There are several questions to ask as there is not enough information to understand the poor
quality in sales for the new product, PA123. Here are a few but not limited questions to ask
before deciding on what data mining techniques to use:
What data for this product is available?
Was there a previous study conducted for predictive analysis for this product?
Was the previous analysis of the new product quantitative or qualitative?
What factors are potentially affecting the product?
How are competitors doing similar and different in the company's new product, PA123?
Who? What? When? Why? and How?
Using qualitative data for quantitative analysis may limit the data and cause a loss of information
about the product (Hetenyi et al., 2019). Another important variable is knowing the factors that
may be affecting the product (Lee, 2020). Romero and Ventura (2020) found there are various
techniques that are unique to the end-user based on the terms, purpose, environment, data, data
set, data system, and type of analytics needed for addressing "the problem". In this case, what are
the factors that have caused the problem of PA123 to not generate sales as the company's
expected/predicted rate previously?
References
Hetenyi, G., Dr. Lengyel, A., & Dr. Szilasi, M. (2019). Quantitative analysis of qualitative data:
Using voyant tools to investigate the sales-marketing interface.
Journal of Industrial
Engineering and Management
,
12
(3), 393. https://doi.org/10.3926/jiem.2929
Lee, M., Cai, Y. (Maggie), DeFranco, A., & Lee, J. (2020). Exploring influential factors
affecting guest satisfaction.
Journal of Hospitality and Tourism Technology
,
11
(1), 137–153.
https://doi.org/10.1108/jhtt-07-2018-0054
Romero, C., & Ventura, S. (2020). Educational Data Mining and Learning Analytics: An updated
survey.
WIREs Data Mining and Knowledge Discovery
,
10
(3).
https://doi.org/10.1002/widm.1355
