Mobile Beacons: How to Measure Their Effectiveness

Some announcements made by Apple turn out to be every bit as big as the hype they put into it (e.g. the release of the first iPhone). Others … not so much. (When was the last time you saw an iAd or used Ping?) However, rarely is something that is merely a footnote in one of their keynote announcements gone on to have a major and still evolving impact on the world. In Apple’s World Wide Developer Conference in June 2013, the term “iBeacon” was not even spoken. It was merely shown as one of many capabilities on a slide.

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We are now hearing more and more about iBeacons and how they can be used in powerful ways to target and craft experiences for mobile users in a defined physical space. To be clear, Apple didn’t invent the beacon technology. It is based on Bluetooth Low Energy (BLE), which is part of the Bluetooth 4.0 specification. Android now supports this standard as well. Given this, I refer to this technology as beacons, whether it is on iOS, Android, or some other platform.

Much has been written on how beacons are currently being used by various industry verticals as well other future applications (see Ray Pun’s post on CMO). However, in this post I discuss how beacons can be tracked, analyzed, optimized, and used by digital marketers and analysts alike to accelerate their businesses forward.

Beacon Analytics

Imagine that a national clothing retailer, such as Geometrixx Outdoors, just deployed five beacons as a pilot within their top selling store—store #45. After much consideration, they decide to place the small, compact, transmitting devices throughout the store as follows: store entry (beacon 1), women’s pants (beacon 2), women’s shorts (beacon 3), men’s polos (beacon 4), and men’s pants (beacon 5). The initial plan is to show various offers to customers as they move around these five areas within the store and see how many customers engage and redeem the offers shown.

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All beacons have three primary identifiers. The “uuid” value remains constant across all beacons (even if they roll out a 1,000 more beacons across their 200 stores). The “major” identifier typically would be set with the same value for a given store. The “minor” identifier would be specific to a given beacon. So for all the five beacons deployed within Geometrixx Outdoors’ store #45, the values for uuid and major identifiers would be the same. Only the minor identifier would be different for each of the five beacons. The beacon “proximity trigger” can be used to increase or decrease the distance from the beacon to the mobile device that has a beacon-aware app. In the illustration above, we can see the various proximity triggers (based on the size of the circle) for the respective beacons. Within the table below, we can see the current configuration for the beacon located within the women’s shorts area.

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The Identifier column contains Adobe Analytics’ ContextData variable name used by the Adobe Mobile Services 4.x SDK. When making beacon calls from the SDK (iOS and Android), we can see a consumer that is interacting with the beacon within women’s shorts.

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With the beacons deployed and analytic data being gathered, the Geometrixx Outdoors analytic and marketing teams analyze the conversion from “Offers Shown” to “Offers Touched” to “Offers Redeemed” within store #45. They also include “Offer Conversion” by creating a calculated metric that leverages Offers Shown and Offers Redeemed. In general, conversion hovers around 30%, except for women’s shorts (18%). Not only does this information illustrate what targeted areas within the store are performing versus those that are not, but it also shows which areas have the highest levels of foot traffic. Not surprisingly, there are more offers shown within the “store entry” than any other area. It also shows that more offers are being shown within the women’s clothing areas than in the men’s department. Obviously there are many insights that can be gained from this and other reports using the same underlying data.

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However, although these and similar types of reports are useful, I can imagine a business need to see beacon activity and conversion overlaid on a store layout map. This visualization would clearly and intuitively show which targeted areas within the store are performing. For example, in the diagram below, I have used a modified set of concentric pie charts showing the location, proximity trigger points (based on circle diameter), and various conversion metrics relevant to beacon usage. In addition, each set of pie charts for a given beacon location represent the Offers Shown (blue), Offers Touched (green), and Offers Redeemed (gold). Also, a more complete or enclosed set of pie charts at a given beacon location would indicate a higher percentage of customers who took action when shown an offer.

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Experience Optimization

To this point I have focused exclusively on analytics generated from beacon interaction. However, an important aspect with crafting an effective beacon experience is the ability to optimize that experience. Adobe Target, which is supported by the Mobile Services SDK, provides the ability to deliver content and experiences to a mobile app based on a mobile users beacon interactions. For example, Geometrixx Outdoors’ marketing team may decide to run an A/B test of the offer shown within women’s short area to help increase conversion. Without needing to make changes to the app, the team simply alters the offer message within Adobe Target and deploys the updated campaign, which takes effect immediately.

Summary

Beacons are a new tool in the digital marketer’s arsenal that can have game changing implications. This is especially true for those who want to target or craft an experience based on location within a physical environment, such as a retail store location, stadium, hotel, airport, or concert venue. The types of analyses and optimizations discussed within this post help digital marketers and analysts more fully leverage beacons to move the needle forward for their business.