In the Masters of Growth series, we ask leaders of highly successful ventures how they help customers scale and solve problems. It’s a window into the minds of some of Ontario’s most successful business people.
Flybits empowers enterprises to connect with customers by creating micro-personalized experiences enriched with contextual data. The company aggregates disparate sources of public and proprietary information while ensuring the highest standards of privacy. Flybits simplifies data normalization and seamlessly layers on contextualization.
Integrated into digital products, the technology powers an intelligent and customizable experience, allowing Flybits’s Fortune 500 clients to build sophisticated and evolving customer engagement programs.
And Flybits’s patented inference engine hides the complexities of artificial intelligence (AI), data intelligence and machine learning, allowing you to focus on creating value for customers and achieving your goals.
Dr. Rahnama, tell us about your journey to Flybits.
I was raised by my mother (a retired academic) and father (a retired computer engineer and entrepreneur) in Tehran. They instilled in me a passion for education and, being surrounded by my father’s computers, I was able to apply it at an early age. My family moved to Canada following the Iranian revolution, and my desire for knowledge grew as I explored my new country. My focus became ubiquitous computing — I’ve written 30 publications and earned 10 patents on the subject — and I continue to explore AI, mobile human-computer interactions and the effective design of data-driven services. These interests led me to found Flybits in 2013, along with my team at Ryerson University.
Flybits has grown substantially since its founding. What were some of the company’s unique challenges while scaling?
One of the great things about the service Flybits provides, is that it can be used and applied in countless settings and industries. In the early stages, we discussed the implementation of our service in a variety of verticals, ranging from fashion shows to public transit systems. We quickly realized this model would not allow our team to scale and truly flourish. We took time to conduct research into which industries would be most beneficial for us. Because we’re based in Toronto, the centre of Canada’s largest financial institutions, we decided that that sector would be the best. We had already engaged with Barclays on a project and saw the potential this vertical could hold. We still have the same ambitions — but the financial institutions sector allows us to most effectively scale our brand to other industries.
There are over 6,000 marketing technologies live today. What is different about the Flybits solution?
Flybits is unique in several areas, the first being our ability to enrich an organization’s existing data with context. In real life, context impacts every decision we make. We have harnessed this power so companies can gain a much deeper understanding of their customers’ individual needs.
We also excel at enabling enterprises to evolve their data ecosystem without the technical challenges encountered when using other solutions. Because we simplify aggregation and normalization, data can be easily augmented with contextual sources and applied to predictive modelling. This is what allows enterprises to achieve the data sophistication required to take advantage of AI and, most importantly, scale as new AI capabilities develop.
Third, our product, the Flybits Concierge, provides a customizable solution that lets companies achieve any use case across any channel. The possibilities are limitless.
One example of how we work with companies is our partnership with TD Bank. TD for Me, a feature of the company’s mobile app, is powered by Flybits. Through a privacy-conscious opt-in process, TD is able to provide incredibly personalized offers to their customers. The company uses this for lending, card services and core banking use cases. TD created this channel to behave differently based on the needs of the customer. If you use the app at home, at the train station, or when travelling abroad, you will receive a different experience.
AI is often misunderstood amongst traditional marketers. How have you been able to break through and change their minds?
We found that many large companies have great use cases for digital personalization, but when it came to implementation, over 90 per cent of their budget and resources were spent on developing the software and managing IT complexities. All the data brought into the organization came with a different contract and structure, meaning companies had to build large hierarchical IT teams to manage. This development stage took a long time, leading to only a handful of rudimentary use cases being brought to market.
Flybits allows organizations to hide the complexity of data and its integrations, and gives companies access to tools where they can easily drag and drop interfaces to create engagement rules and bring use cases to the market faster and more effectively. We’ve focused on the ability to turn data sets into micro-services in real-time, normalizing them, and allowing companies to empower teams to focus on use cases. Companies can now build teams with a data scientist, engineer, designer and UX expert, truly giving them the autonomy to run a personalization strategy.
Our patented, AI-driven inference engine allows predictive models — and the data that feeds them — to be optimized. The engine also applies machine learning and AI to provide recommendations on how to refine or augment the experiences teams design. Marketers should know that Flybits allows them to spend more time improving campaigns and less time analyzing data. That’s how they’ll begin to see the value of AI within their strategies.
What does the future hold for Flybits?
Flybits is made up of engaged, interested and curious minds. We are constantly brainstorming and evolving what our future will entail. We want to thrive within the financial institutions sector globally — but our plans don’t stop there.
Machine learning and AI have become incredibly prominent in today’s market. At Flybits, we have spent a lot of time understanding how we can incorporate these elements into our company. There are about 45 different ways of doing machine learning and understanding data. The context behind this is very important. Depending on what kind of situation customers finds themselves in, different forms of machine learning are required to provide the best structure.
I mentioned our inference engine. It functions, in part, as a router to tell what kind of semantics or context customers are in and apply the appropriate network. If a client wants to target a first-year student and offer them a credit card, a Bayesian belief network would be appropriate, as it is good at understanding risk. Our algorithm would then route the inference to a micro-service and contain the machine learning. If the client wanted to give the next best offer to someone, a deep belief network may be appropriate. Every time there is a new machine learning capability, companies can plug it into the router framework and stay current.
When large organizations are being pressured to incorporate AI into their technology strategy, instead of locking down their infrastructure based on one or two types of AI, companies will be able to rely on Flybits to use whichever form of machine learning is most relevant. This will allow companies to easily scale their infrastructure as new AI capabilities become available.
Finally, give us three words that describe Flybits.
Innovative; team-focused; experimental.
by Nathan Monk