Creating well-developed audience personas is a key element in providing any sort of relevant and meaningful experiences for your customers. As the marketing landscape has shifted and marketers have been able to gather more information on their customers, an opportunity has arisen to move from creating audience personas based on what you think you know about a customer segment, to letting data and insights shape those personas. After all, your audience personas are influencing (or should be influencing) a significant amount of your marketing decisions. By letting data guide your persona development, you can better spend your marketing dollars by more accurately targeting your customers in a manner that truly speaks to them.
Personas are developed by aggregating information to construct profiles of customers at various stages of the purchasing cycle. Typically, these are developed by considering demographic, econometric, ethnographic, and behavioral data, combined with institutional or accumulated insights.
The overarching goal of persona creation is to develop contextual archetypes for customers that can be leveraged across media, products, and engagements. As we gather increasing amounts of data and are privy to new tools within the marketplace, marketers are able to leverage data analysis techniques to develop a more refined and empirical method of persona creation. This ultimately offers alternatives or augmented approaches through the use of customer and web analytics data.
The Risk of False Personas
While this increased amount of data offers a great opportunity, many organizations are still falling prey to personas that no longer define (or may have never defined) their customers. The proliferation of devices, mediums, and disjointed purchase paths only further exacerbates the risk of operating off of false audience personas. It can be easy to let stereotypes, preconceptions, and assumptions about customer intent influence you, and as a result, end up with audience personas that mirror your own understanding, rather than the actual customers you are trying to engage.
If this is how you’ve been developing your personas, there is work to do, but not all is lost. These preconceived personas can serve as important starting points on several levels:
- Performing a competitive analysis with industry or vertical peers: There are numerous ways personas can influence competitive analysis. Some of these include uncovering previously unknown competitors who are marketing to similar personas and refining exactly where your product or service intersects with your competition.
- Serving to capture the variety of customer views within an organization: At Rise, we have often revealed for clients serious discrepancies in how different departments were defining and communicating with their customers. Cross-team persona development created from customer data has helped to address this shortcoming and provide consistency.
- Providing a starting point for validation and testing: When developing testing hypotheses, it is important to be able to segment certain attributes or criteria. Having a well-defined and consistent persona allows for segments to be applied universally.
Data-Driven Persona Creation: Getting Started
Appending data to customer profiles
A practical starting point for data-driven persona curation is demographic data. The ubiquity of web analytics data offers marketers an accessible and relatively consistent framework to begin analyzing and compiling customer demographics. The two most common web analytics platforms, Google Analytics and Adobe Analytics, both provide a vast amount of demographic data. Below are sample screenshots of demographic data available in Google Analytics and the plethora of customer segments across geographies, devices, technology, and more, available through Adobe Analytics.
This data can be used to augment your traditional or existing personas. When back-end customer data files are mapped to web data – the proverbial “closing of the loop” – you can begin to build a more complete customer view. Additionally, CRM or customer data files can be built with a variety of data append options that offer very specific demographic (and in some instances, esoteric) options, along with the more customary alternatives. For example, marketing and technology services company Acxiom offers over 3,000 category options for data append.
However, having a broad and exhaustive data set about your customers is just the first step in deriving data-driven personas. The next phase is to begin building an analysis framework that takes these individual data records and begins forming them into actual personas.
Using cluster analysis
A useful data analysis method for persona creation is clustering. Clustering is an algorithmic approach to exploratory data mining that essentially groups objects (in this instance customers) with similar traits or characteristics.
Marketers can use this technique across a wide variety of testing hypotheses and insights. For example, do you believe your highest value customers buy at a certain price point and possess a given set of demographic traits (such as family size, income or education)?
A cluster analysis can help to validate or refute this hypothesis. At the same time, in the hands of an inquisitive and skilled analyst, clustering can unearth contrary or previously unknown attributes of your customer personas.
Without looking at your data, you may miss a key attribute of your customer base. By appending data to your existing customer profiles and performing cluster analysis, you’re able to gain enhanced insight that you can more confidently act upon.
With increasing amounts of information available, most marketers have the foundational data at their disposal to begin building more robust and accurate audience personas. The brands that are able to leverage this data-driven approach will be set up to better understand their customers and create the most relevant messaging to address their needs. If you’d like more information on taking a data-driven approach to your audience persona development, reach out to Rise.