(101 Introduction To Social Analytics+ 201 Boolean Mastery+301 Data Analysis And Insights Generation)

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301.5 Tips and Tricks for Social Listening Reporting

Please watch the video, read the lesson below and take a quiz at the end of this lesson (bottom of this page).

Charting Guide

 This guide on data visualization chart types from tapclicks is a very helpful reference to see the four main data visualization categories and some chart types that you can use for each of them. We won’t go through each one, but we’ll show you the most commonly used charts for each category.

1.Making Comparisons – As the name suggests, you use this type of charts to compare or show the difference between items.

a.Comparing Items

A.1 If you’re comparing many items, it’s better to use a bar chart because you can easily read the item labels from top to bottom:

Sample usage of this type of graph:

  • Conversation Themes breakdown
  • Product Benefits
  • Product Trial Triggers/Barriers

A.2 A column chart can be used to compare a few items because each item label would be easy to read from left to right:

Sample usage of this type of graph:

  • Comparing total buzz volume/engagements for a maximum of five brands
  • Comparing total buzz volume about a product/brand/campaign across a few audience segments (countries, age groups, etc.)

 

b.Comparing Data over Time

B.1 A line chart is the most commonly used data visualization for showing data over time. It is best used when you’re visualizing data for many periods.

Sample usage of this type of graph:

  • Conversation volume over time
  • Engagement volume over time

B.2 Column charts can also be used for showing data over time provided that you’re only visualizing data for a few periods.

Sample usage of this type of graph:

  • Conversation/engagement volume per quarter/year (provided that only a maximum of five periods will be visualized)

2.Displaying Composition – This type of graph is used to visualize how certain parts contribute to a whole.

a.Static

The most commonly used graph for this purpose is a pie graph.

 

Sample usage of this type of graph:

  • Sentiment breakdown
  • Engagement type breakdown (provided that only a maximum of 5 engagement types will be visualized)
  • Conversation theme breakdown (provided that only a maximum of 5 themes will be visualized and that you have accounted for all themes present in the data – meaning, the sum should be 100%)

b.Data Changing over Time

B.1 A stacked area chart is best used when you’re visualizing how items differ across many periods.

 

Sample usage of this type of graph:

  • Breakdown of sentiments over time
  • Breakdown of engagement types (Likes, Comments, Shares) over time

B.2 A stacked column chart is used to show how items differ for a few periods.

 

Sample usage of this type of graph:

  • Breakdown of sentiments or engagement types (Likes, Comments, Shares) per quarter/year (provided that only a maximum of five periods will be visualized)

3.Overview of Distribution – This type of visualization is used to see how points are spread out in a data set.

A column histogram is the most commonly used graph for this purpose. It’s basically a column chart that shows the distribution of data for a few points

 

Sample usage of this type of graph:

  • Users breakdown in terms of follower count or engagement count (provided that a few date ranges will be defined)
  1. Showing Relationship – So far, we’ve shown graphs that visualize data only for one variable. But how about when you want to show correlations between two or more variables?

a.Two Variables

A scatter plot or scatter chart is the best choice to visualize this type of data analysis. Basically, you plot variable A in the X-axis, while you plot variable B in the Y-axis. How do you know which variable to put on which axis? As a best practice, put the variable with higher numbers in the X-axis so your graph expands horizontally and not vertically.

For example, we want to visualize how ten brands fared against each other in terms of Social Media Mentions and Monthly Average Google Searches, we can create a scatter plot like the one below. We put Social Media Mentions on the X-axis because this metric has higher numbers than Monthly Average Google Searches. Visualizing the dataset this way also helps us see if there is a relationship between these two variables – if a brand has high Social Media Mentions, does it mean that it also has high Monthly Average Google Searches?

b.Three Variables

If you want to add one more variable to this graph, you can use a bubble chart instead. It visualizes the third variable as the size of the circles representing each data point.

In our example, if we were to add a third variable “Social Media Account Followers”, we can visualize it through the size of the bubbles for each brand. We can easily see that those brands with bigger bubbles have more followers on their social media pages.

 

Data Visualization for Social Listening Analysis

  1. Flow and Storytelling

Let us start off by saying that there is not one correct way of organizing a report structure. The flow will depend on what you are trying to uncover with your insights – i.e. research objectives. But, if we could give a suggestion on the best practice to follow when planning your report structure, you could use this as a guide:

Start of the Report: Generic data (looking at an overview/background about the topic)

Main Body of the Report: Going through specific insights that are tied back to the research objectives

Conclusion of the Report: Summary of key insights and recommendations

It would be easier to visualize with a specific case study. Imagine that you are preparing a report about snacking trends – with the objective of understanding consumer sentiment, factors of consideration, as well as a topline competitive analysis. Your structure can be something like:

  1. Overview of consumer sentiment about snacks
    1. Benefits that consumers are looking for in snack products
  2. Factors of Consideration for Product Purchase
  • Topline Competitive Analysis
    1. Sentiment Breakdown per Brand
    2. Brand Associations per Brand
  1. Product Benefit Gaps
  2. Summary and Recommendations

Why does this flow work?

  1. You set a context about the topic by looking at topline consumer sentiment first before going into specific details
  2. After setting the tone of the report, your readers/stakeholders won’t get lost once you go into the specifics like factors of consideration and brand sentiments and associations because you went over the general insights already
  3. Going through benefit gaps before you summarize the report gives a background on your recommendations and is actually the main meat of insights in your report
  4. Integrating Charts and Verbatim

Now that you have a flow, how do you bring everything together into a report? We’ll give you examples of how you can layout three slides of this report.

  1. Benefits that consumers are looking for in snack products

 

Why does this slide work?

  1. When identifying product benefits that people are looking for, it is good to use a combination of Social Media Listening data as well as Google Search data because Search gives a deeper understanding of what people are interested in. Social media shows what things they want to discuss and share with others, but Search shows the things they are truly interested in that they take the time to look for information on those privately.
  2. As we have discussed in the previous section, showing correlations between two or more metrics is under the category of “Showing Relationships” where scatterplots are the best choice for data visualization.
  3. Adding dashed lines to indicate the median Social Media Mentions volume and Monthly Average Google Searches makes it easier for readers to see what benefits they should focus on in this slide – which are the three benefits that got the highest scores on the upper right quadrant of the graph.

B. Factors of Consideration for Product Purchase

 

Why does this slide work?

  1. People share why they buy certain products in social media (especially reviews from e-commerce sites). Google Search data will not be conclusive for this because it’s hard to tell if the people who searched for those products really bought them.
  2. Using a bar chart makes it easy to read social volume percentage for several factors.
  3. Only focusing on the top five factors makes it easier for the reader to know which points to focus on. Also, it’s not necessary to put several snippets for each factor if the mentions can be clustered together into similar themes.

 

  1. Sentiment Breakdown per Competitor

 

 

Why does this slide work?

  1. Sentiment breakdown for a few brands is best visualized using a stacked bar chart because it’s easy to visually compare the percentages against each other.
  2. When showing sentiment breakdown, it is not necessary to show examples of all sentiment categories. When comparing brands against each other, it’s usually better to just compare Positive and Negative sentiments only because these are the most insightful data points.

 

A few points to remember for social listening reporting:

  • Is social listening data fully explaining the insight both in the quantitative and qualitative sense? If not, what external data sources can be used to complement social data?
  • What type of data analysis do you need to visualize? What is the best graph to clearly show this?
  • What are the main points that you’re conveying in your slide? As a best practice, a slide will be easiest to read if only one main point is highlighted, with an additional two secondary points at the most.