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Keywords Reference Guide

Category Keyword Example Notes
Aggregates Total,Sum Total Revenue this month
Average Average temperature by city in may 2018
Variance Variance of humidity this year
Standard Deviation Standard Deviation of ratings this week
Count Count of orders by category this month Count + record name defined for dataset
Max, maximum Max temperature per month of year in last 50 years
Min, Minimum Minimum delivery duration per destination last quarter
Unique Count, Distinct count Unique count of customers with order date today
Cumulative Sum Cumulative Sum Sales monthly this year Accumulates the aggregate by each value of the dimension in your question
The dimension can be of any type (date or text)
Cumulative Average Cumulative Average checkout duration daily
Cumulative Count Cumulative Count orders by city
Cumulative Min Cumulative Min service duration weekly
Cumulative Max Cumulative Max search_seconds hourly
Moving n1 to n2 window Sum Moving 1 to 1 window Sum revenue monthly Returns the selected aggregate of the metric, for each value starting with the X axis value minus n1 and ending with X axis value plus n2.
Requires a date axis (e.g. using an interval such as monthly). If not provided will default to monthly
Moving n1 to n2 window Average Moving 1 to 1 window Average ratings yearly
Moving n1 to n2 window Min Moving 1 to 1 window Min profit by week
Moving n1 to n2 window Max Moving 1 to 1 window Max temperature daily
Moving n1 to n2 window Count Moving 1 to 1 window Count accidents monthly
Trailing n period Sum Trailing 12 period Sum revenue monthly A trailing n period sum has a window starting with -(n-1) and ending at the current value. For example a trailing 12 month average will compute the average of the last 12 months including the current month. It is the equivalent of Moving 11 to 0 window average.
Requires a date axis (e.g. using an interval such as monthly). If not provided will default to monthly
Trailing n period Sum Trailing 5 period Average ratings yearly
Trailing n period Sum Trailing 4 period Min profit by quarterly
Trailing n period Sum Trailing 30 period Max temperature daily
Trailing n period Sum Trailing 6 period Count accidents monthly
Top and bottom Top n Top 10 countries in sales this month
Top, highest,most,descending,desc Top customers by number of orders Will default to the top 10
Bottom n bottom 5 categories in profit year to date
Bottom,lowest,least,ascending,asc Lowest Origin City in average delivery duration
Date Filters After Search queries after may 19 this year daily
Before Organic Sessions before 11/20/2017
From .. To Weekly number of helpdesks from jan 1 2018 to may 14 2018
Monthly checkouts feb-june 2018
Revenue from 2017 to 2018
Average bounce rate from jan 2016 to feb 2019
Orders 12-3-2015 to 12-5-2018
Searches during march 5th 2017 until jan 9th 2018
Use any date format (yyyy-mm-dd, mm-dd-yyyy, etc..)
Use any seperator (space,”/”,”-“,”.”, etc..)
Between Orders between feb 2018 and june 2018 for tv and phones
To date Expenses month to date by category Will show data for this month up till end of today
To date exclude today Profit month to date excluding today by category Will show data for this month up till yesterday
Last Last week keywords with search duration > 2
Last n Closed helpdesks last 7 days by department Will show the last 7 days excluding today
Last n days include today Number of Transactions last 10 days including today Will show the last 7 days including today
This, current This quarter sales by sales person top 10
n (period) ago Average bounce rate 2 months ago
More than n (period) ago Users with LastLogin more than 3 months ago Will filter by the end date being n period(s) ago. periods can be minutes,hours,days,weeks, months, quarters, years
Starting, ending Number of bounced sessions starting jan 2017 and ending feb 2018
Year Number of accidents in 2018 by street name
Month Jan revenue
Quarter Q1 delayed shipments by destination
Latest [Month/Quarter/Year] Total subscriptions latest quarter
Revenue latest month
Gdp per country latest year
Will filter the date by the last available quarter in your dataset. This is useful in the case of data reported on a periodic basis and then appended to the dataset. The latest keyword will always bring the latest reported period. This is especially usefull when used in Dashboards.
First [Day/Week/Month] of [Month/Quarter/Year] Invoices First day of quarter Will return the metric for the first day of each quarter
Last [Day/Week/Month] of [Month/Quarter/Year] Subscribers Last day of month daily Will return the metric for the last day of each month
Mon-Thu Weekly average turnout Mon-Thu Works also with Monday to Thursday. Its equivalent to writing Mon Tue Wed Thu Fri
Sun-Thu Weekly total shipments Sunday to Thursday
Saturday Average Sales Saturdays Works with any day name or combination (separated by space)
Filters Equal,= Number of orders category=electronics or fashion
Number of requests country=US CN BR
Other keywords: Equals,equal to, etc..
