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.

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.

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

Some of these criteria are created in Harvestr by default:

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:

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

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 scores: a RICE score or a weighted average. To create a score field, follow the same steps as other discovery fields and select the score type.

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.

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

When building a weighted average score, you must assign each field a weight from 0 to 100 based on 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 a field's score influence, 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.

If you toggle on the Inverted option for one of your prioritization criteria, this criterion will negatively impact the score. In other words, the higher this criterion is, the lower the score will be.

We always give you a number between 0 and 100 so that the score is easily understandable. 

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:

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

If you toggled on the "Inverted" option for Criteria A, the calculation would become:

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.

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 relative to each other. For example, you can filter Discoveries with more than 30 feedbacks or Discoveries 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.

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

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

Here are a few examples.

A specific set of filtering and sorting options can be saved in a View. You can also display Discoveries as a table or board for all your Views.

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