How to Maximize the Benefits of Your Product Analytics Tool

Product Analytic Tool
This article lists some of the proven ways in which you can take better advantage of your product analytics tool. While many companies struggle with an under-utilization of product and customer analytics, taking these points into consideration will help you advance product development by avoiding common pitfalls and bringing more granularity into the analysis.
Product analytics tools make life easier for companies by helping them better understand their customer needs and make product improvements in relation to those needs. They act as platforms where you can develop custom analytics that are simple to read and easy to take action upon.
However, a product analytics tool can also be one of the most under-utilized techs within companies. Organizations also tend to misinterpret analytics by taking siloed approaches on specific data points that consequently don’t positively impact the overall business goals. What matters to some business model may not matter to yours.
It might be safe to say that most of these issues arise from a lack of understanding of the tool itself. And some of it has to do with understanding how well you know the product-market fit.
That said, let’s take a look at some of the things that you can put the focus on to maximize the effects of your product analytics tool.

Customize Analytics

All of the data that a predefined analytics tool provides you with will not be beneficial until you customize your dashboard and tracking to suit the business model.
Some of the best market advisors stress upon choosing analytics metrics and KPIs that are curated to advance your business model. These metrics can help you advance through product development and engineering, aiding customer acquisition and, in turn, revenue generation.
Are you tracking recurring users, one-time buyers, or regular visitors? What metrics best suit your business aim? Do they correlate with what you are trying to achieve? And you should also know whether tracking those metrics will actually cause a difference in the end.
When you ask yourself these questions, you will have a strong basis to support your hypothesis on what to expect from those actionable metrics you come up with. Unless you can make necessary adjustments in the tool, the data generated will be rendered unactionable for teams. Hence, you will need to spend time finding the ideal market fit for each business use case to make these much-needed changes.

Track Maturity

If you expect results within a short period of time of using an analytics tool, you could be rushing through the stages of maturity, i.e., the stages that determine how much you track and how detailed your product analytics strategy is. The reverse can happen when you lag behind adopting advanced utilities when your product is past a stage of maturity.
Track product analytics maturity in terms of data access, attribution, and the overall alignment of the business for a successful analytics tool adoption.
Suppose a product is not at a stage where there is enough data to pool into analytics; the results may be biased or skewed. Setting the right data collection methods to get the data on which to run the analytics will be the first step in this scenario.
Meanwhile, you also cannot rely on archaic methods of analysis when your company has the potential to rely on better analytics solutions. You cannot rely on pure excel sheets when the data has crossed over to the billions or if there are too many metrics to track manually. Processes that are routine and achieve high levels of accuracy could be at a stage where they can be automated.
Many businesses find out much later in their development phase that they have settled for surface-level analytics tracking when their product analytics tools were capable of much more. One way that you can go wrong is when you track metrics without comparison. Are you tracking your progress month-on-month and quarter by quarter?
You may be surprised to find that the average changes might not be the right reflection of your progress, and a more granular approach with different user segments and cohorts can show you some things completely different.

Iterate Continually

Some of the most popular brands in the world became successful on a completely different business model than which they had started with. For instance, PayPal was initially supposed to be a security software before it became what it is today. Apple started off with successive failures in trying to sell TV appliances before launching the very successful iMac. There are many more examples out there of businesses that had to either radically or marginally change their business strategy to eventually land on the best solution they could sell.
Even if your company does not go through such huge shifts, you may still want to iteratively keep adjusting your market expectations in relation to the market changes and, most importantly, customer feedback.
Use analytics software to understand your customer interests by tracking the customer journey and collecting feedback through surveys, such as NPS® for pointing out where exactly you should be making a change. This could be a frustrating process when you have to keep going back on previous decisions; however, it is always rewarding to see that you did not go further down a road with no end.

Segment Users

Analytics can be misleading if decisions are based upon averages and overviews of data by ignoring the deeper levels of insights. For example, a lot of your users may be converting, yet the revenue generated still seems to remain low. In reality, you may have focused on too wide of an audience all along. This wider category of users has cost you a lot to retain with very little ROI. Thus, it will bring down the average, even though you are making huge strides of progress with another segment of users. They should then be your primary target users to whom you give more attention.
Cohort analysis and user segmentation are two ways in which users can be separated to identify problems in wide analysis. Cohorts are groups of users who exhibit common characteristics and behaviors, while user segments are based on categories such as age, gender, location, etc.
Finding out commonalities that connect users will not only tell you about the customer journey but also the product’s development over time.

Focus on Retention

Retention may not always be the ideal metric to track for all companies and business models. Still, for e-commerce, fintech, medtech, electronics, and many more, retention is a basic metric that cannot be avoided when studying a complete customer journey.
If your product performance is not based on one-time user successes, retaining existing users will bring higher returns than trying to acquire new users. Therefore, maintaining retention by tracking the relevant metrics is another great way to leverage your product analytics tool.
If you would like to know more about product analytics tools and the ways in which they can help your business advance, check out more Countly articles on our blog and academy!
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