Can you guess how many new products in the U.S. fail because businesses don't communicate with customers? One in five? One in three? The actual number is stunning — 85 percent! When companies do not listen to their audience, their products don’t meet customers' expectations.
That’s the case when data analytics becomes crucial to ensure the success of a product. The analytics market is predicted to increase further. By the end of 2023, analysts predict the analytics market will be worth $57 million.
What is data analytics? DA consists of examining data sets to identify trends and develop conclusions about the information they contain. In the same way, big data analytics works with big data to get insights. For instance, hidden patterns, correlations, market trends, and client preferences.
Just one case to showcase the power of data analytics: a supermarket found out a woman was pregnant before her family did. It became possible that to studying the change in the items she started to buy: unscented lotions and soaps, supplements like zinc, magnesium, and calcium, etc. It’s no wonder examining data sets can assist companies in making wise business decisions.
Data analytics aids in identifying factors that appear to change for no visible reason. This way, you can understand why they occur and why your items perform the way they do.
Proactive analytics includes business monitoring to prevent incidents from escalating. Typically, it alerts you as soon as possible if something goes wrong and even before it happens. Thus, you can act sooner to address the issues before they significantly affect your business.
In this article, we will outline the importance of data-driven product development and the use of data and analytics for proactive product development. You will learn more about the best practices for implementing a data and analytics strategy based on the experience of Techstack, a full-stack software development company.
The Importance of Data-Driven Product Development
Data-driven product development obviously works with data. That is, it involves testing product concepts with customers and letting the collected customer data replace guesswork when making further development decisions.
An example of data-driven product development? The Product Experience Analytics team at Facebook ensures that advertisers are provided with a tailored product. To guarantee that marketers have the most meaningful interactions possible with their target audiences while using Facebook Ads products, the team performs deep-dive investigations using analytical methodologies and technologies.
Data-driven product development keeps the user and the product connected. It offers the information required to comprehend how consumers use a product, where it might be lacking, and where opportunities for improvement may exist. This approach prepares the final product for the market, finds its target audience, and produces the expected effects.
Everyone who works on product development will benefit from using data analytics in their position.
- Product managers can pinpoint product flaws and determine what to do to enhance user experience. With access to data analytics, they are in charge of the entire product development process.
- Software engineers understand which elements of the product they're creating will be helpful. Analytical insights allow developers to identify defects and work with the design team to fix them for a more effective final product.
- UX designers can identify the features that users find perplexing so they can fix them. Additionally, with data analytics, there is less back and forth in the process regarding design and development.
- The marketing team gains knowledge to promote the product to users properly. With data analytics, the product will receive the attention it merits in advertising and commercial promotion.
- End users get products designed to meet their requirements and expectations. Thus, they have a better customer experience. As a result, your business increases revenue by expanding its customer base and turning more prospective leads.
Using Data and Analytics for Proactive Product Improvement
Product analytics tools help to use data and analytics for proactive product improvement by monitoring user interactions. Using these, product teams can better analyze user behavior, spot mistakes, and make more informed decisions regarding product modifications.
Let’s look at some of the most popular analytics tools for product improvement.
Google Analytics
Google Analytics (GA) is a convenient and multifunctional service from Google for the analysis of Internet sites and mobile applications.
It is popular among marketing teams. However, fewer people know that it also helps make thoughtful, calculated software product development and improvement decisions.
- Use Behavior Flow to analyze users’ behavior. You can observe user interactions with your product from a close distance using Google Analytics' Behavior Flow feature without personally assisting each one of your consumers.
- Observe what users click on with In-Page Reports. It's crucial to understand what users click on. You may monitor which products your consumers select on the landing page, whether they prefer to click on buttons or photos, and even what they click on the navigation bar.
- Justify responsive design with Mobile Overview. You can choose which view (mobile, desktop, or tablet) to focus on developing a product by seeing which device your consumers use most to access your website.
- Discover popular features using All Pages. Google Analytics can also track the features that your users use the most. GA provides information and rankings on the features and pages that users visit most frequently.
