We often get questions from prospective clients about how to use personas. Like, “okay, after I have these personas, then what? How can I use them?“.
I have two standard responses for this:
Persona analytics is just like any analytics. If you want to understand your customers, there must be some reason for that. What is that reason?
What are your marketing/business/design goals? What do you want to improve? After knowing these, we can then tell how we think personas could be useful.
The point is that we are not some magicians that can tell you what to do. Instead, we should figure it out together. It’s called co-creation — you, as the client know more about your organization, your problems and goals than us. We need that information to figure out if personas make sense and if so, how.
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A tech-savvy social media influencer at work Source: Unsplash
In this post, we discuss if these artificially generated pictures are good enough for use in personas profiles for real-world systems and applications, which are highly dependent on images for the personas. One of the key aspects of generating personas using a data-driven approach is to be able to represent the persona profile with a matching picture.
The following is a post from the APG Team’s summer 2020 intern, Jaad Mohammed.
Here we talk about how the APG system enriches a data-driven persona by adding personality attributes to the persona profile, like marital status.
After APG creates a base persona for a set of user data by following its unique sequential method :
Identifying distinct user interaction patterns from the data,
Linking these distinct user interaction patterns to user demographic groups,
Identifying impactful user demographic groups from the data,
Creating base personas via demographic attributes, and
Enriching these base personas to create rich personas description.
The above method is explained in detail when we talk about how the systems finds picture & age of a persona.
Now that we have a base persona.
The system queries a Facebook Application Program Interface (API) to collect information like related demographics such as the personas marital status by feeding the API details of the base personas interests and demographics data.
These are the steps taken by APG to add an Education Level to a Persona Profile.
While there have been attempts to “modernize” personas by automating their creation and tying the concept to behavioral online analytics data, the question remains: can data-driven personas offer efficiency and/or effectiveness value relative to other analytics approaches and data representations for user understanding tasks?
This question remains largely unaddressed in the previous research, even for personas in general, and it serves as the motivation for the research we are doing with APG, a data-driven persona system.
Overall, there is surprisingly little research in the field of marketing, HCI, or information science about data granularity in user segmentation. Yet, this is an issue that is practically important and theoretically interesting.
Customer segmentation and granularity research has noted aggregation beneficial for marketing practice. The optimal segmentation of user groups has also been pursued without a definitive solution.
While not discounting traditionally generated personas, our position is that data-driven persona has certain inherent advantages, as shown in Table 1.
Remarkably, with the combining of personas and analytics, the strengths of each approach help offset the deficiencies of the other. Conceptually, personas are easy for people to understand and generate empathy for the user, but personas are perceived as not granular and not actionable. Analytics data can be granular and actionable, but analytics can be cumbersome for employment and difficult for end users to comprehend. Nonetheless, the combination of both leverages the strengths and limits the weakness of each.
Creating personas from large amounts of online data is useful but difficult with manual methods.
To address this difficulty, we present Automatic Persona Generation (APG), which is an implementation of a methodology for quantitatively generating data-driven personas from online social media data.
APG is functional, and it is deployed with several organizations in multiple industry verticals.
APG employs a scalable web front-end user interface and robust back-end database framework processing tens of millions of user interactions with tens of thousands of online digital products across multiple online platforms, including Facebook, Google Analytics, and YouTube.
APG identifies audience segments that are both distinct and impactful for an organization to create persona profiles. APG enhances numerical social media data with relevant human attributes, such as names, photos, topics, etc. Here, we discuss the architecture development and central system features.
Overall, APG can benefit organizations in distributing content via online platforms or with online content that relates to commercial products. APG is unique in its algorithmic approach to processing social media data for customer insights. APG can be found online at https://persona.qcri.org.