MicroBuildHS Retail Store Location Analysis Using Household Geo-Demography

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1 White Paper MicroBuildHS Retail Store Location Analysis Using Household Geo-Demography This paper covers an approach followed by a major home-improvement retailer to improve its site location research by employing information data products that accurately and precisely quantify households and populations in target market areas and incorporate detailed household-level demographics. With more than one hundred new stores opened each year, the retail company s annual investment to construct stores can often exceed $3 billion. Given the enormous investment required, the competitive real estate market and the impact to corporate image, retailers must be able to make store location decisions with the most current, accurate and precise data available. The unique approach developed initially with a home-improvement retailer combines the assets of two household-level data products. The solution includes a proprietary process and data product, MicroBuild, produced by the Gadberry Group and Acxiom Corporation s proprietary household-level segmentation system, Personicx. The combined solution, referred to in this paper as MicroBuildHS, brings together the geographic accuracy of census data and the marketing power of household demographic data to address the needs of research and marketing departments across the retail enterprise. Introduction: the question When it comes to real estate, most experts agree that it s all about location, location, location. When you buy a house, for example, you access all the tools available to you school system data, crime statistics, proximity to shopping and services, word of mouth from family, coworkers and friends to make the right decision. For retailers, the effort and expense required for store location research is much more complex. Successful retailers today must consider new locations based on a number of variables such as size and format, the type of merchandise being offered and alreadypresent competitors. Steps include identifying the ideal real estate and working through the complex process to qualify a site. In addition, real estate decisions must be validated against the practical and statistical measures used to determine the right location. Retailers must be able to compete for the choicest real estate using reliable population and demographic data. This process may sound like a straightforward approach to problem solving; however, for companies trying to find the best location, gathering the correct data, using the best tools and making the right decision about a site often becomes very difficult. Why? First, relating population and demographics to the physical locations where consumers reside is not as easy as it may seem. Consider the fact that over 300 million people representing over 130 million households currently reside in the United States. Adding to that complexity, experts estimate that over 20%, or roughly 26 million of these households, relocate every year. That s a lot of moving parts. Even if companies keep up with the sheer numbers, determining exactly where people live presents other challenges. Typically, most people consider mailing address to be the physical identifier for where people live. But this most fundamental assumption can lead to errors because mailing addresses are always in flux, constantly being created and reassigned within postal areas such as new ZIP Codes. Another barrier to the ability to interpret addresses as physical locations comes from the technologies used to

2 translate addresses into location. Using latitude and longitude, these technologies only work with already-recognizable addresses. In the cases of very new or remote rural addresses, results often yield an approximation of the address, which can be great distances from the actual residence. The most commonly used source for location data comes from the U.S. Decennial Census databases and products built using census data. These databases and products provide households, populations, demographics and an array of other variables that become the baseline to determine the population for an area. Understanding the base population provides the cornerstone for finding the perfect spot to build a successful business. Many companies use census data and related products extensively as tools for research, marketing activities and information product development. The breadth and depth of coverage, the consistency of the compilation processes, cost and availability make census data an extremely powerful data asset. However, census data also involves certain limitations. Disadvantages of decennial census data and products constructed from it lie in data currency, and, in some cases, limited sampling. Produced once every decade with only annual estimates in between, census data does not reflect the dramatic changes in population that can occur within much shorter intervals. Most researchers concede that census data is the standard for understanding the distribution and demographic makeup of the U.S. population but accurate analysis becomes a challenge as decennial census data ages. To supplement census data, many companies invest many thousands of dollars in data that is more current and precise. Marketing departments within companies find this supplemental household data very effective for understanding their customers and for conducting direct mailings and other types of marketing campaigns. Companies also employ expensive geographic information systems (GIS) software to define and analyze market areas tied to census or other geographic data that can be rolled up and compared at various levels. So, what are these companies missing when they ask, Where is the best location for my business? They may not have utilized a solution that combines the best that census data has to offer with the most complete and current population and demographic information available, all delivered in a single, cohesive decision-making tool a tool that, in both form and function, can provide research and marketing departments with an effective and accurate way to answer location questions. The problem scenario To illustrate the current problems involved in gathering and working with location analysis data, let s look at a scenario of a day in the life of a typical research analyst. The analyst, Sarah Peacock, works for a major retail chain. She is responsible for identifying, securing and integrating data used by the company to better understand and predict the differences in populations that make up its market or trade areas. These critical differences help Sarah interpret the performance of retail locations. Sarah s job involves analyzing data that describes what occurred in the past, what is happening in the present and predicts the future performance of current locations to help model and identify new locations. Sarah s company has made significant investments in a variety of datasets to help her do her job including consumer household demographic data used by marketing and other

