Watch Video in FullscreenUnderstand your strengths and weaknesses based on what your customers are saying in reviews and surveys.
The Insights tab under Experience groups review and survey feedback into categories using text analytics to provide insights into what your business is doing well and what areas need improvement.
Insights include only reviews and surveys with comments that carry meaningful feedback. The results exclude blank reviews or surveys (i.e., no comments) as well as comments that are too short or generic.
Filters
Top line filters affect the data shown on the tab. You can also filter by Feedback Type (Industry or Employees) and by Surveys (All or check individual surveys to include).
Summary Tab
Insights by Location Tab
The Insights by Location tab is available if you’ve selected two or more locations in the filters.
Impact is not displayed when Group By is set to Location as impact from a single location is typically not significant.
| Column | Description |
|---|---|
| Rank | The selected grouping ranked first according to the sorted column. The default is sorted by the grouping with the highest average rating. |
| Group By Choice | The name of the groping selected in the Group By filter and top 3 categories that contribute most to the impact score (see below). Click on a category to view details by time or view customer quotes and a word cloud related to the selected category for that group of locations. |
| Rating | The average rating of reviews and surveys for the selected group of locations and time period, color-coded according to sentiment. |
| Rating Change | The rating change (in tenths of a point/star) of the group of location's rating previous comparable time period. |
| Feedback Volume | The volume of reviews and survey submissions for the grouping. |
| Impact | The influence a group of locations has on the overall average star rating, measured in hundredths of a point/star. "Low" indicates impact a value between -0.01 and 0.01. Impact is calculated relative to industry baselines. For example, if the industry baseline is 3.5, and a single 4-star review has two positive categories associated with it, each of those categories have a +.25 impact on the rating (4.0 – 3.5 = .5) / 2. |
Insights by Category Tab
Categories are then measured by category sentiment, which is a more precise measure of customer satisfaction than 1-5 star rating. This algorithm breaks down the review content into categories that are scored separately (e.g., a customer gives a 4-star on a review that raves about the polite doctor but complains about parking). The algorithm may score 100 to sentiment in “Staff” (positive) and 0 to sentiment in “Parking” (negative).
After all sentences are given a score (0 negative, 50 neutral, 100 positive), each category score is added and divided by the total number of mentions for that category.
5 are negative = (0x5) = 0
8 are positive = (8×100) = 800
2 are neutral = (2×50) = 100
0+800+100=900
900/15 = 60
The Parking category sentiment equals 60.
Rely on the industry average to determine whether each category sentiment score is ideal (above or below industry average). Generally, the sentiment score breakdown is as follows:
50-75 –> Moderate
75-100 –> High
| Item | Description |
|---|---|
| Category Sentiment | Categories that have the highest average rating for the feedback with that tag. (The average rating is not displayed but calculated on the back end.) See above for how category sentiment is calculated. The grey dot on the bar is the industry average for that category to give you a sense of how you're doing compared to similar categories. |
| Mentions | Total number of times that category is present in reviews and surveys for the given filters. |
| Impact | A conservative estimate of influence of that category over the average star rating. Because the categories in Strengths and Weaknesses are sorted by impact in descending order, focus on these categories first, especially the weaknesses. Addressing the related consumer issues is likely to have the highest impact on the average star rating. Impact is tailored to each industry. (Calculates an average star rating for each industry and estimates the impact of each category by comparing it to the industry average.) "Low" impact indicates that the potential impact of a category is less than 1/100th of a star. |
| Quotes | Sample phrases about that topic extracted from reviews or surveys. The comments selected represent those that the algorithm can identify as most positive or negative and that are most recent in that category. If no quotes are shown, the platform doesn't have the phrase or confidence level to display them. To see all quotes, go to the Sentiment Map and drill-down into that category's tile by Customer Quotes. |
Strengths
Lists sentiment, feedback volume, impact, and a sample of up to six customer quotes for each category . See above for definitions of each item.
Weaknesses
Lists sentiment, feedback volume, impact, and a sample of up to six customer quotes for each category. See above for definitions of each item.
Coloring represents the
category sentiment
rating of phrases within reviews and/or surveys tagged with that category. Each review can have a positive (100), a negative (0), or a neutral (50) category sentiment.
| Column | Description |
|---|---|
| Category Name | Name of the topic for related category(ies) associated with the review and/or survey comments. Each review/survey may have multiple category tags applied (e.g., "Staff" may have mentions related to "Staff Professionalism" and "People"). |
| Mentions – Current Period | This is the total number of reviews and/or surveys with this specific category tag. Each review/survey may have multiple category tags, so this number may not correspond 1:1 with total volume of reviews and/or surveys received during the time period. |
| Average Rating – Current Period | The average star rating (1-5 scale) of reviews and/or surveys that include this category. |
| CATEGORY SENTIMENT (0 to 100 scale) | |
| Category sentiment is a more precise measure of customer satisfaction than 1-5 star rating. The algorithm breaks down the review content into categories that are scored separately (e.g., a customer gives a 4-star on a review that raves about the polite doctor but complains about parking). The algorithm may score 100 to sentiment in "Staff" (positive) and 0 to sentiment in "Parking" (negative). After all sentences are given a score (0 negative, 50 neutral, 100 positive), each category score is added and divided by the total number of mentions for that category. |
|
| Current Period Average | The average category sentiment for the current time period. |
| Change from Prev. Period | The category sentiment change compared to the previous time period. |
| Prev. Period Trend | The category sentiment trend from the previous time period's reviews that include this category. The dotted line represents 50. |
| Current Period Trend | The category sentiment trend from the current time period's reviews that include this category. The dotted line represents 50. |
| Current Period Breakdown | The total volume of reviews that include this category, broken down by positive (green), negative (red), and neutral (yellow). |
Drill Down into Categories
In terms of impact, both the volume of review category and the rating are important.
Note the average star rating from the Summary tab, which represents the average for all locations selected. Let’s say it’s 4.0. Even if an individual category has a large review volume but is also rated equivalent to a 4.0, the impact of that category won’t be as high compared to a category with the same review volume but with a rating that differs from the average.
Drill all the way to the full comment for richer analysis of customer feedback.
To view drill-down data:
- From the Operations menu, click Insights.
- From the Sentiment Map, Sentiment by Location, or Big Movers section, click inside a category to dill down. Choose View by Location, View by Time, or View Customer Quotes.
- Within the pop-up window, click on a chart element to reveal a Word Cloud and related comments.
- From within the Quote section of the table, click to see the full review or survey quote from which the comment was selected.
View the full comment from the survey or review:
