In today’s competitive business landscape, organizations strive to enhance operational efficiency and drive growth. This business case aims to address these objectives by streamlining business operations through advanced technology adoption and process optimization. We will leverage a customer store dataset, analyze key metrics and models, and provide actionable insights and recommendations for the business. By implementing the proposed solutions, the company can improve customer experience, increase revenue, and achieve long-term success.
A customer store facing various challenges in managing their business operations effectively. They were eager to uncover growth opportunities and optimize their processes. The store owners realized that they needed a transformative approach to enhance customer satisfaction, increase revenue, and improve overall business efficiency. Throught the following chapters, I explore the journey we embarked on to help achieve these goals.
# import libraries
library(readxl)
library(tidyverse)
library(lubridate)
library(knitr)# read the data
data <- read_xlsx("store-b-data.xlsx")The store owners were curious about the impact of discounts on their business. They wanted to know the percentage of paid invoices that had discounts applied and the total amount discounted. After analyzing the customer store dataset, we discovered that discounts were applied to 4.17% of paid invoices, amounting to $26,560. This insight highlighted the significance of discount strategies in attracting customers and boosting sales.
data %>%
#create a boolean variable to indciate discount status
mutate(has_discount = ifelse(discount_applied > 0, T, F))%>%
#% of the invoices with discounts applied
summarise(has_discount = scales::percent(mean(has_discount, na.rm = T), accuracy=.01),
#the total amount of $ discounted
discount_applied = scales::dollar(sum(discount_applied, na.rm = T)))## # A tibble: 1 × 2
## has_discount discount_applied
## <chr> <chr>
## 1 4.17% $26,560
To secure more revenue upfront, the store owners wanted to explore opportunities to convert monthly customers into annual subscribers. We delved into the dataset and found that 107 users subscribed to both monthly and annual plans in the past year. This revelation indicated the potential to encourage more monthly customers to upgrade to annual plans, providing the store with stable and predictable revenue streams.
data %>%
group_by(user_id, plan_duration)%>% #group by users and subscription plan
summarise(value = as.logical(n()))%>%
spread(key=plan_duration, value=value) %>%
filter(Monthly==T, Annual==T)%>% #if the user subscribed monthly and annualy
ungroup()%>%
summarise(users = n())## # A tibble: 1 × 1
## users
## <int>
## 1 107
Understanding the revenue composition is crucial for making informed business decisions. We analyzed the store’s revenue in 2023 and compared it to the previous year. In 2023, annual subscriptions accounted for 46.22% of the total revenue, a significant increase from 25.76% in 2022. This exponential growth showcased the importance of promoting and nurturing annual subscriptions to drive financial stability and business expansion.
data %>%
mutate(year = year(transaction_date))%>% #extract the year from the date
group_by(year, plan_duration)%>% #group by year and subscription plan
summarise(revenue = sum(amount_paid))%>% #calculate revenue
#calculate the revenue share per year
mutate(revenue_share = scales::percent(revenue/sum(revenue), accuracy=.01))%>%
filter(plan_duration == "Annual")%>%
select(year, revenue_share)## # A tibble: 2 × 2
## # Groups: year [2]
## year revenue_share
## <dbl> <chr>
## 1 2022 25.76%
## 2 2023 46.22%
The store owners pondered offering discounted annual upgrades to their most loyal monthly customers. To identify these loyal customers, we identified 534 customers who had subscribed to the monthly plan for all of the past 12 months. This loyal customer list provided the store owners with an opportunity to offer exclusive discounts and rewards, fostering customer loyalty and encouraging them to upgrade to annual plans.
loyal_customers <- data %>%
filter(plan_duration=="Monthly")%>% #filter monthly subscribers
group_by(user_id)%>%
summarise(subscriptions = n())%>% #count the subscriptions for each user
filter(subscriptions >= 12) #keep only customers that subscribed at least 12 months
# save the customer list in a csv file
write.csv(loyal_customers, "Loyal Customers.csv")Measuring customer retention is crucial for evaluating business performance and identifying areas for improvement. We examined the retention rate of new monthly customers in the following month, grouped by the month of their first paid invoice. We observed consistently high retention rates, with the exception of a slight dip in October 2022, where the retention rate dropped to 92%. Overall, the retention rate remained around 97%, indicating that the store was successful in retaining its monthly customers.
