By JP Snow, Principal & Founder at Customer Catalytics, September 16, 2024
This article is part of our Customer Concepts series, which covers core ideas inherent to Customer Catalytics’ Customer Creation Model for driving growth, retention and scale.
The Hidden Foundations of Customer-Centric Business
Customer identifiers are not an enthralling topic. That’s one of the reasons so many businesses fail to think about them enough in their early days. Compared to the hustle of finding those first customers, “failing fast” and making strategic pivots, designing a mundane data structure feels like a drag. Planning a customer identification framework is also complex, requiring multiple functional perspectives and foresight about how the business model may evolve.
Businesses that fail to thoughtfully design their customer identifiers and related data structures do so at their own peril. I can’t think of any technical decision that is more overlooked relative to its long-term benefits and potential pain points. Builders make analogous decisions when they plan a new structure. They are meticulous about the location and accuracy of the foundation. They’re careful about laying out the network of plumbing and electrical conduits. If they don’t get these elements right, the building will be cursed with disconnects and workarounds that will be far more expensive to fix later, if doing so is even possible.
A rapidly growing e-commerce startup might initially focus on acquiring customers and processing orders without implementing a robust customer identification system. As they scale, they find themselves unable to track customer lifetime value accurately, personalize marketing efforts effectively, or provide seamless cross-channel experiences. The cost of retrofitting their systems to accommodate proper customer identification could be large enough to keep them from ever fixing it, despite the customer dissatisfaction and lost opportunities.
This Customer Concepts series article is the first of a few that will explain the value of identifiers as a critical enabler of customer insight and experience delivery. This article focuses on the most key components necessary for direct-to-consumer businesses. Future articles will focus on business-to-business needs and more advanced topics where identifiers need to be constructed or inferred.
What are customer identifiers?
In general, an identifier is a unique data element or key used within information systems to distinctly recognize, track and address a specific data record. They’re used to recognize a person or object, connect them with other records and make sure the right actions are applied only as intended. Identifiers are the glue that holds data ecosystems together. Without a Stock Keeping Unit (SKU#), your items at the grocery store wouldn’t scan. Without a survey id, the store wouldn’t know who to contact to resolve a service issue. The list goes on and on. By my count, there are at least 96 types of identifiers used to facilitate retail commerce. That excludes whatever is needed for the manufacturing, human resources and financial processes that don’t directly touch customers.
In the context of customer analytics, customer relationship management (CRM) and operational systems, identifiers are the connectors that enable businesses to know and meet their customers’ needs. Most important is the one used to designate each specific customer. The customer ID can be something unique that they provide, such as their email address or a chosen username, or it can be a distinct number or an alphanumeric code assigned by the company. Customer ids provide a mechanism for accumulating insights about each customer and for acting on that information.
Businesses that are primarily transactional use customer identifiers to accumulate insights about each customer and then act on that information. When a repeat purchaser logs in, they can avoid re-entering shipping and payment information. The company can provide order history, and they can use it to display more relevant buying options. They can also analyze the aggregated data across customers to inform product design and supply chain forecasting. Financial services, healthcare and other relationship-intensive businesses derive even more value from tracking the relationships with customers. In banking and investing, account balances provide ongoing income for both the customer and the firm. Most banking transactions produce no revenue but do still signal valuable information. Customer identification is also the key, literally, to tracking assets and protecting them.
Relationship-intensive businesses also use customer ids to track the relationships between customers and the changing connections between customers and the company over time. The example below illustrates several customer identification concepts used in financial services. The two customers have distinct identifiers. They are each associated with multiple accounts via roles that designate distinct access and information rights. The customers are also grouped into a household, designated by a distinct household id.
Visuals make it easier to project the many uses cases enabled by a well-designed customer identification structure. Without knowing these inter-relationships, a bank could still market to each customer or each account, but it would be blind to its customers’ most important life circumstances. It would miss out on serving financial needs following a marriage or providing advice to a surviving spouse. It could also send the same household redundant marketing messaging, potentially with conflicting signals. It would treat its customers transactionally, rather than valuing the totality of each relationship. Visuals are even more useful for more complex situations, such as a multi-generation household or financial advisor with influence on dozens of accounts.
