Literature RẹevIew LH n ng 11101 1H 111112111 kkg 5 0.0 ~ai 7
Theoretical Background - 1 2222121221112 211222111111 1501111 11tr ru 8 0.0 ~ai 7 8 PIN Hi oi o3 an Ô 8 2.1.1 Consumer behavior segmented by REM: - 2 2222121222211 1122211 re 8 2.1.2 RFM SCOTITE: Q0 2210211101112 15 1111111115011 111115 1x k cu 9 2.2 Market segment definIti0n: L1 1 21222112112 121 1521151111511 151251 1111115128111 k ke 10 2.3 CLV - customer lifetime value? ll
Proposed modeÌL - c c1 1212122111111 11211121110111111 1101110111811 5 1k nha 13 KV) 0 vvi ii
The above model was applied to the research article, divided into 5 main stages, each stage was done well and fully In the first phase, we use the data available from Adventure Works from Microsoft, we identify the elements and subjects that the research section needs in Adventure Works (collect) In the next stage, we select in the database table the necessary items to be able to calculate the factors and objects to be studied and separate them to start the calculation process, converting the necessary data (selective) In the next stage, we rely on those data to calculate the ranges based on quintiles and find the different results so that in the next stage we will classify those results into separate groups, each group will have its own setpoint and evaluate the real effectiveness of the model just studied
AdventureWorks is data provided by Microsoft that is updated to 2021 We are currently applying the RFM model for calculations on this dataset and therefore we focus on only one module, Sales module
1 CurteneyRatelD CurrencyRateDate FromCurrencyCode ToCunencyCode AverageRate EndoayRate
| SalesOrderD OrderDate DueDate ShipDate Status Customeri0
| SalesOrderDetallD CarlerTrackingNumber Orderaty ProductiD SpecialOfedD UniPrice UnitPriceDiscount LineTotal
#Ealss.5pecia|0fferProducl Í 8pecialOfeD Pơ Í ProduttlD
1 ShoppingCartiem|D §h0pplng€arlD Quantty ProductiD DataCreated
— TW Category StanDate EneDate Minôty MarÔty
The sales module contains information about carts, orders, special offers, places of delivery, and salespeople We extract data from three tables:
Sales.SalesTerritory, SalesOrderHeader, and SalesOrderDetail, then select the appropriate columns as below:
SalesOrderID int Sales.SalesOrderH Auto Increment eader
OrderDate datetime Sales.SalesOrderH Date on which an order eader was created
Status tinyint Sales.SalesOrderHea The current status is der labeled from | to 5 1 Processing, 2 Approved, 3 Pre-ordered, 4 Rejected, 5 = Shipped, 6
ClientID int Sales.SalesOrderHea Customer Identification der Number
OrderQty smallint Sales.SalesOrderDet Invalid q'ty allocated ail
UnitPrice money Sales.SalesOrderDet The price of a product ail
UnitPriceDiscou money Sales.SalesOrderDet Discount amount nt ail
Line Total number( Sales.SalesOrderHea Subtotal per
Territory[D Int Sale.Sales Territory Place of delivery
Custom Custome Customer City NTP rcs Country- Postal erKey rID Province Region Code
-1 [Not [Not [Not [Not [Not [Not
Applicabl | Applicable] | Applicable] | Applicable] | Applicabl | Applicabl e| e| e|
Original dataset has total 121253 rows and 15 columns There are error values which are customerID variables not applicable as those may not provide their information or one-time buyers After those error values had been eliminated, the dataset had 60398 rows left
All min values of Order Quantity and Unit Price Discount Pet are equal with their max values times as | and 0 That means the UnitPrice value equals with Sales Amount value
The min and max of UnitPrice are 2.29 and 3578.27 respectively, which means the pricing of products varies greatly Its large range may be caused by some extreme values
0 0.1 0.2 0.3 0.4 tFigure 3.3.3.Iistributtlon oƒ Unit Price Discount Pct
Histogram was used to display the distribution of 3 numerical variables including OrderQuantity, UnitPrice, Unit Price Discount Pct In this case, all of the distributions are right-skewed because most of the observations are clustered in the first bin while in the other bins there are very few values
Figure 3.3.4 Correlation between Order Quantity and Sales Amount
Scatter chart showing quantity and total amount with two trends When the order quantity is around 0-20 then the total amount has positive correlation That means the more customers order, the higher the total amount
However, when the order quantity increases from 20 or more the total amount tends to stay the same But this trend is quite sparsely distributed
RFM (Recency, Frequency, Monetary) is a technique used to assess customer value by analyzing three key metrics: recency of purchase, frequency of purchase, and monetary value of purchases This analysis allows businesses to segment their customer base into different groups based on their purchasing behavior By understanding these segments, businesses can tailor marketing campaigns and provide targeted incentives to specific customer groups to improve customer engagement and drive sales.
