Big Data in New Product Development: Unlocking the Power of Data-Driven Innovation

Data is one of the essential parts of any business, and also includes the area that is mainly focused on product development. Companies can efficiently analyze the data and incorporate it in the different stages of development for product creation. In this way, it helps in developing a product that is more effective than it would be without analysis. It is a time taking process but it is good to introduce the data analysis in starting stage of the product to ensure its quality performance. In this blog, we are talking about the role of big data in improving the development of new products.

Key Advantages of Utilizing Big Data in New Product Development

Big data brings various benefits to new product development. It allows companies to develop products that are more connected with customers and boost consumer value while reducing the risk associated with new product launches. With data mining, companies can easily identify customers’ key needs and fulfil their prime demands, which increases customer value and improves customer brand engagement. The inclusion of predictive analytics and data modelling also helps in forecasting product performance in the market and optimizing marketing strategies to get more customers at the lowest cost.

In short, Big Data is playing a vital role in transforming big data into actionable insights to create effective products and improve existing ones.

Use of Big Data in New Product Development

• Big Data to Get Actionable Insights 

Companies are using Big Data tools like Data Mining, Predictive Analytics, etc. with traditional market research techniques to get actionable insights into customer needs as well as their brands/products’ demand. With this, they will be able to take a proactive approach to develop a new product. Like, they can develop entirely new products, find opportunities to launch new product features and product extensions and improve their existing products. Working on a proactive approach instead of a reactive approach allows companies to ensure better product quality and brand consistency through effective marketing. Also, it reduces the risk of uncertainty associated with new product launches because failure can be highly expensive. There are mainly three methods used for predicting new product success that include the Bass Model, the Fourt-Woodlock Model, and the Assessor Model.

• Develop New Products by Using Big Data

After completing beta testing, companies start determining how to boost product manufacturing and integrate it with existing operations. It contains everything from contingency planning to the evaluation of optimal suppliers. Optimization models are utilized by companies for predicting quality constraints. The data can be used for strengthening decision-making and getting a higher ROI with outstanding performance. The next stage is commercialization when an actual product is finally launched.

With optimization models, companies can predict the region, nation, and location distribution where the chances of their product adoption are higher among consumers at a low customer acquisition cost. It can prove beneficial in getting information about the ideal location (or locations) for the product launch. Next, for optimizing the operational aspects of the distribution chain, Big Data management tools can be used.

Modelling tools are used for optimizing the media planning process to identify the right media channels, like PR, digital, and advertising, through which firms can achieve their marketing goals. The last stage includes the price adjustment to reflect actual supply, production, and distribution costs. Also, it focuses on the key demand of the market, sales, and responses from competitors.

• Big Data to Improve Existing Products

It happens many times when consumer insights captured by market research for the new product include the existing product of the firm. Like customer feedback, this criticism criticizes the firm’s existing product. The company can use such insight when launching a brand extension of the product. Also, companies can get relevant data from different social media networks and other online sources to understand whether their product meets the customer’s needs or not. By collecting such information, firms can develop product solutions according to current market trends or build a planned solution for new product launches or extensions of existing products.

Next, the Internet of Things comes into the spotlight, which allows businesses to connect their products like household appliances via wireless technologies. With this, firms can provide real-time data about consumer usage and identify opportunities to maximize revenue and increase product value for the customer. For example, a smart refrigerator can be programmed to retain diagnostic information that helps the company make the right preventative and emergency maintenance efforts. If a company receives repeated complaints about a particular feature, then it can improve it at cost or for free.

By using predictive analytics, companies can determine the product features which should be included in next-gen products to generate more returns. For example, a company can do a beta test of a new video game console with various features like games, new controllers, etc. and check the customer’s buying behaviour. Also, companies can utilize such data for determining the price of different customized versions of consoles that allow them to reap better revenue and profit goals.

Conclusion

The role of Big Data in new product development is not limited to this extent. Each business is different and has its own unique requirements, so you need to identify how to use Big Data to improve your business’s performance. If you want to leverage the power of Big Data to boost your business, then contact a professional like Ksolves. At Ksolves, we can help you implement the right implementation strategies that efficiently grow your business.