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Use Cases for Personas

This is a joint piece by Dr. Joni Salminen and Professor Jim Jansen. The authors are working on a system for automatic persona generation at the Qatar Computing Research Institute. The system is available online at https://persona.qcri.org.

Introduction

Personas are fictive characterizations of the core audience or customers of a company, introduced into software development and marketing in the 1990s (see Jenkinson, 1994; Cooper, 1999). Personas capture and summarize key elements of key customer segments so that decision makers could better understand their audience or customers, not just by using numbers but also referring to qualitative attributes, such as key pain points and desires, needs and wants. We refer to persona creation as “giving faces to data,” as personas are ideally based on real data on customer behavior. Figure 1 shows an example of a data-driven persona in which the attributes are inferred automatically from social media data.

Figure 1: Data-driven persona.

While personas have been argued to have many benefits in the academic literature (see e.g., Nielsen, 2004; Pruitt & Grudin, 2003; Salminen et al., 2017), we are constantly facing the same questions from new client organizations wishing to use our system for automatic persona generation (APG) (An et al., 2017). Namely, they want to know how to use personas in practice. While we often make the analogy that personas are like any other analytics system, meaning that the use cases depend on the client’s information needs (i.e., what they want to know about the customers), this answer is still a bit puzzling to them.

For that reason, we decided to write this piece outlining some key use cases for personas. These are meant as examples, as the full range of use cases is much wider. We will first explore some general use cases, and then proceed to elaborate on more specific persona use cases by different organizational units.

General Use Cases of Personas

In general, there are three main purposes personas serve:

1) Customer Insights. This deals with getting to know your core audience, users or customers better. For example, APG enables an organization to understand its audience’s topics of interest and preferred social media content. Who uses? Everyone in the organization.

2) Creation Activities. Using persona information to create better products, content, marketing communication, or other outputs. Who uses? Everyone in the organization dealing with customer-facing outputs.

3) Communication. Using personas for communication across departments. While it is difficult to discuss a spreadsheet, it is much easier to communicate about a person. Sharing the persona work across divisions thus increases the chance for realization of benefits. Personas make data communicable and keep team members focused on the customer needs. Who uses? Everyone in the organization.

Specific Use Cases of Personas

In addition to shared use cases of personas, there are more specific use cases. For example, product managers can use the information to design a product that meets the needs or desires of core customers, and marketing can use personas to craft messages that resonate. Here, we are outlining specific examples of use cases within organizational units. More specifically, we allocate these use cases under four sections.

1) Customer Insights and Reporting

Journey Mapping: Plot the stages and paths of the persona lifecycle, documenting each persona’s unique state of mind, needs and concerns at each stage. Understand your website visitors’ customer journey.

Persona Discovery: Document the individuals involved in the purchase process in a way that allows decision makers to empathize with them in a consistent way.

Brand Discovery: Uncover how your core customers feel about your product or service and how they rationalize their purchase decisions.

Reporting and Feedback: Report and review data and insights to drive strategic decisions, as well as provide information to the organization as a whole.

2) Creation Activities

Planning Product Offerings: With the help of personas, organizations can more easily build the features that suit their customers’ needs. Consider the goals, desires, and limitations of core customers to guide feature, interface, and design choices.

Role Playing: Personas help product developers “get into character” and understand the circumstances of their users. They facilitate genuine understanding of the thoughts, feelings, and behaviors of core customers. Individuals have a natural tendency to relate to other humans, and it’s important to tap into this trait when making design and product development choices.

Content Creation: Content creators can leverage personas for delivery of content that will be most relevant and useful to their audience. When planning for content, we might ask “Would Jamal understand this?” or “Would Jamal be attracted by this?” Personas help one determine what kind of content is needed to resonate with core customers and in which tone or style to deliver the content. Naturally, customer analytics can and should be used to verify the results.

3) Persona Experimentation

Channel and Offering Alignment: Align every piece of offerings and marketing activity to a persona and purchase stage, identifying new channels and needs where opportunities exist.

Prediction of Popularity: Predict how a given persona will react to content, marketing messages, or products. This is a particular advantage of data-driven personas that enable using the underlying topical interests of the persona to model the likely match between personas and a given content unit.

Experimentation and Optimization: Carry out well-thought experiments with personas to produce statistically valid business insights and apply the results to optimize performance. For example, you could run Facebook Ads campaigns targeting segments corresponding to the core personas and analyze whether the campaigns perform better than broader or other customer segments.

4) Strategic Decision Making

Strategic Marketing: When you understand where your core customers spend their time online, you are able to focus your marketing spend on these channels. For example, if the data shows that your core customers prefer YouTube over Facebook, you can increase your marketing spend in the former. Think how you might describe your product for this particular type of person. For example, would Bridget better understand your offering as a “social media service” or as an “enterprise customer management tool”? Depending on the answer, the communicative strategy would be different.

Sales Strategies: Targeted offerings can help organizations convert more potential customers to subscribers, followers and customers. You can also use personas to tailor lead generation strategies which is likely to improve your lead quality and performance. By approaching your messages from a human perspective, you can create sales and marketing communication that is tailored to your core customers and, therefore, is likely to perform better.

Executives: Key decision makers can keep personas in mind while making strategic decisions. In fact, a persona can become a “silent member in the boardroom,” evoked to question the customer impact of the considered decisions.

Examples for the APG system

In the following, we will include some use case examples from the APG system that generates personas automatically from online analytics and social media data. The system is currently fully functional, and we are accepting a limited number of new clients with free of charge research licenses. See the end of this post for more details.

Figure 2: This functionality enables the client to generate personas from his chosen data source (currently, following platforms are supported: YouTube, Facebook, Google Analytics). The client can choose between 5 and 15 personas.

Figure 3: The persona profile shows detailed information about the persona. It enables human-oriented customer insights.

Figure 4: This feature enables an easy comparison of the personas across their key attributes. Improves understanding of the core customer segments.

Figure 5: This feature shows which personas most often react with which individual content.

Figure 6: This feature shows how the interests and other information of the personas change over time. Currently, APG generates new personas on a monthly basis.

Figure 7: This feature enables a gap analysis of the current audience and potential audience. The statistics are retrieved from actual audience data of the organization and the corresponding Facebook audience (via Facebook Marketing API).

Conclusion

Forrester Research (2010) reports a 20% productivity improvement with teams that use personas. Yet, using personas is not always straight-forward. Ultimately, the exact use cases depend on the client’s information needs. These needs can best be found by collaborating with persona creators to provide tailored personas that are useful specifically for a given organization in their practical decision making.

Through means of “co-creation,” clients and persona creators can figure out together how the personas could be useful for real usage scenarios. According to our experience, useful questions for defining the client’s information needs include:

  • What are your objectives for content creation / marketing?
  • What kind of customer-related decisions you make?
  • What kind of customer information you need?
  • What analytics information are you currently using?
  • What kind of customer-related questions you don’t currently get good answers to?
  • How would you use personas in your own work?
  • What information you find useful in the persona mockup?
  • What information is missing from the persona mockup?

If you are interested in the possibilities of automatic persona generation for your organization, don’t hesitate to contact us! Professor Jim Jansen will gladly provide more information: [email protected]. However, please note that for automatic persona generation to be useful for your organization, you need to have at least hundreds (preferably thousands) of content pieces published online with a wide audience viewing them. APG is great at summarizing complex audiences, but if you don’t have enough data, persona generation is better done via manual methods.

References

An, J., Haewoon, K., & Jansen, B. J. (2017). Personas for Content Creators via Decomposed Aggregate Audience Statistics. In Proceedings of Advances in Social Network Analysis and Mining (ASONAM 2017). http://www.bernardjjansen.com/uploads/2/4/1/8/24188166/jansen_personas_asonam2017.pdf

Cooper, A. (1999). The Inmates Are Running the Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity (1 edition). Indianapolis, IN: Sams – Pearson Education.

Forrester Research. (2010). The ROI Of Personas. Retrieved from https://www.forrester.com/report/The+ROI+Of+Personas/-/E-RES55359

Jenkinson, A. (1994). Beyond segmentation. Journal of Targeting, Measurement and Analysis for Marketing, 3(1), 60–72.

Nielsen, L. (2004). Engaging personas and narrative scenarios (Vol. 17). Samfundslitteratur. Retrieved from http://personas.dk/wp-content/samlet-udgave-til-load.pdf

Pruitt, J., & Grudin, J. (2003). Personas: Practice and Theory. In Proceedings of the 2003 Conference on Designing for User Experiences (pp. 1–15). New York, NY, USA: ACM.

