Visualizing Preferences in SimplyMap: Credit Cards

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Three weeks ago we utilized the SimmonsLOCAL data set to visualize contiguous America’s mayonnaise brand preference. Staying with the American visualization theme, this week’s report will map the US as it relates to the brand of credit card they own.

To create these maps, we tapped into the Nielsen Financial CLOUT data package. Nielsen Financial CLOUT is a database that contains current year (CY) and five year (FY) projections of market penetration and dollar balances for more than 100 financial products. These modeled estimates and projections include all basic banking products and auxiliary services, such as checking accounts and bill pay, and other services, such as investments and retirement accounts.

The four mapped % Households Owning variables can be found at this path in SimplyMap: Financial CLOUT » Credit Card Products » Bank Credit Card Products

American Express Blue/Clear/Co-branded Card
MasterCard
Discover Card
VISA Card

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Exploring SimplyMap Data: Internet Search Engines

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In this week’s report exploring SimplyMap data, we take a look at the variables available relative to internet search engines used. The source of this data will come from the SimmonsLOCAL data set which, as we’ve mentioned before, contains over 60,000 data variables. With that much data available, it’s safe to say that this series will be around for a very long time. Let’s get started!

Our general assumption here is that Google is the most used search engine in the United States. But does the data backup our assumption? Let’s find out.

First, create a new Standard Report under the New Tabular Report button:

se1

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Visualizing Brand Preference: Mayonnaise

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One benefit of data and applications like SimplyMap is the ability to visualize information. In this week’s report, we’ll take a quick look at some branding preferences in contiguous America using data from the SimmonsLOCAL data set. As a reminder about the dataset, Experian SimmonsLOCAL is a powerful targeting and profiling system that provides insights into consumer behavior for all of America’s 210 media markets on a local level with 60,000+ data variables, including over 450 categories and 8,000 specific brands.

With that said, let’s visualize the brand preferences of mayonnaise (we’ll use Hellmann’s, Best Foods and Miracle Whip) across the United States to see if we can identify some obvious trends in preferences relative to geography.

Quick side note: out of curiosity, we decided to run a search for the word “mayonnaise” to see how many related variables exist in SimplyMap. That total? 202! On to our maps:

HELLMANN’S

hellmanns

BEST FOODS

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Come see us at ALA 2016 in Orlando – June 23-28 [Booth 775]

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Visit us at booth #775 at the ALA 2016 annual conference to meet the SimplyMap team and learn more about the features and functions behind SimplyMap. Have a Ghirardelli Chocolate while we present SimplyMap and offer tips & tricks for current users. While at our booth, don’t forget to enter our raffle – we will be giving away a $100 gift certificate to Amazon.com. See you in Orlando!

 

Exhibit Hours:

Friday June 24 –  5:30 – 7:00pm

Saturday June 25 – 9:00am – 5:00pm

Sunday June 26 – 9:00am – 5:00pm

Monday June 27 – 9:00am – 2:00pm

AC16_WereExhibiting

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Business Search Tip: Searching Multiple Names

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Inspired by a recent support desk question, this week’s SimplyMap report will demonstrate how a user would run a business search that consists of multiple names. In short, the user was attempting to view on a map and report where Walmart and Target stores were located within a city. Here’s how we did it.

First, select a location and map a variable – in this example, we’ll use the city of Philadelphia and map the variable Median Household Income:

philly1

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Exploring SimplyMap Data: P$YCLE

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In this week’s SimplyMap report we’ll take a look at one of our newer data packages offered, P$YCLE® by Nielsen.

P$YCLE® is a segmentation system that evaluates consumers using key demographic factors that have the greatest effect on their financial behaviors, such as income, age, presence of children, home ownership and Nielsen’ proprietary measure of Income Producing Assets (IPA). The result is a total of 58 P$YCLE® segments, within 12 P$YCLE Lifestage Groups, each with distinct usage patterns for financial and investment products and services.

Let’s take a look at the top segments from a few cities around the US. First, create a new Standard Report (under New Tabular Report > Standard Report). Next, open the variables panel and navigate to the P$YCLE® segments folder – from there, select Add All Variables from the action dropdown that appears. Close out the panel to generate your report for the US.

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Using Multiple Filter Conditions

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This week’s SimplyMap report will provide an overview of one of the more advanced functionalities in SimplyMap – applying multiple filter conditions. Filters can be applied to any map or report tabs. For example, a map showing Household income levels by ZIP Code could be filtered to only show ZIP Codes with income over a specific threshold. Another example would be to filter the same income map to only show ZIP Codes with a population over 8,000. Using multiple filter conditions is a great way to hone your focus on a narrower set of geographic locations that meet your multiple criteria. Let’s get started with an example.

Scenario: You are looking for the best location to open up a high end salon in California and want to develop an initial list to further analyze. Let’s assume there are three criteria that must be met before considering a location.

1. The location has to have more than 500,000 residents.
2. The location must have a median household income of at least 75,000.
3. At least 30% of households must report spending money at a salon.

The Setup: First, create a new Location Analysis Report and set your location to California. Next, navigate through some pertinent variables and add the following variables that will serve as our criteria.

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The Landline Telephone’s Descent

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In previous blog entries we highlighted the usefulness of viewing multiple years of data for the same variable. In short, analyzing the same variable across multiple years allows users to identify trends. For example, in one of the previous blogs we showcased the explosion of tablet ownership over recent years (quadrupling!).For this week’s blog, we’ll see if the data matches our own observations on the rapid decline of landline phones in households. Let’s get started!

First, open a Standard Report and navigate through this path: MRI Consumer Survey » Telephone to find the variable, % Households w/ a Telephone: Have a landline telephone, 2015.

Hover over the variable and select the Select Variable Year option from the Action Dropdown menu. You will see the years 2008-2015 on your screen. Select each year as shown below:

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