Introduction
Are you trying to grow a business or improve a product or service? If so, data is one of the best gifts you can give yourself. From watching what customers do when they land on your website, to gaining insight into customer demographic data, or calculating customer lifetime value (CLV), using product analytics can help you gain the insights you need to better serve customers and inform your business strategy. If you are not making data-informed decisions with your analytics, there is no better time to begin than today. This article will introduce you to knowledge and tools to help you start using product analytics to inform your business strategy. If you already have a good grasp on the types of data and tools needed to gain product analytics insights, skip to the end for an example to help you take action.
Note: This is written for anyone who wants to use data to grow their business or product, including product managers, small business owners, and startup employees. Product analytics, business analytics, and data analytics are interconnected and have some overlap. For this article and its examples, I will use the term product analytics, where the focus is on the information you can gather about aspects of your business and customers to help improve, enhance, and inform your future direction.
Why are Product Analytics Important?
Product analytics can inform you of your past business performance in addition to helping you plan for the future. Think of your data like you do directions from one location to another. There are many ways to get to a destination; analytics can give you the shortest path to success. Analytics can expose what is working well and what isn’t, so you can understand the effectiveness of your strategies and focus on the right things at the right time.
Consider this example: after successfully selling your product through an online marketplace, you create a website to sell the product because you want to gain more control and increase your bottom line. You get very few sales after 6 months, even though you are still thriving in the online marketplace. What happened? More importantly, how do you fix it?
The issue could be anything from website traffic to not offering the right payment options. With a huge range of potential issues, where do you start? The answer to these questions very likely exists in your product analytics. While you can start from anywhere and hope that you uncover the problem quickly, it can be much more effective to use your data to point you in the right direction.
What Data Do You Need?
Analytics are fueled by the data available to you. There is an incredible amount of data you can collect about your business, products, and services. What data do you need? The short answer is you should collect information on every area of your business. But don’t let that overwhelm you. Instead, make a conscious choice to increase the data you are collecting over time and based on areas that have the most opportunity. Then, when you need to gain insights through your product analytics, the relevant data will be available to you.
Likely, you are already collecting data about your business through the tools you use every day. You may be unaware of everything available to you, and if so, that’s okay. As time goes on and you uncover new opportunities and establish new goals, you can bring in additional data collection tools and methods. Having all of your data set up will make it easier to gain enough insights to take action in specific areas when you are ready to.
Start With Product Analytics Tools You Use Already
There are many tools out there to help you understand your business and customers. Review the tools you are already using and take inventory of the analytics available to you. These tools can be a wealth of information and perhaps where you need to spend most of your time reviewing data. You will also find valuable data in records you may keep on sales, customer support, and reviews, among other places.
Examples of product analytics tools you may already have include the following:
- eCommerce tools. These might provide sales data, customer information, and product popularity data
- Customer relationship management (CRM) tools. These might have space for you to add identity data, descriptive data, and qualitative data, and could also provide analytics on customer acquisition costs, close rates, marketing funnel information, and sales data points.
- Ticketing systems. These are typically used for customer or technical service requests and can provide information on common problems faced by your users.
- Social media accounts. These might include analytics on subscribers, customer engagement, and your most popular content.
- Vendor tools. The types of data you can collect vary based on the type of vendor. Always check to see what types of data your vendors provide.
It is also good to know the data limitations of the tools you use. Some tools will offer additional analytics for a fee, but you don’t necessarily need those. Focus first on the problems that need to be solved and the analytics you need to solve those issues. If there is a related data gap preventing you from getting the complete picture or understanding the magnitude of the problem, you can then check to see if the upgrade will provide the missing information.
Additional Product Analytics Tools
You may decide additional product analytics tools are needed beyond what is already available to you. There are a lot of options out there. Identify the gaps in the information you need, especially as it pertains to your goals. Do you need a tool that focuses on showing what is happening in your purchase funnel? Do you need a tool that shows how many people are clicking on a specific product page? Maybe you’d benefit from a testing tool that gives objective data before you build a new feature. Think about the use cases that would be most helpful to you and the exact data you need, and then explore what is available.
