Utilizing data to build an exceptional location analytics product

Here are four specific areas where a location analytics product is particularly helpful:

• Market segmentation – when a customer needs to better understand the types of businesses, consumers, or competitors within a specific geographical area (by census track, zip code, etc.), market segmentation allows for that. For example, Starbucks may be looking to expand their locations to Los Alamos, CA. Before doing so, they would need to look at competitors in the area and determine which parts of the city are more populated and have better access to major highways to understand which area the business would be most successful.
• Customer prospecting and analysis – location analytics comes in handy when a sales representative needs to understand where his or her target prospects are located. For example, if a sales rep wants to go to New York to visit a client, they want to be able to understand the surrounding businesses in that area within their targeted industry verticals and pay those a visit as well. A location analytics product can show this information on a map or within an application for ease of access and visual reference.
• Distribution site selection – when a company is measuring the needs of a new build project and they are searching for locations for the store. For example, a couple of years ago Amazon was evaluating where to build their new headquarters. They were researching various cities and states across the country in order to determine the best location for their new offices. They assessed things like weather patterns, demand for products, economic impacts, tourism, state policies, and more.
• Indexing – this is helpful from a consumer perspective to aid in expansion. For example, if a residential real estate contractor is looking to build out a new neighborhood in a certain area, they want to understand the types of consumers within the area, how much money they make, the ranges that homes usually sell for, and more. Location analytics companies allow these types of businesses to do this analysis easily by aggregating consumer counts within a specific geography. These aggregate statistics can then be shown in a report or map for the client to utilize.

A lot of times location analytics companies are looking to expand the data they offer to provide better coverage to their clients, while also making sure that the information is as up-to-date as possible. The ability to expand data attributes, ensure data accuracy and completeness as well as the option to get real-time updates are critical considerations when evaluating a data source. For example, location analytics companies can leverage consumer transaction data to understand how a particular zip code indexes against the whole city. Alternatively, they can access high-quality business data to understand a business’ employee size, industry, and site location (physical address) of competitors in a given area.

When it comes to analyzing businesses, everything from employee size, sales volume, SIC/NACIS or industry, location linkages, and more is often leveraged. When it comes to analyzing consumers, local analytics companies might be more interested in age, household income, children present, spending habits, and more.

This data can be implemented in a variety of formats and is often customizable depending on the client’s needs. Whether they want to manage a full file, make real-time API calls, or a combination of the two, the data delivery can be flexible

When it comes to local analytics, companies should look for these four things when selecting a data partner:

a. Data Quality – where is the data sourced from? How is the data validated? what quality checks are in place for the data?
b. Data Accuracy – how often is the data updated? Do you have access to data that is being updated in real-time? How is the data maintained over time?
c. Data Coverage – what is the coverage of the data? How do you manage chains and franchises? What types of linkage do you maintain? Do you have sales volume and employee size on business locations?
d. Data Delivery – how is the data being delivered? Can you inject data and perform key processing directly in your own platforms as you need it?