In marketing, big data is defined as the ever-increasing variety, volume, variability, velocity, and complexity of information. Big data is so complex, large, and fast that it is difficult if not impossible to process it using traditional methods like manual data entry. After the big data is collected, it is analyzed to improve business operations such as brand awareness, customer awareness, customer acquisition, and a clear understanding of customer needs and meeting them accordingly. With big data, it is possible to know how customers are interacting with a brand (Verhoef, Kooge & Walk, 2016). Big data has a predictive aspect that allows the marketing department to make informed conclusions on what the customers are likely to respond to in the future on certain products.
Structured data is formatted and organized in a manner that makes it easy to search in a rational database while unstructured data does not have any pre-defined organization or format which makes it hard to search. The two are not conflicting but rather they are classified as such based on their nature. A person may choose either of the two not necessarily based on their structure but rather on the application of it. Structured data contains objective facts and numbers that can be collected by analytics software while unstructured data is made up of subjective opinions and judgments of a brand which may be in the form of text which cannot be collected by analytics software (Verhoef, Kooge & Walk, 2016). Structured data can be used to collect data on how many items are sold within a certain period which would help in determining the profit margins. Unstructured data can be used to know how different customers feel about a brand which would informal and different from each other.
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Question 2
Marketing analytics is a practice whereby marketing performance is measured, managed, and analyzed. Big data and marketing analytics methods help in mining, combining, and analyzing data in real-time. Therefore, marketers can discover patterns that may be hidden (Verhoef, Kooge & Walk, 2016). For example, the patterns that inform how various groups of customers interact with a product and how that informs the decision to purchase it or not.
The four marketing analytics are descriptive, diagnostic, predictive, and prescriptive which answer all marketing questions from knowing where the company is to the future. Descriptive analytics is important in summarizing or describing the existing data by applying the existing intelligence tools so that there can be a more understanding of what is happening or what went on. Diagnostic analytics focuses on the past performance to help in determining what happened and how it did happen. Predictive analytics is the emphasis on coming up with a prediction of the possible outcome based on the available data and presenting it on statistical models (Verhoef, Kooge & Walk, 2016). During prescriptive analytics, the marketers use the analyzed data to recommend or prescribe one or more courses of action.
Question 3
Issues and consequences that may arise from not being sensitive to equity, diversity and inclusivity in a person’s behavior and communication are legal actions, hefty costs associated with direct losses due to reduced customer base mainly from the affected, and loss of brand credibility and loyalty. When a certain group of people, for example, the people living with a disability, are directly offended by a certain communication during marketing, they can take the company to court if their rights are breached. When there is racial insensitivity, there may be outrage especially in social media and that may lead to hefty spending in dealing with social media backlash. When a certain group of people feels attacked negatively, they may lose loyalty and credibility to the brand. All that may lead to reduced sales significantly. An example of insensitivity is on H&M advert whereby a black girl was used to model in a sweatshirt written “coolest monkey in the jungle.” The brand and the company, in general, received huge backlash globally from consumers which forced the company’s top management to apologize but the consequences were far-reaching.
Reference
Verhoef, P., Kooge, E., & Walk, N. (2016). Creating value with big data analytics: Making smarter marketing decisions . Routledge.
