How to Analyse Rank (Score)?

Rank allows Respondents to assign a Rank to each Choice in your Question.

  • Rank (%) analysis is the 1st step, and divides your count values by the answered values to get a percentage for each rank in your choices.
  • Rank (Score) analysis (this article) is the 2nd step and multiplies the rank percentages with the rank scores and sums them together to give you a rank score for each choice.

Score Default:

To analyse the Rank score:

  1. Go to any Rank question (with choices and ranks), e.g.



  1. Change the Values from "Total %" to "Score", e.g.

  1. Analyse the results! E.g.

In above table / chart , we can see rank scores for each of our "Hot Sauce: Purchase Factors" of:

  • 2.9 for Taste [BEST] equals
    • Rank 1: (30% x 1) +
    • Rank 2: (20% x 2) +
    • Rank 3: (17% x 3) +
    • Rank 4: (13% x 4) +
    • Rank 5:  (9% x 5) +
    • No Rank: (11% x 6) =
  • 3.7 for Heat Level and 3.8 for Price
  • 5.5 for Diet and also 5.5 for Packaging [WORST]

Simple, right?

Score Crosstab A:

Score Crosstab A permits you to see the differences in rank scores for all your choices in the rows / chart axis (e.g. Price, Brand, Diet), across A FEW segments or demographic filters in the columns / chart bars (e.g. Completes vs. Males vs. Females)

To create Score Crosstab A, simply:

  • keep Rows as "Choices",
  • change Columns to "Filters",
  • keep Values as "Score"
  • change Chart Type to "Group Bar"
  • add Filters, e.g. for "Males" and "Females"

We can now see differences in rank scores across gender in our hot sauce purchase factors, such as:

  • Taste: Completes: 2.9, Males 2.9, Females 2.9 [BEST]
  • Diet: Completes: 5.5, Males 5.6, Females 5.5 [WORST]

How powerful is that?

Score Crosstab B:

The Crosstab B format is GREAT for BIG score crosstabs as you can fit a lot MORE filters in the table rows / chart axis than you can in the table columns / chart bars. E.g. 15 filters across Completes (1), Gender (2), Age (3), Time (4), and Country (5) in the table rows / chart axis.



To create Score Crosstab B:

  • change Rows to "Filters"
  • change Columns to "Ranks"
  • a new Dropdown for "Choices" will automagically appear, e.g. "Price"
  • keep Values as "Score"

E.g. for Choice: "Price" as a hot sauce: purchase factor, we can now see differences in Rank Scores for each Filter of:

  • 3.8 for Completes
  • 3.7 for Males
  • 4.0 for 61-99s
  • 3.7 for Q4 2023
  • 3.9 for AUS

How clear is that?

Score Crosstab C:

However, instead of 1 choice at a time, e.g. price, in Crosstab B above, Score Crosstab C below lets you see rank scores:

  • for MANY filters, e.g. completes (1), gender (2), age (3), time (4), country (4) in the rows AND
  • for A FEW choices at a time, e.g. 5/10 choices - taste, heat, price, packaging, diet in the columns.



To create Score Crosstab C, simply:

  • keep Rows as "Filters"
  • change Columns to "Choices"
  • keep Values as "Score"

We can now see differences in rank scores across 15 demographic filters for 5/10 hot sauce buying factors of:

  • Completes: Taste: 2.9 [best], Heat 3.7, Diet 5.5 [worst]
  • Females: Taste: 2.9, Heat 3.7, Diet 5.5
  • 18-40s: Taste: 3.0, Heat 3.7, Diet 5.3
  • Q2: Taste: 2.9, Heat 3.7, Diet 5.5
  • NZD: Taste: 2.8, Heat 3.6, Diet 5.6

This is the most COMPLEX rank score analysis possible, but how EASY was that?

Score Time Series:

Finally, to track trends over time, simply:

  • add time filters (e.g. Q1, Q2, Q3, Q4) to your table rows and
  • keep Columns as "Choices" (e.g. Taste, Heat, Price, etc)
  • keep Values as "Score"
  • change Chart Type to a "Line Chart"

We can now see scores in each quarter for all statements of:

Rank Score Recap

To recap, these are the 5 most common combos that cover 99% of use cases for Rank (Score) analysis.

  1. Score Default: Choices x Ranks with Score and Group Bar Chart
  2. Score Crosstab A: Choices x Filters with Score and Group Bar Chart
  3. Score Crosstab B: Filters x Ranks x Choice with Score and Group Bar Chart
  4. Score Crosstab C: Filters x Choices with Score and Group Bar Chart
  5. Score Time Series: Filters x Choices with Score and Line Chart

Rank (Score) analysis done! 🎉

In the next article, we'll show you how to analyse Constant Sum (Sum %).

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