A finished MKT-443 Topic 2 customer data quality audit example, sampling a retailer's customer file, scoring it on five quality dimensions and tracing each defect to the moment it enters. Searches like "mkt 443 topic 2 assignment example", "mkt443 topic 2 sample" and "mkt-443 topic 2 example" land here.
What a finished MKT-443 Topic 2 customer data quality audit looks like
The finished audit works from a random sample of 2,000 records drawn from roughly 48,000, and every figure it reports is labeled illustrative. Eighteen percent of sampled records carry no email address. Nine percent of postal addresses fail validation against a standard address file. Manual review finds that about one record in nine duplicates a customer already on file, usually a parent registered once at a store and again when booking a child's lessons online. Thirty-one percent show no purchase, lesson or contact in three years. The audit then does what a cleansing report would not: it traces each defect to where it enters. Store clerks reach the email prompt after the card has been taken and the customer is leaving, so the field is skipped at the till, not lost later.
How an MKT-443 Topic 2 example is structured
The audit is set out as sample, scores, sources and fixes. An opening section states why the file is being examined now, a planned lessons renewal campaign, and what that campaign needs from each record. The method section describes the random draw, the address check and the manual duplicate review, with the hours each took. Results appear in a table scoring completeness, validity, uniqueness, timeliness and consistency, each defined in a sentence and paired with its sampled rate. A capture section follows a record's life from the till, the lessons desk and the web checkout, marking where each defect arises and who is typing at that moment. The next section prices the defects in the campaign's own terms, the share of lesson families it could actually reach. Fixes at the point of entry are ranked above any one-time cleanup in the closing section.
A random draw, not a spot check
Two thousand records chosen at random stand in for the whole file, so the rates reported describe it rather than whichever records a clerk happened to open.
Five dimensions, each with a rate
Completeness, validity, uniqueness, timeliness and consistency are each defined in a sentence and given a sampled percentage, which keeps the audit from judging quality by impression.
Duplicates traced to the lessons desk
Most repeated customers are parents registered at a store and again when booking lessons online, which locates the problem in two entry points that never check each other.
The till prompt that arrives too late
Clerks see the email field only after payment, when the customer is already walking away, so the missing addresses are a screen design fault rather than carelessness.
Defects priced in campaign reach
The audit converts its rates into the share of lesson families the renewal campaign could contact at all, which turns quality scores into a figure a manager weighs.
Fixes at entry ranked above cleanup
Moving the email prompt before payment and adding a lookup at the lessons desk outrank a one-time scrub that the same entry habits would soon undo.
Where marks go in MKT-443 Topic 2
Nothing costs more here than rates drawn from a handful of records someone opened by hand, since no figure from them can be applied to the file. Reporting that the data is poor without a single measured percentage leaves every later topic without a baseline. Many drafts list the quality dimensions from the reading and never score the sample on any of them. A cleanup recommendation with no account of where defects enter repeats the problem within months, because the clerks and web forms producing it are unchanged. Treating duplicates as a storage issue misses that each one splits a customer's history and sends two renewal letters to one household. The least expected deduction comes from blaming staff for skipped fields when the screen asked at the wrong moment.
Get an MKT-443 Topic 2 example written to your instructions
Send the MKT-443 Topic 2 instructions and the rubric posted in your classroom, with the customer data or case your section supplied. We write a custom example to those criteria, with a sampled file scored on each quality dimension, every defect traced to its point of entry and fixes ranked above cleanup, in 24 to 48 hours. The first one is free.
MKT-443 Topic 2 questions, answered
What are the dimensions of data quality?
The audit uses five that most data management texts recognize under similar names: completeness, whether a field is filled; validity, whether the value fits its format and rules; uniqueness, whether a customer appears once; timeliness, whether the record is current enough to act on; and consistency, whether the same fact agrees across systems. Other lists add accuracy against the real world, which usually needs a costlier check.
Why sample the file instead of checking every record?
Because a well-drawn random sample gives a reliable estimate of each defect rate at a fraction of the effort, and some checks, such as judging whether two records are the same person, need a human eye. Two thousand records reviewed carefully tell the retailer more than forty-eight thousand scanned by a rule that misses the hard cases. The example states its sampling method so the rates can be trusted.
Why fix data at the point of entry rather than clean it later?
Because cleaning treats the result while the cause keeps running. If the till still asks for an email after payment, next year's file will be missing addresses at the same rate. A cleanup is sometimes worth doing once, and the example budgets for one, but it ranks changes to the screens and routines that create each defect ahead of it.