# Task Prioritization: Formulas and Examples

*This is part 5 of our series on Task Prioritization**, previous segments are available in our archives.*

Now that we know how to scale each of the factors, we can use a formula to compute a score. The formula I use is:

Using this formula to calculate the score gives you exactly what you want, the confidence-adjusted impact (i.e., combination of Reach and Value) per unit of effort to achieve the task.

Let’s have a look at the two most impactful initiatives from the other day to see how they would be scored and prioritized using this system, starting with the “create a native smart-phone app” task. Suppose we’ve done some customer research and found that about 100 new or existing customers would find an app useful, so we estimate Reach to be 100 customers. The app would be so useful that between reduced churn and new sales we expect a Value of over $20,000 per month. However, these Reach and Value estimates are based on surveys and a few customer conversations, so they are not guaranteed. With regards to Effort, building both iPhone and Android apps is a significant endeavor, so we estimate this as a 5-point task. Given our uncertainty over the Reach and Value, and that new software development inevitably has some bumps in the road, we put our Confidence of our estimates at 50%. Now, let’s scale these results and create a score:

Factor | Estimate | Scaled |

Reach | > 50 customers | 10 |

Value | > $20,000 | 10 |

Confidence | 50% | 5 |

Effort | 5 | 10 |

Now, let’s consider the second task, “integrate with a new data source”. In this case, we have had conversations with 10 current customers who have requested this integration, giving us a Reach of 10. These customers have each signed paperwork committing to upgrading their service to each pay an additional $200 per month to take advantage of the new integration, meaning there is a Value of $2,000 per month. The data integration is similar to one that we’ve done in the past and we’ve already researched their API, so while not completely trial, this is still a pretty straightforward for engineering – we estimate an Effort of 1 point. Given that the paperwork is signed and the execution is pretty clear, this is close to a sure-thing as there is, so we put our Confidence in the estimates pretty close to 100%.

Factor | Estimate | Scaled |

Reach | 6-10 customers | 5 |

Value | $1,000 – $5,000 | 3 |

Confidence | 91-100% | 10 |

Effort | 1 | 2 |

As we can see, even though the creation of a native smart-phone app has a higher Reach and Value, it would take a much more significant Effort and the Confidence in this task is lower, causing the overall prioritized score to be lower than the task to integrate a new data source. So, given the factors we’ve considered, we should prioritize the new data integration over building the app.

After you score each task you are considering, sort them in decreasing order by the score to see what the highest priority tasks are. Remember that this is only an input into your process. You cannot trust these scores blindly for a number of reasons:

- There are a lot of estimates and subjectivity going into these scores. You may have hidden bias.
- There can be other important factors that will influence your decision that are not captured in the four I’ve discussed. It might be best to include these in your scoring if they are common, but if not use your judgement..

So there you have it, a data-driven way to organize your tasks! If you use a different system that works, please share, as I’d love to hear about it!