Creating Entry and Displacement Barriers in the Mobile App space.
Given the ubiquity of the mobile platforms, and the flood of apps how then is one to create a competitive barrier in the mobile enabled space? Here I underline some of my thoughts:
A purely app based play , that uses the same API’s as any other app and leverages the same sensor platform of the mobile device as any other app, will be hard-pressed to withstand rapid displacement.
To rise into the signal level (“signal level”: a mobile offering with a large installed base and/or longer period of continued use) in a landscape with an insignificant signal to noise ratio (“noise level”: just any app, with a few to none one time downloads/one-off experimental use), there are three dimensions of differentiation potential:
1) Great User Experience/UI: This piece is crucial. If it is love at first sight, the app-uptake could be viral and critical mass of installs/use may provide a competitive barrier. However, it is an extremely unpredictable dynamic and hard to characterize and this dimension alone is not sufficient.
2) Custom data front-end: Having a custom accessory that caters to some daily customer pain-point is a great barrier. Such a custom-plug in would have sensors and systems that are not part of the core mobile platform, but complement those therein. It could improve an existing process (e.g. Square) or enable new insights (health and wellness, security systems etc.). This route poses challenges of higher capital cost outlay, supply-chain and production risks but offers a far higher barrier than a UI alone. Also, it creates a rich custom data-base of user-content, in an era where content and data rule supreme.
3) Analytics and insight: Gathering the flood of data from the sensors on the core mobile platform and/or a custom accessory and providing actionable insights into the data is also crucial. While machine learning on big-data can –do much, it is not a magic wand that will unlock hidden secrets. The assumptions underlying non-linear and higher order mathematical models can easily lead to misinterpretations. There are many challenges in leveraging data to providing actionable insights, “actionable” being the key-word drowned out in the big-data hype of late. However, coupled with the mobile platform and given a critical mass of data to allow even first/second order aggregate statistics to be run with significance, the rich data-base, of preferably custom content, can pose a great barrier to displacement.
The overall architecture to allow scalability and minimize downtime is also crucial to preserve the user experience once one acquires the customers. However, there are many reasonably mature approaches to this technical issue and a start-up can leverage services such as AWS to meet this challenge to a large extent.
Once one strengthens ones’ offering in all the above dimensions, there are still significant challenges, given the extremely high noise level, of getting noticed by the market; but at least one will be facing the market better equipped. . Given the declining costs of development, creating a compelling and useful offering to the market that differentiates along all three dimensions is no longer too expensive and greatly increases the barrier to entry and displacement.