Creating an Effective Customer Data Strategy
Organizational leaders know the value of customer data. It shapes marketing campaigns, informs operations, and guides product development, as well as a host of other key business areas.
Yet approaches to customer data are often piecemeal or siloed, leading to fractured customer experiences. A customer might see retargeting ads for an item they returned, for example, or receive a welcome SMS message weeks after joining a loyalty program. These kinds of experiences weaken a brand’s relationship with its audiences and neglect insights that would otherwise energize engagement and increase customer loyalty.
To make the best use of customer data, organizations need an enterprise-wide strategy. That can seem like a tall order, particularly for larger businesses, but the results of having a strategy in place are too great to ignore. Indeed, when McKinsey asked what most helped organizations achieve their data and analytics goals, 21% of respondents ranked having a strategy as the #1 key to success.
Let’s look at how an organization can build a strong customer data strategy that meets business goals and delivers a cohesive, delightful customer experience.
1. Build a dedicated team
The types of customer data available are manifold, including first-party data and third-party data, which can be further subdivided into profile data, transactional data, engagement data, and the like. The range reflects the various departments that compile such data, indicating the cross-functional makeup of the team needed when developing a customer data strategy.
From sales executives and customer service leaders to digital marketers and IT technicians, as well as business analysts, there’s a wealth of in-house experts to involve. Especially important to have on board, however, is a data scientist who’s familiar with statistics, data visualization, and fields like machine learning and artificial intelligence.
Not every organization will have a data scientist or even staff available from each team, but don’t let that stop you. Gather the best group you can to ensure that everyone follows the same playbook. It will ease coordination, foster collaboration, and help roll out plans to various departments when it comes time to introduce the strategy and the tools needed to execute it.
2. Audit your data
Crafting a customer data strategy demands understanding the data you already collect, where there are gaps, and which data can be tossed. This work can be done internally or by an external vendor but should answer such questions as:
- What is the size of your customer data set? Who collects it, and where do they keep it? Remember: Volume isn’t always the answer. If the data doesn’t support your strategy and goals, then it’s just taking up space on your servers.
- Which categories of customer data are compiled, and what are their sources?
- What data security protocols does the company follow? Are there data privacy regulations to abide by, and are there governance policies in place?
- Which technology solutions, if any, are used to gather, store, and evaluate the data? Are those solutions centralized or spread across different business units? If it’s the latter, are any integrated?
- What input types make up your customer data set? Is it composed of structured or unstructured data? This is an important question for technologies that involve machine learning.
3. Establish goals and objectives
While the audit is taking place, set goals and objectives. Broadly speaking, these typically focus on improving top-line revenue and/or bottom-line value via operational efficiency.
You want to drill down on these, though, by tying specific key performance indicators (KPIs) to your goals. For example, is there a percentage by which you want to raise customer lifetime value? By how much would you like to grow your customer loyalty program? Establishing measurable goals will help your team determine concrete steps to take.
4. Decide your technology requirements
Once you’ve completed the audit and have decided on goals and objectives, it’s time to determine the technology solution that best supports your efforts. Data comes in many forms, and there’s mountains of it available, some of which can be handled manually and some that demand specialized technologies to decipher. Here are common options:
- Customer relationship management (CRM) system. CRMs are best known for helping organizations win new business and keep existing clients by making it easier to manage customer relationships. Their primary users usually occupy customer-facing roles, and the data that CRMs hold are often entered manually. While data unification is typically less expensive than with other systems, external integrations can be complicated and licensing costs steep. A CRM also can’t unify incoming data dynamically.
- Data lakes. These offer a central location for storing, processing, and securing structured, semi-structured, and unstructured data in native formats, often with no processing or size limitations — a boon, for sure, but it’s up to organizations to make the information found in data lakes useful. That requires dedicated personnel — think data scientists and engineers — so the usefulness of data lakes really depends on who an organization has on staff and their bandwidth. Use cases also make a difference. For example, if your marketing team needs to use the data, it would likely rely on IT for that. Alternatively, data engineers could install business intelligence (BI) and data visualization tools on top of the data lake to support downstream usage.
- Customer data platforms (CDPs). CDPs may be the best technology for helping organizations (especially those at the enterprise level) make good on their customer data strategy. They collect data across channels and systems to produce a unified customer profile — a “golden record,” if you will — in real time, allowing marketers the hands-on ability to offer the right products and services at the right time on the right channel and with the right message. And, if you’re considering using machine learning (ML) models, a unified customer view is a must, because ML models are built off of them. Remember: ML models are only as strong as the data they’re built on.
5. Implement your customer data strategy
After resolving gaps in your customer data that were identified during the audit, you may need to scrub the data and complete similar tasks as you prepare to implement your chosen solution and integrate it with your existing tech stack. Then, you'll be ready to begin leveraging customer data to meet established objectives. For example, through technology like identity resolution, a CDP can create 360° profiles of individual customers that offer marketers insights into their behavior, such as likelihood to buy or responsiveness to discount offers.
These individual profiles can also be segmented and clustered as patterns emerge, allowing marketers to categorize them into groups like “impulse buyers” or “luxury shopper” and to tailor their outreach initiatives accordingly.
A CDP can also improve personalization efforts, a real advantage. Organizations that successfully deploy personalization realize 40% more revenue than their counterparts who invest less in the tactic, according to McKinsey.
6. Iterate and optimize
Of course, data isn’t static nor are the rules that govern them. Customer behaviors and fortunes will change depending on factors as general as the economy and as personal as life events like marriage. Data privacy regulations, which are on the rise, should also be tracked. You’ll need to monitor these impacts and adjust your tactics so they continue to yield the desired business outcomes.
Execute on your customer data strategy today
The amount of customer data available to organizations can be overwhelming, creating chaotic environments that paralyze teams and hold businesses back. By developing a customer data strategy, organizations can act with confidence and begin to realize the benefits that technologies like customer data platforms promise. To learn more about the tools that can help your organization reach its goals, discover Acquia CDP