retention <- data %>%
filter(plan_duration=="Monthly")%>% #filter transactions with monthly subscription
select(user_id, transaction_date)%>%
group_by(user_id)%>% #group by each user
arrange(user_id, transaction_date)%>% #arrange by transaction date per users
filter(row_number() <= 2)%>% #look at the first two transactions
#calculate the difference between the first and the second transaction (in months)
mutate(diff = interval(transaction_date, lead(transaction_date)) %/% months(1),
#if the user has more then one transaction and the difference between
#the second and the first transaction is less than two months
#this will counted as retention
retention = ifelse(n()>1 & diff<2, T, F))%>%
filter(!is.na(diff))%>%
mutate(month = month(transaction_date),
year = year(transaction_date),
date = format(transaction_date, "%b %Y"))%>%
group_by(year, month, date)%>%
#number of new customers with retention in the following month
summarise(retention = sum(retention),
#number of new customers
new_customers = n())%>%
mutate(retention = scales::percent(retention/new_customers))%>%
filter(!month %in% 3)%>%
select(-month, -year) %>%
select(date, retention, new_customers)
kable(retention)| year | month | date | retention | new_customers |
|---|---|---|---|---|
| 2022 | 4 | Apr 2022 | 97% | 1147 |
| 2022 | 5 | May 2022 | 93% | 107 |
| 2022 | 6 | Jun 2022 | 95% | 96 |
| 2022 | 7 | Jul 2022 | 97% | 75 |
| 2022 | 8 | Aug 2022 | 96% | 81 |
| 2022 | 9 | Sep 2022 | 97% | 70 |
| 2022 | 10 | Oct 2022 | 92% | 65 |
| 2022 | 11 | Nov 2022 | 96% | 132 |
| 2022 | 12 | Dec 2022 | 99% | 153 |
| 2023 | 1 | Jan 2023 | 99% | 130 |
| 2023 | 2 | Feb 2023 | 100% | 88 |
To convert more customers to annual subscriptions, we recommended focusing on offering attractive discount codes specifically for annual plans. Our analysis revealed that more than 37% of annual subscribers used discount codes, compared to only 10.4% of monthly subscribers. By implementing targeted discount strategies, the store owners could incentivize customers to choose annual subscriptions, thereby increasing revenue and customer loyalty.
data %>%
#create boolean variable - True means it has discount
mutate(has_discount = ifelse(discount_applied > 0, T, F))%>%
group_by(plan_duration, user_id)%>% #group by user and plan
#true means the user used a discount at least once
summarise(has_discount=as.logical(sum(has_discount)))%>%
#calculate the % of users who used discount per plan
summarise(has_discount = scales::percent(mean(has_discount), accuracy=.01))## # A tibble: 2 × 2
## plan_duration has_discount
## <chr> <chr>
## 1 Annual 37.19%
## 2 Monthly 10.41%
Additionally, we suggested gathering more data on the demographics, geography, interests, and behaviors of annual subscribers. This data could be collected through surveys, social listening, and customer feedback. By understanding the motivations and preferences of this customer segment, the store owners could tailor their offerings and marketing campaigns to better meet their needs.
data%>%
filter(discount_applied > 0,
plan_duration == "Annual")%>%
#the percentage percentage for the total subscription price
mutate(discount = scales::percent(discount_applied/subscription_price))%>%
group_by(discount)%>%
#calculate number of subscribers and revenue per discount %
summarise(subscriptions=n(),
amount_paid = scales::dollar(round(sum(amount_paid), 0))) ## # A tibble: 3 × 3
## discount subscriptions amount_paid
## <chr> <int> <chr>
## 1 20% 30 $6,158
## 2 25% 148 $28,283
## 3 28% 147 $27,052
Through this transformative journey, the store owners discovered the potential for growth and optimization in their business operations. By leveraging advanced technology, implementing discount strategies, and analyzing key metrics, they successfully streamlined their operations and improved customer satisfaction. The store witnessed a significant increase in revenue from annual subscriptions, enhanced customer retention, and unlocked opportunities to convert more customers onto annual plans.
In this ever-evolving business landscape, continuous improvement is key. By embracing data-driven insights and implementing the recommended solutions, the store owners can stay ahead of the competition, foster customer loyalty, and achieve sustainable long-term success.