Priority Ranking (MaxDiff)
Priority Ranking (MaxDiff, best–worst scaling) ranks a product’s features by how much they matter in the purchase decision. It answers a question such as “Which feature of our coffee subscription matters most to customers, and which matters least?” It doesn’t ask about price; it only compares the features with one another.
Setting up a Priority Ranking study and reading the report
Prepares a feature list for a product in General Research and starts a Priority Ranking study. It then looks at the importance scores, segment breakdowns and Excel file in the report.
Each synthetic consumer (persona) sees the features in groups of four; these groups are called question sets. In each set, the persona ranks the four features from most to least important for its own purchase decision. The feature it puts at the top counts as its Best choice, and the one it puts at the bottom as its Worst choice.
These choices are added up across all sets and personas, and each feature gets a score and an importance rank. Because the persona has to put one feature ahead of the others in every set, the difference between “nice to have” and “must have” shows up more clearly than when you ask about each feature on its own.
When to use it
- You have four or more features and need to decide which ones to put first: product development priorities, marketing messages, packaging claims.
- You want to see how important the features are relative to one another, and you don’t need to measure the effect on price.
- You want to see whether priorities differ between demographic groups. For example, do younger and middle-aged customers put the same feature first?
To measure how many lira a feature adds to or takes away from willingness to pay, use Feature Price Impact. Feature Price Impact takes at most 5 features, so you can narrow a long list down with Priority Ranking first. To compare methods, see Choosing the right method.
Before you start
- Population: You can choose a saved population or create a new one during setup; see Creating a population.
- Credits: To create a General Research study in the New Research flow, your balance must be at least 500 credits. Creating a new population in Fashion requires at least 50 credits.
- Feature list: Prepare the list in advance, following the principles below; the list is what shapes the result most.
Preparing a good feature list
- Number: You need at least 4 features. The more features you add, the more question sets and credits the study needs; keep the list to 20 features at most.
- Same level of detail: A concrete feature such as “30-hour battery life” can’t be compared fairly with a general one such as “good user experience”. Write them all at a similar level of concreteness.
- One idea, no overlap: Each feature should describe one thing. If “Fast shipping” and “Free and fast shipping” are in the same list, personas assess the same idea twice. Merge features that are very close to each other, or remove one of them.
- Description: Personas also see each feature’s description. Use the description field to clarify a name that’s open to interpretation, such as “Premium materials”.
- Duplicates: If you enter a name that’s already in the list (ignoring case), the feature isn’t added.
Setting up the study
- In the sidebar, click New Research.
- Choose the General Research or Fashion card.
- On the method screen, click the Priority Ranking card. In Fashion, this card is called Özellik Tercihi (MaxDiff).
- Choose or create a population. This step is covered on the Starting a new study page.
- On the design page, titled Feature Preference (MaxDiff) (Özellik Tercihi (MaxDiff) in Fashion), enter your product’s name in the Product Name field (required). You can also fill in the Product Description (Optional) field if you like.
- Add features in one of the ways described below. You can write a short description in the Optional description… field under each feature, and delete a feature with the x button.
- Check the status line. Once there are 4 or more features, the line turns green and reads, for example, 10 features · 8 question sets. If there aren’t enough features, a warning such as Add 2 more features appears.
- Click Next Step. While the population is being prepared, the button reads Waiting… and can’t be clicked. In General Research, click Continue in the Are you sure you want to continue? dialog that opens; your design is saved.
- On the Research Preview page, check the Person Count, Feature Count and Credit Usage values on the Experiment Summary card.
- Click Start, then Continue. When the study finishes, the Your analysis is ready card and the Go to Reports button appear; the report opens automatically within a few seconds.
Ways to add features
| Option | How it works |
|---|---|
| Suggest with AI | The AI suggests 10 features based on the product name and adds them to the end of the list. You can’t click it while Product Name is empty. The gear icon next to it opens the AI Instructions field, where you can add guidance such as “Focus on sustainability features”. |
| Add feature… field | Type the feature, then press Enter or click the + button. |
| Pasting | If you paste several lines into the Add feature… field, each line becomes a separate feature. If a line contains a Tab character, the part before the Tab becomes the name and the part after it the description, so you can copy the name and description columns from a spreadsheet together. |
| Bulk Edit | Opens the whole list in a text box: one feature per line, with the name and description separated by a Tab. Edit the text and click Apply. |
| Select from Saved Surveys | Loads a Priority Ranking design you saved to the Surveys library. The card only appears if you have a saved design. If you change a field in the loaded design, the selection is cleared. |
The Clear button deletes all the features in the list. AI suggestions come without descriptions; review the list and add descriptions where needed.
Settings and limits
| Setting | Value |
|---|---|
| Product Name | Required, up to 255 characters |
| Number of features | At least 4; at most 20 recommended |
| Feature name | Up to 255 characters |
| Feature description | Optional, up to 500 characters |
| Features per question set | 4; can’t be changed |
| Times each feature is shown | At least 3 per persona |
How question sets are built
You don’t enter the number of question sets; it’s calculated from the number of features. The formula is in the credit calculation section below. The sets are balanced: each feature is shown roughly the same number of times and appears alongside different features. The features within each set are shown in shuffled order.
| Features | 4 | 5 | 6 | 8 | 10 | 12 | 16 | 20 |
|---|---|---|---|---|---|---|---|---|
| Question sets | 3 | 4 | 5 | 6 | 8 | 9 | 12 | 15 |
Credit calculation
Credits = number of question sets × number of personas
Number of question sets = number of features × 3 ÷ 4, rounded up.
- 10 features, 100 personas: 10 × 3 ÷ 4 = 7.5, rounded up to 8 question sets; 8 × 100 = 800 credits.
