The InsightSquared Revenue Intelligence Platform's Enterprise Search enables you to search by opportunity, name, rep, or any detail you can think of, and get instant access to all customer-related information, spread across your CRM, email, calendars, help desks, websites, and hundreds of other apps your company uses each day.
Search by opportunity, name, rep, or any detail you can think of and get instant access to all customer-related information, spread across your CRM, email, calendars, help desks, websites, and hundreds of other apps your company uses each day. Then click to be taken directly to the content, regardless of in which app it lives, including websites and tools you’ve visited outside of your organization.
Structured queries are about interrogating data that has an inherent structure. Dates, times, and numbers are all structured: they have a precise format from which you can perform logical operations. Common operations include comparing ranges of numbers or dates, or determining which of two values is larger. Below are examples of structured queries:
- Show all references in sales data referring to NDA
- List all of my Q118 opportunities: opportunity.closeDate:[2018-01-01 TO 2018-03-31]
Below are more examples of Structured Queries:
- Opportunities touched after June 1st 2018 (requires email integration):
- Opportunities missing a lead source:
- Opportunities with a lead source:
- Opportunities owned by this email:
- Opportunities of amount greater than 50k:
- You and also combine structured searches using "AND" and "OR". To see opportunities set to close and Q4 and have been touched after June 1:
opportunity.closeDate:[2018-10-01 TO 2018-12-31] AND opportunity.lastTouchDate:>2018-06-01
Search by opportunity, name, rep, or any detail you can think of and get instant access to all customer-related information, spread across your CRM, email, calendars, help desks, websites, and hundreds of other apps your company uses each day.
For example, to see everything related to an opportunity, simply search a company name.
In the Summary, you can see all content related to the company.
Getting Crazy with Advanced Insights
The IS2 Platform allows you to combine many kinds of complex operators to perform many advanced searches.
Wildcard searches can be run on individual terms, using
? to replace a single character, and
* to replace zero or more characters:
Be aware that wildcard queries can perform very badly — just think how many terms need to be queried to match the query string
"a* b* c*".
We can search for terms that are similar to, but not exactly like our search terms, using the “fuzzy” operator:
quikc~ brwn~ foks~
This uses the Damerau-Levenshtein distance to find all terms with a maximum of two changes, where a change is an insertion, deletion, or substitution of a single character, or transposition of two adjacent characters.
The default edit distance is
2, but an edit distance of
1 should be sufficient to catch 80% of all human misspellings. It can be specified as:
While a phrase query (eg
"john smith") expects all of the terms in exactly the same order, a proximity query allows the specified words to be further apart or in a different order. In the same way that fuzzy queries can specify a maximum edit distance for characters in a word, a proximity search allows us to specify a maximum edit distance of words in a phrase:
The closer the text in a field is to the original order specified in the query string, the more relevant that document is considered to be. When compared to the above example query, the phrase
"quick fox" would be considered more relevant than
"quick brown fox".
Use the boost operator
^ to make one term more relevant than another. For instance, if we want to find all documents about foxes, but we are especially interested in quick foxes:
boost value is 1, but can be any positive floating point number. Boosts between 0 and 1 reduce relevance.
Boosts can also be applied to phrases or to groups:
"john smith"^2 (foo bar)^4
By default, all terms are optional, as long as one term matches. A search for
foo bar baz will find any document that contains one or more of
baz. We have already discussed the
default_operator above which allows you to force all terms to be required, but there are also boolean operators that can be used in the query string itself to provide more control.
The preferred operators are
+ (this term must be present) and
- (this term must not be present). All other terms are optional. For example, this query:
quick brown +fox -news
- fox must be present
- news must not be present
The familiar operators
NOT (also written
!) are also supported. However, the effects of these operators can be more complicated than is obvious at first glance.
NOT takes precedence over
AND, which takes precedence over
OR. While the
- only affect the term to the right of the operator,
OR can affect the terms to the left and right.
Rewriting the above query using
NOT demonstrates the complexity:
quick OR brown AND fox AND NOT news
- This is incorrect because brown is now a required term.
(quick OR brown) AND fox AND NOT news
- This is incorrect because at least one of quick or brown is now required and the search for those terms would be scored differently from the original query.
((quick AND fox) OR (brown AND fox) OR fox) AND NOT news
- This form now replicates the logic from the original query correctly, but the relevance scoring bears little resemblance to the original.
Multiple terms or clauses can be grouped together with parentheses, to form sub-queries:
(quick OR brown) AND fox
Groups can be used to target a particular field, or to boost the result of a sub-query:
status:(active OR pending) title:(full text search)^2