3 departments. Because the data comes in a variety of formats including name and address, ZIP Code counts, postal statistics and more Sarah spends a considerable amount of time formatting and assembling the data to use it effectively in models and geographic applications. To complicate matters, the retail analyst must also contend with customer and prospect data currently in a name and address format that does not align with the historic census data critical to trending models. Although Sarah is able to use geo-coding software to assign a location to data, when she tries to geographically summarize this data, significant variances are immediately obvious from geography to geography that cannot be reconciled when compared with expected trends. Think big, look small Over the past 24 months, companies and researchers involved in location analysis activities have expressed an increasing interest in the use of household-level data in their analysis. This interest involves gaining more insight into current customers and prospects at greater levels of precision, which means looking at increasingly more data for decreasingly smaller areas. Currently, the standard geographic unit of measure for most location research is the census block group, which includes all people in a census-defined area and usually ranges from 600 to 3,000 people, with an optimal target of 1,500 people. The 2000 census included slightly more than 208,000 block groups. The next smaller census geography is the block, which in 2000 represented over 8 million boundaries, or 40 times the number of block groups. With constant advances in technology and available computing power, most companies can meet the challenge of slicing data into smaller chunks to focus on more targeted solutions. However, a greater challenge exists in finding data that is both accurate and precise for much smaller areas. Most products built to provide population and demographics begin as summarized data at fairly high levels of geography such as county or ZIP Code. When used to provide directional data, these products give researchers a sense of whether an area is growing, shrinking or stable. However, if the goal is to evaluate growth or demographic characteristics within a market area, these products tend to paint the landscape with a very broad brush and often result in less than ideal information for decisionmaking. With existing issues about the age of census data and potential concerns about the Census Bureau s new American Community Survey, which will be implemented with the next census for the first time, some businesses are considering alternatives to census data. Companies that have already purchased household data want to leverage existing investments across the enterprise from marketing departments that typically purchase household data to research departments that work without the tools or expertise to utilize household-level data. While many believe the market s interest in household-level data will continue to grow, there is a significant challenge to integrating this type of data into geographic-based applications. The ability to associate 120+ million addresses with the physical locations of households is not a trivial task. It is possible to successfully create latitude and longitude for input addresses as well as other attributes such as block group or block ID s; however, integrating data into GIS software and comparing data to historic census databases often result in data that does not portray an accurate number and distribution of households and population. In some cases, these differences can be inaccurate by orders of magnitude.

4 One company s answer A major do-it-yourself home improvement retailer is always looking for new data sources to help determine where to find the best location to grow business. As most retailers know all too well, the battle for success in retailing occurs neighborhood-by-neighborhood, block by block. Winning in a particular market often means getting there first, and remaining successful always means being in the best spot to conveniently reach the highest number of ideal target customers. The home improvement retailer began using the Gadberry Group s MicroBuild product in early 2004 to help them understand population changes in their trade areas. While effectively and extensively using census-based products, the retailer like a growing number of other similar companies realized that accurately quantifying population and population-related demographics also requires more current and precise forms of data. The company also licenses Acxiom s Personicx household segmentation system. With Personicx, marketing analysts for the company can easily identify and market to unique characteristics such as purchase behavior, income, contact preferences and demographics for all segments of the population. Although both products are valuable to the company for marketing and location research respectively, neither product by itself delivers a solution that spans both functions. This scenario is quite common and represents a classic division among data products where the form of data is driven by its function. Data used in spatial applications such as site location is created with the ability to relate to some geographic reference such as census block group. Accordingly, data used primarily by marketing groups is oriented toward direct marketing applications such as mail. To accomplish both goals, companies find themselves forced to invest in multiple data products for multiple departments. This expensive undertaking does not promote the philosophy of the overall enterprise and perpetuates silo thinking and processes. To provide a comprehensive location analysis solution for the above-referenced home improvement retailer and other mutual clients, Acxiom and Gadberry collaborated to produce a solution called MicroBuildHS that provides a geographically accurate, censusequivalent household base powered by Personicx. The result provides current, accurate population and detailed demographics at the census-block level that can easily and accurately contrast to equivalent census variables. This paper describes the process used to develop MicroBuildHS. MicroBuildHS can provide significant value to companies whose marketing and real estate research efforts require precise demographic and geographic views of their customers. MicroBuildHS is targeted toward businesses that use customer and prospect databases for sales forecasting, merchandising and store location analysis and companies that have location research departments. Industries that lead in the use of these concepts and stand to gain the most from the solution described in this paper include retail, real estate and financial services.

