Locations vs Products: Applications

This is the sixth and last in a series of blog posts looking at the parallels between locations and products.

Thus far in this series of location and product posts, we’ve discussed history, universal identifiers, addressing, categorization, and the massive complexity behind location and product tracking. We’ve also discussed in general terms how people use location and product information to drive innovations, tracking, and convention. In this post, we’re going to dive deep into the ways that people use this information in today’s technological environment.

Locations: Getting from Point A to Point B

In my lifetime, we’ve gone through several iterations of one function: getting from point A to point B. Twenty years ago, paper maps were the only way to plot a route. People had maps at varying scales in atlases or those horrible giant paper maps that no one could actually re-fold correctly.  Before you left point A, you would use a map to plot which roads you planned to take to point B. Alternatively, organizations like the American Automobile Association (AAA) would make paper “Triptiks” for their members with turn-by turn directions.

In 1996, MapQuest (then called GeoSystems Global Corporation) created a web service to depart from paper-only travel planning. Instead of spreading maps all over the dining room table in order plan our trips, we could plan them online and then print out the paper directions. Around the same time, Oldsmobile introduced GuideStar, the first GPS navigation available in production cars in the US. Those wealthy enough could depart from paper entirely in favor of in-car GPS navigation. Well, as long as we weren’t stuck “rerouting” all the time, anyhow.

While paper atlases, AAA Triptiks, Mapquest, and in-car navigation systems still exist, most of us use our phones to get from point A to point B. We use Google Maps, Waze, or Apple Maps. While they aren’t perfect (Google maps directed me straight into a construction roadblock last weekend), we’re dependent on always knowing where we are and using our phones to get around.

Infinite Channels for Commerce

Commerce has followed a similar path. We were dependent on only physical purchasing or using mail-order catalog purchasing through the mid-1990s. In the mid-1990s, web browsers became more common. Amazon launched and we started buying products from our PCs. In the 2010s, we saw ecommerce evolve into “omnichannel,” which basically meant brick-and-mortar + web + mobile. In the past few years, however, commerce opportunities have become nearly endless with infinite channels, including smartphones, tablets, laptops, iBeacons, wearable devices, social media platforms, interactive storefronts and apps, virtual stores, lions, tigers, and bears. Oh, my!

Both location and commerce pervade our lives. We take for granted that we always know where we are and what we can purchase. We don’t even have to enter our address or pull out our credit cards—our phones have that info already. Let’s take a closer look at basic uses, interesting data mashups, and killer applications.

Locations and Products: Search

History tells us we need location and product information to power simple searches. If you do a location-based search such as, “Find restaurants near me,” the application needs enough location information to determine the following:

  • Where are you now?
  • What types of businesses labeled “restaurant” are nearby?
  • Once you select a restaurant, where is that location relative to your location?
  • What are the navigation steps you need to get from where you are to your selected restaurant?

For a product-based search such as, “Find a blue cashmere sweater,” applications need slightly different information. They need to determine:

  • Do you have intent to buy (usually searches that contain product do)?
  • What stores carry blue cashmere sweaters?
  • Once you select a sweater, how or where do you buy it?

The information above may be technically sufficient to help identify target restaurants or sweaters, but additional information to the simple search will speed up identification (and therefore commerce). For location, adding reviews, price ranges, dress codes, categorization (e.g., Italian restaurants), and other information helps identify whether to grab a burger and beer or pasta. Similarly, additional information beyond which stores carry a product speeds up selection. These data points include price, in-stock status, reviews, and what other customers have purchased.

The depth of data can also help with analysis. Once you’re ready to choose your restaurant, you need to look at traffic and travel time to ensure you made the best choice. This requires real-time location information that can be difficult to track.

Efficient product decisions rely on the ability to compare items. Many companies have done this effectively with grid views. This doesn’t require real-time information the same way that location does, but it does require thought to select what data to display and how to display it in a way that doesn’t overwhelm the customer.

Locations and Products: Data Mashups

The breadth of data matters as well. Data mashups provide some of the best information to help people and companies make decisions. Combining location data with people, helps us find our friends and meet them at a local bar. Combining location with devices, helps us track our phones or kids. Combining location with time tells us how long it takes to get somewhere, which is key to analysis, as mentioned above.

Combining location and product data helps us figure out where products are, so we don’t run to our local store only to discover an item is out of stock. Combining product and people data feeds into analysis by telling us who bought what; companies like Nielsen do powerful demographic analysis with this type of data to tell retailers which population groups to target with what advertising campaigns.

Locations and Products: Killer Apps

Killer apps find ways to use both the breadth and depth of data to deliver amazing value. Yelp does a brilliant job of using location data in conjunction with reviews and people data  to help us navigate to businesses. Amazon does a slightly frightening job of using product data in conjunction with buying patterns and people data to give personalized recommendations that I actually buy. The next generation of killer apps will figure out how to use both navigation (location) and personalization (product) to push commerce further into our lives.

These killer apps will need good business models to survive. Location data is much easier to implement than to monetize. If your app has anything to do with location, implementation requires a simple integration with the maps API of your choice (although Google is the gold standard). Monetization requires second-order thinking. If you’re a business, driving more people to your location equals more money. If you’re an app like Yelp, however, you have to think about paid subscription or ad revenue to monetize location.

Product information, in contrast, is easier to monetize, but much harder to implement. If you’re using product information, chances are you make products, sell products, or generate affiliate or ad revenue off of product sales—all straightforward ways to monetize. Implementation, however, presents more of a challenge. We don’t yet have a gold standard for an API that provides good product information (spoiler alert: Indix strives to be that gold standard—we have the largest programmatically-accessible product information database). Getting product information can be expensive and messy and implementing that information is messier still.

How do you create a great comparison grid if you don’t have the same attributes from each brand? As we drive toward Conversational Commerce, how much more complex will implementation be?

Just as the product information industry lags behind the location information industry on global identifiers, it lags behind on killer apps and implementation.  Many apps that are already location-aware aren’t yet product-aware. In the next decade, we’ll see this radically change as we settle on a gold standard for product information and fix the gaping holes in unique product identifiers.

We have explored many parallels between location information and product information over this series of blog posts. We’ve discovered that product information lacks the maturity that location information has in evolution, standardization, consumer use, and technological implementation. This immaturity makes product information much more exciting to pursue. This excitement and all of the possibilities that product information presents are why, at Indix, we strive to gather, structure, and provide access to the world’s product information so everyone can act on it. It’s been fun so far, and we have a long way to go.

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