Use cross-shopping data for deeper visitor insights

CRE | Analyze cross-shopping trends to help inform co-tenancy and merchandising strategies

Use cross-shopping data for deeper visitor insights

CRE | Analyze cross-shopping trends to help inform co-tenancy and merchandising strategies
In This Article

It’s important to understand where your visitors shop the most, both in terms of chains and specific locations, to gain a deeper understanding of their preferences for specific brands and businesses. Placer’s cross-visitation location data and XTRA report catalogs help you analyze cross-shopping trends and inform co-tenancy and merchandising strategies.

You’ll be able to answer these questions after reading this article:

  1. How can you see which chains your visitors frequent the most?
  2. How can you analyze your visitors’ cross-shopping trends?
  3. How can you combine store visitation with digital behavior to classify households into segments?

Get started with the following steps:

Step 1: Use Favorite Chains or Favorite Places to understand visitor affinity for specific brands and businesses

a. From the Property report page, navigate to the Visitor JourneyThe flow of commercial activity to and from any property that shows the top "Prior" and "Post" locations. section. 

b. Scroll down to Favorite Chains and adjust the Category filter as desired. Set the minimum visits to your preferred number (this applies to the POIsPoints (Locations) of Interest. in this list, not your chosen POI). This will show you which chains your visitors frequent the most.

Learn about Favorite Chains

c. Scroll down to Favorite Places, adjust the Category filter, and set the minimum visits. Now you'll see your visitors’ favorite locations and their addresses.

Learn about Favorite Places

Step 2: Request XTRA reports that showcase cross-shopping trends

a. In the top navigation bar, select Advanced Reports, and choose Placer XTRA to view the report catalog. Several reports in this section are built to showcase cross-shopping trends:

i) In-center Cross-Shopping (filter using Shopping Center): cross-references customers’ same-day visit trends among tenants within the same complex

ii) Same Day Shared Audience (filter using Retail): compares cross-shoppingPlaces that visitors to your location or center have also visited during the selected time frame. data on the same day (one day only) between specific POIs selected

iii) Shared Audience, Full Timeframe (filter using Advertising): compares cross-shopping data between two specific POIs across a full time period (time period and specific POIs are selected)

b.To request these reports, select a specific report page in the XTRA catalog, enter the Placer POI(s), select the applicable date range, and review the rest of the submission form for additional details. Once submitted, these reports can take between 2 and 4 business days, depending on complexity. Delivery SLAs are located in the bottom right corner of each report’s detail page.

c. Upon completion, the reports will be delivered to the person who requested them.

Learn about Placer XTRA
Pro Tip: The Spatial.ai PersonaLive expansion combines store visitation with digital behavior from social and web interactions to classify households into 80 segments that reveal which brands they follow, publications they read, topics they discuss, and more.

Step 3: For those who have added the Spatial.ai PersonaLive expansion, gain deep demographic and behavioral insights into segmented households across the U.S. 

a. Navigate to Demographics in the left sidebar and refer to Audience Profile.

b. Select the trade area type you’d like to use for the report (True Trade Area, Drive Time, Walk Time, or Distance in Miles).

Learn about Trade Area

c. Select Spatial.ai: Personalive from the drop-down menu as the dataset of choice. If the dataset is not visible in the menu, click View more Datasets to add it.

d. In this example, see how visitors of Madison Yards are classified and read the description of each by hovering over the information icon.

Case Study

Broker uses cross-shopping data to secure restriction waiver and bring Poke restaurant to neighborhood center

The Challenge

A neighborhood center wanted to bring a Poke restaurant to its shoppers, but had a Panera bread as an existing tenant, with a restriction preventing any other restaurants. How could the center show that the Poke restaurant would be complimentary, not a threat, and get a waiver for the restriction?

The Outcome

The landlord broker used Placer to pull trade area and shopper insights for a center in a nearby city, where there was a Panera and a Poke restaurant operating successfully next to each other. One of the main pieces of information shared was that the Poke customers had significant cross-shopping with Panera Bread, showing opportunity for the existing store and leading the Panera Bread owner to waive the restriction.

Case Study

Broker uses cross-shopping data to secure restriction waiver and bring Poke restaurant to neighborhood center

The Challenge

A neighborhood center wanted to bring a Poke restaurant to its shoppers, but had a Panera bread as an existing tenant, with a restriction preventing any other restaurants. How could the center show that the Poke restaurant would be complimentary, not a threat, and get a waiver for the restriction?

The Outcome

The landlord broker used Placer to pull trade area and shopper insights for a center in a nearby city, where there was a Panera and a Poke restaurant operating successfully next to each other. One of the main pieces of information shared was that the Poke customers had significant cross-shopping with Panera Bread, showing opportunity for the existing store and leading the Panera Bread owner to waive the restriction.

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