In business, ideas that everyone likes are especially dangerous:
🤨 the presentation is convincing;
🤨 the manager nods;
🤨 the team is already discussing scaling;
🤨 the market doesn't yet know it's now obliged to buy this.

To avoid learning the truth after serious investments, use the HADI cycle — a method for testing hypotheses through short experiments.

🔤- Hypothesis
🔤- Action
🔤- Data
🔤- Insights

In plain English: Hypothesis → Action → Data → Insights.

🔤 — Hypothesis

A hypothesis is not: "I think clients will like it."

That's an opinion. Sometimes expert, sometimes collective, sometimes voiced by the person with the highest grade. But still an opinion.

A working hypothesis sounds specific:

If we do X for audience Y, metric Z will change from A to B within a certain period, because…


For example: If we offer a test box instead of a pallet, the conversion to first order will increase because the risk of a trial purchase is reduced.

We define the success criterion in advance. Otherwise, the absence of sales can always be explained by "increased awareness."

🅰️ — Action

Next, we need a minimal experiment that will provide an answer.

It's not necessary to immediately build production, release a year's supply, and write a strategy until 2035.

You can create a prototype, a test offer, a demo sample, or a small batch.

The goal of the experiment is not to create a perfect product, but to quickly and cheaply obtain useful knowledge.

🔤 — Data

Before launch, we define:
— what we measure;
— how long the test lasts;
— what result we consider success;
— at what result we stop.

It's important not to confuse business metrics with vanity metrics.

Views and reach look nice. But business is confirmed by orders, willingness to pay, repeat purchases, and margin.

🔤 — Insights

Data shows what happened. Insight explains why it happened and what to do next.

"We received 20 applications" — data.

"Clients are interested, but the minimum order quantity is too large" — conclusion.

"In the next cycle, we test box delivery through a distributor" — decision.

A hypothesis can be confirmed, partially confirmed, or not confirmed. The latter is also a result.

Better an inexpensive experiment today than expensive heroism in a year.

🌏How it works at global companies

📺Netflix tested replacing the five-star rating with "like" and "dislike" buttons. The simpler mechanics increased user activity and improved recommendations.

📱Dropbox before full development tested the need through research and clickable prototypes. First, they found out if the scenario was needed by the client, and only then built the solution.

🏠Airbnb tested the impact of high-quality photos on bookings. The accommodation itself didn't change — the way to show its value changed.

🟠And what about my projects/products?

I don't rush into building and launching new production; it's pointless in the early stages when the product is not popular, has no confirmed demand, and the colossal CAPEX would simply be unjustified under uncertainty.

The approach is extremely simple:
1️⃣ produce a couple of lab samples of different formulations for field tests
2️⃣ visit 2-3 clients for testing on real sites
3️⃣ determine the working formulation, calculate unit economics based on the market
4️⃣ produce a pilot batch to cover at least 2-3 distributors
5️⃣ launch trial sales to "feel" the market

When the experiment metrics show that demand is confirmed, you move to the next step: scaling.

It should be noted that HADI does not guarantee the success of every idea. It helps to quickly understand what to scale and what is better to stop before the idea starts demanding a budget.

If it was useful, hit 🔥