Nav Salooja
“Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019”
<script type='text/javascript' src='https://prod-useast-a.online.tableau.com/javascripts/api/viz_v1.js'></script><div class='tableauPlaceholder' style='width: 1920px; height: 915px;'><object class='tableauViz' width='1920' height='915' style='display:none;'><param name='host_url' value='https%3A%2F%2Fprod-useast-a.online.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='/t/torontofimarketgeovisprojectsa8905fall2019' /><param name='name' value='TheTorontoFIMarketDashboard/TorontoFIMarket' /><param name='tabs' value='yes' /><param name='toolbar' value='yes' /><param name='showAppBanner' value='false' /></object></div>Introduction & Background
Banking in the 21st century has evolved significantly especially in the hyper competitive Canadian Market. Big banks nationally have a limited population and wealth share to capture given Canada’s small population and have been active in innovating their retail footprint. In this case study, TD Bank is the point of interest given its large branch network footprint in the Toronto CMA. Within the City of Toronto the bank has 144 branches and is used as the study area for the dashboard created. The dashboard analyzes the market potential, branch network distribution, banking product recommendations and client insights to help derive analytics through a centralized and interactive data visualization tool.
Technology
The technology selected for the geovisualization component is Tableau given its friendly user interface, mapping capabilities, data manipulation and an overall excellent visualization experience. However, Alteryx was widely used for the build out of the datasets that run in Tableau. As the data was extracted from various different sources, spatial element and combining datasets was all done in Alteryx. The data extracted for Expenditure, Income and Dwelling Composition was merged and indexed in Alteryx. The TD Branches was web scrapped live from the Branch Locator and the trading areas (1.5KM Buffers) are also created in Alteryx. The software is also used for all the statistical functions such as the indexed data points in the workbook were all created in Alteryx. The geovisualization component is all created within the Tableau workbooks as multiple sheets are leverged to create an interactive dashboard for full end user development and results.
Data Overview
There are several data sets used to build the multiple sheets in the tableau workbook which range from Environics Expenditure Data, Census Data and webscrapped TD branch locations. In addition to these data sets, a client and trade area geography file was also created. The clients dataset was generated by leveraging a random name and Toronto address generator and those clients were then profiled to their corresponding market. The data collected ranges from a wide variety of sources and geographic extents to provide a fully functional view of the banking industry. This begins by extracting and analyzing the TD Branches and their respective trade areas. The trading areas are created based on a limited buffer representing the immediate market opportunity for the respective branches. Average Income and Dwelling composition variables are then used at the Dissemination Area (DA) geography from the 2016 Census. Although income is represented as an actual dollar value, all market demographics are analyzed and indexed against Toronto CMA averages. As such these datasets combined with Market, Client and TD level data provide the full conceptual framework for this dashboard.
Tables & Visualization Overview
Given the structure of the datasets, six total tables are utilized to combine and work with the data to provide the appropriate visualization. The first two tables are the branch level datasets which begin with the geographic location of the branches in the City of Toronto. This is a point file taken from the TD store locator with fundamental information about the branch name and location attributes. There is a second table created which analyzes the performance of these branches in respect to their client acquisition over a pre-determined timeframe.
The third table used consists of client level information selected from ‘frequent’ clients (clients transacting at branches 20+ times in a year. Their information builds on the respective geography and identifies who and where the client resides along with critical information that is usable for the bank to run some level of statistical analytics. The client table shows the exact location of those frequent clients, their names, unique identifiers, their preferred branch, current location, average incomes, property/dwelling value and mortgage payments the bank collects. This table is then combined to understand the client demographic and wealth opportunity from these frequent clients at the respective branches.
Table four and five are extremely comprehensive as they visualize the geography of the market (City of Toronto at a DA level). This provides a trade area market level full breakdown of the demographics and trading areas as DAs are attributed to their closest branch and allows users to trigger on for where the bank has market coverage and where the gaps reside. However, outside of the allocation of the branches, the geography has a robust set of demographics such as growth (population, income), Dwelling composition and structure, average expenditure and the product recommendations the bank can target driven through the average expenditure datasets. Although the file has a significant amount of data and can be seen as overwhelming, selected data is fully visualized. This also has the full breakdown of how many frequent clients reside in the respective markets and what kind of products are being recomened on the basis of the market demographics analyzed through dwelling composition, growth metrics and expenditure.
The final table provides visualization and breakdown of the five primary product lines of business the bank offers which are combined with the market level data and cross validated against the average expenditure dataset. This is done to identify what products can be recommended throughout the market based on current and anticipate expenditure and growth metrics. For example, markets with high population, income and dwelling growth with limited spend would be targeted with mortgage products given the anticipated growth and the limited spend indicating a demographic saving to buy their home in a growth market. These assumptions are made across the market based on the actual indexed values and as such every market (DA) is given a product recommendation.
Dashboard
Based on the full breakdown of the data extracted, the build out and the tables leveraged as seen above, the dashboard is fully interactive and driven by one prime parameters which controls all elements of the dashboard. Additional visualizations such as the products visualization, the client distribution treemap and the branch trends bar graph are combined here. The products visualization provides a full breakdown of the products that can be recommended based on their value and categorization to the bank. The value is driven based on the revenue the product can bring as investment products drive higher returns than liabilities. This is then broken down into three graphs consisting of the amount of times the product is recommended, the market coverage the recommendation provides between Stocks, Mortgages, Broker Fees, Insurance and Personal Banking products. The client distribution tree map provides an overview by branch as to how many frequent clients reside in the branch’s respective trading area. This provides a holistic approach to anticipating branch traffic trends and capacity constraints as branches with a high degree of frequent clients would require larger square footage and staffing models to adequately service the dependent markets. The final component is the representation of the client trends in a five year run rate to identify the growth the bank experienced in the market and at a branch level through new client acquisition. This provides a full run down of the number of new clients acquired and how the performance varies year over year to identify areas of high and low growth.
This combined with the primary three mapping visualizations, creates a fully robust and interactive dashboard for the user. Parameters are heavily used and are built on a select by branch basis to dynamically change all 6 live elements to represent what the user input requires. This is one of the most significant capabilities of Tableau, the flexibility of using a parameter to analyze the entire market, one branch at a time or to analyze markets without a branch is extremely powerful in deriving insights and analytics. The overall dashboard then zooms in/out as required when a specific branch is selected highlighting its location, its respective frequent clients, the trade area breakdown, what kind of products to recommend, the branch client acquisition trends and the actual number of frequent clients in the market. This can also be expanded to analyze multiple branches or larger markets overall if the functionality is required. Overall, the capacity of the dashboard consists of the following elements:
1. Market DA Level Map
2. Branch Level Map
3. Client Level Map
4. Client Distribution (Tree-Map)
5. Branch Trending Graph
6. Product Recommendation Coverage, Value and Effectiveness
This combined with the capacity to manipulate/store a live feed of data and the current parameters used for this level of analysis bring a new capacity to visualizing large datasets and providing a robust interactive playground to derive insights and analytics.
The link for this full Tableau Workbook is hosted here (please note an online account is required):https://prod-useast-a.online.tableau.com/t/torontofimarketgeovisprojectsa8905fall2019/views/TheTorontoFIMarketDashboard/TorontoFIMarket?:showAppBanner=false&:display_count=n&:showVizHome=n&:origin=viz_share_link