5 Micro-to-macro The Gadberry Group s MicroBuild product provides the most current, accurate and precise representation of U.S. households and population available in the market today. The product is designed and constructed to identify occupied, residential U.S. households. MicroBuild is produced quarterly and is the only product of its kind available at the block level. MicroBuild uses a unique three-step process to yield the most accurate population-to-address data based on a patent-pending methodology. Creating the household baseline Gadberry utilizes a proprietary national address source consisting of more than 117 million base records. This data set is regularly updated by the provider and is integrated into the MicroBuild product build process every quarter. After it is acquired, the data is geo-coded and associated with the most precise level of geography possible. On average, this process results in identifying 91% of all address points to an exact street segment. Addresses that do not geocode precisely are allocated to their respective block using models based on the distribution of exact street addresses and trend models that incorporate census and postal statistics. The first phase of Gadberry s MicroBuild micro-to-macro methodology provides a unique and valuable solution to the challenge of accurately placing households and addresses within the correct census boundary. Building household counts Unlike the U.S. census, which attempts to identify and describe every person in the country, compiled household files, which are built for delivery of mail campaigns, naturally understate or overstate the total household population for the nation. Therefore, any commercial household file must be adjusted to account for accurate population counts prior to any meaningful comparison to census data. A key component of the MicroBuild methodology includes high and low series comparisons to other trusted national data sources that serve as control totals. Calculating population Using the resulting household counts from the preceding step, MicroBuild calculates household population using Census 2000 average household size at the block level. Further population numbers based on Census 2000 group quarters, which include all people not living in households subdivided by factors such as sex and age, are added to the final population to provide a total population count. The power of household-level segmentation Key criteria for the selection of a market segmentation system include data accuracy, freshness and, perhaps most importantly, the availability of data at the household level. Personicx is a household-level consumer segmentation system driven by Acxiom s InfoBase data. Personicx can provide accurate consumer portraits and analyses to help businesses understand current customer demographics and behaviors, to conduct market analyses and planning, and to develop acquisition, cross-sell/up-sell and retention campaigns through various marketing channels.

6 MicroBuildHS provides an ideal application of the strengths of Personicx at a detailed level of geographic precision. Classic segmentation tools assume market segments resemble each other at the neighborhood or ZIP Code level. And even if most tools claim to provide household level segmentation, the segments often project down to a lower level based on a geographical segmentation. Consider your own neighborhood which may include a retiree at the corner, a single parent across the street and a five-member family two doors down. Each household has very different interests and needs. Acxiom s Personicx suite of products, including Personicx LifeChanges, a Personicx Classic tool for tracking when consumers experience life stage changes and migrate to different Personicx clusters, are built from the ground up at a household level using the renowned, multi-sourced InfoBase household-level demographic data. Actual household-level segmentation enables greater targeting precision and true classification accuracy, allowing companies to address household-level needs and providing companies with information they may use in making marketing decisions that result in a major difference in their marketing success. Solution approach The Personicx ground-up approach complements the micro-to-macro methodology of MicroBuild. In fact, MicroBuild was designed with this data integration in mind. Together, these technologies bring something unique to the market by combining census block-level population data and geographic accuracy with household-level segmentation and demographics to help companies decide exactly where to build to reach the most desirable and profitable target consumers. Step 1: Geo-standardize The approach begins with the Acxiom InfoBase marketing list file, which includes Personicx clusters. The Gadberry Group processes, or geo-standardizes, the file using the MicroBuild methodology. This first step in the process consists of two critical components: Incorporating a householding routine to ensure that all addresses net to produce a census equivalent household count. This is necessary because of the manner in which compiled list files are built around surname households and address points. This very important step enables the final product to be accurately contrasted with historic census data. Creating geo-codes for the resulting addresses and associating those addresses with the correct census block. Step 2: Summarize After the base address file becomes geo-standardized, the solution summarizes the file by household counts and Personicx cluster for each census block. It is important to note that only addresses that receive an exact geo-coding result (latitude/longitude) at the rooftop are included for the block-level summarization. In this way, the Personicx clusters are used later as input data to aggregate each block within a specific block boundary. In almost all cases, this step in the process yields a variance in the number of Personicxencoded addresses versus the MicroBuild-published household counts. These differences are resolved in subsequent steps of the process.

