What Does Buying Customer Data Look Like?
What Does Buying Customer Data Look Like?
A Comprehensive Look at Common Data Appends
People struggle to understand what customer data exists. They’ll hear ‘data is the new1 oil' and know that it’s correspondingly valuable.
But if you pressed on specifics, almost no one would be able to tell you:
What kinds of data are valuable and why they’re valueable
Where you can get them
And why they’re valuable
In an earlier post (below), we covered the common groupings of customer data that a business might generate.
For this post, we’ll cover what can be purchased rather than collected organically.
You’ll understand what can be added to your dataset or what might be valuable to others.
What Can Be Appended to Enhance Your Dataset?
Enriching your customer data involves adding more information to your existing records.
You’ll often hear this referred to as a ‘data append’ because you append information about existing customers. Generally you don’t see companies buying data about random people.2
You’d want to append your data when you believe that understanding a specific aspect of your customers will let you increase their purchase behavior.
Data appends can quickly become expensive, so appends should only include the data that is actually valuable.
A considered field append can provide a more comprehensive view of your customers, enabling better decision-making and personalized marketing.
You can simultaneously create better customer segments, save money by suppressing unlikely-to-purchase audience ads, and target those most likely to purchase.
Here’s a summary of the types of potentially valuable data you can purchase and append to your datasets:
Use the info below as a reference, but don’t feel the need to remember it all
I have an example of some of the data categories offered by Axciom for appends in the footnotes for reference.3
Demographic Data
Age, Gender, and Income: Knowing basic demographics helps in tailoring your marketing strategies and understanding your customer base.
For example, advertising a Lamborghini to a household that makes the median income is likely a waste of advertising dollars.
Education and Occupation: These attributes provide insights into your customers' professional and educational background, aiding in segmenting and targeting.
Spending money to target an MBA holder for an executive MBA program would be money thrown away.
Geographic Data
Location Information: Address, city, state, and zip codes help in regional targeting and understanding geographic distribution.
It makes a lot of sense to advertise local deals to a local audience. I’ve talked about how Autozone does a great job here
Neighborhood and Housing Data: Information about home ownership and property values can be crucial for industries like real estate and home improvement.
Someone who has lived for a while in a neighborhood that’s changed values significantly might not know the worth of their house. Maybe sending them a flyer about a HELOC makes sense
Behavioral Data
Purchase History: Data on past purchases helps in predicting future buying behavior and personalizing offers.
Someone identified as a gardener would be dozens of times more likely to buy mulch than a random person picked in a city.
Online Behavior: Web browsing history and online activity can be used for targeted online advertising.
I guarantee you’ve seen an ad follow you around on different websites after you’ve browsed something specific
Financial Data
Credit Scores and Financial Health: These insights are vital for financial institutions to assess risk and offer appropriate financial products.
Spending Patterns: Understanding how customers spend can help in crafting better loyalty programs and promotions.
Lifestyle and Interests
Hobbies and Interests: Data on hobbies and interests can enhance customer engagement by providing content and offers that align with their passions.
Life Events: Information on significant life events such as marriage, childbirth, or moving can trigger personalized marketing campaigns.
Women who have just given birth for the first time are likely to create new habits and brand loyalties—this is a valuable time to spend money on an audience with a high Lifetime Value
Automotive Data
Vehicle Ownership and Preferences: Knowing what vehicles your customers own and their preferences can aid automotive sales and service industries.
Without direct financial info, you can guess with good accuracy someones financial situation by what car they drive
Contact Information
Email and Phone Numbers: Ensuring you have the latest contact information helps in maintaining communication and reducing bounce rates.
Note: Validating email addresses isn’t really an ‘append’ in the traditional sense, but it makes sure you aren’t spamming fake inboxes or catch-all mail addresses.
Health Data
Health Conditions and Preferences: This is particularly useful for healthcare providers and wellness companies to offer relevant services and products.
Note: Highly regulated. Be careful
Providers of Data Enrichment Services
Experian: Offers over 900 data elements for appending, covering demographics, financial data, and lifestyle attributes.
TransUnion: Specializes in credit-related data and financial health.
Epsilon: Provides detailed consumer insights, including purchase behavior and preferences.
Acxiom: Known for its extensive data on demographics, geographic, and lifestyle attributes.
LiveRamp: Focuses on linking offline data with online behavior for comprehensive customer profiles.
You can sign up for these services as needed to add to your dataset.
Is That It?
I tried to keep an even hand on this post about the merits of purchasing customer data, but I had to break out the section about the dark side of data into another post.
Check out the follow-up piece in a future article.
Can we really still say its new?
You will, however, see companies spending a lot of time putting together information about ‘lookalike audiences’
