Who’s reading your technical blog? Hopefully someone! But more than that, hopefully your intended audience - preferably at scale, and with repeat, long-term viewership. However the audience that your technical blog reaches (and hopefully compels) and the intended audience of the blog are not always in sync.
Having a clear, detailed understanding of the target audience is a necessary foundation for effective, compelling technical writing - and grasping that readership contingent you want.
Balancing readership insights and intended audience
There are two ways to determine an intended audience, the first is a qualitative “pull” method, the second a marketing “push” effort.
Quantitative readership insights from existing data
There are ways to gain current readership insights from data on hand:
- Identifying reader location patterns from website GeoIP data
- Form collection identifying company type, size, role
- Determining level of expertise and experience from comments on technical blogs
Crafting content with an audience in mind
- Using language and marketing to focus on a particular organizational role/job
- Publishing via particular channels (e.g. Hackernoon, LinkedIn)
- Creating pieces that would only appeal to / apply to a certain audience
There’s inevitably some balance of these factors - identifying the audience from data vs. choosing the audience - as a technical blog evolves.
If you find from your data collection that you aren’t capturing your intended audience, you’ll need to rethink strategy - perhaps pivoting from developers to C-Suite, or changing your language.
How to do target audience research
Market research for your target audience reads much like product development target audience research - just with a layer of technical comprehension and viewpoint over the top. Ask yourself the following:
- What are the needs of the audience? What do they value?
- What knowledge and thought patterns are typical for the audience?
- What level of English comprehension does the audience have?
- What is the cultural background of the audience?
- What are the attention span and interest level of the audience?
- What is the level of technical comprehension of the audience?
- What sort of style compels the audience the most?
While these seem like simple questions, eliciting the answers can be elusive.
How can you find the answers to your audience questions?
There are traditional ways to uncover audience sentiments.
Reader interviews, feedback/rating systems, article comments, GeoIP data, and webpage bounce rates can all provide some foundation “pull” insight, but these only begin to scratch the surface. Further, these insights can be biased, inaccurate, or statistically insignificant.
A tale of Survivorship Bias
Consider “Survivorship Bias,” where data based on a subset of cases result in biased data analysis. A standard example of this bias is the study of aircraft returning from an active combat zone during World War II, to determine where to reenforce the planes better for maximum efficiency during war.
The intuitive answer was to examine returning aircraft, for where bullets made it through the armor, and reinforce there. However, the correct answer is where the bullets hadn’t penetrated the aircraft - the aircraft that hadn’t returned from duty, those rendered incapacitated.
What’s perhaps most interesting here - the intuitive answer of “reinforce where the bullets made it through the armor” might be the worst choice for reinforcement since that’s where the aircraft can afford to be shot, and still make it back home.
How does Survivorship Bias relate to your technical blog audience?
Survivorship Bias pops up in consideration of technical blog audiences. Reader interviews, feedback/rating systems, article comments, GeoIP data, and webpage bounce rates are all based on a sort of Survivor Bias.
That is, the data doesn’t include the readers who never viewed the article because the title was uncompelling, they didn’t receive the marketing, or for logistical reasons couldn’t access the content (a slow internet connection, region blocking, distraction, etc.).
While GeoIP and bounce rate data, for example, might appear quantitative and free from human bias, these metrics do reflect a level of Survivorship Bias - perhaps all the more sinister because real numbers feel so objective.
How to gain insights from the audience you aren’t receiving
Understanding and shaping the target audience for a technical blog is an ongoing activity, with both qualitative “pull” and marketing “push” factors being helpful.
A plurality of perspectives, tools, and approaches can reduce bias and provide richer, more holistic insights. Utilize marketing tools like Hubspot (and techniques like A/B testing with Titles), analyze with Google Analytics, and use automation connectors like Zapier to eliminate manual concatenation and transformation of intelligence.
With this improved understanding and shaping of the target audience, we can achieve more meaningful, valuable technical blog pieces.