Location-Based Services (LBS) – Bringing consumers a unified shopping experience
I recently became a new mom, and like many others, there are many frustrating moments for me, big and small. Stepping into a whole new world of products that I’m not aware of ever existed, playing the guessing game on what size anything my baby will need and for how long, and that late-night diaper run to the store, just to name a few. As a consumer, I’m confused, time-strangled and desperately looking for ways to simplify my shopping experiences, both online and offline. I can’t help but wonder, “What if my local retailers can just help me simplify and save more time?”
Bringing offline back
The brick-and-mortar Retail industry is at a critical turning point to regain their glory, implementing digital strategies to improve their customers’ experiences, both online and offline. Not only are they building more eCommerce presence, but many have been looking for new opportunities to leverage the physical location of consumers to experiment with new ideas and make their offline experiences comparable to their online ones.
According to a survey by Accenture and the Retail Industry Leaders Association, more consumers than ever want retailers to personalize their shopping experiences. 63% of consumers surveyed indicated that they were interested in personalized recommendations, while 64% revealed they were willing to share personal data in exchange for benefits like loyalty points and automatic credits for coupons.
Thanks to blazing fast innovations, technologies are bringing this vision together for the retailers, including cloud-based POS technology, beacons, wearables, augmented reality, virtual reality, RFID and of course mobile apps. One of the most common use cases we often see with our retail customers is location-based services that recommend retailers that offer certain products at nearby – so people like me will hopefully never need that mid-night diaper run again.
What are Location-Based Services
Location-based services tracks one’s location using GPS technology on their mobile devices, if one allows, and identifies opportunities to send recommendations, coupons, notifications and so on based on their location. For retailers, they can detect when a customer is in the store, and send personalized recommendations and coupons accordingly based on a combination of analytical analyses, using data including similar behavior of other consumers, weather forecasting in the local area and the customer’s personal purchase history.
In my case, my local retailer can potentially remind of me the next time I’m in the store that I may need to buy more diapers. And what’s even better, based on my purchase history, online and offline, they can recommend me that my baby may need to size-up. Oh, and did I mention there’s a coupon for that? These methods allow retailers to make a timely engagement with the customers while they are still in the store, ultimately keeping their loyalty and increasing the customer lifetime value.
What’s happening in the background
It is no easy task to pull together a location-based service. To start with, geo-data and customer data from one’s smartphone sends the location of that person to the backend. In the example of the retail industry, the streaming data is collected in real time to report which store they are in and who they are. The LBS application in place is then triggered, which processes real-time data, and sends back the content from historical analytics based on similar consumer behaviors and purchase histories, such as a product recommendation engine. At the same time, the real-time data is also collected in a data store, often the raw data zone in a cloud data lake, for future analytical or data science projects.
Putting it all together
To make it happen, many requirements go into the data management approach. Among many other things, Retailers need a robust integration platform. To start with, your integration platform needs to be able to handle streaming data and be resilient enough to catch and handle the dynamics of streaming data timely to make sure it signals the right trigger at the right time. Needless to say, data from various sources needs to be collected, cleansed, and transformed.
Meanwhile, that is such a cross-functional effort that not only your data engineers will need to build integration pipelines, but other data professionals including the data analysts and data scientists will also need to be able to bring better insight and explore more opportunities with appropriate tools you provide. Last but not least, data governance standards must be established because of the huge amount of customer data you are handling from so many disparate sources. And the list just goes on.
Earlier this year, we introduced a new UI called Pipeline Designer and can help with this type of projects. Talend Cloud Pipeline Designer is built for citizen integrators and data engineers to easily prepare and transform their data for analytical projects, and it is designed to handle streaming as well as batch data. You can read more about it here. We are happy to share that there are now more Azure related features and connectors added to the product including Azure Blog Storage, Azure ADLS Gen2, Azure SQL Database, Azure SQL Data Warehouse, and Azure Databricks. You can try it for free here. Read along and there are also more examples of how Retail customers succeed with Talend.