First, the team prepared a sheet of data on Excel consisting of CustomerID, Order ID, Order date, Money Then proceed to insert the Pivot Table
To ensure the recency of data, the maximum value is used to identify the most recent customer order date The number of days since the last order is then calculated by subtracting the current date from the last order date.
For Frequency (F), using the Distinct count function to count the number of non- duplicate order codes, thereby viewing F
For Monetary (M), using the sum function to sum up each customer's money In which, the amount of money will be equal to the unit price multiplied by the amount set minus the discount
Figure 3.4.1, RFM values in Excel
9 œ~iơln ki Ma Neunnorstea Q Gò 0 0U C 0ò UU 2 www Nn Q2 0ò NÓ Œ Gò PR RR RwWR
In the data file, there are 29483 customers, the team applies the scoring method according to the quintile method in case the data file is continuous from 0 to the max value From there, the research team evaluated the customer's score and produced the results to segment into eleven customer groups, including the loss customer
F 0to2 3to4 5 to 15 16 to 40 41 to 70
After we have the values for Recency, Frequency and Monetary parameters, we deduce to R, F and M Score respectively Each R, F, M Score will get a value between 1 and 5 for each parameter The RFM score is calculated by using quintiles Each quintile contains 20% of the population The reason for using quintiles instead of setting ranges based on customer’s expected behavior is it will be more flexible as the data will be
21 adapted to the ranges which are reality deviated and would be better if there is diversity of customer behavior
Table 3.5.1 The RFM score of each customer
We will divide the customer into 11 segments based on the R, F and M scores The description of the segments is as the following:
Customers bought recently, Champions 555, 554, 544, 545, 454, 455, frequently and spent a lot of
Customers spend good Loyal 543, 444, 435, 355, 354, 345, money and regularly buy
Customers recently buy with Potential 553, 551, 552, 541, 542, 533, average frequency value or
Loyalist 532, 531, 452, 451, 442, 441, regularly buy with average
New Customers 512, S11, 422, 421, 412, 411, Customers bought recently but seldom buy with low
Promising 525, 524, 523, 522, 521,515, | p and M scores but have not
Customers have above Need Attention 535, 534, 443, 434, 343, 334,
325, 324 average RFM scores but may not have bought very frequently
Customers have below average recency and monetary values We will lose them if not reactivated
Similar to “Can not lose them” but they have smaller monetary and frequency value
Customers did not buy often, spent big money but it was a long time ago
Customers whose last purchase was long back and have a low number of orders
Lost customers LU, 112, 121, 131,141,151 Customers buy rarely with small spend and it was far from day since last lable 3 5 Customer segmentation
We visualize the matrix result We can easily see hibernating customers account for the highest percentage - 48.38% whereas at risk and can not loose segmentations have the same lowest percentage 0%
I Champions I Loyal ® Potential Loyalist @ New Customers ® Promising ®@ Need Attention @ About To Sleep @ AtRisk
@ Cannot Lose Them @ Hibernating customers MM Lost customers
Figure 3.5.2 The customer segmentation matrix
Experimental Resuẽfs c0 212221112211 112212111021112811 12281211 txxrườ 25 (@/1:>i0)/>a41>.⁄)ỶÝỶÝ
Figure 4.1.1 The distribution of Recency
Figure 4.1.2 The distribution of Frequency
Customer behavior exhibits contrasting patterns based on recent purchases versus frequent purchases Recent customers primarily fall within points 4 and 5 on the recency scale, indicating a more recent engagement In contrast, frequent customers are concentrated in points 1 and 2, suggesting a higher frequency of purchases over time.
Finally, we look at the distribution of our segments by bar chart because it is a better fit for comparing quantities
5.00% 9.03% 0.05% 1.14% i 0.00% 0.00% 1.99% © > we © oF 5 ° cố gề & & & se sề ot 5 é
Sà xe FF ôŠ & we về s * & vs „$Ÿ ti Se co?
