This article is part 2 of our ongoing identity series. To read part 1, click here.

Customer segmentation helps successful organizations form a more nuanced understanding of their buyers and improve personalization to address customer characteristics. By tailoring your product and message to each customer’s industry, use case, and need, businesses increase their ability to reach, delight, and retain customers. 

But, as businesses scale, focusing on every customer equally is a losing game. Some might not even want attention unless they reach out first. Others may feel neglected and frustrated if not consistently engaged. Even once your customers are grouped into well-defined segments, it’s still possible to misallocate resources. This is where customer tiering comes in. 

What is Customer Tiering?

Essentially, customer tiering is prioritization. It helps your business maximize customer lifetime value (LTV). Businesses need to decide how much effort to invest in particular customer segments in order to be profitable. This is how they make sure they’re not investing too many resources in low value relationships or insufficient resources in potential high-value relationships.

Every business has a different way of tiering customers. 

In industries like fintech and investment research, this usually involves finding tiers of potential customers and/or markets that have the highest value. For example, because fintech companies make a lot of their money from debit fees, they want customers who make more money and transactions of a larger size. And, when these customers are identified and onboarded, they want to direct ample resources dedicated toward their satisfaction and retention, i.e. to the top tier.

Data Used Today for Customer Tiering

Today, there are two kinds of data used by businesses to tier customers:

1. Information the customer provides when they onboard

In some cases, this data can be pretty sparse. Even for financial services, the data customers provide is usually just their name, date of birth, address, and social security number. The business has to collect the rest, in compliance with current data privacy laws. They may have the data in house or obtain it elsewhere, such as a credit file, but regulations limit their ability to use such data.

2. Information related to past transactions 

This data can be hard to collect, unless businesses attempt to gather the information through a third-party service. They aren’t always able or successful in collecting such data.

Even with both of these data types in-hand, the full picture is still missing. It’s still unclear what the customer would do if incentivized, if they have a separate, lucrative sole-proprietorship spawned from a hobby or other venture, a long history in a relevant industry, etc.; essentially the rest of their identity. 

The Cost of Bad Customer Tiering 

A lack of proper customer tiering has both upfront and back end costs. 

The upfront costs include pursuing the wrong customers, or pursuing the right customers with the wrong offering because you don’t know them well enough to offer the right thing. The biggest upfront cost is the opportunity cost of not capturing the business in a timely manner. And, timing is everything.

The backend costs are incurred because people are agents of change. Financial service providers need a clear understanding of their customers’ risk or value. But, people’s circumstances change all the time. If all your customer insight is based on transactional data, you have no way to reflect the real commercial life events that impact an individual’s risk and value. 

However, there is a massive opportunity for businesses to capture and win their market share with customer tiering. Just consider some of the following statistics:

• 44% of businesses focus on customer acquisition, while only 18% focus on customer retention.

• 76% of companies agree that customer lifetime value is an important concept in their organization, but only 42% of companies can accurately measure the lifetime value of their customers.

• Selling to an existing customer has an average probability of 60 - 70%, while selling to a new customer has an average probability of 5 - 20%.

• The average American company will lose 23% to 30% of its customers each year due to a lack of customer loyalty. 

For more interesting statistics, read the Semrush/Statista Premium publication.

Addressing Data Challenges in Customer Tiering

Successful customer tiering depends on gathering the right data and making sure that data remains fresh and accurate over time. The biggest question businesses need to ask themselves is: “What are the problems I need to solve?” 

It’s not enough to simply arrive with a laundry list of data elements. This may lead you to miss out on added information or contextualizations that in-house data experts or vendors could have provided. A business needs to understand its own challenges before it can employ data to solve them. 

Once you have an understanding of your problems, apply a problem-centric approach when collecting data either in-house or with a data vendor. Keep in mind, stitching all of this data together is an additional challenge, especially for smaller firms with limited resources. Do not assume you’ve asked all of the right questions. Seek answers from data experts.

How the Right Data Powers Better Customer Tiering

There are two ways choosing the right data inputs can improve customer tiering:

1. Fills in the gaps to better understand customers

While credit bureaus can provide a snapshot of some people’s identity, there are many valuable cohorts they leave out. People older than 65 with shifting interests, people under 25 with a developing career, new immigrants, or even wealthy people who don’t use credit are just some examples of people that make up a large set of individuals that traditional means of data collection misses.

2. Increases the depth and reach of current and potential customers

When companies enlist the help of data providers with special expertises, they increase the depth of each person’s profile, helping  them prioritize and tier. 

In the case of People Data Labs, this means adding a layer of context drawn from professional data to understand people’s career path, education background, and commercial relationships. In addition, the kinds of data available is broadened to include specific sets to address particular problems within each industry.

At a certain point (which is realized more quickly than you think), having more of the same data doesn’t help you. You need many different kinds of data to look for that 1% lift that makes the difference to your business. A proper data-enabled customer tiering approach helps you find entities that you can’t see, and deepens and expands relationships with entities that you already engage.

To learn more, book a demo!

To read part 3, click here.

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