How AI is Reducing Product Time-to-Market
Exploring how machine learning is bringing better products to market, faster than ever before.
A new product's time-to-market is how quickly the product is developed and brought to the market. A quick time-to-market for a product can be a competitive advantage, as seen by companies that have enjoyed the first-mover advantage, like Amazon and eBay.
If the company has a significant head start when they come to market, then they are more likely to grab market share before their competitors can even launch their own product. Even being a second-mover comes with benefits, like the opportunity to free-ride from the investments made by first-movers, the resolution of market uncertainty, technological discontinuities that open for a new entry, and more.
There are other advantages to having a quick time-to-market as well. For example, it allows for better pricing power from an earlier stage in development, and it may allow for faster monetization by generating early revenues from licensing agreements or from early sales.
The average time-to-market for a new product depends on many factors. The first factor is the type of product. For instance, some products are tracked through different industries than others, such as medical devices versus personal care products. The second factor is what type of market it is in, such as pharmaceuticals versus consumer electronics. Lastly, how much capital is available plays a role in how quickly a company can develop and launch its new product.
Across industries and market categories, AI is reducing product time-to-market in a number of ways.
AI Forecasts Demand for New Products
AI helps companies discover opportunities for new products by monitoring customer reactions to existing products and services.
Commerce.AI’s data engine monitors customer conversations on social media, and then accurately predicts what people want to buy before they know it themselves. Commerce.AI does this by monitoring millions of conversations a day between customers and brands, using an automated data engine that identifies consumer trends by analyzing text, emotional sentiment, hashtags, and other key data points.
This information can be used to help companies forecast demand for new products or even offer new features in existing products.
AI Automates Market Research
AI-powered technologies have been changing the way we do business, and market research is just one of the many fields that will be affected. In the past, marketers had to conduct qualitative research to gain insights about their customers. However, this was both time-consuming and expensive. Marketers had to send people out into the field to provide in-person interviews and surveys, which could take weeks to complete. Today, AI automates this process by providing insights about consumers from billions of data points.
One of the most popular uses for AI is sentiment analysis. Machines can quickly read millions of reviews or social media posts and identify the sentiment behind them. For example, if someone is complaining about a product on Twitter, the AI can determine that they are likely angry and frustrated with it. This insight can be used to make changes that will keep customers happy which will likely result in more sales.
Instead of taking weeks or months to conduct qualitative research, marketers can now collect important insights in a matter of hours or minutes. This allows them to make more informed decisions about how to market their products and services which will lead to better ROI from their work.
AI Automates Competitive Strategy
A comprehensive competitive strategy is a prerequisite for bringing any product to market successfully. Just look at the Microsoft Zune, which failed due to competitive pressure from the iPod.
Commerce.AI enhances the competitive position of brands by automating the accumulation and synthesis of over a trillion data points across products, categories, and brands. The next step is to put this data to work, and to make it actionable.
Commerce.AI then applies advanced machine learning algorithms to identify patterns in the data that provide insights on what is happening now and what will happen next.
Ultimately, AI has the potential to reduce time-to-market by automating the most essential aspects of product strategy, from demand forecasting to market research and competitive strategy.