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Power BI Blog

  • Retail Location Analytics using Power BI

    This blog post was authored by Shish Shridhar, Director of Business Development - Retail, Microsoft

    By combining demographic data like Median Income, Education Levels, Median Age and customer purchasing data such as preferences, past purchases, and online behavioral data, retailers gain a more in-depth understanding of customer needs and wants than with just past purchase data. Power BI provides powerful capabilities for combining data from various sources and enabling visual correlation: you can certainly use the Data Analysis Tookpak in Excel and run a Correlation Coefficient on the combined data as well.

    To test this out, I came with the assumption that Seattle has the most Starbucks and that demographics affect the number of stores. To learn what the answer was, I used Power BI and Excel. Here is what I did:

    I looked for oData sources relevant to retail and found one via Socrata: https://opendata.socrata.com/

     

    I ran a search for Starbucks, to see if there was a list of Starbucks Restaurants from around the world. I did find a oData source of with a listing of all the Starbucks Restaurants around the world: https://opendata.socrata.com/Business/All-Starbucks-Locations-in-the-World/xy4y-c4mk

     

    Here is the oData source link to access the data: http://opendata.socrata.com/OData.svc/xy4y-c4mk

    Using Power Query for Excel, I was able to access the data using the oData option. This returns 20, 621 rows of data containing details of Starbucks restaurants around the world:

     

    To get a better insights from the data, I used Power View for Excel to create a visualizations. A quick drag & drop of the Brands against the count of the StoreIds showed me the brands represented by the data:

     

    I was curious about the countries with the most Starbucks, so I dragged in the Country information along with a count of the StoreIds. Here is the result:

     

    And interestingly, Seattle is not the city with the most Starbucks, as I’d assumed:

     

    Power Map for Excel enables visualizing this data on a Map as a layer of information:

    I was able to obtain US Census Data from Neustar and I imported this data into Excel. This data included Zip codes as well as detailed information about every Zip code. I could potentially use this information to correlate things like median age, median income, population around each of the Starbucks in the US. The Data looks like this:

     

    When I overlay the Census Data on top of the Starbucks Store Locations, I get a visual correlation between demographics data and Starbucks locations:

     

    Here is a Power Map for Excel video of two layers: Starbucks store locations with Median Income by Zip code:

    There are several sources of interesting public data that you can use to analyze Retailers: proximity analysis of retailers and their competition using data from Yelp and Foursquare; Correlating retail yelp rating and FourSquare CheckIns against Demographic data; Correlating Weather data against store performance.

    Here's the actual live visualization I created with Power View:

    You can check out some more examples at my blog.

    #power query#Power BI#Power Map#Power View#Power BI Training#Excel#live demo#data sources#visualization#Starbucks#store#zip code#retail#location#geo location

    Wed, 24 Sep 2014 16:00:00 GMT byPower BI Team2 Comments

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  • London's Metro Bank uses Microsoft’s Power BI to help improve customer service

    While we love talking about the latest out of Microsoft, the best part of talking about the latest and greatest is hearing how our customers are able to use our products to meet – and beat – goals, increase efficiency, or get a leg up on competitors. This last benefit – gaining a competitive edge – was of particular importance to Metro Bank, which in 2010 became the first new retail bank in Britain in more than a century.

    Metro Bank prides itself on being different. From round-the-clock call centers staffed by people (not machines) and “Magic Money Machines” to entertain kids, to the ability to open accounts and issue debit cards within minutes, Metro Bank’s priority is unrelenting customer service. This approach paid off, resulting in thousands of customers joining the ‘banking revolution’ over the last four years. As the bank continued to grow, it needed a business intelligence solution that could help it understand how customers used all of its services, from in-store to mobile and online. This information would help Metro Bank fine-tune its services and move toward its goal of 1 million customers by 2020. Instead of a third-party BI system, Metro Bank went with Power BI for Office 365.

    Over the last year, Metro Bank put Power BI through its paces, creating a variety of dashboards to track bank operations, including the launch of a mobile banking service. Features such as Power Q&A enable executives and colleagues alike – regardless of previous experience with business intelligence – to ask questions in natural language, accelerating adoption throughout the company.

    For more information on how Metro Bank is using Power BI for Office 365 to reach a million members, check out the brand new case study.

    If you’re interested in trying Power BI, do it for free today.

    If you’re an Excel power user or are simply interested in growing your analytics skills, check out the free “Faster Insights to Data with Power BI” training

    #Power BI#Customer Story#Office 365#Metro Bank

    Thu, 11 Sep 2014 17:00:00 GMT byPower BI Team0 Comment

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