How Analytics Can Be Used to Accelerate Time-to-Market Processes

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Who doesn’t want faster time-to-market? Faster time-to-market means you waste less time with a profitable idea that isn’t earning any money. But, how does one achieve that goal?
powering intelligent manufacturing

The answer lies in analytics. They can help you accelerate time-to-market by determining what your customers want, how the product will perform in the market, and how to manufacture it efficiently.

What Do Your Customers Want?

Your customers are talking to you on a daily basis. They’re sharing more information with you than ever before through their mobile devices, their browsing habits, their connected devices, and social media. When you listen to that information, you glean an extraordinary amount of insight.

By applying predictive analytics to customer-generated information, you can get a better sense of what products your customers want and need, as well as how they’ll use them. This information allows you to design products that will make it to market sooner because you know they’re going to sell.

How Will the Product Perform in the Market?

Bringing a product to the market used to be a guessing game. Sure, you might have a killer product, but a number of factors can turn a dream boat into a sinking ship. If a product has poor marketing, isn’t priced right, or isn’t perceived to be different enough from other products out there, it will fail.

Predictive analytics have a role to play in product performance, meaning that there’s less time wasted researching how it will do once it’s on the shelves. With the information derived from predictive analytics, you can figure out what your optimal pricing should be, how to segment your marketing so that you reach the right people at the right time, and how to differentiate your product so that customers believe it to be a striking new addition to the market.

How Can You Manufacture Your Product Efficiently?

In a survey of 1,600 integrated-circuit-design projects carried out by consulting firm McKinsey, over 80% of those projects were delivered late. The average cost overrun was nearly 30%. Moreover, projects were 80% as likely to suffer a cost overrun as they were to finish on-time – not exactly great statistics.

Yet again, predictive analytics have a role to play in efficient manufacturing. They fill in the gap that exists when engineers don’t accurately estimate how long it will take or how much it will cost to add all of the required features while maintaining high levels of quality. In addition, project specifications have a nasty habit of changing during the course of the design phase. Predictive analytics help manufacturers estimate how long it will take to produce an item and how much it will cost, enabling them to get it to market faster.

The longer it takes to get a product to market, the longer it takes to see ROI on the investment you’ve made in this product. Predictive analytics help you recoup that investment by shortening the time to market so you can achieve high ROI.

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