Cloud Native solution for Targeted/Context-aware Notifications in QSRs using Beacons and GPS
Authors
Amit Sharma — Director, Engineering
Lourdhuraju Periyathambi — Director, Engineering
Introduction
Location awareness is the heart of virtually all context-aware mobile apps. Although GPS technologies have enabled rough estimates of a person’s location, unfortunately, it does not provide the accuracy required for context-aware in indoor environments. This is especially true in large multi-level buildings such as restaurants, hotel and food hall environments. Therefore, it is highly likely that merging mobile marketing efforts with Beacon technology could result in higher click-through, quicker conversion, more sales and greater loyalty.
Integrating beacons with a mobile strategy will help level the playing field by responding to customers’ timely contextual content needs.
Imagine you are deciding where to have lunch. All of a sudden, your phone alerts you to an offer of two free sodas with the purchase of any entrée at a nearby restaurant. That kind of interaction might influence your decision to visit the restaurant.
This added value is critical as restaurants look to rise above their competition in a highly crowded space. Beacon technology can also be leveraged to capitalize on consumer habits.
In addition to this type of customer interaction, there are also numerous operational efficiencies that restaurants will experience when using Beacon technology.
One example is drive-thru.
For QSRs, drive-thru serves as a major way for customers to receive their orders. But what if customers order ahead and decide at the last minute that they would like to eat inside instead? Thanks to Beacon technology arranged at the parking lot entrance, the customer can be prompted upon arrival with the question of whether he or she plans to eat inside or pick up the order at the drive-thru, so the decision can be made on the spot. This allows the staff to act accordingly, change gears if needed and have the order ready the way the customer likes it.
Challenges today
Data Collection
The process of data collection and the ethics surrounding it are increasingly under fire, as evidenced by growing concerns over user privacy and the regulatory legislature aimed at protecting it. While the extensive push for data regulations creates challenges for marketers, it also provides an opportunity for businesses to build a foundation of trust with customers. By obtaining user consent and offering visibility into how location data is being used, marketers can maintain customer trust and deliver meaningful content in the path to purchase.
A lot of opt ins (customers need to install the app, always enable Bluetooth, agree to receive Push Notifications).
Accuracy/Poor User Experience on Beacon-enabled Apps
According to a recent survey conducted by Oho interactive with 35 of the top Beacon CMS and hardware providers, nearly half of the companies claimed poor user experience as one of their top concerns. These mainly fall under two categories:
1. Sending irrelevant notifications
2. Not providing for seamless experience
Specifications Issue
Lack of open standard, thus requiring businesses to purchase, install, and manage beacons that support each platform in addition to developing apps for both iOS and Android. Drives up the management and cost of the solution.
Reliability/Scalability
Data is as good as when captured and processed for the right use. IT solution exists to help with Beacons technology, but constantly struggle with the reliability and scalability of the data. On-prem solutions don’t scale with the needs and often fail to capture the data reliably to share with other systems. Poor data quality affects the efficiency and the timing of the notifications.
The Framework to Solve the Challenges
The following sections provide mechanics of the framework:
Cloud Native
Our solution framework brings Cloud-native to the forefront, which has become the new norm to help businesses operate.
1. Faster with greater flexibility
2. Offers elasticity, resilience, high availability and responsiveness
3. Cost-effective, as you can pay per use
4. Supports fastest Idea-to-Delivery time for Pilot Use Cases
Streaming/Real-time Data
Real-time is the key for the proximity-based notifications. Our framework uses serverless streaming services which capture data from beacons or GPS tracking at scale. Being serverless gives the ability to scale when needed without any management overhead.
Relevant notifications
As we capture the data real-time, the feedback loop to the relevant customer can go out at the same rate. With the use of right data processing, we apply personalization rules which make the notifications relevant according to their history, location, weather pattern, etc.
It’s important not to overwhelm users with notifications, as it could nudge them towards opting-out or un-installing an app. The key here is to have a deep understanding of the value offered to customers and deliver it in the best way possible. For example, allow customers to bookmark favorite dishes on the mobile app. This is so that when they walk by a restaurant any day, the restaurant can automatically push notifications with an enticing offer on the dish the customer bookmarked earlier.
