Why Your Most Profitable Customers May Not Be the Best Customers
Secrets to ideal customers and analyzing customer data
In marketing, we like to talk about “ideal customers” for businesses and emphasize the importance of “knowing your customers.” How this performs properly is still a mystery to many companies.
Simply defining Business-to-business (B2B), college students, or parents as your ideal customer can leave many opportunities on the table.
As part of the Customer Lifetime Value analysis, the first step is to identify your ideal customers. Creating an ideal customer profile or a buyer persona helps you track and calculate whether the group you identified as ideal customers is, in fact, ideal for your business.
In this guide, I want to talk about what an ideal customer means to your business, where to get your customer data, and how to analyze that data to create an ideal customer profile.
First, let’s make sure we are on the same page when referring to ideal customers or clients.
Which customers are considered ideal? If your answer is “customers that buy the most or that are most profitable,” you may want to continue reading.
Who are Ideal Customers or Clients?
According to HBR’s “Choosing the Right Customer,” identifying the best primary customer involves these three dimensions: perspective, capabilities, and profit potential.
Perspective is the business’s culture and mission. Capabilities is the company’s core competency. Profit potential refers to the level of profit that a customer brings to the company.
Basically, the right customer is someone who’s aligned with the culture and mission of the business, taking advantage of the company’s core competency, while bringing a high level of profit.
It’s not only about being the most profitable or loyal, although that is a big part of the formula.
One common misconception is that ideal customers are only the ones with the highest profitability.
Customer Profitability Example
Consider this scenario:
Customer A results in a profit of $5000 but only bought once and will never return.
Customer B results in a profit of $500 but will buy monthly for the next three years, for a total lifetime profit of $18,000.
Which customer would you prefer? I’d say Customer B.
Let’s add one more element to this.
The same Customer A still buys only once but gives referrals to five new customers that are just like him/her. That’s a $30,000 total profit.
Customer A: (($5,000 profit) + ($5,000 profit referrals x 5 = $25,000)) = $30,000
What’s your answer now? My answer changed to Customer A.
Let’s add one more, and last, element to this scenario.
The same Customer A costs you $20,000 in their lifetime to service. On the other hand, the same Customer B costs $5,000 to maintain throughout their lifetime.
Customer A: (($5,000 profit) + ($5,000 profit referrals x 5 = $25,000)) — ($20,000 cost to service) = $10,000 profit
is less than
Customer B: ($18,000 profit) — ($5,000 cost to service) = $13,000
Therefore, I now prefer Customer B.
The point of this example is;
1 You can’t merely calculate a customer’s profitability (revenue minus cost) to determine their value to your business.
2 You need to consider the costs to acquire, service, and retain customers. This cost calculation doesn’t have to be perfect, but it has to be consistent in what you include vs. do not include.
3 Calculating the Customer Lifetime Value is not as simple and clear cut as you may think. However, it is valuable and well worth the exercise.
4Customer’s value goes beyond how much profit you receive from their purchases. How much they purchase, how often they purchase, how long they stay with your brand, and how many new customers they give referrals to all counts towards measuring the lifetime value.
Now that you better understand what an ideal customer looks like, let’s look into how you can go about finding them using your own customer data.
By the way, one of the common questions I get is — how many ideal customer types can you have?
How to Create an Ideal Customer Profile From Your Data
What happens if you don’t precisely know which types of customers you have?
For many companies that are not regularly reviewing their customer data and investing time and resources on the Customer Lifetime Value process, it may be difficult to know where to start.
Although this process can be made more complex, it doesn’t have to be.
Here are some of the customer data that you can review:
Demographics:
- Gender
- Age
- Marital status
- Income
- Employment
- Education
- Etc.
Behaviors:
- When are they interacting with you the most?
- What device are they using?
- What do they frequently buy together?
- How often do they repurchase?
- Why would someone purchase from our competitors instead?
Interests:
- What are their pain points?
- What are the most significant purchase motivations? Discounts, value, brand loyalty, etc.?
- What triggers their purchase intention?
Other information you can consider are:
- Competitors: Who are my main competitors going after?
- Market trends: Are my main products being replaced by a new product? Is my target audience still looking for and wanting my products? Will they in 3–5 years?
Knowing the details of your best customers helps you build a buyer persona, which visualizes the person you’re marketing to.
And based on your buyer persona profile, you can create their buyer’s journey and marketing funnel that all fit into your target audience.
Where to Get the Customer Data
Fortunately, modern-day businesses have access to a lot of data about anything and everything.
Unfortunately, many marketing teams or executives don’t exactly know how to process all that information to turn them into an actionable plan.
Properly collecting as much relevant customer data as you can is a crucial first step.
Let’s look at where you can look to collect your data.
Internal first-party customer data
You can use your CRM, POS, eCommerce or website platform, email marketing system, first-party analytics, and other first-party systems used to collect customer data.
Your CRM can contain a wide variety of data depending on how it’s set up and how the teams are using them.
The website or eCommerce platforms and analytics, such as WordPress, Magento, Shopify, and Google Analytics, provide plenty of information about your traffic and customers.
In a perfect world, we would have all of our systems combined to give one data source.
However, in the real world, we most likely will continue to have multiple systems that store our customer data in different places.
Therefore, it’s important to keep in mind what type of customer data you might need before deciding how to set up and use your first-party customer data systems.
