Why is Defining Your Ideal Customer Profile so Important?
Perhaps an even bigger question: how can you make sure you get it right?
In January of 2020 Google announced plans to phase out the use of third-party cookies causing cold sweats in marketers everywhere. As privacy concerns, a lack of transparency and the potential unethical use of data became widely discussed within the industry, other browsers like Firefox and Safari announced similar plans.
Various new technologies were discussed as potential replacements to the almighty cookie during subsequent years, with the use of Data Clean Rooms quickly becoming the most likely successor. While the industry scrambled to find new ways to support the growing desire for marketing personalization, tactical execution and campaign attribution, the value of first party data was becoming increasingly pronounced.
As is often the case what’s old is new again, as businesses reaffirm the value of their existing audiences and the comprehensive level of understanding their current customers can provide. While the idea of the Ideal Customer Profile (ICP) is nothing particularly new, it gains relevance and importance in a cookie-free world; one where data sharing within clean rooms can allow for the continuation of personalization and performance, but only with a deep understanding of the audience you want to reach in the first place.
All of this makes the strategic research component of any sales enabled marketing campaign so important. The more we can recognize our Ideal Customer Profile, and the better we can articulate the Total Addressable Market (TAM) available to us, the more intricate and defined the campaign buildout can be. And while this level of detailed analysis of zero-party and augmented first party data can be complex (especially when scaled across numerous data sources and manually verified line-item by line-item to ensure data-health) the power of these insights is impossible to overstate. The ICP is where everything starts in that it really helps us to avoid the core marketing mistake.
In short, it’s all about the consumer. What’s old is new.
So, if it all starts with the Ideal Customer Profile, let’s talk a bit about how it’s defined. Once the data sources are identified and collected, it can be analyzed and segmented to identify patterns and trends, which can ultimately be used to create a detailed picture of an ideal customer. And what characteristics are considered when building out the Ideal Customer Profile? Here are 10 powerful datapoints that represent just a sample of what might be included:
Ten Important Ideal Customer Profile Datapoints
1. Demographics: The “traditional” view of an ideal customer profile. We’ve come a long way since this was the primary targeting mechanism, but it’s still an important look. Segmentation based on characteristics such as age, gender, income, education level, and location.
2. Behavioral: Segmentation based on behavior and actions, such as purchase history, website browsing history, and engagement with a brand’s content. Perhaps one of the most critical ideal customer profile components.
3. Psychographics: The ideal customer profile element that most effectively guides creative messaging. Segmentation based on interests, values, beliefs, and lifestyle.
4. Technographics: Segmentation based on technology usage and adoption patterns, such as device preference, mobile app usage, and internet connection speed.
5. Firmographics: For B2B marketers, this is the ideal customer profile datapoint that is most important. Segmentation based on the characteristics of their employer, such as industry, size, and location.
6. Social data: Segmentation based on social media behavior, such as interests, demographics, and interactions with a brand’s social media accounts.
7. Transactional data: Segmentation based on purchase history, such as their lifetime value, recency, frequency, and monetary value.
8. Engagement data: Consumers can be segmented based on their engagement with a brand’s email campaigns, such as open rates, click-through rates, and conversions.
9. Intent: A cumulative datapoint, often characterized as a predictive model that considers numerous other areas of segmentation, here consumers are segmented based on their likely purchase intent, and whether they are in the research, consideration, or decision stage of the buying process.
10. Occupation: Consumers can be segmented based on their occupation, such as whether they are working in a professional or blue-collar job.
Wrapping Up
While the future of personalization and scalable marketing campaigns remains in flux, the value of truly understanding the customer has never been clearer. For many years 3rd party cookies provided businesses and marketers with an affective shortcut. A cookie-free future will almost certainly require more effort up-front, but the potential long-term benefits offer many reasons to feel encouraged.
