Data-Driven Product Development Trends for 2017

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Data-Driven Product Development Trends for 2017: An article about how to build your product with data rather than guessing.

The numbers don’t lie, but they don’t tell the whole story either.

When it comes to building a better product, relying solely on data is a mistake. The numbers speak for themselves, but you also have to know what to ask. Quantitative data can provide insight into who your audience is and how they behave, but it doesn’t provide context or explain why.

That’s where qualitative research comes in. Whether you are designing a new product feature or a new business model, talking with customers (and prospective customers) is essential for understanding the market and customer needs. This is especially true when it comes to the early stages of product development when the market is still forming and there isn’t much quantitative data to draw from.

If you want to build a better product — one that people want to use and pay for — then invest in user research before you begin building and again throughout the process. Good customer research is an essential part of a data-driven approach to building products because it provides the context needed to make sense of user behavior and make decisions based on real customer insights rather than

Our report on the Data-Driven Product Development Trends for 2017 reveals that product people are:

Stepping up their experimentation practices to test hypotheses, find pinch points and prove assumptions.

Getting more serious about using big data to make better decisions.

Using a mix of qualitative and quantitative research to fill in gaps in understanding.

Investing in the right tools to help them make smarter decisions.

Our goal was to uncover emerging trends in data-driven product development so we can better understand how product teams are using data to drive decisions and meet their goals. We also wanted to find out what challenges they face as they do so.

Data-driven product development is no longer a nice-to-have; it’s an essential practice. But how will this trend play out in 2017? Here are the top two trends we expect to see over the next 12 months.

In order to build a successful product, you need to make informed decisions. If you’re making decisions based on guesswork and hunches rather than real data, you’re essentially throwing darts in the dark and hoping for the best.

Data-driven product development is no longer a nice-to-have; it’s an essential practice. But how will this trend play out in 2017? Here are the top two trends we expect to see over the next 12 months.

To understand what these trends mean for your team, let’s first define data-driven product development so we’re all on the same page. According to a recent blog post by Aaron Shapiro, CEO of Huge, “Data-driven product development is the process of using actual data about users and their behaviors to inform your decisions about how to shape a product.”

Data-driven product development is not new. Over the past few years, we’ve seen more and more companies make data-informed decisions.

As we move deeper into 2017, the need to be data-driven will remain critical, but the approach will change. Here are five notable trends in data-driven product development that we expect to see in 2017:

1.Personalization through machine learning

2.Proactive and automated customer service

3.Data-driven marketing decisions

4.Apps for machine learning

5.The rise of “smart” products

One thing is clear: The stakes for being data-driven have never been higher.

The products and services we use are often powered by software, and the best of them are powered by data.

Data is the fuel for product innovation. It allows us to make more informed decisions, detect problems before they happen, and design solutions that are better at meeting a customer’s needs.

Here are some of the trends we expect to see in 2017:

1. Data-driven product development will be essential to remain competitive.

2. You’ll need to collect feature usage data (no matter what kind of software you build).

3. More products will be built with data science at the core (and not just Analytics products).

4. Successful products will derive insights from multiple data sources (including customer interactions).

5. More effort will be put into using analytics to build better products (and not just better marketing materials).

The year 2016 has been an exciting year in the field of data science. A lot of companies have started making their data resources available to the outside world in the form of APIs, Platforms and other abstractions. This has made it easier to use advanced machine learning techniques to solve real life problems and build innovative solutions. This year also saw a few breakthroughs in deep learning techniques which are taking us closer and closer to human level artificial intelligence.

It is safe to say that 2017 is going to be another exciting year for the field of Data Science. In this post, I am going to list down some of the trends that I think will define the year 2017 for data scientists, data analysts and machine learning engineers:

1.)Data-Driven Product Development: Many companies are now relying on their data teams to take key decisions instead of their marketing team or product managers. For example, when Uber wanted to release their service in India, they did not just rely on their intuition but also used data science techniques like market basket analysis (MBA) on purchase patterns to determine the most appropriate price for their service which resulted in them becoming one of the leading ride sharing companies in India within a short time period.

2.)More focus on productization: Companies have realized that

Predictive analytics is a powerful tool for product managers looking to make data-driven decisions. With it, the product manager can identify and apply patterns from historical and transactional data to better predict the future performance of products or services.

Predictive analytics has many uses in product management. It helps with everything from determining what features to add to a new product, to optimizing user experience, and even predicting sales or other consumer behavior trends.

Predictions are based on existing data trends, so the more data collected over time about specific events, the more accurate the predictive analytics can become. For example, if a product manager wants to predict how many widgets will sell next month, they’d first look at historical sales patterns of that widget over several months or years. The predictive analytics would then use these patterns and any recent external factors (like economic trends or weather) to come up with a number of widgets that are likely to sell next month.

An organization’s business intelligence team may be responsible for setting up the predictive analytics model and generating these forecasts for the product manager, but it’s up to them to interpret the results and act on them appropriately.

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