Another way to utilize first-party data is through surveys you conduct with your prospects and customers.
You can learn a lot about why they’re choosing you over your competitors, their primary pain point(s) you’re solving, what attracted them to your offer, and other subjective aspects.
External third-party data
If you’re advertising in Google, Facebook, LinkedIn, or any other Pay-Per-Click (PPC) platform, you will receive a lot of data about your targets and paid traffic.
You can also use your social media analytics to see who is likely to engage with your brand.
They don’t give you any identifiable data, which means you can’t link which click or traffic is associated with which specific person.
However, it can tell you what type of people are more likely to click on your ad and convert. It can also tell you what type of people are more likely to spend more on their first purchase or advocate for your brand via social media channels.
You can also learn a lot about your prospects’ and customers’ interests and behaviors by testing different options and measuring the results.
For example, Facebook allows you to build your target audience and test different options to measure their performance.
If you have your first-party data, you can use that to help you determine the starting point. You can test different options to narrow in on precisely which groups are more likely to convert.
How to Analyze Your Customer Data [Best Practices]
You probably have multiple sources of data, with each providing a lot of different data points.
However, you don’t know what you’re looking for or where to start.
If that’s you, this section will help.
Customer data analysis should be methodical and systematic so that you can maintain its consistency and don’t over-complicate things.
The following are tips on how to make this happen:
1. Define your revenues and costs
When determining a customer’s profitability, we normally use their revenue or sales and costs.
However, revenue and cost are not always clear unless you explicitly define what they are.
Are you collecting shipping fees or other administrative fees such as late fees or processing fees?
If so, choose which of these revenues and fees that you collect will count towards your customer lifetime revenue.
Some companies that can track customer service, sales, and other admin times spent managing and servicing accounts may choose to use those expenses as part of the cost.
Other costs, such as shipping, marketing, and retention programs, should be clearly noted, so you or your team can pull the same numbers the same way every time.
It’s okay not to include all possible costs as long as you’re consistent in what you include every time you run the report.
That’s because you won’t be using the Customer Lifetime Value at its face value to determine anything. It’s a guide for making decisions and measuring performances rather than using a specific number figure.
2. Segment your customers
Any average is merely that, an average across the board.
If you want meaningful and relevant data, you need to implement customer segmentation in your strategy.
Segmenting your customers simply means you’re separating them in different relevant smaller groups to review them separately.
The key is to make your groups meaningful and relevant.
A group of customers with the first name of John isn’t meaningful to your company, and it isn’t relevant because there’s nothing you can do with that data set.
If half of your customers buy $10 each time and the other half buy $100 at a time, your average order is $55 per order. If you run a campaign to raise the average order value to $70, that helps neither the $10 nor the $100 group.
Some frequently used segmentation examples include:
- Acquisition channel: groups of customers acquired from PPC, social media, organic traffic or Search Engine Optimization (SEO), or email
- Product line: groups of customers buying your product line A, B, or C
- Profit margin: groups of customers lower than 30% profit margin vs. 31–50%, 51–75%, and over 76%
- Demographics: male or female, age groups, locations
- Customer behaviors: buying frequency, average order value
3. Start with segments with the highest financial impact
Unless you have unlimited amounts of time and resources, you’re better off starting with a few segments that make the highest impact on your business.
Let’s say you’re happy with where you are with lead generation and new customer acquisition, but you want to expand on your retention program to reduce customer churn.
You have customers that return monthly, every three months, every six months, and every year.
Ideally, you want more monthly returning customers to stay with you longer because they buy more often. However, after analyzing their CLV, you conclude that they’re more costly to retain than someone that returns every three months.
You decide to specifically target the 3-month returning customers for your retention program by sending them targeted emails, relevant offers, and other benefits for continuing to buy from you.
How does this approach differ from a typical remarketing strategy?
Many tend to target their whole customer base, trying to see what sticks and hope for the best.
Even if they segment the customers to retarget, they may choose a group that sounds like the best option rather than analyzing the data.
Starting with the customer segments with the highest financial impact allows your program to dedicate all of your available resources, including the budget, to focus on the group that would make the most difference.
It avoids spreading your campaign too thin across the board.
4. Track and monitor each segment over time
Your company decides to be more data-driven and start analyzing customer data.
You create an action plan based on your findings, but now what?
For example, your analysis uncovered that your customer group with a profit margin between 31–50% has the highest Customer Lifetime Value (CLV).
Your marketing team implements a campaign to retain these customers. They also go after new customers that are more like them.
How do you measure its success or know if it’s working?
We can run the analysis of this customer segment regularly to see the effects of these campaigns by looking at their CLV improvements.
Is there a significant increase or decrease?
What is driving their CLV increase or decrease?
Next Steps
By the time your team completes the process of identifying your ideal customers, you will feel much better about who you’re targeting and why.
It gives you a sense of clarity. It allows you to prioritize what’s important to you.
So, what’s next?
You need to figure out where to find more of them.
Using your marketing funnel and the analysis of your ideal customers, you can identify various customer touchpoints where you interact with your potential customers and clients.
Identifying the touchpoints with your ideal customers will help you focus your budget and efforts on acquiring the right customers at the right time and place.
This is Part 1 of the Customer Lifetime Value (CLV) Optimization Process Series, originally published on https://blog.digitalpowerup.com/ideal-customer-profile/
