As PR teams often deal with brand health monitoring and crisis situations, it is critical for them to monitor relevant conversations with the minimum possible noise. Any noise/irrelevant data can hamper the accuracy of PR reports leading to wrong decisions which can further lead to inefficient results.
Thus, setting up precise story queries allows them to accurately measure the crisis to drive their efforts in the correct direction and maximize their impact. Keyword selection & Operator choice can define how good a story query is and can often determine the accuracy of your PR reports.
In this article, we will try to understand how to effectively use the keywords and the operators and build a good story query.
Best practices while creating Story Queries
Below mentioned practices can help you set up robust queries –
Keyword selection is the most important step to set up relevant queries. Collect the relevant keywords with respect to the entity that you are tracking (brands, competitors, etc. as discussed earlier). Keywords used to set up the story query should not only consider direct keywords but also popular abbreviations, misspellings, hashtags, sub-brands or products or campaigns, etc.
Keyword Examples for Constellation Brand – [Constellation Brands, @cbrands, #cbrands]
You could leverage the wildcards operator to capture subtle variations of keywords instead of listing down all the combinations. For example, "iPhone14*" can be used to capture data around "iPhone14", "iPhone14pro", "iPhone14plus", "iPhone14promax", etc.
There could be certain messages that you would not require for your monitoring or analysis. In order to exclude mentions from your search, use the operator "NOT". Some possible scenarios are:
Brand names could be often correlated or used in a different context. For example, brands such as Constellation could be used in the context of Star Constellations. So the query can be improved as ("constellation" OR "Constellationbrands" OR "cbrand”) NOT ("celestial" OR "star" OR "pattern" OR "Mythological")
Brand wants specific events/campaigns around the brand to be excluded. For example, the brand’s ex-employee in a viral event.
When using generic keywords like "Constellation" & "Sprinklr", it is advised to use the "NEAR/X" operator with other brand or industry-relevant keywords to ensure relevant data is pulled in. The query for Constellation brands can be written as (constellation OR “Constellationbrands” OR cbrand) NEAR/10 (alcohol OR spirits OR beverage OR drinks OR <additional keywords>)
When you need to monitor brand keywords around key corporate pillars such as sustainability. For example: (constellation OR "Constellationbrands" OR cbrand) NEAR/10 (sustain OR sustainable OR eco-friendly OR eco OR “green energy” OR <additional keywords>)
Understanding how prominent your keywords of interest are placed in the media coverage is a way of measuring brand prominence. One way to do is by filtering media coverage where the search keywords are mentioned in the title of the message. The "title" operator should be used for the same.
For example: title: (("Ragy" OR "Ragy Thomas" OR "CEO") NEAR/5 ("Sprinklr"))
Keyword Lists allow you to consistently maintain a set of keywords that can be leveraged across multiple story queries. For example, you could build a keyword list with all your brand/sub-brand keywords and use the Keyword List within multiple campaign queries. So any changes needed for brand keywords can be done in one place in the keyword list which will reflect across all campaign story queries. This will save you time and ensure reports have consistent measurements.
Keyword lists can also be used to slice and dice your data and add another level of analysis within your reporting. Let’s imagine you are monitoring different brands for your organization or that of competitors, and you want to understand how these brands are performing on corporate reputation pillars such as Sustainability, Inclusion & Diversity, etc. Using appropriate keywords, you can set up each reputation pillar as a separate keyword list and add the layer of reputation pillar analysis to your brand monitoring. The keyword list can be directly used as filters in dashboards, sections, and widgets or they can also be used as a part of story queries.
Note: Your user needs to have permission to add, edit and delete keyword lists within the platform.
You can use different entity tags such as Story Query Tags, Domain List Tags, etc. to group story queries, domain lists, etc. respectively. These can help you to visualize multiple brands under single widgets and allow you to plot widgets like Share of Voice, Brand Scorecards, Side by Side trend analysis, etc.
If you have a set of filters that you consistently apply on the dashboards, the same set of filters can be applied within the Story Query itself as well. This will ensure that users of the dashboards do not miss out on adding any filters at the dashboard level.
For example, you are creating a story query and dashboard for analyzing the latest campaign with respect to the United Kingdom. You can apply the filter of "Country" containing "United Kingdom" within the Story Query itself. Then the Story Query can be directly added to the dashboard and data can be used directly without any additional filter.
So, use filters in the story query builder such as language, country, etc. for a targeted query search.