Self Service Technology Evolution
I'd like to distill my experiences into a simple trajectory of the space. I hope it will help inform other self service developers to build better products. It sums up quite simply... self service technology lags behind the technology of the web.
I have a long career in the self service space, since 2000 when I joined WebRaiser Technologies. I've built self service PaaS products, consulted on dozens of large deployments, and have seen some really novel applications.
Put another way, you can anticipate the next major technology shift in the self service space by looking at the last major technology shift of the web.
(Unfortunately my blogging tool uses markdown formatting -- which does not have table support. So bear with my list format please!)
The web and the kiosk industry have been more or less parallel, with products going through these major phases:
- Web: static web sites of academic, corporate or personal information
- Kiosk: digital signs, way finding, directories
- Data driven
- Web: search, online business services, behavioral content targeting
- Kiosk: Internet terminals, eHR, job application, check in
- Web: online ordering, product subscription, digital media
- Kiosk: photo kiosks, ticketing kiosks, dvd vending, key duplication
- Social Media
- Web: social sharing, identity providers, peer to peer communication tools
- Kiosk: user generated content sharing, interactive marketing, jukebox
- Big Data / Data Science
- Web: social graph, knowledge graph, semantic web, deep learning
- Kiosk: none yet -- opportunity to generate unique data sets that can optimize kiosk performance and create new services
- Future? Web of Things. Smart objects. Beacons. Presence.
Items 1 through 3 are mature technologies.
Item 4, social media, has become entrenched and is now the buy in for products and companies to compete. Kiosks leveraging social media as a core experience are just emerging over the past couple years. This trend will continue and "old" kiosks will embrace social, build relationships with their audience and start to drive their own k factor (and not just be dependent on foot traffic).
We now the rise of big data and data science including deep learning and machine intelligence. Examples include Google's knowledge graph and Amazon's recommendation system. Google through it's Cloud Platform and Amazon through AWS are even providing machine learning as a service.
Kiosk networks can generate large amounts of raw data on users that are unique and actionable. This data, respectfully collected and transparently used, can be the foundation for new services and optimizations that benefit the kiosk provider, the merchant, and the consumer.
As you consider building a new kiosk application, think through the trajectory of the web in order to build an offering that is competitive and that delights users. Many kiosks today still feel "old" because the core of the application is far behind what consumers experience on the web or on their phone.
As your mature kiosk networks begins to get that "old" feeling and might start to lose traction, look to the web to find new opportunities and inspiration to leverage your device footprint, user base, and merchant relationships to launch new offerings.