Marketing Analytics

Marketing Analytics 1
MARKETING ANALYTICS
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Marketing Analytics 2
Marketing Analytics
Question 1
“Marketing Analytics is just a hype, and does not necessarily create value for the firm.”
Marketing analytics refers to a technology-centric and model-sensitive approach towards the collection and
evaluation of market and customer data for the purpose of decision making in business. Therefore, marketing
analytic seeks to enhance the customer experience from the moment the product/service is introduced to
potential buyers to the final stage when the customer makes the purchase. Marketing analytics has been
employed in businesses throughout history. However, the technological boom of the 20th century created big
data that has lead to the need for specialized knowledge and personnel in matters of marketing analytics. In the
modern world of business, marketing analytics is a critical determinant of success for any organization in a
competitive marketplace. A business needs to understand its niche in the market, customer wants, market size,
market segmentation, and purchasing patterns of consumers; and, the only way a business can gather such
information is through market analytics. As a result, marketing analytics is a value adding proponent for any
business (France & Ghose, 2019).
Marketing analytics has a lot of application in the realm of business strategies and development. An
organization like Walmart employs market analytics to gather intelligence on sales transactions, as a means to
determine which products are enjoying high purchase volume and which products are not being procured by
consumers. Based on historical purchasing trends gathered by Walmart, the organization is able to make
projections on how they should stock their stores to increase revenue based on location, demographics, and
average income of people. Moreover, market analytics allows Walmart to send promotional offers to customers
based on items they have purchased in the past and products they have shown preference towards. Online video
streaming service providers like Netflix and Hulu utilize marketing analytics techniques to gather information
on the movies to recommend to their subscribers based on their viewing history and trending films and TV
series. For example, when a person views a movie on Hulu, he/she automatically get suggestions to view its
sequel or similar films (same genre, subject, director, country, or actors/actresses) (Hauser, 2007).

Marketing Analytics 3
Marketing analytics is recommended for organizations in extremely competitive markets as a way for them to
recognize gaps and exploit shortcoming in service provision. For example, the drive-through service at fast food
restaurant chains was introduced to facilitate the need by customers to procure food without having to search for
parking or leaving their vehicles. Marketing analytics was used to gather information on how customers would
feel about procuring their food without leaving their vehicles. Marketing analytics techniques are also
appropriate for organizations facing limited competition as it allows them to optimize their business models and
services to better their sales revenue. Firm performance is highly dependent on customers; therefore
understanding the changes in customer preference will safeguard the going concern of an organization. For
example, Kodak was not able to recognize the shift in consumer preference towards digital picture and they
ended up losing millions in sales, market share, and brand loyalty (Corrigan, 2014).
Question 2
Hotel Type that Customers will Book
The problem being investigated here is the determination of the hotel type that a customer is likely to book out
of the 100 using qualitative data exploratory techniques. The qualitative data exploratory method that will be
used is observation: this involves the sorting, filtering, and counting of hotels types that are regularly booked by
the customers. The assumption is that the online travel company will be able to better their services to
customers if they are able to determine the hotel types that are booked by customers. The data was loaded into
Microsoft Excel and transformed by applying filters to the package and hotel type columns. Packages were
filtered with regard to a value of 1 and then the hotel types that were filtered out were recorded. Looking at the
data we are able to see that out of the 100 hotel types only two were filtered out when booked was set as
requirement for inclusion. The two hotel types are 38 and 60; hence this two can be removed from the catalogue
as a way to improve the results (Akinci & Sadler-Smith, 2012). The most booked hotel type is 65. Therefore,
customers are most likely to book hotel type 65 because it has been booked by the most people (55). With the
exception of 38 and 60 we expect that customers will be willing to book any of the remaining 98 hotel types.
With regard to the number of children and adults involved the customer is most likely to book hotel 28 if he/she