Unstructured Data and Analysis in Decision Making Essay

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Ranking 3 Types of Unstructured Data Used by PulteGroup: Google Searches, Website Visits, and Social Media Conversations

The case study presents three sources of unstructured data the consumers use to purchase a product. First is Google search. Consumers use search tools to research their prospective purchase. Hence, the group used Google Insights to gather information regarding the competitor’s products. The second tool that is used the comparison of the official websites of its competitors and the third tool is monitoring the consumer discussion about their and their competitor’s products. The most important tool that I feel for understanding consumer research trend is online consumer discussion on social networking websites, company websites and third is Google searches. The most reliable source for gaining consumer insights is through discussion on social networking websites. This form of marketing is word of mouth promotion that helps consumers gain the first-hand experience of purchasers and their insights.

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Description of the COBI Index and Its Validation Against Other Data

The COBI Index follows a five-step process to validate data. The first step is to determine a competitor’s set globally as the company has its operations in different countries. Hence, the company selected a set of 15 builders who operated nationally and created the competitor’s set. The second step was to collect data from Google searchers, website traffic, social networks, and builder new orders reported to the SEC to create a database of the competition’s customers. The third step is to convert the raw data collected into monthly share figures, which is then used to generate the scoring coefficient of a single factor. The fifth step is to scale the score from zero to 100 that helps to factor the scores and increases ease of understanding.

Validation of the data is necessary as it increases ease of understanding. Further, it gives a clear picture of the brand’s online presence and the purchase of the brand’s products that shows how marketing is being converted to actual purchase.

Sentiment analysis, its accuracy and reliability

Online sentiment analysis is done through text analysis of the communication of the consumers regarding the brand or product over the Internet. The threads of conversation available on the Internet explode with consumer preference and choices regarding the product and brands that the consumers express with great sincerity. Analysis of these data can help in creating a great database of sentiments that the consumers have towards the products.

Text mining enables the company to explore unstructured data without preconceived hypothesis regarding the possible outcome. It allows the companies to gather data from the enormous amount of information available through volumes of online conversations. This helps companies look into the brand or product that is discussed most by the consumers. Text analytics helps in creating a link between the collected conversation and their products and services to establish a possible marketing relation.

Data mining is a reliable source of gaining insights into consumer preferences. When these data are combined with other qualitative and quantitative research data, they can become a powerful tool for understanding consumer demand and preferences.

Incorporation of Unstructured Data Into a Decision Making Process

As the Chief Marketing Officer of PulteGroup, the marketing data that I would use today is the text analytics data from online conversations in social media websites, product information websites, and Google searches. Consumers today rely on information derived from the Internet and research though user reviews to form a definitive purchase decision. Hence, data and text analytics of online data mining would become one of the most significant tools for my marketing decisions. Other data that I would incorporate for the decision-making process are qualitative and quantitate research data collected through direct consumer surveys. For my decision-making purposes, I would require these data updated on a weekly basis, as this would provide a real-time trend of change in consumer preference.

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IvyPanda. (2020, June 23). Unstructured Data and Analysis in Decision Making. https://ivypanda.com/essays/unstructured-data-and-analysis-in-decision-making/

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"Unstructured Data and Analysis in Decision Making." IvyPanda, 23 June 2020, ivypanda.com/essays/unstructured-data-and-analysis-in-decision-making/.

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IvyPanda. (2020) 'Unstructured Data and Analysis in Decision Making'. 23 June.

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IvyPanda. 2020. "Unstructured Data and Analysis in Decision Making." June 23, 2020. https://ivypanda.com/essays/unstructured-data-and-analysis-in-decision-making/.

1. IvyPanda. "Unstructured Data and Analysis in Decision Making." June 23, 2020. https://ivypanda.com/essays/unstructured-data-and-analysis-in-decision-making/.


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IvyPanda. "Unstructured Data and Analysis in Decision Making." June 23, 2020. https://ivypanda.com/essays/unstructured-data-and-analysis-in-decision-making/.

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