7 Step 3: Sample size The aggregation process for Personicx data begins with a statistically valid sample size threshold established by Gadberry. Beginning with census block, the sample requires that the base Acxiom InfoBase marketing list file to be used represents a minimum of 20% of all MicroBuild households. If a block fails to meet the sample threshold, then the solution uses MicroBuild and Acxiom InfoBase marketing list file household counts for the block group instead. Step 4: Apply demographics to MicroBuild households As mentioned previously and known to experienced direct marketers, the Acxiom InfoBase marketing file is built to insure a high deliverability of mail campaigns, not to balance census numbers. Households can be excluded for a variety of reasons incomplete name or address, timing differences resulting from recent moves, seasonal occupants and other factors. To account for the differences between Acxiom InfoBase marketing list file data and MicroBuild households, Gadberry developed a process that adjusts the Acxiom base address counts to align with the MicroBuild counts. This proportional allocation model uses a ratio of qualified Acxiom addresses for each block to map total PersonicX counts. Multiplying this ratio against the total for MicroBuild households yields a result value, which is then used in whole number form to create a whole value. Figure 1 illustrates this model: Figure 1: Allocation model for incorporating demographics The final calculation completes the alignment of Acxiom InfoBase marketing list addresses to MicroBuild household counts by removing the fractional portion of the result value and assigning the remaining count to cluster 1 to create final counts. The end result of these calculations provides an accurate block-level representation of Personicx clusters to tie those values to the MicroBuild counts. This model provides the most critical component of this solution by creating the precise demographics researchers want and presenting those demographics in a form that can be compared and trended against census data. Step 5: Contrast household demographics to census The final development step for the MicroBuildHS solution accurately compares Personicx clusters to certain 2000 census demographic attributes. The aforementioned home improvement client and many other major retailers use census data and census-based products extensively for site-selection analysis. These companies must be able to compare

8 current results for a given geography to historic census databases to produce effective trend and sales forecasts. To develop this solution, Gadberry uses PersonicX metadata for two specific variables that are very important to companies: income and net worth. The methodology derives values for these two variables that yield weighted ranks and aggregate totals by block for each variable. The approach also ensures that the resulting data, and more specifically the income variable, matches the format including ranges, averages and medians for the 2000 census income data. Retailers involved in the complex tasks of deciding where and what to build need data that is more relevant than 2000 census data. They need to be able to tie the performance of a location to the consumer by combining attributes such as population, household income, purchase behavior, ethnicity and retail sales history into ongoing trend analyses with current, accurate and precise household-level intelligence. This [MicroBuildHS] solution does an outstanding job of putting the necessary household demographic data into location intelligence systems to provide retailers with the decision-making tools they need to define a trade area based on the most current, accurate and precise data. Nobody out there is doing what MicroBuildHS is doing; this offering takes us in the right direction by linking household level data to performance to help retailers move beyond just siting a store it takes it to the level of defining what to put on shelves, allowing retailers to model the most highly-profitable locations today and the most potentially profitable in the future. Dr. James Webster, Adjunct Professor of Finance for the Sam Walton College of Business at the University of Arkansas, Recently retired after 16 years as the Director of Real Estate Research and Planning for Wal-Mart, Inc. The combined results By using MicroBuildHS in their location analysis, companies can tie demographic insight to geographic locations in ways never before available. The following section illustrates the power of this solution in action. Delivering the data The development effort offers the best combination of household-level demographics and standardized census data to create a data asset of 8.2 million blocks for the U.S., each containing the following variables: MicroBuildHS households MicroBuildHS population Personicx cluster distributions Personicx sampling percentage Weighted rank for income and net worth Maximum and minimum income and net worth rank Aggregate income Average income MicroBuildHS benefits The following chart illustrates the value of using MicroBuildHS to geo-standardize household level data. The example compares Census 2000 households and Acxiom InfoBase marketing

