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Operations Insights

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Understand your strengths and weaknesses based on what your customers are saying in reviews and surveys.

See how other busineses are operationalizing feedback!

The Insights tab under Operations 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

Proportional volume of top five review sources. Hover your mouse over a source to view percentage of total.

Average star rating from reviews and surveys (with a rating question included). Percentage of sentiment is broken into Positive, Neutral, and Negative.

View trends in sentiment by feedback source, broken down by positives (green bars), neutrals (yellow bars), negatives (red bars), and sources with no rating (gray bars). Consider using this chart to compare feedback from surveys against third-party review sources to better understand the questions you should be asking in your surveys to gauge accurate sentiment. You may also use it as a control mechanism against “gaming” the system whenever incentives are offered with first-party feedback (surveys).

Insights by Location Tab

The Insights by Location tab is available if you’ve selected two or more locations in the filters.

View how groups of locations impact the business’s overall star rating. For example, if you select State in the Group By filter, you can estimate the influence each state and its corresponding locations has on the overall star rating for the business.

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.

Lists Locations with the most rating change (positive) within the given time period. Features the categories that contributed most to the improvement. Filter out locations with fewer comments for more meaningful insights. Click on a category to view details by time or view customer quotes and a word cloud related to the selected category for that location.

Lists locations with the most rating change (negative) within the given time period. Features the categories that contributed most to the decline. Filter out locations with fewer comments for more meaningful insights. Click on a category to view details by time or view customer quotes and a word cloud related to the selected category for that location.

Insights by Category Tab

Customer comments within reviews and surveys are transformed into text analytic categories. These categories are grouped within the domains that are customized for your account (e.g., Staff, Transaction, Satisfaction, Experience, etc.).

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.

Example: 15 total mentions for Parking
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:

0-50 –> Low
50-75 –> Moderate
75-100 –> High

Strengths and Weaknesses provides a list of categories that impact your star rating the most. Strengths are those categories that pull your star rating up the most, and weaknesses are those which drag your star rating down the most.

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.

The size of the box in the map indicates the volume of reviews/surveys associated with the category. Hover over a box to see the review/survey volume in that category as well as the category sentiment score out of 100 points. The “Other” box represents categories that may show up in fewer than 1% of reviews and surveys.

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.

Shows all categories sorted by the highest volume of mentions in reviews and surveys. View by average star rating (1-5), and compare previous and current time periods by category sentiment (0 to 100).

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:

  1. From the Operations menu, click Insights.
  2. 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.
  3. Within the pop-up window, click on a chart element to reveal a Word Cloud and related comments.
  4. 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:

Related Articles
Customer comments within reviews and surveys are transformed into text analytic categories. These categories are grouped within the domains that are customized for your account (e.g., Staff, Transaction, Satisfaction, Experience, etc.).
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.
Example: 15 total mentions for Parking
5 are negative = (0x5) = 0
8 are positive = (8x100) = 800
2 are neutral = (2x50) = 100
0+800+100=900
900/15 = 60
The Parking category sentiment equals 60.
Customer comments within reviews and surveys are transformed into text analytic categories. These categories are grouped within the domains that are customized for your account (e.g., Staff, Transaction, Satisfaction, Experience, etc.).
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.
Example: 15 total mentions for Parking
5 are negative = (0x5) = 0
8 are positive = (8x100) = 800
2 are neutral = (2x50) = 100
0+800+100=900
900/15 = 60
The Parking category sentiment equals 60.
Customer comments within reviews and surveys are transformed into text analytic categories. These categories are grouped within the domains that are customized for your account (e.g., Staff, Transaction, Satisfaction, Experience, etc.).
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.
Example: 15 total mentions for Parking
5 are negative = (0x5) = 0
8 are positive = (8x100) = 800
2 are neutral = (2x50) = 100
0+800+100=900
900/15 = 60
The Parking category sentiment equals 60.
Customer comments within reviews and surveys are transformed into text analytic categories. These categories are grouped within the domains that are customized for your account (e.g., Staff, Transaction, Satisfaction, Experience, etc.).
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.
Example: 15 total mentions for Parking
5 are negative = (0x5) = 0
8 are positive = (8x100) = 800
2 are neutral = (2x50) = 100
0+800+100=900
900/15 = 60
The Parking category sentiment equals 60.
Customer comments within reviews and surveys are transformed into text analytic categories. These categories are grouped within the domains that are customized for your account (e.g., Staff, Transaction, Satisfaction, Experience, etc.).
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.
Example: 15 total mentions for Parking
5 are negative = (0x5) = 0
8 are positive = (8x100) = 800
2 are neutral = (2x50) = 100
0+800+100=900
900/15 = 60
The Parking category sentiment equals 60.
Customer comments within reviews and surveys are transformed into text analytic categories. These categories are grouped within the domains that are customized for your account (e.g., Staff, Transaction, Satisfaction, Experience, etc.).
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.
Example: 15 total mentions for Parking
5 are negative = (0x5) = 0
8 are positive = (8x100) = 800
2 are neutral = (2x50) = 100
0+800+100=900
900/15 = 60
The Parking category sentiment equals 60.
Customer comments within reviews and surveys are transformed into text analytic categories. These categories are grouped within the domains that are customized for your account (e.g., Staff, Transaction, Satisfaction, Experience, etc.).
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.
Example: 15 total mentions for Parking
5 are negative = (0x5) = 0
8 are positive = (8x100) = 800
2 are neutral = (2x50) = 100
0+800+100=900
900/15 = 60
The Parking category sentiment equals 60.