14 Persona Myths – Debunked
Personas are a powerful tool to drive leads, conversions, and revenue, no question! However, many myths around them need clarification to make the most out of them.
This article will highlight the biggest myths and pitfalls of buyer persona creation that crossed our way of serving customers for the past years.
- I know my customer, so I know my personas
- Human Bias and personas
- Persona creation is highly time-consuming
- Persona creation is expensive
- There is not enough data to create personas for my business
- The more data, the better
- Quantitative data is all I need for persona creation
- Persona creation is a one-time effort
- Persona creation is a marketing department thing
- The more information the persona contains, the better
Organizations often believe they have a clear understanding of their customer base, which I have no doubt. However, basing persona creation on these beliefs, experiences, and assumptions is not the best idea.
The trend that persona creation should be 100% data-driven is rising; however, many organizations still add human bias to the creation process. They have a clear picture of the clients and tend to find data that supports their assumptions. But this approach can be very misleading. The second, where you add your own experiences and beliefs in the process, the results will always be falsified. We all become operationally blind over time, and therefore, it is essential always to be open to change in the market. That’s why it’s so important to create personas that are 100% data-driven and not based on assumptions. They will reflect current market trends and reveal blind spots that the company may not know about.
Historically, no doubt, this statement was true! Why? Because persona creation was heavily dependent on gathering as much information as possible from different departments to find common ground within assumptions. Trying to balance and support the different beliefs takes time and effort, especially in larger decentralized organizations.
However, once you free yourself from the idea that experiences and assumptions are the core for persona creation, the process becomes more accessible. Focusing on internal and external data and thereby sorely listening to customers, the persona creation process finally becomes efficient. You do not need to argue why one assumption is correct, and the other one is not but, let the data provide and prove the results.
Steadily increasing computational power is a crucial factor in speeding up data-driven persona creation. AI models that historically took weeks to deliver results are now powered by hardware that allows information to be processed in almost real-time.
Price is relative, right? Since personas should build the foundation for the whole business endeavor (check out our post on application areas), creating personas “quick and dirty” using templates and dumping in some assumptions is a bad idea. If you do not have the funds at the moment, do not bother. Educate yourself about persona outcomes and acquire resources and support by presenting their importance to the stakeholders.
Answering the question “What is it worth to have a clear understanding of my customer?” might be a good starting point. Once an organization sees how data-driven personas can positively impact lead generation, sales, and customer retention by tailoring offerings and messaging around the customer needs, answering that question becomes easy.
Historically, the cost for persona creation was between 150k and 300k for larger corporations, undoubtedly a significant investment. But let us do simple math – let us say you do 3 million revenue a year and by better addressing the customer needs, you can increase sales by 4%, the cost already amortized within one year. Some research even states that personas can lead to a 171% increase in revenue from marketing, so the investment is undoubtedly worth it.
But why was such a high cost associated with persona creation? Because it was a mixture of external consultants who charged horrendous fees for workshops, conducted surveys, acquired expensive market research papers, and manually analyzing data.
The good news is, these days are over. The latest developments in Artificial Intelligence are beneficial for persona creation. Automated clustering, sentiment analysis, emotion recognition, GAN, and other tools are incredibly efficient in extracting the information needed to build personas.
However, be critical – offerings of automated persona creation for 40 USD might also be too optimistic. There are providers that state that they use AI to build a persona from your Google Analytics which in the end is sorely an RFM analysis on Google Analytics data labeled as personas but has nothing to do with data-driven AI-powered personas. You will have a target audience in front of you, but that is it. The core of personas reflects real needs, pain points, and triggers, which you will lack in this case. You will likely include human bias by manually adding these missing features based on assumptions- and we just learned that this is a bad idea.
Unless you just developed a suit that makes you fly and invisible simultaneously, yes, you might be right that there is probably not enough data to build personas because something like that does not exist yet in any shape or form. However, if you have an existing product in the market or competitors, do not worry about a lack of data.
