Reporting - Business Requirements and Data Definition

Defining Business Requirements - Key Business Questions

Questions about your business and customer behaviour are often the best way to define business requirements for reprting. Generating a list of questions and prioritising them provides a basis for a development roadmap, and a context for interrogating data from which to drive decisions for action.

The following is an example useful for interrogating ticketing sales data.


Analysis Type Questions this analysis will help answer
Customer Base analysis - total customers, active and lapsed How many Unique customers in My Area / My Branch?
* How many are Active (bought last 12 months)
* Lapsed (not bought in 12 months) 
Frequency Distributions by no. of attendances, tickets, spend, time (months) since last attendance How many customers: 
* have purchased 1,2, times? 
* have bought 1, 2, items? 
* have spent $0-20, $20-40, $40-60...? 
* last spent 1,2,3... months ago? 
Customer behaviour analysis - recency, frequency, monetary How many times per month/year do consumers attend events? 
How long since their last event? 
How much are they spending per month/year? 
Genre Analysis What are the popular Categories in My Area? 
What combinations are popular among customers? 
How is this changing over time?
Geographic Analysis - Customer Origin by UK District What percentage of our customers are local or out-of town? 
Where do most of our customers come from? 
Customer Profiling (Advanced)* What is the typical profile of our consumers across key customer metrics?
How do different groups of customers compare in their profiles, by
* Recency=Frequency-Monetary category
* Product Preference
* Geography
* Other bespoke segmentationcustom
*Geodeographics may be included here if client can supply them e.g. Mosaic, Personics

Data Definition

The following is a simplistic example definition of the data sets to be supplied by data providers - please note this definition is not comprehensive and it is possible to accommodate flexibility within reason. It is recommended for all data providers to consult with our data team prior to submission.
  • Customer Data - Important fields:
    • Customer Id
    • Location or 'Geo' - Postcode,  Country
    • Other
      • Age or DOB
      • Gender or Title
      • Other attributes
  • Sales History – Important fields:
    • Customer Id
    • Order Date
    • Product ID
    • Sales Location and or Agent
    • Quantity
    • Price
    • Other e.g.  Payment method
  • Product Data
    • Product ID
    • Type
    • Category
    • Product date (e.g. for events)
  • Marketing Activity Data
    • Customer id
    • Marketing Acitivity (e.g. mailing)
    • Outcome - Sent, Opened, Clicked


  • Location or 'Geo' analysis may only be conducted at the level of granularity provided
  • Data may be provided in 1 single table (denormalised) or multiple tables (normalised)
  • Data Quality - reporting is based on data provided as is - data cleansing e.g. matching and deduping of individual customer records is generally not included however may be arranged via external service providers upon request.


Reports Examples

Here are just a few examples of reports possible from data provided similarly to the defiition above:

  • Customer Base analysis
    • Number of active and lapsed customers defined by last purchase inside 12 months (or other periods as required)
  • Frequency Distributions - customer counts by
    • Number of purchases
    • Quantity of products purchased
    • Total customer spend, Average Customer spend
    • Time (months) since first / last purchase
  • “RFM” - Recency, Frequency, Monetary
    • Recency - number of months since purchase
    • Frequency - number of purchases
    • Monetary - total spend
  • Geographic Analysis
    • Percentage of customer in local postcodes
    • Frequency distribution by postal area
  • Marketing Effectiveness
    • Campaigns by month by open/click rate
    • Frequency distribution of customers by open/click rate
  • Website user analysis
    • Number of active, inactive customers
    • Customers by month since last login
  • Data inventory & quality
    • Number of records in each data source table
    • Time period range from/to
    • Data Completeness (fields, contactability (email, phone, address)

There are myriad ways to slice and dice even the simplest data to show trends, yearly or regoiinal comparisons, ratios etc  Talk to our data team about the reporting that's possible from your data.