| 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 | ||
| Sorting and Limiting Data | 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 | ||
| Sort By + Column + Asc/Desc | Revenue by Country Sort by Country Ascending | use Asc/Ascending | |
| Revenue by Country Sort by Category Desc | use Desc/Descending | ||
| Revenue by Country Sort by Category Desc top 5 | Combine Sort by + Top/Bottom to sort and limit | ||
| Sort by + Column +Asc/Desc+Top/Bottom | Revenue by Country Sort by Category | Will default to Ascending order if not specified | |
| 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 |
