Pokémon GO is an incredible case study in the power of relevant hyperlocal targeting and geospatial analytics.
In case you don’t know what we’re talking about, Pokémon GO is a free-to-play, location-based augmented reality game based on a popular cartoon franchise, where cartoon monsters roam the lands, and your job is to find, capture, and train them. The user plays by using your phone’s GPS for your real-world location and augmented reality to bring up those cool-looking Pokémon on your screen, overlaid on top of what you see in front of you. The game has become the biggest mobile game ever, with rapid growth to 21M daily active users, surpassing Twitter and increasing the value of Nintendo (who makes related games) by $23 billion almost overnight.
Beyond just being a fun game to play, Pokemon GO is turning into a case study in the power of hyperlocal advertising and the geospatial analyses that power them. The Japanese unit of McDonald’s Corp. briefly hit a 15-year high after the company announced that fast-food chain’s restaurants across Japan will be “PokéStops” and “Gyms” for “Pokémon GO”. A pizza restaurant manager in New York says he paid $10 to have a dozen of the Pokémon characters lured to the store. It drew in so many players, the shop’s business went up 75 percent. Niantic has indicated that “sponsored locations,” where companies would pay to become locations in the virtual world in order to drive foot traffic, will be coming to the game.
Be in the right place at the right time
As Ray Kroc, who joined McDonald's in 1954 and built it into the most successful fast food operation in the world, once said, “The two most important requirements for major success are: first, being in the right place at the right time, and second, doing something about it.” Luckily you don’t need a hit app to do this. The ability of data analytics technologies to be able to process and analyze massive quantities of data, along with new era of geospatial analytics, mean that delivering relevant messages to people’s phone based on where they are is now feasible.
Here are just a few types of analyses you can do to take advantage of the local mobile opportunity.
Capacity and Demand Pattern Analysis
Capturing geospatial event data allows you to analyse of patterns of demand (when, where, etc. people buy) and periods of peak or decreased capacity. This in turn allows you too:
- Refine sales and marketing segments: understand where specific target segments live, identify similar “look alike” segments, enrich and create new features and variables for segmentation like distance traveled
- Plan store locations or merchandising decisions: use geospatial modeling to determine the optimal merchandise mix for a flagship store, plan inventory, identify high footfall locations, and reduce the number of locations required to serve markets
- Evaluate supply chain and logistics planning: monitor actual vs. planned time for deliveries, evaluate alternate routes, identify bottlenecks, and speed up the last mile to the customer
Personalized Offers and Recommendations
Adding geolocation data to your customer data set, then using predictive models to suggest who to target with what message, can dramatically enhance click through rate for ads. For example,:
- Addressing specific wants and needs, e.g., commuting to work, concerts or special events, etc.
- Optimize spend by prioritizing locations, e.g., predicting ideal locations and expected ROI based on cost per impression
- Identify intent from search, e.g., someone search for travel to another city
When combined with closed-loop response data, geospatial information can increased personalized engagement with consumers.
Geofencing and Push Notifications
Geofencing is using GPS coordinates to define a virtual geographic boundary, in order to to push content to consumers who enter a specified vicinity at specific times. Geofencing combined with personalized targeting can be a powerful tool to deliver unique, customized content and offers to customers at the right time and right place at a far lower cost than traditional broadcast spray and pray media. Examples of what marketers can do include:
- Demographic targeting to hard to reach segments, e.g., marketing to mothers within the vicinity of a school
- Special offers or flash sales, e.g., imagine offering a coupon for umbrellas to nearby customers when it’s raining
- Order ahead service, e.g., offering to have a customer’s favorite product or reservation checkin prepared in advance at the touch of a button
Of course, marketers need to be able to analyze historical geospatial in order to do this.
Build Your Team, Hit the Gym
While some geospatial strategies seem like rocket science, there are easy progressive steps to increase maturity that deliver incremental value.
- Assess current state capabilities to harvest, enrich or analyze geospatial data
- Define strategic objectives, implementation roadmap, and opportunities to deliver incremental value
- Develop the team with the right skills to implement with agility while increasing maturity
What kind of team do you need? A team that balances particular kinds of strategic, engineering and scientific skills.
- Strategy: Developing strategies for engaging geospatial data requires the right mix of creativity and technical aptitude. It’s important to understand the art of the possible and the art of the practical - there’s a wealth of opportunities to deliver immediate ROI on geospatial event data while keeping a big picture and future state in focus.
- Engineering: Managing, enriching, analyzing massive amounts of data generated by geospatial events is complex. It’s important to define an infrastructure and architecture that will scale to volume and complexity of geospatial data. Achieving scale and maximum ROI requires skills with a variety of big data technologies for both batch and real-time latency.
- Science: Optimizing analytical capabilities requires data scientists who understand both the technical rigor of building, managing and governing statistical models. Geospatial analytics employs specialized techniques for analysis, feature, and model development, but this process can be accelerated with tools for geospatial visualization.
The most effective programs use a combination of specialized augmentation and smaller agile teams to facilitate communication, prototyping, faster cycle time, and cross training. Teams with a mix of skill and experience levels can employ experts to guide strategy for engineering and modeling approaches, while providing hands on leadership to less experienced team members engaged in data preparation and visualization.
Find out how the Clarity team can help your business increase your consumer engagement through strategy, engineering and scientific evaluation of geospatial data.
Tripp Smith, CTO for Clarity Solution Group