Prioritize what to build next

Use Harvestr to build your own opportunity prioritization system

Valentin Huang avatar
Written by Valentin Huang
Updated over a week ago

Making the right decisions is key to Product Success. That's why we have built a flexible and powerful prioritization system to help identify opportunities that drive customer satisfaction and business impact.

The Product interface

In Harvestr, the Product interface helps you get a central view of all the Discoveries you are working on. This view has two main purposes: reviewing your backlog and deciding what to build next by prioritizing Discoveries.

Add prioritization criteria for Discoveries

You can evaluate and prioritize Discoveries based on a set of criteria.

Some of these criteria are created in Harvestr by default:

  • Feedback = the volume of feedback currently linked to a Discovery

  • User and company segments = the segments assigned to the users/companies who are linked to a Discovery

In addition to those standard criteria, you can create custom Discovery fields to represent criteria that matter to your specific product and business, such as revenue, user impact, effort, etc.

You can create three types of prioritization fields for your Discoveries:

Field type





# of users impacted

# of days required to build the feature


Value from 0 to 5



Customer attributes aggregation

Computation of customer attribute values

Cumulated ARR of all customers linked to a Discovery

To create those fields, click Manage fields and select the type of field you want to add.

Build your own prioritization score

Once you have added all the fields you need to evaluate your Discoveries, you can also build a score that will compute those fields to compare and rank Discoveries.

You can build two types of scores: a RICE score or a weighted average. To create a score field, follow the same steps as for other discovery fields, and select the score type.

RICE score

RICE stands for Reach, Impact, Confidence, and Effort. It's a very common prioritization methodology that weighs the potential impact of an opportunity versus the effort it would take to tackle it.

More precisely, the RICE score = [Reach x Impact x Confience] / [Ease]

In Harvestr, you can map each element of the Rice score with one of your Discovery fields.

Once you have set up the mapping, click save, and your RICE score will be created and calculated for all your Discoveries.

Weighted average score

The weighted average score available in Harvestr lets you build a score that :

  • takes into account as many criteria as you wish

  • assigns a weight to each criterion based on how it matters to you

  • lets you easily evaluate Discoveries with a final score that will always be an integer between 0 and 100


When building a weighted average score, you must give each field a weight from 0 to 100 according to how much each criterion should count in the final score.

The "Score influence" column tells you how much each field weighs in the final score relative to the others. The bigger the Score influence of a field is, the more it will affect the Score.

If a field has a Score influence of 80%, it means that this field accounts for 80% of the Score value.

Score calculation

In order for the Score to be easily understandable, we always give you a number between 0 and 100. 

To do that, Harvestr first converts all your fields' values into a number between 0 and 100 for all your Discoveries. How? By dividing the field's value for a Discovery by the highest existing value for this field among all your Discoveries. This process is called "normalization".

For example, let's say I have a financial field where I put the expected additional revenue of a Discovery. One Discovery has an expected revenue of $10,000. Among all my Discoveries, the highest expected revenue is $100,000. Then, Harvestr would give the former Discovery an intermediary score of 10% (10,000 divided by 100,000) for this financial field. 

To calculate the final Score, Harvestr does exactly the same for each field. We then multiply each field by its weight and sum all these values, giving you a final score between 0 and 100.

To give you a complete example, let's say you have three prioritization criteria: A, B, and C.

You set the following weights for each criteria: Weight_A = 20%, Weight_B = 30%, Weight_C = 50%

You have the following list of Discoveries with the following values for each criterion:




Discovery #1




Discovery #2




Discovery #3




Discovery #4




Discovery #5




The value of the final score for Discovery #1 would then be equal to:

[20% x $10000 / MAX(A) + 30% x 10 x MAX(B) + 50% x 1 / MAX(C)] x 100

= [20% x $10000 / $15000 + 30% x 10 x 70 + 50% x 1 / 4] x 100

= 30

Filter and sort Discoveries to identify priorities 

Once you have set your custom fields and weights, you can use Harvestr's advanced filtering and sorting capabilities to extract a subset of Discoveries according to your needs and to prioritize Discoveries.

Filtering options

In Harvestr, you can filter Discoveries by tags, user groups, states, date created, last updated, last feedback, and numeric fields.

Date and numeric fields can be sorted in a relative way. You can for example, filter Discoveries that have more than 30 feedbacks or Discoveries that were created more than 14 days ago.

Filters can be added with "AND" / "OR" conditions. If we go on with the same example, you could filter Discoveries with more than 30 feedback AND were created more than 14 days ago.

Sorting options

Discoveries can also be sorted alphabetically and numerically according to the field you want to sort by.

Prioritize what to build next

All these filtering and sorting options can be combined to prioritize Discoveries and decide what you should focus on according to your own criteria.

Here are a few examples.

Prioritize trending feature requests from large accounts

  • filter Discoveries by tag "Feature request"
    AND filter by user group "Large accounts"
    AND filter by last feedback "is before one month ago" (for the trending criteria)

  • sort by feedback volume

Prioritize quick wins

  • give each Discovery an impact and ease score between 1 and 5

  • give a 50% weight to each of these criteria to compute the final score

  • sort Discoveries by the final score. The ones with high impact and high ease are your quick wins and will have the higher score

Prioritize Discoveries according to customer segments and a multi-criteria score

  • filter Discoveries by the user group you want to focus on, "Power users," for example

  • create and weigh fields of building a score that takes into account feedback volume, impact, ease, expected revenue

  • sort Discoveries by Score 

Use views to get the right information at the right time

A specific set of filtering and sorting options can be saved in a View. For all your Views, you can also choose to display Discoveries as a table or as a board.

Here are a few examples of Views you can set up.

Top feature requests view

  • type: list

  • tag filter: "Feature request"

  • state: all active states, exclude "Shipped" and "Will not do" Discoveries

Prioritization view

  • type: list

  • user group filter: chose only the user groups you want to focus on

  • fields: display all the fields you take into account for prioritization (user groups, numeric fields, score)

  • sort: rank Discoveries by Score to see priorities come on top

Roadmap view

  • type: board

  • state filter: display only active states, exclude "Shipped" and "Will not do" Discoveries

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