Salminen, J., Sercan, Ş., Haewoon, K., Jansen, B. J., An, J., Jung, S., Vieweg, S., and Harrell, F. (2017). Generating Cultural Personas from Social Data: A Perspective of Middle Eastern Users. In Proceedings of The Fourth International Symposium on Social Networks Analysis, Management and Security (SNAMS-2017). Prague, Czech Republic. Available at http://www.bernardjjansen.com/uploads/2/4/1/8/24188166/jansen_mena_personas2017.pdf

Creating Buyer Personas: Common Interview Questions

Introduction

At Qatar Computing Research Institute (QCRI), we are developing a system for automatic persona generation (APG). The demo is available online at https://persona.qcri.org

As a part of this research, we’re interested in the information needs of end users of personas [1]. People working in different domains are interested in different information, after all. For example, journalists want to know what type of news the personas are consuming, while e-commerce marketers want to know what products they are buying.

We have reviewed a lot of material relating to interviewing customers in order to create the persona profiles because, although our approach is based on automation and computational techniques, we have an interest to experiment with mixed personas utilizing qualitative data to enrich the automatically generated personas [2].

This brief post shares some of the key insights we’ve found.

Persona Information

In general, when creating personas we need to query two types of information:

  1. Information needs of persona users => this means what information people inside our organization want to know
  2. Customer information => this means what information we can learn about the customers

For the former, we have developed an Information Needs Questionnaire with eight questions:

  1. What are your objectives for content creation / marketing?
  2. What kind of customer-related decisions you make?
  3. What kind of customer information you need?
  4. What analytics information are you currently using?
  5. What kind of customer-related questions you don’t currently get good answers to?
  6. How would you use personas in your own work?
  7. What information you find useful in the persona mockup?
  8. What information is missing from the mockup?

The purpose of these questions is to discover the interviewee’s professional information needs. This is useful for developing analytics systems, e.g. automatic persona generation, but also extends to traditional persona creation.

In the following, we summarize some questions intended for customers.

From Mr. Steve Cartwright (2015) [3]

“I know that when I am preparing buyer personas I have a whole heap of questions that I ask in fact I have a PowerPoint I go through with clients, this enables me to generate the personas that I need. However, if you start by simply asking:

·      Who are they?

·      What do these people do?

·      Are they married, singles, living with a partner?

·      What problems or concerns do they have, that your industry niche can solve?

·      Where do they hang out and what do they do online?

·      Are these people decision makers, influencers or referral sources?

Just those six questions are all you need to get started and to start to understand who you’re customers are and to turn your business into a customer centric one.”

***

From “Nisha” (2013) [4]:

“Questions for B2B marketers to delve into while creating buyer personas include:

  • Buyer experience and reporting officer of the prospect
  • Professional background of the prospect
  • Kind of organization
  • Organizations’ segment focus
  • History of purchases
  • Change in role in past few years
  • Market forces influencing buyers
  • Most urgent problems
  • What funded initiatives does the buyer have
  • What are the motivations that drive the buyer
  • What the buyer’s needs?
  • What is the budget?
  • Who are involved in the decision-making?
  • Attitude of the company towards the product/service

***

From Jesse Ness [5] (2016):

“Demographic questions:

These are the most basic questions that you should be asking your target customers, such as:

·      Are they married?

·      How old are they?

·      Where do they live?

·      Do they have children? How many? What ages?

·      Which country/city did they grow up in?

·      Education questions:

Our early school and college education help us shape as adults. People usually tend to answer these questions more honestly.

·      What level of education did they complete?

·      Which schools did they attend? Public or Private?

·      What did they study?

·      Were they popular at school?

·      Which extra-curricular activities (if any) did they take part in?

·      Career questions:

Questions about the working life of your prospects reveals a lot of interesting details about them.

·      What industry do they work in?

·      What is their current job level?

·      What was their first full-time job?

·      How did they end up where they are today?

·      Has their career track been traditional or did they switch from another industry?

·      Financial questions:

Your customers finances will tell you what they can afford and how easily they make their purchasing decisions.

·      How often you buy high ticket items?

·      How much are they worth?

·      Are they responsible for making purchasing decision in the household?

Keep in mind that people tend to answer financial questions incorrectly, even in anonymous online surveys. Some might even construe this as an invasion of their privacy. Temper your results accordingly (usually by decreasing the stated average income).”

Conclusion

There is a myriad of questions one can ask from the customers when creating persona profiles. However, they should be based on first defining internal information needs. In the persona creation process, the above question lists serve as inspiration.

Interested in automatic persona generation for your company? Contact Dr. Jim Jansen: [email protected]

Footnotes

[1] Personas are fictive characters based on real data about the underlying audience. Their purpose is to make customer analytics more easily understandable than numbers and graphs.

[2] Salminen, J., Şengün, S., Haewoon, K., Jansen, B. J., An, J., Jung, S., … Harrell, F. (2017). Generating Cultural Personas from Social Data: A Perspective of Middle Eastern Users. In Proceedings of The Fourth International Symposium on Social Networks Analysis, Management and Security (SNAMS-2017). Prague, Czech Republic.

