Editor’s Note: This is the fourth in a series of posts on “The Marketer’s Guide to Measuring Social Media Marketing Engagement.” The first post was entitled, “Why Measure Social Media Marketing Engagement.”
The last post in the series, “How to Calculate Engagement Rate for Social Media Marketing,” explained how to calculate different engagement rate KPIs based on the needs of a business. The next step in establishing a standardized measurement framework is to determine how to categorize the results of campaigns and maximize the relevance of the insights generated.
Today’s businesses operate social media properties across hundreds of networks, brands, products, and geographic markets. Splitting properties on these dimensions has various benefits: it helps keep brand equities distinct, it allows for localization of language and messaging, and it focuses relevant content towards a more interested audience.
Below is an example of how PepsiCo, a global food and beverage manufacturer, splits its Facebook pages:
- Brand: Pepsi, Diet Pepsi, Mountain Dew, Lay’s, Gatorade, etc.
- Market: Pepsi Brasil, Pepsi Italia, Mountain Dew Thailand, Lay’s Canada, etc.
- Other: Pepsi Center, Pepsi MTV Indies, Pepsi IPL, etc.
Splitting properties, however, also comes with the disadvantage of reaching a smaller audience per property (thus limiting virality), as well as increasing management overhead and reporting complexity. For this reason, it is rare that businesses create properties for individual products, limiting splits to brands and major markets.
Whether or not an organization decides to split properties, there are many advantages to being able to analyze the performance of activities at a granular level along different dimensions.
One obvious benefit is the possibility of extracting detailed, business-relevant, and actionable insights from the data. If it were possible to track and compare engagement for individual products for each market, the insights generated can have broader business implications beyond marketing; for example, measuring how the social buzz around a product launch affects sales volume.
How data is sliced affects not only the granularity, but also the accuracy of conclusions. Whenever data is aggregated, information is lost and results can become misleading. For example, when a particular type of messaging works for one market and not for another, the results in the bigger market will dominate when the two markets are aggregated together.
Since more granular insights are both more accurate and more actionable, there is a strong case to report not only on property-level results, but also post-level results grouped by business-relevant categories.
There are a number of ways to categorize posts. Tagging and classifying posts at time of creation is possible via some social media management tools, which is useful for broad and static classifications that do not change over time. However, more agile and detailed insights call for flexible classification methods that can be performed after the posts have already been submitted.
One such method that allows post-mortem categorization at scale is by using a platform that allows searching through posts and tagging them based on various criteria.
With on-the-fly categorization, results can be freely sliced and diced at a later date, regardless of how the posts were initially classified. This allows for more detailed and flexible insight generation, as well as greater responsiveness to changes in objectives and reporting requirements.
Choosing Categories for Reporting
The categories in which to group results depends on how the business is organized. Standard business dimensions such as brands, markets, and product categories are common, but other types of categorization, such as by marketing strategies (branding vs. promotion, acquisition vs. retention, etc.), may also prove useful.
Below are three types of categorizations that improve business relevance and accuracy of results.
- Based on how marketing budget is allocated
Organizing marketing results along the same categories as budget makes it easier to track run rate and report on ROI.
- Based on how business revenue is reported
Categorizing results based on the broader business helps to show the impact of social media on revenue and informs investment allocation by identifying areas of focus.
- By combining similar-behaving segments
Grouping by similarity has the benefit of reducing reporting complexity, while maintaining accuracy of insights and conclusions.
Each method has its respective advantages and use cases and should ideally be used in parallel. With technology that enables on-the-fly aggregation of data on different categories, it becomes possible to view results from multiple dimensions at once, increasing the potential to generate useful and actionable insights. One example of such a view is shown below.
Next in the series: Advanced Techniques for Measuring Social Media Marketing
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