For example, Aarong can easily check their sales data of Women’s section to find out whether
the sales has been increased or decreased over the time.
Model building: Model building is the second objective of secondary data which is more
complicated than fact finding. Model building is the use of secondary data to help specify
relationships between two or more variable. It involves the development of descriptive or
predictive equations.
3 common objective under Model building are given below:
Estimating market potential for geographical areas: Business researcher use
secondary data for estimating their market potential for geographical areas.
For example, Coca Cola can do projection in some specific area to find out in which areas people
consume more Coca Cola. By doing this Coca Cola can invest more in those geographic area
where people consume more Coca Cola that means where potential market is high Coca Cola
should invest there.
Forecasting sales: Sales forecasting is the process of estimating future total sales by
using past sales record.
For example, Frutika can use its past sales data which is readily available to Frutika to forecast
its future sales.
Analysis of trade areas and sites: Often business researchers analyze their trade areas
and use site analysis techniques to find out which area will be the best for opening their
shop. The index of retail saturation describes the relationship between the retail demand
and the retail space.
For example, Raymond wants to open a new shop in a new area. For that Raymond will calculate
index retail saturation and by calculating it Raymond will find out in which area’s there will be
highest market potential. In that areas Raymond will open new shop.
Database marketing: Database marketing is a direct marketing and a systematic approach to the
gathering, consolidation and processing of consumer data. Database marketing may be called
customer relationship management. CRM (customer relationship management) systems are a
decision support system that manage the interactions between an organization and its customers.
It involves collecting customer data like names, addresses, emails, phone numbers, transaction
histories, customer support tickets, past purchases, responses to past promotional offers, and
other relevant data such as demographic and financial data. Database marketing also contains
how much a customer has purchased in past.