In case you do not have sufficient internal data, look on the web for relevant information. Social listening, Blogs, Review sites, etc., usually have a plethora of data sources you can use.
It is a false assumption that the more data you have, the better the persona outcome. What matters is the data quality. 1 Million Social Media posts that contain mainly emojis or text fragments only might be worth little compared to a proper survey of 1000 customers. Therefore, especially when using a data-science approach, the better the data quality, the less data you need.
This is straightforwardly false, historically and especially in the future. Google Analytics can be a good source for persona creation, but using it exclusively is a bad idea. Why? Because the maximum you will get out of it is target audiences based on existing customers. You will miss the opportunity to uncover personas for your organization you were not aware of prior.
But this is precisely what you do not want when creating personas – you want to go beyond target audiences. Qualitative data brings your personas to life and contains crucial insights about the needs, pain, points, emotions.
To gather these insights, investigate qualitative data sources such as Social Media, blogs, reviews, customer surveys, or CRM data.
Especially since Google and now FB announced that they would no longer provide audience insights, it is time to focus on what matters: The voice of customers and prospects!
False – markets, products, and customers change continuously. Therefore, persona creation should be an ongoing process. Using personas that are outdated is as bad as using personas based on assumptions – they will lead your organization potentially in a completely wrong direction. Therefore, frequently monitoring the market and the available data is crucial to keep the personas up to date and effective. Not to mention that Covid impacted everyone.
False – ideally, personas should be a holistic company endeavor, especially when customer-centricity is the top priority. Only if you follow a holistic persona-based approach can you maximize the benefit of persona usage.
You will start creating stuff people want and creating a tailored strategy on whom you will sell to become easier and more effective.
Once you figure out where and how to gather information about your customers, you might be stunned by how many insights you suddenly have at your disposal. However, only focus on those insights that are essential for your business. Otherwise, your staff will be overwhelmed with the given information. For the majority of organizations, the critical questions the personas should answer are the following:
- Who are they?
- How can you help them?
- How can you reach them?
- What are their drivers?
- What do they feel?
- How important are they?
Once you have gathered all the information, do not make a mistake and segment the customer clusters as granular as possible. You might end up with 20 personas for a single product, and it will undoubtedly be overwhelming to build a personalized strategy for every single one of them.
At the same time, do not force different customer segments into the same bucket just because of convenience. Here is a little real-life example: We were asked to create personas for a segment in a massive online store. Within the product portfolio were beds, lamps, and cargo trousers. It would be very misleading to develop personas for the entire portfolio since you throw someone looking for a new bed in the same bucket as buying a new pair of trousers.
Summed up, the ideal number of personas can not be generalized and depends on the product range and how homogeneous the customer base is. AI-driven clustering helps a lot with this challenge since data analysis is the primary driver that decides which customers belong in the same bucket. On average, we see 4 to 8 personas for a particular product segment.
False; each persona is representative of a homogeneous customer segment but not an individual. Therefore do not see every word in a persona profile as a fact but more as guidance. Few small examples: If the profile states that the persona is a massive fan of the New England Patriots, it does not mean that every customer presented by this persona is a patriots fan. Some might be on the bandwagon to Tampa.
Creating generic personas for a product and believing that they are sufficient to optimize the customer journey is false. Indeed, they are a perfect basis for understanding your customer segments and drive a customer-centric approach. However, if it is your task to optimize a specific step in the customer journey, you need personas within this step – so-called customer journey personas. Of course, creating numerous personas for every step might be too much, but picking one step and creating personas for it is the way to go.
We disagree – personas are equally crucial for B2C and B2B, if not more. All decisions are not based only on product features, but emotions, personal benefits, etc., play a significant role. Even if the persona benefits are not as trivial as for B2C, 68% of B2B buyers who see a personal value will pay a higher price for a service. Therefore, identifying them is essential in the B2B sphere.