Adaptive Apps: Reimagining Customer Engagement
Last December, my family flew to Cabo San Lucas for a short vacation. On the way to the airport, I was pleasantly surprised to receive a notification from Google Now, informing me that my flight was 15 minutes late. I was amazed at how Google Now alerted me to the delay before my online travel broker or my airline did. Google’s only role in my travel was receiving the order confirmation email from the travel broker.
And yet, Google Now adapted its behavior to alert me that the flight was delayed—it paid attention to any useful signal, anticipated that I’d want to know about the late departure and that I would be in the car heading to the airport, learned that I’m an active smartphone user, and adapted its behavior in real time so I got a push notification on my phone (and not an email).
This is a classic example of an adaptive app, the kind that we’ll soon expect as standard. Today, this kind of experience is only available from a handful of digital innovators like Google, Waze (which Google acquired in 2013), and Netflix.
Why adaptive apps?
Your customers experience a journey composed of a sequence of interactions that span channels and devices, and they expect every interaction to be easy, relevant, timely, tailored to their needs, and available on any device. Designing an interaction at one point in time thats exhibits those characteristics is doable. But making every interaction easy, relevant, useful for each customer and doing it over and over again—that’s really hard.
Adaptive apps make every interaction engaging because they anticipate what the customer wants, they adapt to the customer, and they learn from each interaction, setting in motion a dynamic cycle that keeps your customers coming back, all while protecting their privacy.
Adaptive apps are much more sophisticated than a traditional web or mobile app because they:
- Operate with full knowledge of the customer’s journey—every interaction that’s within reach. This includes information internal to the company such as prior purchases or web visits, as well as external data, such as social media or third-party marketing data. They do not suffer from blind spots like apps built for a specific channel do.
Focus on the individual. They go beyond trying to fit you into a large segment that’s typically based on demographic and profile information and then “personalizing” your experience based on rules for that segment. Instead, adaptive apps focus on identifying what’s right for you.
Pay attention to your current behavior and your context in real time. Adaptive apps use signals from your location, the weather, and time of day, for example, and dynamically change behavior. This makes their recommendations more relevant and valuable.
Learn from your behavior. Similar to how Google adapts the ordering of its search results based on which result is clicked on the most, adaptive apps learn each person’s likes and dislikes, fueling a continuous feedback loop that enables improved recommendations over time.
Adaptive apps in retail
A retail adaptive app recommends products based on all browsing and purchase activity—on the web, on the phone, and in the store. Next time you visit a store, the app shows you “in-store offers” that take into consideration your decision to enter the store. As you browse around the store, the in-store offers can change based on your browsing behavior (this will require you to use their app when in store so it can pick up your in store location via WiFi or beacons).
Adaptive apps in telecom and communications
An adaptive app in telco recommends plans and options based on your behavior. For example, if you consistently cross your data usage threshold but underuse your voice plan, the app can recommend a plan with higher data limits but lower voice limits. If your usage behavior changes in the next few months, the app adapts again and suggests a better alternative.
Adaptive apps in financial services
In financial services, the adaptive app goes beyond simple account management and bill payment and analyzes each person’s credit card spending behavior to recommend different products that fit their preferences and lifestyle. For example, if the bank sees a relatively high number of transactions at restaurants (person is eating out a lot), the app recommends offers from restaurants similar to the ones visited and near them too. And if that spending shifts to a different city because of, say, frequent business travel, the app adapts to offers in that location, and delivers them via push notification when a customer is most likely to use them.
As these examples demonstrate, adaptive apps anticipate, learn, and adapt to each customer’s needs and current context, and deliver contextual, individualized interactions on any channel. As a result, adaptive apps always deliver fresh, relevant experiences that engage customers and accelerate business growth.
There are many others examples of adaptive apps that are now possible with big data, predictive analytics, and APIs. In an upcoming post, we’ll explore how big data, predictive analytics, and APIs come together to enable adaptive apps. For more on the power of adaptive apps, check out Apigee CTO Anant Jhingran's keynote at I Love APIs 2014.
image: Christian Schnettelker/Flickr