Analytics best practices for store locator software

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Analytics best practices for store locator software

Store locators have a unique set of important events and need special attention when tracking user behavior.  Of course, we need to understand number of unique visits, visit duration and bounce rate.  Those types of events should be tracked by your existing analytics package.  This article explores the locator-specific behavior that should be analyzed so that you can measure the performance of your locator, locations and important user actions.

Overall, store locator analytics should serve multiple dimensions of data.  First the “locator” level, which includes actions such as number of searches, number of visits, click heat maps and so on.  Secondly, the “location” level, which includes per-location analytics such as the number of clicks on that location, the number of searches that included that location as a result, “conversion” clicks like an email address, get directions and so forth.  Some locators might also have product-level analytics.   Product finders often allow a visitor to segment the search results according to a those stockists that carry a certain product.  This search parameter has special meaning in a locator and should be a readily available data segment.

Below we present a series of critical measures for tracking locator software effectiveness.  These analytics are above and beyond the commonly available website analytics such as total visits, page views, hits etc.  Those analytics are certainly essential, but not explored in this article.

  • Conversion Activity: Conversions in store locators might include the user receiving directions to a location, an in-store visit, a click to the phone number to call or an email lead to that location.  Locators often have a different definition of what constitutes a conversion and analytics solutions should be flexible enough to handle that variable definition.  Tracking conversions is essential to focusing on meaningful behavior in a store locator.  Conversions should measure the return on investment, (ROI) or business impact of a store locator.  Without these measurements, a business cannot say with certainty how much revenue is coming from their locator and therefore what marketing spend is appropriate.
  • Search Demand Map: Store locators commonly leverage a geographic search.  This search can bring a unique dimension of analytics around the geography of the user’s search.  For example, when a user searches for 80205, and the locator has no locations nearby, that is certainly important information.  Displaying this demand information as a heat map is common solution.  Overlaying that demand information with a location map can highlight areas of unmet demand.  In MetaLocator, we geocode every user search term.  This latitude and longitude then contribute to a the demand heat map found under the Analytics > Heat Map menu.
  • User Location: Geographic information regarding the user’s search does not always directly correspond to that user’s physical location.  MetaLocator also track’s the user’s location and displays that information a real-time map and a map which can be segmented over time.  Understanding your user’s physical location is essential to interpreting locator analytics.  Similar to search location, these data can reveal pockets of demand.
  • Interaction Visits: These are the total number of visits, as compared to the total number of visits, that included a user interaction.  This factor reflects overall engagement, and brings meaning to overall statistics like overall page views, “hits” and visits.  Interactions should include any interaction with the locator, such as a location click, a search, map pan and zoom and so forth.
  • Referral Source & Path:  This metric explores how the visitor reached the locator, and the original source of that visit.  A pie chart visualization of direct visits, search engine visits and other referral sources should explore how users are reaching the locator and the common paths they took to reach the locator.  Referral source should also be viewed in the context of conversion activity.  Conversions by referral path and source can show the effectiveness of a given channel.  It is critical to understand if conversion activity is arriving from Google searches to your product pages, a referral partner or a location page.
  • Segmentation:  This best practice involves a feature of the analytics system, as opposed to a specific metric.  Segmentation allows the user to view analytics filtered by a certain parameter.  The most common segment is a date range.  E.g. the number of searches that occurred in October.  However, segmentation in store locator analytics should allow the user to segment by nearly every metric available such as user location, product choice and locator.  MetaLocator allows it’s user to create an unlimited number of locators.  Therefore, a common top-tier segment is the locator itself.
  • Visitor click stream:  Every visit should include a drill-down of every action taken by that individual user.  This allows for real insight into user behavior and conversion path analysis.  Well-tracked locator behavior often provides summary data that is not immediately understood.  The ability to drill-down to view individual visit logs can shed light on why users might choose a certain path or abandon a conversion funnel.
  • Mobile Activity: Mobile users are especially important in locators.  First, they have high purchase intent and are typically indicative of motivated users.  Secondly, they often have more accurate geo-location.  It also helps to understand which users are viewing the mobile responsive version of the locator.  Segmenting traffic from mobile users allows your business to understand user behavior on the go.
  • Search Activity: Of course, the total number of searches in the locator is essential.  Locator searches often include a number of options such as products, categories or keywords.  Locator analytics should include a report of the total number of searches and a breakdown of those with options.  Below that should be the value choice of each of those options.  For example, if a locator includes options for text input of a keyword and a drop-down list for products, a search report should include both the number of searches with and without those parameters, but also a pie chart with the values of those secondary parameters.  This allows a product finder analyst to view activity based on certain product choices in the search form.
  • Location Activity Reports: Each location must include it’s own report that provides overall exposure and conversion activity for that location.  The total number of impressions, where a location was included in a search shows the location’s exposure.  The number of clicks to reveal details of the location might not constitute a true conversion, but they certainly show interest.  Conversion activity such as email form submits, total detail page clicks, “get directions” clicks, link clicks and more should be included.  The conversion activity might vary based on the type of locator.  For example, dealer finder might be concerned with the number of lead form completions, whereas a product finder might want to know the number of “get directions” clicks.  Location activity reports should also show location page activity.  Location pages seek to draw in traffic from searchers looking for products and services.  This report should include the total number of inbound searches attracted by this location.
  • User Distance from Stores: Automatically detecting the user’s location can determine, on average how far away a given user is from a store.  Similarly, direction’s requests, and the radius drop-down can contribute to an overall understanding of how far away users are from your locations.  This information can help inform your understanding of how far users are willing to travel to obtain your products.  Does the distance from a user’s location impact the probability that the user will click “Get Directions”?
  • Searches With No Results: The number of searches resulting in no results informs how well your locations are serving your visitors, and can provide insight into how well configured your locator is.

This summary includes a number of essential measures toward understanding activity on your store locator.  Ensure your platform of choice includes this data and provides it in an easy to use format.


About the Author:

Michael is the owner and Chief Executive Officer of He has operated an independent IT consulting firm for over 20 years, working with over 500 companies in all areas of technology application. In that time he has created multiple technology startups, including His proprietary Web site software products have been purchased by over 1600 companies in 82 countries around the world. Michael was awarded an engineering degree in Computer Science from the University of Wisconsin Milwaukee in 2001. Michael has volunteered extensively, serving as a board director for an after‐school technology program for underprivileged youth and a nonprofit focused on computer hardware recycling. His ability ranges over a wide array of technologies with a focus on Web‐based data‐intensive applications. Michael is a highly experienced software engineer, fluent in hundreds of programming languages, APIs and platforms.