Audience Breakdown - v1

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Strategy properties

Strategy id audience_breakdown
Version 1
Supports grouping? Yes
Learn more...
Cost (#groups + 1) x #dimensions
Learn more...

Strategy usage

Overview

This strategy gives you a breakdown of an audience by multiple demographic dimensions.

The strategy allows you to select an audience using demographic attributes, by a topic being engaged with, or both. The strategy also allows you to specify an audience to compare your audience against.

For each demographic dimension you analyze the strategy tells you:

  • the number of members in each segment who have engaged with the topic in the analyzed period.
  • relatively how engaged each segment is compared to your selected comparison audience.
  • how many members in the segment were active on LinkedIn in the period analyzed regardless of whether they engaged with the topic you specified.

Use cases

The strategy helps you answer questions such as:

  • For a defined topic, which are the most engaged audience segments?
  • For a defined audience, what additional demographic details can I see?
  • For a defined topic, what is the size of the 'active audience' for each engaged segment?

Parameters

To execute this strategy without grouping you must provide specify at least one of the keywords, concepts, or audience parameters. To execute this strategy with grouping you must specify the groups parameter.

Parameter Type Required? Description
audience audience

At least one of 'keywords', 'concepts', and 'audience' must be given.

Selects the audience to analyze.

concepts concepts

At least one of 'keywords', 'concepts', and 'audience' must be given.

Concepts for the topic to analyze.

keywords keywords

At least one of 'keywords', 'concepts', and 'audience' must be given.

Keywords for the topic to analyze.

dimensions

array(string)

No.

If specified a list of between 1 and 5 valid dimensions.

Defaults to ["seniorities", "functions", "company sizes", "sectors", "industries"].

Demographic dimensions to analyze.

Valid dimensions: seniorities, functions, company_sizes, sectors, industries, custom_segments, gender, age, skills, occupations, country, metro_area.

period period

No. Defaults to 28 days.

The time period to analyze.

sort_by sort_by

No. Defaults to engagement_ratio.

The output field to sort results by.

comparison_audience comparison_audience

Yes.

The audience to compare engagement with.

groups

No.

Groups to perform analysis for.

See Learn more...

Example requests

The following examples show which parameters should be submitted to the API to answer the stated questions.

Which US audience segments are most engaged with the topic of 'cloud', compared to the global audience?

{
    "type": "strategy",
    "name": "example",
    "subscription_id": "cd99abbc812f646c77bfd8ddf767a134f0b91e84",
    "parameters": {
        "strategy": "audience_breakdown",
        "version": 1,
        "parameters": {
            "keywords": {
                "any": ["cloud", "azure", "aws", "google cloud", "vmware"]
            },
            "audience": {
                "countries": ["united states"]
            },
            "comparison_audience": "global"
        }
    }
}

Which custom segments in the US are most engaged with the topic of 'cloud', compared to the global audience?

{
    "type": "strategy",
    "name": "example",
    "subscription_id": "cd99abbc812f646c77bfd8ddf767a134f0b91e84",
    "parameters": {
        "strategy": "audience_breakdown",
        "version": 1,
        "parameters": {
            "dimensions": ["custom_segments"],
            "keywords": {
                "any": ["cloud", "azure", "aws", "google cloud", "vmware"]
            },
            "audience": {
                "countries": ["united states"]
            },
            "comparison_audience": "global"
        }
    }
}

Note this analysis is the same as the example above, but requests only analysis of the custom segments dimension.

Which decision makers in the UK are most engaged with the topic of 'cloud', compared to the general UK audience?

{
    "type": "strategy",
    "name": "example",
    "subscription_id": "cd99abbc812f646c77bfd8ddf767a134f0b91e84",
    "parameters": {
        "strategy": "audience_breakdown",
        "version": 1,
        "parameters": {
            "keywords": {
                "any": ["cloud", "azure", "aws", "google cloud", "vmware"]
            },
            "audience": {
                "countries": ["united kingdom"],
                "seniorities": ["manager", "director", "vp", "cxo", "partner", "owner"]
            },
            "comparison_audience": {
                "country": "united kingdom"
            }
        }
    }
}

Here the parameters request that only members in the UK in senior positions are analyzed and their engagement is compared to all members in the UK.

Output

If the strategy is successfully executed it will return output with the following structure:

{
    "redacted": false,
    "seniorities": {
        "redacted": false,
        "unique_authors": 92000,
        "interactions": 103000,
        "segments": [
            {
                "seniority": "cxo",
                "unique_authors": 10000,
                "interactions": 12000,
                "engagement_ratio": 1.4,
                "active_audience" 57000
            }
        ]
    },
    "functions": {
        ...
    }
}

A set of results will be returned for each dimension analyzed. In this example two dimensions have been analyzed; seniorities and functions.

The following properties are returned for each dimension:

  • redacted - indicates if there was too little data for the analysis.
  • unique_authors - how many members engaged in total for all segments in the dimension.
  • interactions - how many interactions were analyzed in total for the dimension.
  • segments - the segments for the demographic dimension.

For each segment the following properties are given:

  • unique_authors - how many members in the segment engaged with the topic specified.
  • interactions - how many interactions were carried out by members in the segment relating to the topic.
  • active_audience - how many members were active on LinkedIn in the period analyzed regardless of whether they engaged with the topic.
  • engagement_ratio - relatively how engaged the segment is with the topic compared to the same segment in your selected comparison audience.

Grouping

The strategy supports analysis by multiple groups. Learn how to run strategies with grouping by reading our Performing grouped analysis developer guide.

As an example the following parameters will analyze the audience engaged with the topic of 'cloud' for each of the two countries specified:

{
    "type": "strategy",
    "name": "example",
    "subscription_id": "cd99abbc812f646c77bfd8ddf767a134f0b91e84",
    "parameters": {
        "strategy": "audience_breakdown",
        "version": 1,
        "parameters": {
            "keywords": {
                "any": ["cloud", "azure", "aws", "google cloud", "vmware"]
            },
            "comparison_audience": "global",
            "groups": {
                "list": {
                    "countries": ["united kingdom", "france"]
                }
            }
        }
    }
}

When run with grouping the output of the strategy gives a set of results for each group within the 'groups' property. For example:

{
    "groups": {
        "united kingdom": {
            "redacted": false,
            "seniorities": {...},
            "functions": {...}
        },
        "france": {
            "redacted": false,
            "seniorities": {...},
            "functions": {...}
        },
        ...
    }
}

Usage costs

The cost of running the strategy is based upon the number of dimensions and groups you specify.

If you run the strategy without grouping then the cost is:

Analysis tasks consumed = 2 x (number of dimensions analyzed)

If you run the strategy with grouping then the cost is:

Analysis tasks consumed = (number of groups + 1) x (number of dimensions analyzed)

note icon

Note that if you specify groups for your analysis using the 'top' parameter the strategy will run an extra analysis task to fetch the top segments to be used for the grouped analysis. As a result one additional analysis task will be consumed.