Below is a short list of potential tools that you might look to incorporate in order to understand your users better. You will want to devote a bit of time to research what makes the most sense for your company or product right now. Many of these overlap but also have different strengths and primary purposes.
- Google Analytics 4
- Adobe Analytics
- Fullstory
- Content Square
- Hotjar
- Logrocket
- Mixpanel
- Amplitude
- Optimizely
- Pendo
- Unbounce
- Qualtrics
All of the product analytics tools mentioned thus far are strong in the quantitative area. With these tools, you can get a clear picture of what is happening. Qualitative data, or understanding why, is a different story.
Key Types of Data: Qualitative and Quantitative
While there are many different types of useful data, quantitative and qualitative data encompass much of the data you will use. Together they will paint a detailed picture of your business, allowing you to have the context needed to understand your greatest opportunities for improvement.
Quantitative data is numerical, objective, and measurable. You will be able to understand how often or with how many customers something is true. This data can be analyzed statistically.
Qualitative data is non-numerical and subjective. You’ll draw conclusions from this data to understand “why” customers behave in certain ways. This data can be analyzed by interpreting observations and grouping information into themes or based on patterns.
Gathering Qualitative Data
Qualitative data pairs with your quantitative data to help you understand the customers’ perceptions, points of view, and why certain actions are taken. It is eye-opening and even alarming to gain these insights.
How to Gain Qualitative Data
Common ways to gain qualitative data are through surveys, questionnaires, observation, and interviews. Reviews and customer support tickets can also provide a wealth of qualitative information. While it tends to be more challenging and costly to obtain qualitative data, it’s imperative if you want to move in a faster and more informed way. It also involves a specialized skill set, centered around research. We won’t get into the details here but know that these skills can be honed over time or outsourced if necessary.
Finding participants for some of these methods can be challenging, and paying people for their time is very common. There are companies that can help with this too. Building it into your existing systems is ideal.
Quality is Key
Two of the most important things to remember with qualitative data are the quality of participants and the quality of questions asked.
For example: imagine you want to understand customer preferences related to customer support channels. Do most want to communicate via chat, call, text, or email? You need to interview people who fit your user persona – the actual individuals who will use your product. It will only help to interview people who are the buyers if they are also the end users. I’m terms of question quality, the best thing you can do is get specific examples of how a customer has behaved in the past. It’s not what they think they will do; instead, it’s what they have done because past behavior predicts future behavior. For this example, here is a good sample question: Tell me about the last time you had to interact with a support professional to help you solve a problem.
How to Take Action with Your Product Analytics: Steps with Examples to Get Started
To take action with your data, there are several steps to follow. While these are not quick, they are likely not very complex and can lead to great change.
Step 1: Understand your business goals.
You need to know where to focus your efforts. What is important for your business right now? Create an objective related to that focus area. This is what you are looking to do to win in the next few months.
Example: grow customer loyalty
Step 2: Understand the state of your focus area through data.
You need a high-level understanding of the data to identify key areas where changes will make a meaningful difference. Look for data that doesn’t meet your expectations, and that indicates major gaps between where you are and where you want to be. You can use market research to tell you if you’re on par in key areas. You’ll get deeper into the data in another step, based on where you see changes that need to be made.
Example: For customer loyalty, you might look at customer satisfaction, churn rate, CLV, and any loyalty program metrics, as applicable.
Step 3: Create measurable goals.
Create key results (if you use the objectives and key results (OKR) format) or otherwise measurable goals that will help you reach your objective and make a positive change in your area of focus. To guide you, answer the following two questions:
- What is one area where you can make change that will contribute to making a meaningful difference in the strategic focus area for your business? This question is to help you define your goals.
- How will you know when you have accomplished that? This question is to help you decide specific metrics or targets that indicate you have made the progress you seek, as well as benchmark where you are currently.
