Chapter 11. Eliminate Waste – Data Versus Opinions

Once product development is underway, there are new feature ideas that will make their way into the product backlog. Having a data strategy can help us to evaluate new feature ideas and how our product performs in terms of success metrics. As we saw in Chapter 7, Track, Measure and Review Customer Feedback we need to track, measure, and review customer feedback. We need to seek qualitative and quantitative insights. However, teams can be easily distracted when looking at the wrong data and biases. We need to eliminate wasteful processes in how we seek and interpret data. Product teams need to have a data strategy as an inherent part of their way of working.

Accordingly, this chapter will address the following topics:

  • Defining the hypothesis that we seek to validate
  • The problems with data

Defining the hypothesis

"If you don't know where you're going, any road will take you there." - Lewis Carroll

Product feedback can come in many ways. Chapter 7, Track, Measure, and Review Customer Feedback, outlined some of the internal and external channels used to gather feedback. The problem is that when there is a lot of input from all these channels, we can get overwhelmed. There can be distraction from having too many voices in our ears. We don't know which voice to respond to because we often don't know where we're headed. Many times, businesses respond by listening to the loudest voice they can hear.

After all, the squeaky wheel gets the grease! A complaint on social media, or a suggestion from a close friend or a respected advisor, can suddenly become our focus of interest. It is indeed important that we respond to an influential consumer, whose opinion can cause damage to our brand. However, not every instance requires that we change our product because one user (however influential) complained. We can manage expectations with messaging, PR, and customer service too.

In fact, early-stage products frequently fall into this trap of getting feedback and advice, and not knowing which to heed and which to ignore. If you're a parent, you probably know what I mean. Raising an infant is one of the most challenging times for a parent. When we're fumbling about with no experience in having to handle a delicate infant, any support seems welcome. We look to our parents, neighbors, doctors, friends, and the internet to constantly check if we're doing the right thing. However, one untimely burp from the baby is all that is needed for advice to flood in. Grandparents, neighbors, and random strangers on the street advise you on what your baby is going through, how to hold him, what to feed him, or not feed him, and why you need to be a better parent. When we're in unsure territory and need all the help we can get, it's difficult to turn down advice. However, free advice soon becomes a bane. Knowing whose advice matters and more importantly knowing if we even need advice, is important. Every parent sooner or later figures this out and so should product teams!

Working around strongly opinionated stakeholders requires people management skills. Having a data strategy as an inherent way to make product decisions can help us to navigate the quagmire of opinions. Knowing how to leverage data to drive decisions and curb opinions is a key skill for product managers today. Data is key to learning about a product's success. Finding the pulse of the consumer is an important aspect of product building. We need to know whether we're headed in the right direction, whether our features are being used or not, who is using them, and how to measure the success of what we're building. On the other hand, gathering data without an idea of what we seek to validate or falsify, can be wasteful.

Success metrics are a good way to define what we want to achieve, based on our capacity/capability. The core DNA of the business teams determines whether we put out bold, ambitious success metrics and the necessary operations to ensure that we deliver them. We also need to track our progress in meeting those success metrics.

There are two key phases of a feature idea (or product) where data-driven decision-making is crucial. The first phase is before we start to build a feature idea and second is after we have launched the feature idea. The type validation we seek at each stage can vary. In the first phase (before we build), we try to validate whether our assumptions about the impact that we expect our feature to have, hold good. In the second (after we launch), we try to measure how well our product is performing on the success metrics. Let's explore more about these two phases of data collection.

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