3_2: Market Analytics: Analysing and Predicting aggregated Demand and Competiton Flashcards
What is Customer Segmentation?
Segmentation: slicing a pie
is the process of dividing a heterogeneous customer base into distinct groups based on shared characteristics, behaviors, or needs
–>Groups are homogenous within and **heterogenous
Name the 3 difficulties of customer segmentation?
Finding actionable outcome: is the result meaningful for the business need=
Choice of method: there is no method that is priori preferable to others
Iterative approach: much time and rounds of data collection
RFM analysis is used to predict …?
used to predict the response rate and profitability generated by marketing campaings
–>tool to identify a organization´s best customers
What is the meaning behind “RFM”?
Recency (R): numbe rof time units that have passed since last purchased
Frequency (F): average number of purchases per time unit
Monetary value (M): total doolar amount spent per time unit
For what is the RFM method used
- it is used to identify the most engaged customers based on the observation that recency, frequency, and monetary metrics are often correlated with the probability of response and lifetime value
- common to use same discrete scoring for all three metrices –>three dimensional cube
- targeting decisions by selecting a subset of segment from the RFM cube
4 Steps of the RFM Analysis
- recency: sort databse in terms of most recent transactions and score your customers
- frequency: re-sort the dartabase on frequency
- Monetary value: Re-sort the database on sales dollar volume (monetary value)
-
Selection use the three columns and each customers total score
–>highest scores are the best customers
Advantages and Disadvantages of RFM Analysis?
Pros:
- Valuable for short term financial orientation
- requires no marketing strategy
- Fast, simple, and easy to use, exlain and implement
Cons:
- Liimited marketing usage as iit is only about engagement
- does not measure the factors that impact customer behavior
What is Behavioral segmentation?
Goal to understand customer behavior (marketing orientation)
- uses behavioral data instead of financial data
- –>Customer core behavior doen´t change
What is Supervised learning (Classification)?
Group affiliation: is known
Goal: to predict outcome data from independent variables
From RFM Analysis to Behaviroal Segmentation
Picture
What is Unsupervides learning (clustering)?
Group affiliation: unkown
Goal: discover grouppings from data structue
What are the two methods of distance-based clustering?
Hierachircal clusteirng
K-Mean-based clustering
–>minimize the discance between the group member while max. distance to members of other groups
What are the two methods of Model-based clustering?
General description of Model-based clustering?
Model-based clustering
Latent class analyis
–>Model data so that the observed variance can be represented by a small group with specific distrib. characteristics
How does Heararchical clustering work?
observations are group acc. to their similarity (distance matrix) clust method used complete linkage method
What are the steps in Hierachical Clustering?
(Distance-based clustering)
- Calculation of distance between the observation by Euclidean distance dissimilarity matrix
- the model uses the complete linkage method, comparing distance between all group members
- Output dendogram which is interpreted by height and where observations are joined
Dendogram: Hierarchical clusteirng
(distance-based clustering)
At the lowest level, the groups are combined into smaller groups that are relatively similar.
–>These groups are sucessively combiine with less similar groups
Height = dissiminlarity
What is k-Mean-based clustering?
(also k-mean clustering) = find groups based of sum-squares deviation from the multivariate center of the assigned group
–>centers need to be specified
What are the steps in (k-)mean-based clustering?
-
Choose number of clusters and maximum distance
–>requires numeric data - Find observation for cluster 1
- Take second obersavtion if far enough from 1 –>Cluster
- > Take next observation and compare with 1 and 2 (ggf. cluster 3)
What do k-means cluster plots show?
What are the limitations
whether it is possible to differentiate groups based on key variables
Limitation:
K-means requires arbitrary specification of clusters (use different values for k)
–>difficult to determine whether one solution is better than the other
What is the problem with K-means cluster plots?
Difficult whether one sultion is better than another
–>Repeat analysis for several number of clusters to compare the results
How can the outcome of the k- mean based model be tested?
Distance based
- Check mean values by ussing aggregate()
- Plot k-mean cluster to chech if it is possible to differentiate groups based on key variables
- Alternatively plot two continous variable by segment