As I blogged about earlier this week, today’s “networked BI” platforms make it easier than ever to integrate data generated by the Internet of Things (IoT) into enterprise environments. Advanced 2-tier BI platforms plug into centrally managed data sources – data warehouses, data lakes, business applications, and big data – and seamlessly unify them with data generated by decentralized teams throughout the organization. Repositories collecting data from IoT devices are just another data source, although the fact that they are generally unstructured creates additional challenges in making these data sources analytic ready. After the data is refined and business rules are added, the combined data is ready to be used.

coffeeShaping marketing campaigns with IoT data

Let’s see how a 2-tier BI analytics platform can help marketers at a tech company that is branching out into IoT consumer products. The company has two main products, an IoT coffeemaker and an IoT teakettle. The products use a shared mobile app for remote control but can operate manually. (Note: Belkin is not a Birst customer. This is a hypothetical example.)

The marketers want to cross-sell both products, and energize the base of customers who are using the products infrequently. They know from the company’s e-commerce system and survey data that 35% of the customers who use their coffeemaker twice a day overall, use the remote control features of the coffee maker, and go online to the company website to frequently purchase spare descaling products, tend to eventually own both the coffeemaker and the teakettle. This group is also likely to buy accessories and cleaning tools for the teakettle.

The product warranty registration database contains another pool of customers who bought either of the products at a retail store and registered online. Finally, the mobile app databases contain the email address and operational information for everyone actively using either or both the coffeemaker and the teakettle.

With a two-tier BI solution, the marketers could virtually combine data from multiple databases, including:

  • Commerce sales
  • Warranty registration
  • Coffeemaker section of the mobile app and the usage of remote control features
  • Teakettle section of the mobile app
  • Data on promotion trade credits

By creating a virtual data instance that incorporates these sources, the marketers could learn which customers:

  • Have either or both products.
  • Are light, moderate, or heavy users (via data from the mobile app databases).
  • Have never activated the mobile app or are heavier users who have the remote control feature programmed via their smartphone.

The marketers could then develop targeted campaigns and offers for specific groups of people:

  • A 20% off coupon on the coffeemaker, for those who own the teakettle.
  • A 20% off coupon on the teakettle, for those who own the coffeemaker.
  • A $10 “thank you” electronic gift card at a national grocery chain to customers who own both.
  • A $20 “thank you” gift card to customers of both, who are heavy users.
  • An activation incentive offer of a $10 gift card, directed at owners of either product who have never used the product with the mobile app.

The delivery of these coupons could be personalized via either email or through the mobile app. In this way, marketers can deliver personalized offers at the right time in the ownership lifecycle, boosting sales and further engaging customers with the brand.

Making sense of martech data

That’s a lot of slicing and dicing of data, but the analysis has to be “in line” and rapid to be able to adjust to fast changing consumer behavior. While we all know that this is a highly generalized example, it’s what many marketers dream of being able to do not just with IoT data, but with the silos of data already collected by the 12 martech tools they already use, on average to really understand the customer’s omnichannel journey.

Advanced “networked BI” platforms don’t just make it possible for marketers to integrate IoT data into their campaigns, they make it easy. Want to learn more about extracting more value from your marketing data? Download this eBook, “10 Ways to Put Your Marketing Data into Sharp Focus.” And join the conversation on Twitter @Birst. Thanks.