Estimating Business Targets

2002/4/10 IDSL seminar

Estimating Business Targets

Advisor: Dr. Hsu

Graduate: Yung-Chu Lin

Data Source: Datta et al., KDD01, pp. 420-425.


Abstract

Propose a new solution to the classical econometric task of frontier analysis

Combine nearest neighbor methods and classical statistical methods

Identify under marketed customersBenchmark regional directory divisions


Outline

MotivationObjectiveHistorical approachesTarget estimation methodologyCase studyConclusion Personal opinion


Motivation

Setting targets is a critical taskSetting the target of each entity to the

average amongst the entities traditionallyTwo challenges

– The characteristics of the entities will have a heavy influence on the outcome

– The inherent unsupervised nature of the problem


Objective

Provide a methodology for estimating unsupervised maximal or minimal targets

Setting revenue target expectations for individual customers

Revenue target setting for regional yellow page directories


Historical Approaches

Mathematical programmingEconomics


Mathematical Programming

where is the target for xi, a vector for the ith observation

Sensitivity to errors or outliers since it assumes that all observed targets define the possible space

)( ii xg

i


Economics

where is a non-negative error term

The requirement of a model for the error term and for g

iii xg )(

i


Target Estimation Methodology

Nearest neighbor vs. clusteringThe neighborhoodsThe distance functionTarget estimation from the neighborhoodsA heuristic for comparing neighborhoods


Nearest Neighbor vs. Clustering

Time complexity– Clustering is better than nearest neighbor

Problem of clustering– Two similar entities fall into different cluster– Dimension higher, influence more serious– But nearest neighbor is not so


The Neighborhoods

xi: ith observationyi: the variable containg its target valueni: neighborhood for xi, where ni is a set of

observations {xi, xj, …}


The Distance Function

Continuous standardizee.g. Continuous- (2,1)(3,4)

Nominal- (a,b)(a,c) 2

22

1 )*3()*1( ww

220 w


Target Estimation From the Neighborhoods

Let yi(1), yi(2), …, yi(k) be the order statistics, so that yi(1) is the largest


A Heuristic for Comparing Neighborhoods

Maximal frontier E(xi) will range from 0 to 1Minimal frontier E(xi) >=1


Case Study

Target revenues for directory book advertisers

Target revenue for regional directories


(1) Target Revenues for Directory Book Advertisers

Goal– Find businesses that have low spending

relative to those with otherwise similar characteristics

Three categories of data available– Advertiser: e.g. number of employees– Directory: e.g. distribution size– Market : e.g. median household income


Calculating Nearest Neighbors

Standardize continuous data: natural logK=4Weight the variables equally

– But decrease the weights for many of the directory and market variables


Distribution for E(x) for Advertisers


A Decision Tree to Predict phi -xi


(2) Target Revenue for Regional Directories

Goal– Benchmark regional directory divisions

Separate the data into two sets– Training set: 80%– Test set: 20%

K=4


Book Type

System book– an entire serving area

System-neighborhood book– A smaller number of geographic areas in the

franchise areaNeighborhood book

– Areas outside of the telephone company’s franchise area


Four Different Distributions labeled according to the legend


Neigborhood books System books Non-system books

The x-axis shos log(distribution) and the y-axis E(x)


Conclusion

Present a general data mining methodology for estimating business targets by frontier analysis

First case– Increase sales focus on the under-marketed customers

– Increase the potential revenue by several million

Second case– Estimate optimal revenue performance targets for

directory divisions

– Increase for directory books is a minimum of several million dollars


Personal opinion

Combine several existed methodologies or disciplines can make new powerful one

Documents

Estimating Business Targets