Figure 4.1.3 The distribution of customer segments
This data shows that the company had quite a few customers who tend to buy infrequently and spend little money ( account for 48,38%, it is nearly half of total ) Lost customers account for a small percentage (1.99%) but still more than champions, loyal segments
Based on the above RFM score, we can calculate customer churn rate by region The graph below visualizes this data
Figure 4 2 Customers churn rate by region
According to experts, the best chum rate that companies should aim for is less than 3% We can see that the customer churn rate at this company is low, with the lowest rate of 0.00% in the Southeast, Northeast and Central of the United States and the highest at 2.60% in the Northwest of the United States This shows that the business has rather effective policies and good quality products to retain customers
To identify key issues driving customer churn, companies should assess: whether pricing is a concern; if products lack desired features; if competitors offer more affordable options; if there have been negative experiences such as outages or poor support; or if other unresolved problems exist.
The next stage is to explicitly ask these questions to your clients who are about to leave, though you can always add more to the list Online survey platforms can be used to build straightforward polls that reveal a clear client turnover pattern
A root cause analysis (RCA) is a fantastic tool for better understanding the mindset of your target audience You ask a series of "why" inquiries in a progressive manner to identify the root cause of an issue RCA looks further into a reported issue to identify the underlying cause rather than stopping at the first indication of a problem
5.2 Effective solution to reduce customer churn
Provide superior customer service and support
A big reason why customers leave is due to poor quality customer service According to an Oracle study, 89% of customers turn to a competitor due to a poor customer service experience with the original brand Customers want to feel like they're being listened to by your organization, so prove to them that they do The company can provide superior service and support by being proactive Don't just wait until the customer makes a complaint to communicate with them indifferently If an upgrade is now available for their purchased product, or a glitch was detected in the product they purchased, try to contact customers privately
Provide value beyond the purchase
Besides providing excellent service and support, there are other ways that you provide value to your existing customers and prevent them from leaving It's all about making customers feel like the company has more to offer than a single product that they need to meet a single need Show your customers that your organization provides unparalleled value Some examples include sending out daily or weekly newsletters, sharing with them relevant blog posts, or updating them on upcoming events and programs hosted by your company Encourage them to sign up for your email newsletter Experience personalization reduces customer churn
Any of us want special treatment and to be remembered That's why most customers are satisfied when called directly at restaurants or online call centers Therefore, businesses should organize customer appreciation events, invest in customer care programs as well as learn about their needs Sending customized communications with incentives to the consumers may persuade them to do business with the company again
Customer feedback allows the company to learn what they like and hate about the products and services Instead than sending out surveys and forms, use a chatbot to collect feedback and communicate with customers After that, take action on the unfavorable comments Create a win-back strategy based on the customers’ complaints Build customer loyalty
Customer satisfaction is an invaluable asset to every business With loyal customers, they are less inclined to leave you Therefore, the company needs to find a way to build the credibility and trust of their customers, keep them in a long-term relationship by providing optimal benefits, and always keep their promises in any case Let customers know what the company is doing for them Those are values, benefits that they can't find anywhere but the business
Restore customer trust in your business by strengthening relationships
Losing customers means losing trust in a variety of ways Therefore, the company needs to research methods in advance to avoid this happening Create brand engagement to keep customers and improve brand loyalty to keep them from giving up on the company
It's important for companies to reflect on the reasons for the departure strategies and come up with strategies to restore their trust in it To rebuild their trust in your
29 company, use the methods listed above and continue to develop the company's own new ones To have a greater impact on lost customers, track and tailor the company re- engagement campaigns The category is quite useful for small businesses and aspiring entrepreneurs who don't have a website WhatsApp Marketing is a great approach to developing long-term relationships with customers and improving your brand For greater results, branch out from WhatsApp and become an authority on all the mediums your customers use Use Telegram and Facebook to reinforce the company's marketing strategy with email notification and web promotion campaigns
5.3 What are the various channels you can use to regain customers?
Live chat is the perfect tool for facilitating real-time engagement with clients on your website or mobile app Ideally, you should make the most of live chat even before customers leave Assume they click the "unsubscribe" or "cancel membership” buttons, or that they move their cursor to the leave tab In that case, you might immediately give them crucial chat messages to sway them away from their relocation plans