Formulate a statement with your answers, using this phrasing: I want to [insert goal] from [current state, also known as baseline] to [desired state].
If you don’t have the baseline or desired state yet, that’s okay. It’s part of the reason for this exercise! You can write your statement to reflect the numbers you need and the direction if needed.
Example: I want to decrease our customer churn rate from 17% to 12%.
Step 4: Identify where you can have the most impact.
Now it’s time to look deeper into your product analytics to figure out some of the tactics you’ll use to achieve the metric you created in the previous step. There are several data points you’ll use to identify the most effective actions to help you accomplish your goal. For customer churn, you may look at the following areas, for example:
- Customer demographic data: differences in demographic data of those who churn versus those who don’t.
- Customer journey data: percentages of churn by stage in the customer journey.
- Customer satisfaction data: differences in satisfied versus unsatisfied customers, including scores and anecdotes.
Identity themes, or patterns in the data to help you identify actions and also additional data points that will help with your tactics.
Example: through your data, you’ve discovered that the greatest churn takes place in the onboarding area. You decide to focus your efforts here.
Step 5: Identify the specific problem to solve.
You might be eager to start making changes to your customer onboarding journey, but not so fast! You need to use data to inform where it will be most advantageous to make changes. Onboarding can include many processes, steps, communications, and people, so gather some deeper product analytics about your onboarding first. Qualitative data could provide a ton of value here. You can gain this in a few ways:
- Anecdotal feedback from internal staff focused on onboarding
- Surveys sent to your customers who have recently completed onboarding
- Sitting in on onboarding calls
- Interviewing customers, both those who have completed onboarding and those who haven’t.
All of this data should be substantial enough to identify and validate the most important problems or opportunities to address. Once you identify it, write out your problem statement concisely to keep everyone focused on the same effort.
Example: through your interviews, you’ve uncovered a theme that came up consistently across customers who have participated in onboarding: a manual process related to customer reporting that takes so much time to complete that the customer cannot finish it within a timeframe they deem reasonable. Problem statement: The customer reporting step of our onboarding process takes too long to complete, resulting in frustrated users and users who do not complete this step.
Step 6: Identify your solution.
Note: This is a complex multi-step process in itself. I am condensing it into a high-level explanation for the purpose of the article.
There are likely several directions you can take to solve your problem. You can use analytics throughout this step to help you determine the most impactful solution. Below are some ways you may use data to help identify your solution.
- You can use market analytics to get ideas and then gather survey feedback for opinions on those ideas.
- You can validate the proposed solution with concept testing.
- You can conduct usability testing with user interviews.
- You can perform A/B or click tests before building to identify anything from feature usage to placement.
Example: You decide you want to automate one piece of customer reporting after learning in previous steps that it was the most time-consuming area and led to the most drop-off. Your team has 5 ideas for how to accomplish this, and conducts a survey to identify the viability of this solution. This leads to your ability to narrow to one solution. You then take 7 users through two mock-ups of this solution to identify which one is more desirable. After that, you conduct usability interviews using a prototype to ensure a good customer experience.
Step 7: Build and measure.
After you believe you have the right solution, you will build and then measure to learn whether your churn rate has improved or if you have more work to do. This can take time, and you may need to use leading indicators or directional data to identify whether you are on the right track.
Example: You sit in on 10 onboarding calls and listen for comments regarding the customer reporting process. 5 people positively commented on the automated piece. 2 months post-build, you were able to see that churn was reduced by 3% – not quite to your goal, but on the right track. You then decide whether or not to continue making changes in the onboarding process and go back to review where you can make more impact.
Conclusion
Product analytics are valuable across the business to inform everything from your objectives to your solutions. A variety of product analytics tools are at your disposal to help you understand where your greatest opportunities lie, in addition to helping you validate the problem, solution, and impact. Using your goals as a guide, you can focus on the most important data and shorten your time to problem resolution. While this is a complex process with many steps, it can lead to more effective output, less risky development, achievable outcomes, and customer success.