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One-time buyers strategies basedon customer portfolio analysis.
A static approach
Przemysław Tomczyk, PhD
Institute of Value Management
Warsaw School of Economics
About me
PhD in Management
research interest include:customer lifetime value management
customer portfolio analysisquantitative researchqualitative research
Business problem: one-time buyers– ok. 88%
multi-time buyers – ok. 12%
Research problem:
is to identify the one-time buyers purchase potential to
migrate them to the multi-time buyers segment. This migration equals the lengthening of the customer lifetime cycle(from transactional to relalationship approach)
Current marketing model:transactional approach with minimum customer relationships building
customer service costs (direct costs) minimization
Key customer value factorpurchase frequency
Research purposeis to identify the purchase frequency determinants
Research object:
internet shop offering garden plants
time market activity: since 2010
the period of analysis: 2010-2013
customer portfolio size: 1122
random sample – 256
Discrimination function canonical coefficients
Function
11. revenues ,012
2. pieces purchased ,010
3. the average purchase value -,016
4. the average product value -,007
5. product categories purchased ,009
6. pieces in the main category -,002
(Const.) -,448
Non-standardized coefficients
purchase frequency determinants for one-purchasing customers – boldpurchase frequency determinants for multu-purchasing customers – normal
y = -0,448+ 0,12X(1) + 0,010X(2) – 0,016X(3) – 0,007X(4) + 0,009X(5)
– 0,002X(6)
Results:
purchase frequency determinants for one-time buyers:
big one-time revenues
expensive products sold
a lot of products sold in the main category
purchase frequency determinants for multi-time buyers:
big overall revenues
big overall number of products sold
big overall categories of products sold
Recommendations (strategies)
If you want to have more multi-time buyers, you have to:
1. sell more products and more categories, but not too expensive ones
2. try not to sell the most expensive products during the first transaction
3. try not to sell too much products during the first transaction
4. try to be a rational adviser. Try to ecourage the purchasing for a long period of time.
![Customers Churn Prediction using Artificial Neural ... · devising of churn policy [16] is depicted in Fig. 2. Fig. 2. The Six Steps for Customer Churn Prediction. Churn prediction](https://img.pdfslide.us/doc/110x75/5e71a26fb4acff71e10cc1fe/customers-churn-prediction-using-artificial-neural-devising-of-churn-policy.jpg)