- Use exit pages to track where users leave. It's crucial to monitor where and when visitors leave your sites or platform. This way, you identify places of frustration or closure. Otherwise, you can prove that your customers used your product as intended.
- A/B test with Experiments. It's best to support your ideas for improved formatting, layout, and other aspects of your work with data from actual users. This feature enables experimenting with A/B testing various elements of your page or platform.
- Segment your data. Not all users and sessions are equivalent, so, you can segment your data in every report using different criteria, such as region, devices, age, date periods, etc.
FullStory
FullStory is a web-based digital intelligence solution that helps to improve the customer experience. It allows following and keeping an eye on each customer's activity. Everything, including page transitions and clicks, is automatically indexed.
This solution enables recording end-user flow (while keeping their anonymity) to understand their demands better and pinpoint the reasons behind their actions.
FullStory is quick to set up and allows you to specify user interactions in the past, thanks to the automatic collection of all user activities. It begins capturing click user interactions right out of the box, enabling you to filter user sessions by users who click on a particular text string, a specific CSS selector such as a button, or anything else.
Suppose you want to track something outside the typical clicks, page views, and text inputs (or taps, scrolls, screen components, and user engagement metrics on mobile apps). In that case, you need to import user interactions from another program using FullStory's API.
Gainsight PX
Gainsight PX is a full-featured product experience platform. With it, product teams can use thorough product analytics and pertinent customer input to determine which product features are most popular with users and which areas are prime for improvement. They get this data without writing any code.
Gainsight PX provides direct input from users. The interviews can be gathered using this tool to assist in outlining effective feature adoption tactics. Auto-tracking is enabled with the tool’s JavaScript code snippet. Gainsight PX is a user analytics solution that emphasizes product uptake and engagement, but is more enterprise-focused.
This tool is more beneficial to customer success teams. With it, they increase user engagement and reduce churn from current users. Yet, it is less effective for product teams who need to comprehend general user behavior. However, with Gainsight PX, you can also monitor each stage of the customer journey and learn how consumers navigate and engage with your product.
Tableau
Tableau is a visual analytics tool used in the business intelligence (BI) sector. It enables extended personalization of analytical data by knowing your customer by their company, title, location, and flow.
Tableau assists in reducing raw data to a format that is very simple to interpret. Thanks to Tableau, professionals of all levels can easily understand the data and conduct data analysis time-efficiently. Additionally, it enables non-technical users to design unique dashboards, graphs, maps, stories, and worksheets to aid in the visualization and analysis of data and making business choices.
Tableau has many other distinctive features. Let's explore some of the additional Tableau features in more detail.
- Robust data search and exploration capabilities. Users of Tableau may quickly find answers to crucial queries.
- Linking to various data sources. Tableau users can integrate and generate reports from various datasets that other BI solutions do not support.
- Tableau Server. This helps to manage all published data sources inside an organization in a centralized location.
Prometheus
Since its creation in 2012, Prometheus has become a well-liked monitoring tool that a diverse group of contributors supports. Prometheus joined the Cloud Native Computing Foundation (CNCF) in 2016 and has now graduated from the organization.
Prometheus is an open-source tool that offers monitoring and alerting capabilities for cloud-native environments, such as Kubernetes. It can gather and store metrics as time-series data, recording information with a timestamp. Prometheus can also collect and record labels, which are optional key-value pairs.
Prometheus' key features include:
- Using a multidimensional data model. This uses key-value pairs and the metric's name to identify time-series data.
- PromQL. This is a querying language that is adaptable and can make use of the multi-dimensional data model.
- No dependency on distributed storage. Each single server node maintains autonomy.
- Pull model. By actively pulling data through HTTP, Prometheus may gather time-series data.
- Pushing time-series data. This uses an intermediary gateway to push time-series data.
- Monitoring target discovery. This is possible via service discovery or static configuration.
- Visualization. Prometheus provides several graph and dashboard types.
Best Practices for Implementing a Data and Analytics Strategy
Developing a data analytics strategy is the first step in creating a data-driven business. What are the key building blocks of a successful data analytics approach?