[3] https://website-designs.com/online-marketing/content-marketing/buyers-personas-allow-you-to/

[4] https://www.xerago.com/blog/2013/08/why-buyer-personas-are-not-the-same-as-customer-profiling/

[5] https://www.ecwid.com/blog/how-to-create-buyer-personas-for-an-ecommerce-store.html

Argument: Personas lose to ‘audience of one’

Introduction. In this post, I’m exploring the usefulness of personas in digital analytics. At Qatar Computing Research Institute (QCRI), we have developed a system for automatic persona generation (APG) – see the demo. Under the leadership of Professor Jim Jansen, we’re constantly working to position this system toward the intersection of customer profiles, personas, and analytics.

Three levels of data. Imagine three levels of data:

  •  customer profiles (individual)
  • personas (aggregated individual)
  • statistics (aggregated numbers: tables and charts)

Which one is the best? The answer: it depends.

Case of advertising. For advertising, usually the more individual the data, the better. The worst case is the mass advertising, where there is one message for everyone: it fails to capture the variation of preferences and tastes of the underlying audience, and is therefore inefficient and expensive. Group-based targeting, i.e. market segmentation (“women 25-34”) performs better because it is aligning the product features with the audience features. Here, the communalities of the target group allow marketers to create more tailored and effective messages, which results in less wasted ad impressions.

Case of design and development. In a similar vein, design is moving towards experimentation. You have certain conventions, first of all, that are adopted industry-wide in the long run (e.g., Amazon adopts a practice and small e-commerce sites follow suit). Many prefer being followers, and it works for the most part. But multivariate testing etc. can reveal optimal designs better than “imagining a user” or simply following conventions. Of course, personas, just like any other immersive technique, can be used as a source of inspiration and ideas. But they are just one technique, not the technique.

For example, in the case of mobile startups I would recommend experimentation over personas. A classic example is Instagram that found from data that filters were a killer feature. For such applications, it makes sense to define an experimental feature set, and adjust if based on behavioral feedback from the users.

Unfortunately, startup founders often ignore systematic testing because they have a pre-defined idea of the user (à la persona) and are not ready to get their ideas challenged. The more work is done to satisfy the imaginary user, the more harder it becomes to make out-of-the-box design choices. Yet, those kind of changes are required to improve not by small margins but by orders of magnitude. Eric Ries call this ‘sunk code fallacy’.

In my opinion, two symptoms predating such a condition can be seen when the features are not

  1. connected to analytics, so that tracking of contribution of each is possible (in isolation & to the whole)
  2. iteratively analyzed with goal metrics, so that there is an ‘action->response->action’ feedback loop.

In contrast, iterative (=repetitive) analysis of performance of each feature is a modern way to design mobile apps and websites. Avoiding the two symptoms is required for systematic optimization. Moreover, testing the features does not need to take place in parallel, but it can be one by one as sequential testing. This can in fact be preferable to avoid ‘feature creep’ (clutter) that hinders the user experience. However, for sequential testing it is preferable to create a testing roadmap with a clear schedule – otherwise, it is too easy to forget about testing.

Strategic use cases show promise. So, what is left for personas? In the end, I would say strategic decision making is very promising. Tactical and operational tasks are often better achieved by using either completely individual or completely aggregated data. But individual data is practically useless at strategic decision making. Aggregated data is useful, e.g. sales by region or customer segment, and it is hard to see anything replace that. However, personas are in between the two – they can provide more understanding on the needs and wants of the market, and act as anchor points for decision making.

Strategic decision aid is also a lucrative space; companies care less about the cost, because the decisions they make are of great importance. To correctly steer the ship, executives need need accurate information about customer preferences and have clear anchor points to align their strategic decision with (see the HubSpot case study).

In addition, aggregated analytics systems have one key weakness. They cannot describe the users very well. Numbers do not include information such as psychographics or needs, because they need to be interpreted from the data. Customer profiles are a different thing — in CRM systems, enrichment might be available but again the number of individual profiles is prohibitive for efficient decision making.

Conclusion. The more we are moving towards real-time optimization, the less useful a priori conceptualizations like target groups and personas become for marketing and design. However, they are likely to remain useful for strategic decision making and as “aggregated people analytics” that combine the coverage of numbers and the details of customer profiles. The question is: can we build personas that include the information of customer profiles, while retaining the efficiency of using large numbers? At QCRI, we’re working everyday toward that goal.