Analytics Efforts are Only Beneficial if Tailored to Address Specific Product Performance

Technology, innovation and ever-changing trends have entirely changed the way businesses operate and how we have been looking at products. Be it marketing or product design and development, everything is customized these days.

Customers are looking for a more tailored and personalized approach to everything, be it how you sell them the product or how you design and develop the product itself.

Data analytics has played a crucial role when it comes to designing and developing customer-centric products. But, how exactly do we use data analytics for addressing product performance?

It’s true that leveraging the power of data analytics to the maximum can enhance the proficiency of the products, improve advertising techniques, and support business growth. But, it’s also true that analytics efforts are only beneficial if tailored to address specific product performance.

You must be right in your approach here. Let’s see how this unfolds and what exactly do we mean.

The Role of Analytics

In the simplest of terms, analytics measure the state of the product. This can be anything how users are interacting with the product, what they are doing, where they are clicking and so on.

The purpose of analytics is to judge what is going on with the product, as measured by various metrics. And all of these insights when interpreted the right way, help with product improvement.

Analytics is the primary source of feedback you get on your product. Analytics is crucial to product management and product improvement. Without analytics, you won’t really know ever what’s going on with your product or if you are headed in the right direction or not.

The key results, insights and metrics brought to the forefront by analytics helps product teams make informed decisions about what’s not working out, what product functionality needs to be upgraded or what specific feature demands additional capabilities.

And, this is also the primary reason why your analytics efforts should always be focused on a specific part of the product performance, rather than taking everything into picture at once. Without analytics, product teams would never realise or understand if the revisions implemented have been able to solve customer’s problems or not.

What you don’t measure, you can’t improve.

And, if you measure as a whole, you can’t pinpoint where exactly the issue lies.

Directing Your Analytics Efforts in the Right Direction

What a lot of businesses do while implementing their analytics plans is to throw in a lot of seemingly complex and rich-in-insights analytics packages and track almost all sorts of data relevant to the product “as a whole”.

However, this approach seldom works!

 

Don’t do this!

This approach never helps because to begin with, you, as a product manager, didn’t know what you are looking for.

Not every feature of the product is data driven and not every feature plays the same role in making the product a success. Before implementing your analytics efforts, you should think about studying what analytics would help you reflect upon the performance of the product the fastest.

Going the other way round, you’d just end up with an overwhelming volume of data. You won’t have any vision about it and you would just feel drowned in this sea of data ending up latching onto the vanity metrics.

Thus, it’s super important that before you implement your analytics plan, you should be crystal clear about what parts of the product performance you need to track, and what exactly your end goal looks like, what data is relevant to you.

 

The key is to track relevant data points, not a whole lot of data!

 

Start with creating a plan that couples the data points you measure with the product vision you and your team had at the beginning of the development and design process along with the product’s key performance indicators (KPIs).

Pros of Working With Specific Data Points

Easy to Report

When studying the feedback for a product, you are expected to define if the improvements introduced have been a success or a failure.

And, for that to happen, you must understand the architecture of the product very well and see what metrics define the success or failure of what feature and what still needs to be worked on.

As against being drowned in a sea of data, tracking data relevant to achieving KPIs makes it easy to report and interpret.

If you don’t report the analytics you track, it’s a waste of time to track them anyway.

 

Common reporting methods such as trends and comparisons make sense only when you report them specifically for a functionality. It would be a great value addition if you are also capable of reporting them using visualization techniques.

For instance, if you are managing a social media platform, it makes more sense to individually track and report analytics on specific features such as the share option or the search option.

You should be focused on understanding what issues the audience is encountering with these data driven features of the product, rather than the product as a whole.

Helps Deliver Relevant Products

While you are focused on improving one feature at a time, you deliver better products with relevant features. Understanding customer insights and improving a particular part of the product helps decrease complexity.

Effective data collection and analysis helps companies stay competitive and on top of trends. Plus, leveraging predictive analytics helps get insights on what is expected from brands in the coming times and what pain points people are struggling with.

Thus, in addition to improving existing products, companies have an excellent opportunity to expand on new markets and develop new products. The optimization of the trial page of Volusion can be a good example of tailoring analytics efforts to a specific part of the product. To improve the lead generation rate, a new registration page was created and an A/B test was run against the then-current trial page.

The previous trial page was overloaded with information about the product and had a lot of CTAs and places to click around. Analyzing this, the newly designed trial page was modified and had some information about the trial (“No credit-card required” ) and removed all the possible distractions.

Thus, rather than modifying the entire product the lead conversion analytics were used to address the issue with the trail page alone. Further, when this didn’t work, the analytics efforts were narrowed down by segmenting the audience on the basis of location.

Informed Product Decision Making

Focusing on one feature at a time makes it easy to make informed decisions and wise choices.

Delivering relevant products also includes NOT overloading your product with irrelevant features. It is important to design the architecture of your product in such a way that it solves the problem of the user without feeling overcrowded.

And, this is possible only when you think about each of the features individually rather than focusing on the product as a whole. Analytics are vital for product design, development and improvement as they tell you what exactly is going on with your product and how your audience has been receiving it.

 Before you think you are all set to launch your product, you must understand and decide what needs to be tracked and reported. This forms the criteria for further choosing what data points out of all are relevant, how to measure it and how to use it for product improvement.