You’ve built an early version of your product, and it works. You have early adopters that really like what you are doing. You have validated your idea and built a product that is valued. But now you’re getting several requests for new features—a fork-in-the-road moment for startup businesses. What do you prioritize first? Is adding these requested features the right move for your product?
The answers are in the user data. The next step is to tailor your product to reflect the ideal user experience (UX)—the total of all perceptions and responses resulting from interaction a user has with a product or service.
Each user is a potential customer. By bringing the power of product analytics for the data warehouse to bear on user data, product teams can better develop an understanding of the ways users prefer to behave. Entrepreneurs tend to value their intuition in building a business. However, once the business has more concrete data on which to base decisions, analytics can confirm or challenge those assumptions.
Say your intuition tells you that users aren’t clicking a ‘subscribe’ button because it has been poorly designed and the color obscures it at a glance. If true, your product creates a poor UX by interrupting potential customer conversion. By leveraging analytics—even something as simple as an A/B test—you can confirm or refute your hypothesis. Once a product is viable and reaches a reasonable threshold of users, their decisions are the best data upon which to build the future of your product.
Get actionable feedback
There are myriad ways to solicit feedback from users of your products, including surveys and planned check-ins with customer success representatives. However, the easiest way to see what customers think of your product is to have your product team constantly querying user data with the help of a product analytics platform.
Your CFO or CIO might baulk at the idea that the product team needs another business intelligence (BI) tool. However, it’s crucial to understand that using product analytics for the data warehouse is not just about dashboards and funnels, it’s about equipping product teams with the tools to answer hundreds of questions that come up on a regular basis. While traditional BI tools can tell you what is going on with your business, product analytics gives you the context behind why something happened.
Research by McKinsey demonstrates that organizations that leverage customer data in internal decision-making outperform peer companies by 85% in sales growth and by 25% in gross margin. Leveraging customer data is a strategic win for the entire organization, but especially the C-suite.
According to Holger Hürtgen, an analytics partner at McKinsey, “CFOs are well-positioned to provide that vision and to lead the widespread adoption of advanced analytics. They have most of the necessary data in hand, as well as the traditional quantitative expertise to assess the real value to be gained from analytics programs.”
Where are users churning? What kinds of users subscribe to newsletters and how long does it take them to do so? How often do newsletter subscribers use the product compared to non-newsletter subscribers? These are just three questions that you might ask. You can answer these with product analytics.
Lay out the product roadmap
Armed with actionable insights from your analytics platform, set out with the development team to prioritize features that drive revenue and create a more successful user experience. There are two simultaneous goals to achieve at this juncture—solve pain points for existing users and begin adding features that support the ideal customer journey. At the junction of these two goals is the ideal product roadmap.
Roadmaps are focused on driving goals associated with the product’s strategic objectives. Measuring the progress towards those objectives is critical to understanding success. If we fall short of those objectives, we need to understand why and how to improve. Analytics is the lens for product teams that brings into focus the efficacy of their work.
At our company, Indicative, we maintain several key metrics related to our core product objectives. When thinking about our roadmap, nearly every new initiative is evaluated based on its impact on achieving these objectives. Examples could include engagement, retention, or reaching key points in our product. We can understand if the effort we invested into our product development efforts achieved the outcomes we had hoped for. If they don’t, our product analytics is the first place we look to see why.
For existing users, focus on the customer journey. Meet with the marketing team and break customers into segments by referral channel and other behaviors to identify the kinds of customers that are struggling through the conversion funnel. Lay out solutions to address those problems. Customers who enjoy your product and convert at high rates become segments to target for marketing purposes. Identify how these customers arrived at your site and seek to replicate that experience for others.
In developing new features, expand on existing successes. Is there a way to move reliable customers up the value chain by developing additional features that drive revenue? Customer retention is critical to build sustainable growth, as new customers are significantly more expensive to attract than existing customers are to retain. Even a 5% increase in customer retention can yield more than a 25% increase in profit.
Communicating to customers an enticing product roadmap for the future will keep them coming back.
The process of refining the product roadmap might start when the product itself becomes viable, but it never really ends. Even the most well-executed product launches fall short of the desired outcome. Your product team should have access to records of every user interaction since the product launch. That user behavior is constantly changing as different customer personas emerge and as product features change over time.
For example, Indicative recently launched a free trial. The goal was to allow potential users to evaluate our full product offering quickly. After the launch, the initial data pointed to a healthy conversion rate in starting the process. However, there was a higher than expected dropoff which resulted in a relatively low number of users starting a free trial.
By exploring the free trial’s full journey, we narrowed the dropoff to a specific step that included asking the user to complete a registration form consisting of eight fields. We concluded that we were asking too much information from the user before they had fully bought into our value proposition.
We designed a test to prove this hypothesis by A/B testing a variation of only collecting the user’s email address. This consequently saw a dramatic increase resulting in an outcome closer to our original expectations.
Analytics enabled us to iterate and achieve our desired outcome quickly. Incorporating A/B tests based on your hypotheses, and measuring the customer journey response is a key component of product management. Have a defined success metrics around user experience. In absence of measurement, there is no hypothesis.
Once the product team has successfully deployed a minimum viable product, many of the decisions on where to go next can be answered by listening to those users. Customer success check-ins and surveys are useful, but nothing can better demonstrate what users think than the way they behave.
Product managers must concern themselves with the creative and the practical. Product analytics measure the creative decisions that product teams make to determine their efficacy.
Companies who tailor their product decisions in such a way are primed to outperform competitors because they understand how and why customers are behaving in a certain way. Their response lights the way for continued iteration in the product roadmap. There is no perfect product without a need for improvement. Take full advantage of product analytics for your data warehouse and incorporate necessary changes. When one product cycle finishes, the next one is just beginning.