Seperate filter values by OR or Spave
Not equal, <>,!=, excluding Revenue by country excluding us and japan
>, above, over Customers with over 5000 in bookings this year
Companies with count of employees > 100
Last week keywords with search duration above 2
You can include a field (e.g. revenue) or
aggregate + field e.g average revenue)
in the filter expression
<,below,under, less Patients with stay duration < 2 by age group
Begins with, starting Keywords starting with iphone over time this month
Ends with,ending, .. Customers with name ending with smith or jones
Containing, similar, like Average order completion time for category containing electronics
Sales for orders containing note%phone
% is equivalent to *
Not + filter Gdp by country continent not north america You can use not with any other filter to reverse it
Not blank, not empty Sessions by channel with channel not blank Will exclude empty string values while null and other values will be included
Blank, empty Reviews with comments empty
Products with description is blank
Will include only empty string values and will exclude null and other values
Not null Sessions by channel with channel not null Will exclude null values (will show empty strings and other values)
Null Products with description equal null Will include only null values (will not show even empty strings)
Not empty or null, not blank or null Sessions with channel not empty or null Will not include records with empty or null values
Empty or null, blank or null Products with description equal null or empty Will include records with empty or null values
Space orders from customers with name not equal space Use pace to denote an empty space ” “, you can use it with any filter operator like equals, not equal, contains, etc..
searches with keyword containing space
Exclude,Excluding Orders by category excluding express shipment Exclude + column value removes the records with that specified column value
Between Landing pages with bounce rate between 10 and 50 this week
Intervals Daily, over time, trend Total revenue daily last month -Other keywords: by day, Per week , every year, each quarter, etc..
-If the date is not specified and not selected from the search
guidance menu, it will be defaulted to the primary date of the dataset
Weekly Weekly sessions from US
Monthly Average monthly temperature in texas
Quarterly Number of accidents quarterly in 2017
Yearly Yearly average gdp by country last 10 years
Hourly Helpdesks by hour of open date
By day of week Patients by day of week of admittance date Will be grouped by day name Mon,Tue,Wed,Etc..
By day of month Orders by day of month last 6 months Will be grouped by day number (1-31) for each month
By day of quarter Average attendance by day of qaurter last 24 months Will be grouped by day number (1-91) for each quarter
By day of year charges by day of year last year Will be grouped by DayMonth
By week of month Sum of mortgage amount by week of month Will be grouped by week number (1-4) for each month
By week of quarter Number of shipments by week of month Will be grouped by week number (1-13) for each quarter
By week of year Count of sessions by week of year Will be grouped by week number (1-52) for each year
By month of quarter Profit by month of quarter last 2 years Will be grouped by month number (1-3) for each quarter
By month of year Shipments by month of year of shipped date Will be grouped by month name : Jan,Feb,etc..
By quarter of year Sales by quarter of year 2015-2018 Will be grouped by quarter number Q1,Q2,Q3,Q4
By Hour of Day Conversion rate by hour of day last 60 days Will be grouped by hour number 0-23
Monthly Yearly (2 intervals) Sales monthly yearly last 5 years
Sales weekly quarterly last 5 years
Use any combination of two interval keywords to plot different lines representing each value of one interval (series designated by color), accross the values of the other interval (X-axis)
Comparison Date vs Date Revenue in US this year vs last year daily You can compare any two date expressions.
You can use any of the synonyms such as Versus, compared to, comparing to, etc..
Ratings month to date compared to previous year Alternatively: vs same month last year
Attribute vs. Attribute Average closure duration for high priority vs low priority requests You can add as many segments as needed separated by Vs
Attribute vs. All Revenue in US vs All yearly Will show revenue for US compared  to total revenue for each year
Filter vs Filter Searches keywords containing apple vs keywords starting with Samsung vs keywords starting with sony You can compare any number of filter expressions
Filter vs. All Profit for category = furniture vs subcategory = accessories vs all You can  compare any number of filter expressions to the total
Percentage Share
Percentage
Share of delayed shipments weekly
Percentage Percentage of keywords with search duration > 2 seconds daily
Share of new users organic vs referal
Chart type List, table, pie, stacked column, line, etc.. Pie of sessions per browser You can use any chart type instead of Pie or stacked column, if the data returned does not fit the graph, the keyword will be ignored
Stacked column of gdp per country
List of delayed orders Will show a table of records matching the criteria specified
Search Duration Histogram Histogram keyword will ensure the metric is returned without aggregation to be drawn on a histogram chart
Row Data Revenue Cost Profit Country 2018 Row Data Row Data will ensure the columns are returned without aggregation, it will just show the values of the columns for each row matching the criteria in your search
Growth Growth + interval Growth of revenue monthly Will default to a waterfall chart
Will default the comparison to previous month
Growth  + interval + Year over Year Growth of sales monthly year over year Other keywords: Month over month, MoM, YOY, Quarter over Quarter,QOQ,Week over week, WOW, etc..
Growth + date1 vs date2 Growth profit this month vs last month for EMEA
Growth + date Top growing destinations by bookings this quarter If date2 not specified, Answerdock will compare
with the previous equivalent period
Growth + date + Year over Year Growth of sales today month over month Alternativel: Growth of revenue today  vs previous month
Geo Where Where are orders mostly coming from Will default to the primary Geo column in the dataset
Dataset Dataset name Transactions Log Typing the dataset name will return the top 100 records in the dataset