making every opinion count

and November 15, 2017

In the era of digital marketplace dominated by the narrative of consumer engagement, celebrity brand endorsement is fast becoming obsolete. The endorsement space has now been usurped by ubiquitous ‘influencers’ with a large fan following on social media. These influencers, with varied amount of expertise in myriad domains, are being increasingly approached by brands for reviews and feedback. In a survey conducted by Dimensional Research, an overwhelming 90% of respondents who recalled reading online reviews claimed that positive online reviews influenced buying decisions, while 86% said they were influenced by negative online reviews. Conventional marketing literature also acknowledges the influence of friends and reference groups on consumer behavior. (See the framework below)

1231-4In this context, Natural Language Processing (NLP) and Artificial Intelligence (AI) are technologies that have the potential to disrupt the landscape in business intelligence, marketing, ecommerce, and enterprise information systems. We are moving to an era where critical business decisions and marketing will rely increasingly on unstructured data. By leveraging this until-now largely unexploited treasure trove of data, businesses will be better poised to react in real-time and more importantly, be proactive about their strategy.

But the million dollar question is: is there a way to analyze the vast deluge of unstructured data and quantify the influencers’ opinion for actionable insights? The answer is a resounding ‘yes’!

In this article, we have explored the consumer experience management (CEM) tool as an aid for marketers to make sense of unstructured data and redefine consumer experience.

making sense of unstructured data

The deluge of unstructured data is a real-time business problem and most organizations do not have an idea of how this can be harnessed to enhance consumer experience. As per Techopedia, unstructured data represents any data that does not have a recognizable structure. It is unorganized and raw and can be non-textual or textual. The other examples of unstructured data include e-mail messages, word processing documents, videos, photos, audio files, presentations, webpages, Facebook posts, and tweets. It is now estimated that 80% of data generated are unstructured, meaning they have no predefined data model2. Unstructured data can be analyzed to reveal trends, patterns, insights, and relationships that can provide a better understanding of business. The analysis of structured data provides the ‘what’, ‘where,’ and ‘when’ of a business challenge, whereas unstructured data analysis provides the ‘why’ and ‘how’.

The CEM tool is a real-time automated intelligent diagnostic tool developed to generate insights from unstructured information in forms of millions of online consumer reviews/opinions across platforms. It generates insights via the application of NLP and other forms of AI. Consumer opinion/inputs from various online sources are fed into the CEM tool for decoding the sentiment expressed by the consumers and identifying the aspects talked about in the feedback. The step-wise process flow of the CEM tool’s working is depicted below:

step 1: illustration of an online user review of a smartphone and the CEM tool’s critical information deciphering model

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CEM’s offerings make it an indispensable tool for businesses to meet their requirement for 360-degree digital intelligence on the 4Ps of marketing—product, price, place, and promotion. Some of the important inputs provided by the CEM tool for positive business outcomes are as follows:

  • product:
  • factors considered while buying, winning product specifications by consumer segments.
  • helping marketers focus on factors to build or highlight to consumers in various segments and help marketers take product-related decisions
  • place: feedback on various channels of launch and after-service, geographical dissection of the product’s reception
  • price: competitor’s action points, consumers’ take on pricing and tonality on future movements of price
  • promotion: most-talked features—positive and negative, providing marketers with key communication aspects for targeted communication

The shopping landscape has witnessed a paradigm shift wherein consumers reward relevance and punish irrelevance. Today, if they are disengaged and dissatisfied, they simply stop communicating, clicking, and returning. In this context, the tool delivers marketing optimization and also facilitates critical multichannel interaction capabilities to engage the digital consumer. The future challenge shall be to replicate this model to harness the consumer share of voice for non-sticky/low-involvement categories like grocery and laundry which do not figure in digital media conversations. Secondly, there are challenges faced in opinion mining of online reviews as multiple opinions are expressed in a single review, there is ambiguity related to word sense with same words having different meanings and paraphrasing issues with different phrases having the same meaning, and the human subjectivity element. In order to fully realize the advantages of using NLP, there is immense scope for improving the precision and accuracy of results related to various undefined human contexts such as sarcasm as well as increasing interaction between NLP systems and other systems of the business.

01 https://www.ft.com/content/de15414e-ebad-11e1-985a-00144feab49a

02 http://www.economist.com/news/united-states/21576694-cities-are-finding-useful-ways-handling-torrentdatanumbers.