- People. You should convey information in a way that is most accessible for the stakeholders who will read it and use it to make decisions. Educate your staff to employ data effectively and draw the right conclusions.
- Processes. You can gain rich insights through analytical models, but managers and staff must be able to comprehend and use them. Using the right technologies to integrate analytics into routine company processes can frequently provide the answer. You need to ensure that data analysis is part of the decision-making process and set rules and guidelines regulating the use of data.
- Data infrastructure. Having the right data architecture is essential for any analytics strategy. The technological infrastructure needed to implement a sound analytics strategy is a component that is frequently disregarded. You can have a great idea, but the proper infrastructure is indispensable if you want to put it into practice. Your data should be stored securely and easily accessible for analytics.
The following best practices address people, processes, and data infrastructure.
Create Data-First Company Culture
Make data a top priority across the entire organization. With everyone on board realizing the importance of data, a data strategy will be successful. It requires awareness and acceptance from all your employees. Data needs to be viewed as a strategic asset and a value to the company.
Monitor KPI and Metrics
In a data-driven organization, everything is measured. The most effective approach to do this is to align your key performance indicators (or KPIs) with your strategic goals. This does not imply measuring all vanity metrics, but rather concentrating on the most important ones. While teams and individuals are frequently given KPIs, important projects and initiatives should also have measurable objectives.
Ensure Data Security
Data security is everyone’s concern. The more data is exchanged between teams, the more access control and distribution considerations you should make. People should have only the data they require. Software engineers don't require access to specific client information or sales statistics, for instance.
Use a Single Source of Truth
The data lineage can grow complicated as it moves between teams and systems. As a result, there are questions regarding the quality and accuracy because it is difficult to trace and check. You need a Single Source of Truth (SSOT) that everyone can trust. Thanks to the SSOT idea, everyone will base their decisions on the same reliable and consistent information.
Boost Productivity with Data Automation
Obtain, transform, and store data using automated methods rather than doing it manually. For teams that rely on obtaining web data, like sales representatives looking into leads and possibilities, data automation can save a ton of time. You can use a data automation solution that extracts data, converts it, and loads it into your CRM for analysis to automate data sourcing.
Techstack Case Study: Introducing Product Analytics System for Sales and Marketing Enablement Platform
How do we work on products? At Techstack, data analysis is integral to the custom software development process. Information and insights let our interdisciplinary tech experts identify problem zones and drive a constant improvement process.
Our intelligence systems enable making data-driven decisions and validating hypotheses. We believe that this approach enhances the value of every product layer. We have worked on an open sales engagement platform that combines content, messaging, and valuable insights to improve business results.
For example, one product required a module for gathering, evaluating, and presenting data on the effectiveness of sales efforts due to the high customer attrition rate caused by poor performance, disrupted experiences, and inconvenience.
To develop a solution for this sales and marketing enablement platform, our engineering team used the following technologies: Java, React.js, JavaScript, Selenium, Kubernetes, Prometheus, Tableau, Google Analytics, Gainsight PX, and FullStory.
Retaining current consumers requires piquing their interest in the product, providing more value, and expanding subscription packages. We developed effective tool integrations for product analytics with Tableau, Gainsight PX, and FullStory.
As a result, we reduced client turnover and increased the platform's client base. All of the upgrades resulted in the product being purchased by one of the top sales enablement companies at the beginning of 2021.
Maximize Your Data with Techstack
Data analytics is vital to ensure your product's success. It allows the discovery of information such as patterns, correlations, market trends, and customer preferences that help businesses make informed business decisions.
Whether you call it the ‘new black’ or ‘new oil,’ data and data analytics are crucial to understanding why user behavior changes. You need these insights to apply proactive product development and improvement—and ultimately, to keep up with your customer’s preferences.
At Techstack, we offer custom software development services to create precisely what will work for your product. Our engineering function fosters engineering culture across Techstack by putting in place methods for engineering quality improvement and scaling tech expertise. We use data analytics as a crucial step to identify issue areas and drive a continuous product improvement process.
