Sequencing

What you will learn How to order several candidates that all passed the three questions. And why starting with the biggest impact is a mistake.

Impact order versus starting order

Impact grows as you go down the three principles.

But the starting order is generally the reverse.

The table makes the reason clear.

Why not start with the big one

Here is the most common failure path, in order.

graph TD
  A["Month 1: start with revenue<br/>because impact is biggest"] --> B["Month 2: data is scattered"]
  B --> C["Month 4: still cleaning data"]
  C --> D["Month 5: 'how's it going?' starts"]
  D --> E["Month 6: nothing to show"]
  E --> F["Budget and attention leave"]

The judgement was not wrong. Revenue work really does have the largest impact. What was left out of the calculation is the organisation's patience as a resource.

Organisations do not wait indefinitely. A first project in particular is under the "is this actually going to work?" gaze, and that gaze usually reaches a verdict in three to six months.

What a first project needs

A first project should not be the one with the biggest result. It should be the one that can be proven.

Do not treat the fifth lightly. A technically perfect project fails if the person doing that job is uncooperative, because only they know how the work really runs.

The table for ordering

With several candidates, lay them out like this. A real example:

The "current value?" column effectively decides the order. If the value exists you can start immediately; if not, you have to build it first, so it goes to the back.

After the first project — the second is different

A successful first project gives the organisation three things.

1. Trust — the experience of "this works"
2. A baseline — you learned how to measure and how to judge
3. People — someone has now done it

With those in hand you can start longer projects. A six-month revenue project will be waited for, because the first one proved itself.

graph TD
  A["1st: cost reduction<br/>2–3 months · solid proof"] --> B["Trust · baseline · experience"]
  B --> C["2nd: quality or mid-sized<br/>4–6 months"]
  C --> D["3rd: revenue<br/>6 months+"]

Can you not run several at once?

Do exactly one first project. Two reasons.

One, you lose causality. Run three at once and when a metric moves you do not know which one did it. Then you cannot decide what to expand.

Two, the first project is also a learning process. You learn how to measure, who to work with, and where things stall. Run three at once and you make the same mistake three times.

From the second round on, parallel is fine.

Common misconceptions

"Start small but also high impact — isn't that contradictory?"

Two different axes.

• The three questions ask "is it worth anything" → they filter candidates
• Sequencing asks "which one first" → it picks among the survivors

Among worthwhile candidates you do the small, fast one first. You do not pick the small one among the worthless ones. That is why the three questions come first.

"Leadership wants the big one."

The point is not to skip the big project but to stage it. Explained this way, it usually lands:

"Price optimisation needs competitor price data, which we don't have. Phase 1 is automating that research, and that alone saves X hours a month. Once the data accumulates, phase 2 is the price optimisation."

Break a big project into stages and the first stage very often turns out to be a cost project.

Check yourself

1. Why is starting with the highest-impact project prone to failure?

2. Which column effectively decides the order of candidates?

3. Two reasons to run only one first project?

That is how to choose. Now score your own candidates → Actually score your candidates