ROI in Every Byte: The Many Benefits of Robust Customer Identification
Customer ids and related identifiers yield benefit through the data connections they enable. The most basic use cases are table stakes in a digital economy. Having a distinct login is necessary for facilitating account security and privacy protection. When a transaction on a shared investment account needs to be investigated, a bank needs to know not just which account was affected, but also which specific customers acted on it, including their trail of clicks.
A well-maintained customer profile serves as the foundation for reducing customer effort and delivering personalized experiences. Order history can be summarized in one place, backed by drill-down details for each purchase. Clothing retailers can store each customer’s size and tailoring preferences. Rewards points and other loyalty program mechanics create a stronger relationship over time. Apps can track rewards points automatically. In-store purchases typically require a clerk to prompt for a rewards number, which is another form of customer identifier.
Customer identifiers are the keys that enable a comprehensive view of each customer. The biggest challenge in data science is often access to the right data. If a retailer knows a given customer’s unique identifiers, data analysts can create a comprehensive picture inclusive of demographics, purchase history, in-person interactions, digital footprints and survey feedback. They can use this information to create more robust predictive models, which in turn enables more personalized marketing and better aggregate forecasting.
Limitations in business metrics and performance measurement often come down to the level of granularity in the underlying data. When a restaurant chain sees a drop in same-store sales, their next step is to understand why. A hypothesis that repeat customers are becoming more price sensitive can only be confirmed if the data provides a way to identify repeat customers.
Customer analytics complexity increases when business problems require analyzing inter-customer relationships, especially when these relationships evolve over time. Such complex use cases can also yield some of the best opportunities for unrealized lift. Most relationship-intensive businesses calculate some form of customer lifetime value (LTV). Forecasting customer longevity and retention requires predictive modeling. Changing family dynamics and household-level decision making can be significant drivers. Good investment advisors know the importance of addressing risk tolerance for both members of a two-person household. For certain types of insurance contracts, such as second-to-die policies, the actuarial calculation requires joint-probability equations based on both person’s age and health.
One of the clearest proof points supporting a solid customer identification design comes from seeing what happens when companies don’t construct them well. Starting with the login experience, customers suffer through unclear and changing user ids. For the company, compiling a complete customer picture requires extra steps to bring data together, often with mismatches that require further checking or create missed opportunities. The workarounds add cost and time to every data project. When companies finally decide to fix the root cause, they find themselves remediating multiple, deeply entrenched systems. When a retailer prompts every user with a message that they’re requiring everyone to set up new login credential, that’s likely an example where they have decided to finally remedy a bad initial design. A robust customer identity framework is instrumental to any modern company’s long-term scale.
The above examples highlight just a few of the top benefits. The chart below shows more, demonstrating the pervasive impact of this fundamental data concept.
Justifying the Right Start Requires Looking Ahead
With so many benefits enabled by an effective customer identification framework, it might seem surprising that so many businesses implement inferior approaches. There are several factors that lead to this costly misstep. Most importantly, all the design costs are immediate, while many of the benefits are years away, assuming the company lasts long enough to benefit from long-term relationships and future scale. The costs include expert data modeling based on requirements that need to anticipate a future business model still in development. The data fields that provide customer “join keys” need to get implemented with the very first data record. When securing initial sales, businesses must defer many longer-term decisions. Unfortunately, customer identifiers are hard to change later.
Customer relationships often seem complex and ambiguous — because in reality, they are. It’s much easier to report metrics based on each user ID or each account, rather than trying to count their overlapping relationships. Some gas and electric companies send their customers an estimate of their usage relative to their neighbors, intending to prompt heavy consumers to take steps toward energy efficiency. The highest users are often larger households, who dismiss the message because the utility company didn’t take their household size into consideration.