Increase Reaction Time
Information is most valuable when it’s up-to-date, and in the hands of key decision-makers. The faster they’re able to respond to changes in data, the further ahead they’ll be. Proactive, smart alerts empower key decision-makers with the information most relevant to them and empower them to react immediately.
Open Source Specifications
Eddystone is an open source beacon format developed by Google and designed with transparency and robustness in mind to support Android and iOS devices (unlike iBeacons which is only compatible with iDevices). Eddystone is cross-platform and discoverable by any Bluetooth smart device.
Eddystone also supports the multi-beacon concept. This means that a single hardware beacon sends out multiple transmissions that can be used independently. The telemetry (metadata about how the beacon is operating that includes both battery and temperature) packet transmission, separate from the main identifier packet, is one example of this. Google made to combine multi-beacons into a single standard.
Reference Architecture and Best Practices –
Cloud-native architectures can be built with different cloud providers. AWS, GCP and Azure are leaders in this. We have captured the reference architecture for AWS and GCP below to help with the understanding:
With AWS Cloud
1. Real-time data will be captured from BLE/GPS notifications. AWS IOT Greengrass can broker the conversation with IOT sensors via the IOT rule to convert it to kinesis streams which can then be massaged with Kinesis firehose.
2. Real-time data is processed by applying windows and identifying data types to be fed to notifications system. Managed SNS service can help with this.
3. Real-time data will be used along with historical data to use AI models for personalization notifications.
4. Other AI services like text-to-speech, etc. can be used to make the messaging more engaging for customers.
5. Appsync along with AWS amoplify can help build mobile app which gives customers real-time alerts.
6. Data is also shared with Analytics engines like Amazon Redshift/Athena to do the BI. Dashboards can help with visibility to management for the success.
7. AWS CloudWatch helps with proactive monitoring with alerts, uptime checks and thresholds. This is keeping the system healthy and help automated remediations of the problems.
8. AWS IAM provides the security and access management of the data for compliance purposes.
9. Terraform and Jenkins are used to automate the deployments and management of the infrastructure.
With Google Cloud Platform
1. Real-time data will be captured from BLE/GPS notifications. Cloud IOT can broker the conversation with IOT sensors to convert it to streams which can be captured in Cloud pubsub and then be massaged with Cloud data flow.
2. Real-time data is processed by applying windows and identifying data types to be fed to notifications system. Cloud pub/sub is a perfect fit here.
3. Real-time data will be used along with historical data to use AI models for personalization notifications.
4. BigQuery ML/AI Platform can be used to feed off the data from data lake to build models for forecasting and accurate predictions. Out of the box recommendation algorithms can be used as well.
a. BigQuery ML supports SQL queries-based model on its storage or federated storage.
b. Auto ML can use the structured data for forecasting model without the data science knowledge.
5. Firebase can help build mobile app which gives customers real-time alerts.
6. Data is also passed on to data lake in Cloud storage or Cloud Bigtable for time series analysis and then processed further using ETL processes.
7. Data is also shared with Analytics engines like BigQuery to do the BI. Dashboards can help with visibility to management for the success.
8. Cloud Monitoring/Logging helps with proactive monitoring with alerts, uptime checks and thresholds. This is keeping the system healthy and help automated remediations of the problems.
9. Cloud IAM provides the security and access management of the data for compliance purposes.
10. Terraform and Jenkins are used to automate the deployments and management of the infrastructure.
Conclusion
Smartphones have made it possible for QSR brands to reach their customers more effectively and to provide them with a more engaging, more valuable brand experience — but only if they approach it the right way. Adding Context-aware technology like Beacons can be a cherry on the top.
Our framework helps brands that are still approaching this technology as a pure acquisition play with more meaningful or personalized messages to drive likeability rather than sending one-size-fits-all blast messages that drive a lot of customers to opt-out of messaging or uninstall apps.
