Scenario Analysis III

Scenario Analysis II

Preliminary Competitor and Opportunities Analysis

Objective
To generate broad insights on competition and opportunities based on Melbourne suburbs data.

Background
This is a preliminary competition analysis of two retailers (which we shall refer to as A and Z hereon) by different suburbs in Melbourne. In addition, this analysis seeks to identify potential suburbs for the opening of new stores.

For simplicity’s sake, the analysis is only based on the population and the median weekly income per household of the suburbs. The data for the analysis has been sourced from the internet, cleaned and visualised in the above dashboard.

Dashboard notes

• This is an interactive platform. Click on (most parts are clickable) any part to either highlight or change a criteria.
• The difference in sizes of the symbols in the scatterplot is meant for easy differentiation of stores (A vs Z vs A and Z vs no stores) only. It does not mean a smaller/larger population or median weekly household income for the representative suburb.
• Some suburbs in regional Victoria are included for information only.

Key insights obtained

1. On the overall, Z has half the number of stores compared to A whereas in the western region, A and Z have an equal number of stores – three each. That being said, in that region A is in a leading position as its stores are generally situated in the more affluent and populous suburbs (median weekly household income of ~$1,600 and total population of 86k) as compared to Z (median weekly household income of ~$1,200 and total population of 66k).
2. This pattern of A being present in the more affluent and populous suburbs can be observed in the rest of Melbourne’s regions as well. Overall, A’s stores can be found in 8 out of 17 affluent and populous suburbs (above $1,500 median weekly household income and over 20k population). Z on the other hand is only present in two of such suburbs.
3. Similarly, A is dominant in the middle-income and populous suburbs ($1,000 - $1,500 median weekly household income and over 20k population). A has stores in 11 out of 27 such suburbs while Z only has 5 stores.
4. A and Z only have one store per suburb. A key thing to note here is that A and Z do not appear together in most suburbs. The only exceptions are in Frankston, Preston and Ringwood where they have a store each. There are also suburbs where A and Z are not found at all.
5. Currently, the affluent Williamstown and Newport suburbs area does not have a nearby store.  Combined they have a population of 25k with a median weekly household income of just above $1,700.

Conclusion and recommendation

• The two competitors tend to avoid direct competition in the same suburb.  The continuous increase in the number of stores may lead to more direct competition in the future.
• The current dynamics of the competition within the 3 suburbs where both A and Z are present could be examined to predict if direct competitions would be more frequent in the future. Examples of dynamics include aggressiveness of local promotions, size and profitability of the stores.
•  A, who is currently in a comfortable leading position, both in terms of total number of stores and presence in the affluent and populous suburbs (above $1,500 median weekly household income and over 20k population) would most likely continue to dominate the brick and mortar side of the business. On the flip side, a larger number of stores means a larger fixed-cost footprint. This makes A more susceptible to disruptive innovations, especially to the types that cannibalise brick and mortar business (e.g. new e-commerce models). With a much smaller fixed-cost footprint, Z would be more flexible in reacting to such innovations.
• The Williamstown and Newport suburbs area represents an opportunity for a future store as the nearest store is about 7km away.

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Work done:

1. Defined the scope of the analysis – restricted coverage of analysis to Melbourne region and to only A and Z stores.
2. Identified the most convenient sources of data for easy extraction.
3. Obtained and cleaned data in Excel (with VBA).
4. Linked data sets from various sources together using common criteria.
5. Formatted data for easy analysis.
6. Visualised data in Tableau (a powerful data visualization software).
7. Designed interactive dashboard.
8. Derived insights, drawn conclusion and made recommendation.