Typically it is not easy to analyse the performance of
store/shelf space in relation to sales data. Commonly, merchandising
data is stored independently of sales data and linking the disparate
sets of information in unachievable.
For example, after analysing sales information for a particular
listed product you may decide that it is not performing well, so you
withdraw that product from the range. An important question which
may remain unanswered is the distribution level of the product
across the store chain. Even though the product did not have full
distribution, it may be performing better than a similar product
with greater distribution.
An outline of some of the typical data analysis problems to be
resolved by Merchandisers and Buyers today:
- Data is not easily presented to analyse information
- Time is wasted and human error increased because of the need
for double entry
- Well presented reports are created manually (for
presentation to the management team)
- Non standard/adhoc reports are time consuming and difficult
to create
- Required information is stored in many sources - ie,
accounts/admin/warehouse
- Various systems required to read the disparate data and
training is inefficient
- As business requirements change, the flow of information
grinds to a stand still as the business logic / requirement
changes
- Manually populated Excel spreadsheets become the predominent
reference while laying out the plan
- Data between back-end systems is not synchronised
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