- 16 features, 250 personas: 16 × 3 ÷ 4 = 12 question sets; 12 × 250 = 3,000 credits.
Every four extra features add three more question sets per persona. The estimated credits appear on the Research Preview page as Credit Usage, in the form “balance/estimate”, for example 1.250/800 (a balance of 1,250 credits against an estimate of 800). If your balance doesn’t cover the estimate, a red info icon appears next to the value, and you can go to Buy Credits from the icon.
Credits are deducted after the responses are generated and saved; a study that fails before any responses are generated doesn’t spend credits. Your balance is checked again when the study starts; if it’s insufficient, the study doesn’t run and no credits are deducted. In General Research, the credits deducted are calculated from the number of saved responses (1 credit for each question set completed by each persona), so they may differ slightly from the estimate. For details, see Credits.
Reading the results
Completed studies are listed on the Reports page; use the Priority filter to see only this method’s reports. The Refresh, Download PDF, Download Excel and Delete buttons at the top of the report are covered on the Reports page.
Key measures
| Measure | Meaning |
|---|---|
| Best | Number of times the feature was chosen as most important in its sets (total across all personas) |
| Worst | Number of times the feature was chosen as least important in its sets (total across all personas) |
| Net / Net Score | Best minus Worst. If it’s positive, the feature was put first more often; if it’s negative, it was put last more often. |
| Score (0-100) | The net score converted to a 0–100 scale. The most important feature you tested sits at the top end of the scale, and the least important at the bottom end. |
Example: in a study with 100 personas and 10 features, each persona answers 8 sets, so 800 Best and 800 Worst choices are made in total. If a feature was chosen as Best 150 times and as Worst 30 times, its net score is 120.
Summary cards
The report has five summary cards:
- Participants: The number of personas included in the analysis.
- Total Features: The number of features tested.
- Best Feature and Worst Feature: The first and last features in the ranking, with their scores.
- Most Differentiated Feature: The feature whose net score is closest to zero; it was either chosen as Best and Worst about equally often, or rarely chosen at either end. It doesn’t mean “most liked”; it shows the feature on which personas were split or indifferent.
Best / Worst tab
In this tab’s chart, each feature’s Best choices are shown as a green bar and its Worst choices as a red bar, with the net score on the right. A feature with both a long green bar and a long red bar is very important to some personas and unimportant to others; to see who it matters to, check the Segment Analysis tab.
Importance Ranking tab
The Feature Importance Ranking section has a card for each feature, showing its rank (#1, #2…), its score out of 100, its importance label and its Best, Worst and Net counts. Labels are assigned by score:
| Score | Label |
|---|---|
| 75 and above | Very Important |
| 60–75 | Important |
| 40–60 | Medium |
| 25–40 | Low Importance |
| Below 25 | Not Important |
The MaxDiff Feature Details table below the cards has Rank, Feature, Score (0-100), Net Score, Best and Worst columns. You can sort the table by clicking a column heading, search for a feature with the Filter… box and download the table with Download CSV. At the bottom of the page, the Most Important Features and Least Important Features boxes summarise the top three and bottom three features.
Results are language model output; they show direction and magnitude. For high-stakes decisions, see Accuracy and limitations.
Analysing results by segment
The Segment Analysis tab shows how priorities change across demographic groups.
- Choose a dimension from the Select Demographic list, for example Age, Gender, Education, Income Group or SES (socio-economic status). If you added a custom feature to your population, Custom Feature is listed too. When the page opens, the first dimension in the list is selected.
- In the Segment Comparison Chart, compare the top 8 features across segments. Choose Score (0-100), Net Score or Ranking from the Metric list.
- In the Analysis by … section below the chart (e.g. Analysis by Age), read each segment’s number of personas, its share and its Most important: feature.
- In the Feature Comparison table, compare the rank and score of up to 10 features side by side across the five largest segments.
The report has no free-form filter: breakdowns are limited to the dimensions in the Select Demographic list, and you can’t filter by two dimensions together (e.g. “women aged 25–34”). To look at a specific group, filter the Segment Impact and Raw sheets in the Excel file, or set up a new study with a population made up only of that group (Filters and attributes). For details of the dimensions, see Segmentation.
Exporting
- Download PDF: If the report includes segment dimensions, the PDF segment selection dialog opens first. Tick the dimensions to include in the PDF and click Generate PDF. The PDF report includes importance ranking, best/worst, breakdown and findings sections.
- Download Excel: An Excel file containing all the data is generated and downloads when it’s ready. The file is in your interface language, but the sheet names stay in English.
| Excel sheet | Contents |
|---|---|
| Meta | General information about the study |
| Executive Summary | Feature MaxDiff Summary: Personas, Features, Sets, Top Feature, Bottom Feature, Response Completion and the Feature Importance Scores chart |
| Feature Rankings | Feature Rankings: Rank, Feature, Shown (how many times the feature was shown), Best, Worst, Net, Normalized Score |
| Segment Impact | Segment Impact: for each feature and segment, Segment Dimension, Category, Persona N, Share, Rank, Normalized Score, Best, Worst |
| Population | The personas’ demographic details |
| Raw | Responses per persona, with the number of completed and expected sets and the completion rate |
The Feature Rankings and Segment Impact sheets come with Excel filter buttons already in place. For download details, see Exporting and downloading.
Tips and common mistakes
- Watch out for small segments. In a segment of only a few personas, even a handful of choices change the scores noticeably. Check each segment’s number of personas in the Analysis by … section.
- Reuse the design. You can use a Priority Ranking design you saved to the Surveys library with different populations (Survey library and question design). To run a saved design on a ready-made population from a single screen, see Quick Research.
- Get help from Argus. Argus, SCL’s AI research assistant, can fill in this form for you in a conversation; see Working with Argus.