "how much will it cost to improve customer data capabilities?" The cost to improve customer data capabilities can vary widely depending on several factors, including the size of the business, the current state of the data infrastructure, the complexity of the data environment, and the specific goals the business aims to achieve. A comprehensive customer data strategy should include a prioritized list of short-term and long-term improvements, a roadmap to visualize the implementation timeline and dependencies, and high-level investment guidance.

Can a business take an incremental approach to investment? For instance, as a business leader, I control the roadmap so I can choose to invest heavily up front or take an approach that spreads out investments. Does this “staggered” approach pay for itself?

The answer depends on what changes are being planned and what the roadmap looks like. It may very well include opportunities for quick wins that require less of a financial commitment. The plan might also span multiple fiscal periods, allowing companies to spread their investment over a longer span of time.

Keep in mind there are significant financial benefits to be realized. For example, by understanding your customers better, you can personalize marketing campaigns, improve product recommendations, and upsell or cross-sell more effectively. This can lead to increased sales, higher average order values, and ultimately, a greater return on every customer. Additionally, you can target your marketing efforts more precisely, reaching the right people with the right message. This reduces wasted ad spend, and improves the efficiency of your customer acquisition efforts.

John Coniglio is a Senior Consultant and Practice Lead at OSF Digital Strategy. He has spent 20+ years as a CIO and technology leader at Alice & Olivia, Linens ‘N Things, Costume Supercenter and Ashley Stewart.

OSF Digital Strategy is the business consulting arm of OSF Digital, a global digital transformation company. Over the past 17 years, OSF Digital Strategy has helped 600+ businesses leverage digital and data to accelerate growth.

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https://insights.osf.digital/lp/data-strategy-assessment/

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