While much of the critical attention on the most recent crop of D2C platforms is focused on the comparative depth of their respective content libraries and the discovery mechanisms available to navigate those libraries, the long-term success of any of these platforms will be determined by the depth and breadth of the entire consumer experience. Building out a D2C streaming platform with a compelling and competitive consumer experience requires a significant and ongoing investment driven by analytics as well as inspiration. How can media publishers best leverage that investment to win and stay ahead?
In going over-the-top, media publishers are fundamentally altering their revenue ecosystem. In bypassing the traditional intermediaries (local stations and MVPDs), they are breaking free of consumer experience (CX) limitations and gaining direct access to consumer behavior data. For ad-supported programming, the combination of CX freedom and first-party data can open the door to much more engaging, targeted, and lucrative advertising than was ever achievable with traditional linear spots (no matter how addressable). Regardless of monetization model, these services can now leverage CX and user navigational data to directly impact subscriber acquisition and churn.
But this also means D2C media owners have to stand up the infrastructure necessary to scale, support, and maintain millions of direct relationships. They’re also flying in a wider sky, competing not just with other streaming services for share of wallets and eyeballs, but with more interactive forms of media for share of attention. All of which is to say they need to keep innovating at Web, not TV, speed. To do so requires a clear answer to the question: “How will you keep up?”
The path forward is complicated by a number of technological and cultural challenges:
From our perspective, long-term success will depend upon:
With new D2C services appearing almost monthly, it would be easy to assume that these launches are one-time disruptive events, but the reality looks much more like an ongoing war of innovation. Netflix has internalized that viewpoint, building their innovation processes to continually deliver new features as they are developed, eschewing the more traditional release-based model for agility and velocity. Facebook, Amazon, and Google work in much the same way.
But software development agility is just one part of the puzzle; iterating quickly on mediocre product concepts only exposes their mediocrity faster. Stepping back to look at the big picture, we see that the whole ideation-creation-validation-implementation-deployment sequence must become a closed loop driven by feedback and metrics.
Iterate to Innovate
The CX innovation process consists of two sequential cycles that in turn feed the production software development life cycle. Agile software development is driven by a backlog of clearly defined and validated product feature requirements; these two cycles work together to build that backlog.
Cycle #1: Innovation Piloting
The purpose of the innovation piloting cycle is to create new feature candidates through a process of generation, clarification, testing, and evaluation. Led by a product owner, the innovation piloting team should be made up primarily of product strategists and designers, with a few full-stack developers to provide implementation perspective and do the rapid prototyping. This dedicated team should reiterate the piloting process on a regular basis.
Innovation piloting focuses on the following:
The output of each cycle iteration is a working prototype of each new feature candidate.
Cycle #2: Scale-Up
The purpose of the scale-up cycle is to flesh out each of the feature candidates produced in the piloting cycle to the level of detailed definition necessary for production implementation. Led by a software architect, the scale-up team should include designers and senior developers, as well as the original product owners from the piloting cycle.
Scale-up focuses on the following:
The output of each cycle iteration is a loaded backlog of detailed requirements, design assets, and reference implementation.
Both cycles require the exchange of ideas and work product between designers and developers; this requires a set of tools and approaches that facilitate this exchange as well as the transfer of features from pilot to production. For example, design tools that output detailed development specifications and assets make it possible for designers and developers to work side by side, collaborating in near-real-time rather than in sequential, stovepiped workflows.
A reference architecture will facilitate piloting, allowing any prototyping to be decoupled from the often challenging task of integrating with enterprise systems. As new features are scaled up, a reference architecture will guide and ease the integration of those features with production back ends.
On the other side of feature development, a common development platform, a device-agnostic presentation engine, and an automated test environment will allow one intelligent design to be deployed across every type of device in a high-velocity yet cost-effective manner.
The difference between a procedure and a process is the use of metrics to drive a feedback path, so the ability to close the loop in this case depends upon the selection of useful metrics. Each of the typical monetization models has a set of customary Key Performance Indicators (KPIs): subscription-based services typically look at Cost Per Acquisition and Customer Return Rate, transaction-based services look at things like Take Rate and Bounce Rate, and ad-supported services are measured by CPMs, CPCs, and conversion rates. But these are only first-order metrics – they can be used to measure overall business performance, but they don’t offer insights as to how to improve specific aspects of the product or process.
Advanced analytics can be of great use, providing much more meaningful metrics and insights. Analyzing the user clickstream using machine learning and artificial intelligence techniques can provide a wealth of information:
The key to building a sustainable competitive advantage is to treat product innovation as part of a closed-loop process that uses consumer behavior analytics to continually enhance the consumer’s experience. This is a fundamental shift in perspective from that traditionally held by media owners and publishers, but it is vital to compete against the other players in the Attention Economy.