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Campaign Tracking & Analysis Module (CTAM) ~ Concept Design. October 2002. Hugh McKay. Why CTAM ?. Post Campaign Analysis and tracking has always suffered from reprioritisation when there have been resource limitations

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Presentation Transcript
why ctam
Why CTAM ?
  • Post Campaign Analysis and tracking has always suffered from reprioritisation when there have been resource limitations
  • The need to remove the ad hoc nature of campaign analysis to regular reporting for efficiencies
  • Accessibility of Campaign results by all of Portfolio Management and wider Cards in a consistent format
what is in ctam
What is in CTAM

Accessed through Essbase there will be 4 modules once fully implemented

Phase I - implementation 1/10/02

Base Campaign

Portfolio

Phase II – Implementation due ??

Complex Campaign

Niche

Demographic measures

More complex key measures

phase i implementation
Phase I implementation

Base Campaign

Allows the end user to view campaigns and drill down by dimensions :

Total Campaign, Cell, Offer

and compare Mailed to responders to control groups(where available)

Data is available as either tabular or graph time series for key performance Measures as requested in the BRD

Portfolio

Allows the user to view the same key performance measures as for base campaign but at the portfolio level. The portfolio dimensions that can be selected are : product, revolving status, market segment, loyalty, scheme, acquisition channel, customer segment, bill code

By providing this information users can compare campaign performance versus portfolios in addition to controls.

monthly updating
Monthly Updating

Campaigns

Data on a campaign becomes available once responders are loaded, a parameter file drives the number months of data presented pre and post campaign. Typically, the campaign will be tracked for 6 months of historic data pre mail date and 2 yrs post mail date. All campaigns will be updated monthly, with data typically available mid month.

Portfolios

Portfolio measures will be updated monthly in line with campaigns going forward. The Statement measures are available for 27 months.The End of Month measures will start from July 2002

Niche

Typically 12 months of historic data (where available) will be loaded and subsequently tracked forward according to requirements

building a system key milestones
Building a system – Key milestones
  • Design and build key cyclic measures table (AUSSCORE) including initial population of 24 months data and monthly load process - 1 record per account,(1.87m records) - 1923 fields per record - 27 months of rolling data - approx 36K cylinders of data storage ($8k per month) - approx CPU cost of $60K to load initial data
  • Develop Weekly loading procedures to AUSPROMO
  • Develop monthly extract and merge process from AUSPROMO and AUSSCORE to SAS
  • - approx cost of $30K to extract to date, approx $10K per month ongoing
  • Develop Driver file for campaign monitoring (start and end dates)
  • Develop in SAS summarised data for Essbase cube load
  • Develop Essbase reporting suite
  • Load past campaigns to AUSPROMO
ctam structure

MONTHLY

AUSSCORE

Current

AUSSCORE

Previous...

Banking & Demog Data x Campaign / Portfolio x Account

Market Pulse Campaign Module (CTAM Phase II)

AUSPROMO

All Customer Contact x Campaign x Cell x Response

Summarise by Segment / Cell

Contact Selection (Market Pulse, DMIS, SAS)

WEEKLY

Selected

Mailhouse & Systems Excludes

Contacted

REPORTING

Customer Segments x Month x Performance

Response

Response Rules (Market Pulse, DMIS, SAS)

CTAM structure
campaign selection and contact

CAMPAIGN BRIEF(Business Case, Extract, Analysis, Fulfil)

Historical SelectedTarget & Control

Weekly Change Report

SELECTION DATASETS

Extract targets from Market Pulse / DMIS

AUSPROMO

All Customer Contact x Campaign x Cell x Response

SelectedTarget & Control

WEEKLY APPEND

FORMAT

Manipulate data in SAS and assign cells

BACKOUT CODE

MAILHOUSE

SYSTEMS

SYSTEMSAUSSTMT fields – Statement mailed, hopper code

WEEKLY UPDATE

Exclusions – Address, block code, delinquency

Direct Mail

Statement

FORMAT

ContactedTarget

PIF

FORMAT

Triad

MAILHOUSEReturned mailfile - formatted with required fields

Acquire

Historical ContactedTarget

MAILFILE DATASETS

Insurance

Campaign selection and contact …
collating performance data by segment

AUSSCOREAccount dimensionsDemographyAccount status

AUSSCORE

Statement dimensions

Spend,FeesInterest

Loyalty

Match each Campaign / Portfolio / Niche record in AUSPROMO with banking behaviour and demographic profile and tag with processing month

Automatically identify Portfolio or Niche membership and extract data

DIMENSION: Campaign / Portfolio / Niche DATA: Banking / Demographic / Time LEVEL: Account

Autotag records if Campaign analysis required for processing month

SUMMARISE / AVERAGE FOR EACH DIMENSION

AUSPROMO

All Customer Contact x Campaign x Cell x Response

DIMENSION: Campaign / Portfolio / Niche DATA: Banking / Demographic / Time LEVEL: Dimension Statement & EOM

Collating performance data by segment …

The whole process requires automation, driven by Campaign analysis periods and Portfolio / Niche segment definitions. It will run automatically when calendar month data is available each month.

reporting campaign
Reporting Campaign

Choose campaign

Choose

cell

Choose

period

Choose

view

Control (Type = C)

Mailfile (Type = M)

Responded (Y, N or P) ~ Campaign specific

View results

ausscore the file
AUSSCORE The file

Purpose:

Predictive Datamart Extracts

Initial subset of around 20,000 accounts

Validation of key algorithms pre campaign

CTAM Extract

Application of key algorithms over whole base

Scoring and cycling of bad and doubtful debts

Features:

Combined account view covering transferred accounts

There is no central account number.

Identifies and uses the correct statement

Changes in cycle date can cause statement skips

Transferred before / after statement date

preselection process
Preselection process

Extract

20000

Records

Measures by Dimensions

AUSSCORE

SAS Dataminer produces Algorithm

Convert to M204 UL

SASResults

Compare Results

M204 Results

AUSSCORE

Results stored

ausscore the file1
AUSSCORE The file

File size:

Uses 36730 Cylinders

70 fields have 27 months history

33 fields are either Static or Invisible

Design:

Monthly update

Single or many records

Single record chosen

Multiple or Single Occurrences

Due to potential Data Quality we chose Single

Move monthly 1 to 2 ,2 to 3 etc

User Language only available

ausscore the file2
AUSSCORE The file

Features:

About 1200 fields are Non Occurs

Scanning time and space used (3600 bytes)

Define fields as occurs fields limit of 743 individual fields

Multiply Occurs could solve this

Maximum of one page of occurs fields

Define first occurrence as occurs front of record

Fast access as the latest is access most often

Using Float 4 could cause rounding errors

Account number is 16 digits cannot be Ordered Number

Packed with DP would save space

Extension records using Prime IRNs

Increased