← Learning Path / Step 1
Step 2 of 10 • ~2 min read
Identify Your Audience
Know who will see your story — and what they need to hear.
📚 Theory
Knowing your audience is what turns a technically correct analysis into a story that actually lands. Beginners often skip this step and jump straight into tools, then wonder why managers look confused, bored, or unconvinced.
Different people care about different things: a frontline team lead might care about shift schedules, while a director cares about trends and risk. If you talk to both in the same way, you’ll miss one of them. When you consciously ask, “Who will see this? What do they already know? What language do they speak? What do they worry about?”, you start tailoring, not dumbing down.
You may keep the same data but change the examples, reduce jargon, or highlight different parts. This step also protects you from miscommunication: you’re less likely to overload a non‑technical audience with technical caveats, or give leaders too much detail and not enough direction.
In data storytelling, the audience isn’t an afterthought; they are the main character. When you see them clearly, your story becomes sharper, kinder, and much more persuasive.
“The success of your presentation will be judged not by the knowledge you send but by what the listener receives.”
Lilly Walters
✏️ Aisha’s Application — Step 2 in Practice
Aisha lists her key characters and writes one line about what each needs:
- Ben (Manager): Cares about complaint trends, agent performance, and efficient use of training hours.
- Mei (HR): Cares about schedule feasibility, room usage, and overall training calendar.
- Team Leaders (indirect audience) : Care about staffing and fairness across shifts.
She then writes:
- For Ben the story must show: “Is attendance really down, and is it linked to the time change?”
- For Mei, the story must show: “Can we justify moving slots again, and is the impact big enough?”
This pushes Aisha to focus on sign-ups and show-ups across time slots rather than on secondary metrics. It also nudges her towards a recommendation framed as a low-risk test rather than a permanent overhaul, which Mei is more likely to accept.
💡 Try it now: Write down the name and role of the one person your next data story is really for. Then answer: What decision do they need to make? What do they already know?