Crimson Hexagon & Equity Research

This guide introduces the concept of “brand fitness” and lays out how equity researchers may apply it to derive value from social data by:

  • Quantifying perceptions of brands & their direct competitors
  • Understanding fluctuations in sentiment over time 
  • Identifying potential brand health issues

Financial Services

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Conceptualizing & Measuring Brand Fitness

Deriving key performance indicators

Offering a lifetime warranty or “satisfaction guaranteed” indicates that a brand has confidence in the reliability of its products or services. Similarly, marketing activities send signals about a brand’s fitness in by way of perceived expense and effort. Research demonstrates how signals of brand fitness have a powerful impact on consumers’ attitudes and purchase intentions (1)(2)(3).

Social listening tools may provide a clear view of perceived expense and perceived caring as signaled by dialogue, quantifying the volume, tone, and content of conversations between consumers and brands. Further, using engagement conversation at a leading indicator of brand fitness allows for repeatable research methodology with comparable like-for-like metrics and benchmarks.

The Data

Locating the signal in the noise

The biggest challenge for researchers using social data is separating the signal from the noise. Brand fitness metrics are derived primarily from enagement conversation, a subset of data that is easy to isolate and rich in meaningfulness.

Online conversation about a brand or organization is composed primarily of two streams. First, “owned conversation” centers on social media posts directed at a brand–or more specifically, a social account or property owned by the brand. This compares loosely with traditional media analytics in terms of paid advertising. This can provide insight into a brand’s “fitness” by measuring its efforts to gain traction through the lens of consumer engagement.

Second, organic or “earned conversation” aligns with the concept of earned media, traditionally thought of as brand mentions in newspapers and magazine articles. This measures what people are saying among themselves. This data has much to offer, however, it can be time-consuming to execute. An “earned conversation” dataset is best employed for industry-level research or trend spotting.

The Analysis

Surfacing actionable insights

Beginning with a clear business question really does make a big difference (e.g., How does LensCrafters brand fitness compare with its main competitors?). Next, employing a framework for the analysis helps keep my exploration of the data focused and deliberate.

Recommendation:

Start with brand fitness

Begin your research process by isolating the “owned conversation” for a single brand and its direct competitors. Compare the volume, tone, and content of conversation to access their relative position to each other in terms of perceived brand fitness. These metrics provide a good snapshot of what people think and requires minimal effort to execute.

Sample Frame Design

Search Terms

Data Sources

Twitter Only Analysis

Some researchers rely primarily on Twitter data only for brand fitness analysis. Twitter has been validated as a broadly representative source for conversation data that can serve as a surrogate for the larger social sphere when necessary.

[B]ecause of the way it is used and perceived by users, Twitter seems to us to be most representative of the broader social sphere. Facebook as typically used for connecting to friends and family, whereas Twitter was seen as an information platform for discovering, sharing, and learning (Millward Brown Digital).

Analysis Outline

Volume – Are the brands being talked about?

  • The share of voice (proportion of total conversation)
    • When does proportion change?
      • One brand increases – Possible increase in market share/ marketing spend
      • One brand decreases – Possible drop in brand salience/ product or services becoming stale
  • Volume comparison over time (volume for each brand)
    • When does volume spike?
      • Do the brands move together? – May indicate seasonality
      • Does it increase momentarily? – May indicate an event or viral content
      • Does it increase steadily? – May indicate brand growth or investment in marketing

 

Sentiment – What is the tone of the conversation about each brand?

  • Positive/Negative by brand (all time)
    • How does sentiment compare?
      • High positive/negative sentiment for all brands? – Possible industry trend (e.g., high negative sentiment for all cable companies)
      • Low positive/negative sentiment for all brands? – Possible low-interest industry (e.g., mostly neutral sentiment for life insurance)
  • Positive/Negative change by brand (year over year)
    • How does sentiment change? – Possible consumer attitude shift (e.g., the brand has improved customer service)
      • Sentiment change by brand (volume each brand or Net Sentiment)
  • When does positive/negative sentiment spike or skew? (all time or past year)
    • Does either pop momentarily? – Possible product issue or bad PR
    • Does either slowly change? – Possible change in product quality or aging facilities

 

Content – What are the topics of conversation about each brand?

Please Note: This piece of the analysis requires the most work and is the most subjective. The primary tools to assist you are found under the “explore” tab, however, our machine learning solution is ideal when a more quantitative analysis of content is required.

  • What are the overall topics and sub-topics of conversation? (overall or by brand)
  • Are any topics defined by an emotion or sentiment?
    • What are the topics among positive and/or negative conversation?
      • Are people expressing concerns about product/service fundamentals?
      • Are people excited about a particular feature or experience?
  • What are the top sites and URLs about the brand being shared online?
    • Which articles/content are being amplified by social media?
  • Can machine learning provide additional support?
    • Do you want to quantify the components of positive and negative sentiment?
    • Do you want to easily compare conversation topics across brands?

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