Getting to Know You: The Power of Understanding Customers’ Personalities

Ever thought about why you prefer certain things over others? Turns out, it’s not just random! Your personality shapes the stuff you like and the way you shop. Cool, right?

In this blog, we’re diving into the world of customer personalities. No jargon, just simple insights into how knowing what makes you tick can help businesses make your shopping experience way better.

Imagine it like putting together puzzle pieces. Each person has their unique set of likes, dislikes, and quirks. And guess what? Businesses can use that puzzle to create stuff you’ll love even more.

From how you talk with customer service to what you add to your cart, your personality is the star of the show. We’re here to show you how this behind-the-scenes action impacts your shopping adventures.

So, get ready for a fun ride where we break down how your personality influences your choices. We’ll share some neat tricks that help businesses make things you’ll adore. Let’s explore together and see how understanding personalities can make shopping a whole lot more awesome! ✨

Dataset:

Data supports Customer Personality Analysis which is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors, and concern of different types of customers. While this dataset covers multiple attributes to the sale of multiple products, I will be focusing on three attributes that I believe could have the most impact on a single product. The analysis is focused on age, education, and income and how they possibly correlate with the amount of money customers spend on wine over the last two years in this particular dataset.

Dataset contains the columns are as follows:

People

• ID: Customer’s unique identifier
• Year_Birth: Customer’s birth year
• Education: Customer’s education level
• Marital_Status: Customer’s marital status
• Income: Customer’s yearly household income
• Kidhome: Number of children in customer’s household
• Teenhome: Number of teenagers in customer’s household
• Dt_Customer: Date of customer’s enrollment with the company
• Recency: Number of days since customer’s last purchase
• Complain: 1 if the customer complained in the last 2 years, 0 otherwise
• Products
• MntWines: Amount spent on wine in last 2 years
• MntFruits: Amount spent on fruits in last 2 years
• MntMeatProducts: Amount spent on meat in last 2 years
• MntFishProducts: Amount spent on fish in last 2 years
• MntSweetProducts: Amount spent on sweets in last 2 years
• MntGoldProds: Amount spent on gold in last 2 years

Promotion

• NumDealsPurchases: Number of purchases made with a discount
• AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise
• AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise
• AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise
• AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise
• AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise
• Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

Place

• NumWebPurchases: Number of purchases made through the company’s website
• NumCatalogPurchases: Number of purchases made using a catalogue
• NumStorePurchases: Number of purchases made directly in stores
• NumWebVisitsMonth: Number of visits to company’s website in the last month

PredictEasy Analysis:

Using the Google Sheets add-on PredictEasy a classification model was built. In order to learn more about how to use the tool, please refer to my previous blog posts.

We start by putting every variable in X and the target variable in Y. After doing this, we see the summary:

The predictive model achieved an accuracy of 0.88, indicating that it correctly classified 88% of the instances.

• The predictive model achieved an accuracy of 0.88, indicating that it correctly classified 88% of the instances.
• The precision score is 0.86, which means that when the model predicts a positive response, it is correct 86% of the time.
• The recall score is 0.88, indicating that the model can identify 88% of the positive responses.
• The F1 score, which combines precision and recall, is 0.86.

Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers.

Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.

Feature Rank

Feature value

recency: with low values have a higher probability of being a Response.

mntmeatproducts: with low values have a higher probability of being a Response.

acceptedcmp3: with low values have a higher probability of being a Response.

mntwines: with low values have a higher probability of being a Response.

mntsweetproducts: with low values have a higher probability of being a Response.

numwebvisitsmonth: with low values have a higher probability of being a Response.

mntgoldprods: with low values have a higher probability of being a Response.

numdealspurchases: with low values have a higher probability of being a Response.

• The number of accepted campaigns (acceptedcmp5, acceptedcmp3, acceptedcmp1, acceptedcmp2) have the highest impact on the target response.
• Other features that contribute to the prediction include kidhome, recency, numwebvisitsmonth, mntmeatproducts, mntwines, teenhome, numstorepurchases, numcatalogpurchases, and more.

Potential Ideas

• The marketing team should focus on improving the effectiveness of campaign 5 (acceptedcmp5) as it has the highest impact on the target response.
• Campaigns 3, 1, and 2 (acceptedcmp3, acceptedcmp1, acceptedcmp2) should also be optimized to increase the likelihood of a positive response.
• Targeting customers with children at home (kidhome) and teenagers (teenhome) might be beneficial for improving the response rate.
• Encouraging more web visits per month (numwebvisitsmonth) and increasing purchases of meat products (mntmeatproducts) and wines (mntwines) could positively influence the response.

Insights and Recommendations

• The predictive model shows promising results with high accuracy, precision, recall, and F1 score.
• The marketing team should focus on improving the effectiveness of campaigns, especially campaign 5, to increase the response rate.
• Targeting customers with children and teenagers might yield better results.
• Encouraging more web visits and promoting specific product categories, such as meat products and wines, could lead to a higher response rate.
• Continuous monitoring and analysis of campaign performance and customer behavior will help refine the marketing strategy and improve future campaigns.