Loyalty programs are a cornerstone of modern marketing strategies, offering incentives and recognition to customers based on their engagement and spending. Automating the assignment of loyalty tiers—such as **"Bronze," "Silver," and "Gold"**—not only saves time but also ensures consistency and fairness in how customers are rewarded. By using data-driven rules to categorize customers according to their total spend, marketers can quickly identify high-value segments, tailor communications, and optimize rewards. This approach enables you to scale your loyalty program efficiently, adapt thresholds as business goals evolve, and maintain an up-to-date view of your customer base—all with minimal manual intervention.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import pandas as pd

class TestTask(unittest.TestCase):
    def setUp(self):
        self.df = pd.DataFrame({
            "CustomerID": [101, 102, 103, 104, 105],
            "TotalSpend": [1000, 999.99, 500, 499.99, 0]
        })
        self.user_code = importlib.import_module("user_code")

    def test_loyalty_tier_column_added(self):
        importlib.reload(self.user_code)
        result = self.user_code.assign_loyalty_tier(self.df.copy())
        _dynamic_test(
            self,
            "LoyaltyTier" in result.columns,
            "'LoyaltyTier' column is added to the DataFrame.",
            "'LoyaltyTier' column was not added to the DataFrame.",
        )

    def test_gold_assignment(self):
        importlib.reload(self.user_code)
        result = self.user_code.assign_loyalty_tier(self.df.copy())
        golds = result[result["TotalSpend"] >= 1000]["LoyaltyTier"].tolist()
        _dynamic_test(
            self,
            all(t == "Gold" for t in golds),
            "Customers with TotalSpend >= 1000 are assigned 'Gold' tier.",
            f"Expected all Gold for >=1000, got: {golds}",
        )

    def test_silver_assignment(self):
        importlib.reload(self.user_code)
        result = self.user_code.assign_loyalty_tier(self.df.copy())
        silvers = result[(result["TotalSpend"] >= 500) & (result["TotalSpend"] < 1000)]["LoyaltyTier"].tolist()
        _dynamic_test(
            self,
            all(t == "Silver" for t in silvers),
            "Customers with 500 <= TotalSpend < 1000 are assigned 'Silver' tier.",
            f"Expected all Silver for 500-999.99, got: {silvers}",
        )

    def test_bronze_assignment(self):
        importlib.reload(self.user_code)
        result = self.user_code.assign_loyalty_tier(self.df.copy())
        bronzes = result[result["TotalSpend"] < 500]["LoyaltyTier"].tolist()
        _dynamic_test(
            self,
            all(t == "Bronze" for t in bronzes),
            "Customers with TotalSpend < 500 are assigned 'Bronze' tier.",
            f"Expected all Bronze for <500, got: {bronzes}",
        )

    def test_original_structure_preserved(self):
        importlib.reload(self.user_code)
        result = self.user_code.assign_loyalty_tier(self.df.copy())
        original_cols = set(self.df.columns)
        new_cols = set(result.columns)
        _dynamic_test(
            self,
            original_cols.issubset(new_cols) and "LoyaltyTier" in new_cols and len(new_cols) == len(original_cols) + 1,
            "Original DataFrame columns are preserved and only 'LoyaltyTier' is added.",
            f"Original columns: {original_cols}, New columns: {new_cols}",
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

Unlock the power of Python to solve real-world marketing problems. This course guides marketers through practical Python applications, from analyzing campaign data to visualizing trends and automating repetitive tasks. Each section blends engaging theory with hands-on challenges tailored to marketing scenarios.

Learn to harness Python for analyzing marketing data, uncovering trends, and making data-driven decisions.

Explore how Python can automate repetitive marketing tasks, saving time and reducing errors.

Delve into advanced analytics techniques to uncover deeper insights and optimize marketing strategies.

Challenge: Automate Loyalty Program Assignment

Solution

Challenge: Automate Loyalty Program Assignment

Solution