Semantic Brand Score
The Semantic Brand Score is a measure designed to assess the importance of one or more brands, in different contexts and whenever textual data (even big data) is available. This metric has its foundations in graph theory and combines methods of text mining and social network analysis. The Semantic Brand Score was developed based on the conceptualizations of brand equity proposed by Keller and Aaker. These well-known models inspired the measurement of a different construct on textual data: brand importance.
Brand equity is traditionally assessed through a series of models, which are often based on the administration of questionnaires to consumers or, for example, on financial evaluations. By contrast, the Semantic Brand Score is calculated on texts that potentially represent spontaneous expressions of different stakeholders: they are not subjected to direct interviews, thus reducing possible cognitive biases. The metric can be calculated, for example, by analyzing newspaper articles, consumer dialogue on online forums, or posts published on social media.
Definition and calculation
The calculation of the Semantic Brand Score requires that the analyzed texts are preliminarily transformed into networks of words, i.e. graphs in which each node represents a word. Links between words are given by their co-occurrence within a given range, or within a sentence. A pre-processing of natural language is advisable to clean up texts, for example by removing stopwords and word affixes (stemming). Consider for example the following network, obtained from the pre-processing of the sentence "The dawn is the appearance of light - usually golden, pink or purple - before sunrise.".
This dimension measures the frequency of use of a brand name, i.e. the number of times a brand is directly mentioned. Prevalence is linked to the concept of brand awareness, with the idea that a brand that appears more often in a text is more familiar to that text authors. Similarly, the fact that a brand name is frequently mentioned increases its recognition and recall, for those who read it.
This dimension measures the diversity of the words associated with a brand. These are textual associations (and not mental ones as in the brand image theorized by Keller), i.e. the words that are most frequently used in conjunction with a certain brand. Calculation is obtained by means of the degree centrality indicator, which corresponds to the degree of the node representing the brand. Alternatively, it was suggested to calculate diversity through the measure of distinctivness centrality, which gives more value to less redundant brand associations. The idea is that many distinctive textual associations make the discourse around a brand more informative, leading to greater brand strength and importance.
This last dimension measures the level of connectivity of a brand with respect to general discourse, i.e. its ability to act as a bridge between other words (nodes) in the network. Ideally it represents the brokerage power of a brand, i.e. its ability to link different words, groups of words, or topics. Calculation is based on the metric of weighted betweenness centrality.
Semantic Brand Score
The Semantic Brand Score is the standardized sum of prevalence, diversity and connectivity. The three components are all important and only together they represent the full construct of brand importance. Consider for example the case where a brand is frequently mentioned, but in a repetitive way with many posts having the same phrase "InventedCola is the best drink of all time". Prevalence in this case would be high, but diversity would be low. On the other hand, a brand frequently mentioned in a heterogeneous context would have both high prevalence and diversity. However, connectivity may still be low if the brand is discussed only as a niche of a wider discourse. When a brand is in-between different topics - it is important and acts as an intermediary for the whole context - then its connectivity is also high. The "InventedCola" brand could be central in one discourse (e.g. soft drinks) and peripheral in another (e.g. bar cocktails).
Some tutorials for the calculation of the metric using the Python programming language can be found online.
Sentiment of textual brand associations
The informativeness of brand importance can be complemented by comparing its value with that of brand associations sentiment. The fact that a brand is frequently mentioned, even in diverse contexts, and is at the heart of a discourse, defines its importance. However, it may be useful to understand whether the feelings and opinions associated with it are positive or negative.
Not only "brands"
The Semantic Brand Score can be used to measure the importance of any word, or set of words; it is therefore not limited to the analysis of brands in a strict sense. By "brand" one can also intend the name of a politician, or a set of words that represent a concept (for example, the concept of "innovation" or a corporate core value).
The measure was used to evaluate the transition dynamics that occur when a new brand replaces an old one. The Semantic Brand Score is also useful to relate the importance of a brand to that of its competitors, or to analyze importance time trends of a single brand. In some applications, the measures obtained have also proved useful for forecasting purposes; for example, in the political scenario, a link has been found between brand importance of candidate names in online press and election outcomes.
There are no limits on the text sources that can be analyzed: newspaper articles, emails, posts on online forums, blogs and social media, open text fields of interviews administered to consumers, etc.. The measure also works with different languages.
- Big data
- Brand equity
- Brand management
- Brand valuation
- Distinctiveness centrality
- Graph theory
- Natural language processing
- Network theory
- Semantic analytics
- Social network analysis
- Text mining
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- "Semantic Brand Score - Analytics Demo". semanticbrandscore.com. Retrieved 2019-02-15.
- https://semanticbrandscore.com. Website which further describes the metric.
- https://towardsdatascience.com/calculating-the-semantic-brand-score-with-python-3f94fb8372a6. Tutorial for the calculation of the Semantic Brand Score using Python