Predicting customer behavior

PHASE 1

Background

The purpose of this capstone project is to analyze data to determine if a marketing campaign will be successful with customers. Specifically, this dataset is from a bank that is marketing a new product to some of their existing customers. The end goal is to be able to provide a framework for identifying the best customers to target to help make the business more efficient with their time and effort.

Why

Since Covid-19 has caused many businesses around the world to take large hits to their bottom lines, finding ways to be more efficient are more important than ever. With the cost of upselling an existing customer only being 24% of the cost to acquire a new one, this is a great opportunity for companies [1]. By identifying and understanding the characteristics of the customers most likely to buy into this marketing campaign, time can be focused more on that cluster of customers.

Existing methods like the Apriori Association Algorithm and Market Basket Analysis seem to be better suited for predicting pairings in purchases for things like groceries [2]. The focus of this project will be for transactions of a single product in higher quality existing relationships.

Dataset

The dataset is from the UCI Repository website and is free to access [3]. The dataset includes information about the specific customers contacted, such as their age, marital status and education level. Additionally, there is information on the timing and duration of the calls. There are 17 columns and 45,212 rows.

PHASE 2

EDA

While looking through the data several trends were identifiable. The first thing that stood out was how many of the short calls resulted in no new business, so all calls less than three minutes were removed from the data. Once those short calls were removed, more trends were identifiable.

PHASE 3

Trying Additional Algorithms

In Phase 2 the Decision Tree was 71.04% accurate so the goal of Phase 3 was to see if trying other algorithms would produce better outcomes. The three algorithms tested in Phase 3 were K Nearest Neighbor, Naïve Bayes, and Random Forest.

Outcome

The Decision Tree algorithm was the best performer of the four tested with this data set. Although I was hoping for results around 80% accuracy, the model was still fairly accurate. For a business trying to make predictions on their customers' behavior, this is a positive thing because the Decision Tree algorithm is easy to code and easy to understand [5]. Additionally, using the Decision Tree algorithm does not require special investment in hardware or software.

Equipped with good predictive analysis on their customer base, companies could focus their efforts where they are most likely to be fruitful. As data collection continues to grow, companies could then add their new data to help make the model even more accurate.

references

[1] "The Economics Of The Upsell," insightsquared.com, para. 9. [Online].

Available: https://www.insightsquared.com/blog/the-economics-of-the-upsell/. [Accessed Sep. 21, 2020].

[2] R. Moodley and F. Chiciana and F. Caraffini and J. Carter, "A product-centric data mining algorithm for targeted promotions," Journal of Retailing and Consumer Services, Vol. 54, May 2020.

Available: ScienceDirect, https://www-sciencedirect-com.proxy-bc.researchport.umd.edu/science/article/pii/S096969891831169X. [Accessed September 21, 2020].

[3] "Bank Marketing Data Set," uci.edu. [Online].

Available: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing. [Accessed Sep. 9, 2020].

[4] E. Grimaldi, "Decision Tree: An Algorithm That Works Like the Human Brain," para 12. Sep. 10 2018. [Online]. Available: https://towardsdatascience.com/decision-tree-an-algorithm-that-works-like-the-human-brain-8bc0652f1fc6. [Accessed Oct. 28 2020].

[5] "Advantages of a Decision Tree for Classification,' pythonprogramminglanguage.com, para. 9. [Online].

Available: https://pythonprogramminglanguage.com/what-are-the-advantages-of-using-a-decision-tree-for-classification/. [Accessed Dec. 6, 2020].