How do I optimzie association analysis for the rules to make sense?

#1
I have a dataset of customers, that I want to define a frequent criteria, to paint a picture of an ideal customer. The dataset has the following fields: -email -fullname -Job (title) -company web domain -company description (string data) -company founded (year) -company employees (number) -company city -company state -company country -linkedin groups followed -created -updated

Except for Company Employees, Company Founded, Created and Updated there is no numerical data. The dataset has other useful data, like age (interval) and sex, but it has too many missing values, so I removed them for the analysis purposes.

I ran the code in R:

data1 <- read.csv("final_account_list.csv")

library(arules)

str(data1)

data1$Company.Founded <- factor(data1$Company.Founded)

rules1 <- apriori(data1)

rules1

inspect(rules1)

options(digits=2) inspect(rules1[1:5])

I am getting a list of 59 rules, but they don't make much sense. For example, {Company.Employees = 500} => {Company.Country USA} lift 1.176, confidence = 0.083, support = 0.109

The fact that majority of customers have 500 employees and are in USA does not bring much value. How do I make my analysis more meaningful?
For example, how do I find association for the title, geographies (city, state) and linkedin groups?

Thanks!