Decision makers within companies find discrete accounting based on individuals or accounts easier to work with. Accounts are distinct and get included in one and only one column on cross-tab reports. Groups of accounts could get included in multiple columns, leading to questions about where to put them or how to consider the math of double counting. It’s often easier to take the simpler route. Ignoring the more complex information also means ignoring potentially valuable information. A common example in financial services is the opportunity with estate transfers. Providing the data fields necessary for marketing to descendants seems like an expensive add-on when the systems are first developed. Decades later, when companies look to monetize the base of customers they have built, inter-generational wealth frequently arises as an untapped opportunity. The cost and complexity of adding family relationships to existing systems are often prohibitive.
Designing Effective Customer Identification Systems: A Practical Guide
Customer identification fields are mundane enough to be overlooked and complex enough to convince many decision makers to move on to clearer, more pressing concerns. Whether you’re thinking ahead or remediating an established data design, in this section I provide a starting point for getting it right.
First, it’s helpful to understand that a good identifier needs to have the following properties: uniqueness, persistence, accessibility, scalability, security and fit for purpose. System developers, information architects, and data modelers specialize at designing for these qualities while balancing trade-offs with cost and complexity. Functional stakeholders and business leaders should also be consulted to make sure the requirements reflect all future use cases, which will span cybersecurity, analytics, marketing, customer service, digital interfaces and financial reporting. A great way to reality check identifiers and how they relate is to watch field sales and service reps in action, and then translate what they do into the digital equivalent.
- When designing an identification system, it’s crucial to anticipate exactly what needs to be identified and consider the full spectrum of potential use cases. The object being referenced can be a specific person, an account, a transaction or a relationship. Focusing on the customer as a person, some companies differentiate prospects from customers using different fields. Others assign a customer ID as soon as they can. There are always trade-offs to consider. The use cases should consider all the potential benefits mentioned in this article. The more of these that apply, the more value is at stake and the more work will be required from data modelers. What works for a predictive analytics use case might not be sufficient for a real-time customer relationship management system.
Identifiers themselves can vary in their level of certainty. Account numbers and usernames are examples of authenticated customer ids that get directly confirmed through supporting processes. Other identifiers get constructed by individual systems. For example, marketing automation systems generate campaign ids and lead ids as communications are sent. Some match-key identifiers can only be inferred. Cookie ids are used to remember website user preferences and deliver targeted ads. The combination of privacy features and shared devices means companies can’t be certain about the customer they are interacting with. This process of connecting multiple identifiers to a single customer, especially when dealing with inferred data, is known as identity resolution in marketing and data analytics contexts. Evaluating these three levels of precision relative to the business need is an important part of planning for customer identification.
If you’re at the beginning stages of implementation, you have the advantage of starting with a clean slate. You also have the challenge of anticipating how the business model and supporting systems will evolve. As much as it’s ideal to get the key fields right the first time, there’s merit in starting with the available information and planning some future milestone for evaluation and adjustment.
If you already have systems in place and realize your customer identification framework is slowing you down, you get to consider the relative cost-benefit between workarounds compared to revising the underlying structures. Having a robust identification system is different from effectively utilizing it. If you’re not taking advantage of all the benefits shown in the above list, you should ask what’s possible that isn’t being done.
Actions to Catalyze Your Success
- Conduct a comprehensive audit of your current customer identification framework to understand how it’s enabling or impeding your growth and scale
- Evaluate the use cases enabled by customer identification (see list above) to see if you’re fully realizing the potential benefits
- Create a roadmap for implementing or upgrading your customer identification framework, prioritizing quick wins while planning for long-term scalability and flexibility
This article is provided by Customer Catalytics, a customer analytics and strategy consulting firm. We help companies grow through insight, automation and leadership. For a complimentary consultation focused on your company’s growth needs, contact us or schedule an introductory meeting at www.customercatalytics.com/connect.
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