9 list file results after householding and geo-coding and MicroBuildHS households for a specific block group on U.S. Hwy 256 beltway in Palestine, Texas. As you can see, the original number of Acxiom addresses associated with this block group is significantly higher than the number of households present in 2000 according to the Census Bureau. The reason for the large number of Acxiom addresses in this block group is due to the presence of 606 post office boxes. These post office boxes, all of which contain valid names and addresses, are placed at a single point in this block group during the geo-coding process. These non-specific addresses represent a significant challenge to anyone attempting to relate household address data to actual population. Additional research shows that postal trends for 12 consecutive quarters support the fact that no significant population growth occurred in this area and certainly no population growth of this magnitude. Using unadjusted geo-coding results alone would place all of the 920 addresses within one block group and produce unrealistic population growth for an area that is only experiencing slight population growth. By contrast, using the proposed solution with MicroBuildHS households depicts a much lower population growth rate for this block group that reflects only households rather than households and post office boxes combined.

10 Figure 2: Mapped view of selected area The following figure provides another view of the same block group, this time showing the relationship between block group and the neighboring block groups. As a unique and added benefit to the process, the patent-pending MicroBuild methodology allocates the non-specific address points to the correct block group, creating a result that reflects only households rather than households and post office boxes combined.

11 Figure 3: MicroBuild allocation methodology Personicx at the block level A key requirement of the solution involves leveraging Personicx demographics for small geographic areas. Because MicroBuild, like Personicx, is constructed using household-level sources, the solution can associate Personicx clusters at the block level, something no other location analysis dataset can do. The following figure illustrates the use of an application called Magnify to depict Personicx cluster distributions with a view of net worth rank for a specific block in a high-growth area near Phoenix, Arizona.

12 Figure 4: Personicx demographics at the block level This image highlights the following key pieces of information: Growth The pop-up box demonstrates that no households were present in this block in 2000 according to the census; but using MicroBuildHS data, we see that this block now contains 1,136 households. Sample size Virtually every household in this block receives a Personicx cluster assignment yielding a 92.1% sample size. Compare this percentage with the Census Bureau s national average block group sample size of 17%, and you can quickly see that the demographic information available using this approach is very powerful. Block-level demographics MicroBuildHS, powered with the accuracy of Personicx, provides a comprehensive view of demographics, purchase behaviors and other key pieces of information at the block level. This additional information is not even available from census data and can provide important background information for decision-making.

13 Conclusion Site location analysis using household-level data offers significant advantages to companies that demand more current, accurate and precise population and demographic data. The MicroBuild and Personicx-enabled solution, called MicroBuildHS, helps companies understand changes in population at the smallest level of geography available. Using Personicx as a segmentation system, companies can take advantage of a new level of precision with demographics and behavioral data to support the location research and decision-making process. The new geo-demographic solution outlined in this paper offers major advantages to companies whose success depends on accurate, fresh and demographically rich data to support business plans and growth. MicroBuildHS can provide companies with a unique ability to interpret and accurately place address-level consumer households within the correct geographic boundaries. Armed with this one-of-a-kind information set, research and marketing departments alike can access data and tools that support their business goals data rich in demographics and marketing potential that can be compared to equivalent census databases for complete and accurate trending and forecasting across the enterprise Gadberry Group LLC. All rights reserved. Gadberry Group and MicroBuild are registered trademarks of Gadberry Group LLC. Acxiom, Personicx and Personicx LifeChanges are registered trademarks of Acxiom Corporation. InfoBase is a trademark of Acxiom Corporation. All other trademarks and service marks mentioned herein are property of their respective owners