In the digital age, there is an unprecedented amount of data available to companies, allowing them to refine products and services to match the constant change in customer expectations.
However, some organisations still aren’t proactive, and are finding that their decision making and release cycles are holding them back. Even worse is the tendency to obsess over this data for so long that decisions never get made, causing the product to remain stagnant – otherwise known as analysis paralysis.
Companies can stay ahead of the game by having developers abandon the quarterly product cycles that have been so historically prevalent; making way for an agile approach, and iterative updates to digital products and services. This allows for a much faster process, enabling teams to implement features that align with customer feedback. But before this can happen, businesses must address the trend of analysis paralysis, and change their product development culture for the better.
What is analysis paralysis?
When product teams are handed the task of “improve conversion rate”, or “increase newsletter sign-ups”, there is no ambiguity in the desired outcome. Yet, the road to the required result is not always as clear. When working out the best way to achieve success, it can be easy for product development teams to get stuck in what is known as analysis paralysis. This is where the product team gets caught up in strategising and planning the best theoretical route forward, without actually taking any tangible steps towards their goal or discussing the options with the wider business. Planning in silos then means a lot of effort is wasted as teams play catch up with each other while trying to move a project forward. All the while, precious time rolls on and the needs of both the business and the customer go unaddressed.
This time lag has a huge impact on targets, such as conversion rate, because by the time the team scopes a new feature, competitors may already have a tried and tested feature, otherwise known as a beta, and will be in the process of improving it. Ultimately, if the development team continues to get stuck in analysis paralysis at crucial hurdles, this will have ramifications far beyond the product teams’ KPIs.
Experimental DevOps teams
There are steps businesses can take to snap out of analysis paralysis. Part of the solution for enabling new products to be implemented faster is equipping developer teams with the skills and tools to not only test ideas, but maintain product stability and reliability during these tests.
For example, if the DevOps team comes up with three possible solutions to a customer pain point, they should have the right technology in place that enables them to test out all three. This will mean they can find the best possible result, rather than choosing one off instinct, rolling with it and hoping for the best, before inevitably having to work their way down the list of options until finding the right solution. Not only will this experimental approach save time, but the deployment of new features will never negatively impact users and enables the team to see real-time interactions and fix issues as they occur. Development teams must ensure they have a kill switch in place as a safeguard. This means that if one customer has a bad experience, you have the power to use the kill switch and roll back the new, untested feature.
Having the means to deploy quickly and more frequently gives businesses a strong competitive edge when it comes to launching new and innovative products. However, there must also be clear communications between the engineering team and those who are involved with other parts of the customer journey that recognise common pain points. Quick communication between developers and those teams will mitigate any issues that arise during product testing and eliminate any confusion about what the end goal is.
A cultural shift is necessary for success
It isn’t just the engineering team that should focus on developing the product offering or key consumer touchpoints. Employees across the organisation are valuable as they all interact with different stages of the customer journey, and can provide valuable insights into pain points. They are capable of delivering a constant flow of new ideas to improve the digital customer experience, asking what will help to add value for your customers while engineering teams actually integrate a process to make it a reality. It’s no longer about the waterfall approach of working in segments, but rather coming together as a collaborative business and empowering the devops team to make the technical decisions needed to make the ideas a reality.
Never underestimate the importance of collaboration in innovation. Giving employees at all levels the opportunity to get involved with their own ideas, perhaps via collaborative brainstorming sessions with the engineering team, can mean the risk of analysis paralysis will be averted, as everyone is involved from the beginning. It is essential for the management team to provide employees with not only the opportunity to share their thoughts about ways to develop the business, but the training to help them use their data and technology to bring these ideas to life.
Customers aren’t going to lower their expectations any time soon, and dynamic, data-driven product teams are setting the bar higher than ever before. Businesses stuck in rigid development cycles are finding themselves left behind, and it is only a matter of time before these archaic processes become altogether extinct. Change isn’t coming, it is here, and business leaders who recognise this and react accordingly will ultimately win out.
About the Author
Justina Nguyen is Developer Evangelist at Optimizely. Optimizely is the world’s leading experimentation platform, enabling businesses to deliver continuous experimentation and personalization across websites, mobile apps and connected devices. Optimizely enables businesses to experiment deeply into their technology stack and broadly across the entire customer experience. The platform’s ease of use and speed of deployment empower organizations to create and run bold experiments that help them make data-driven decisions and grow faster.
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