We’re living in a time where data is a crucial part of everything. Every day, we generate around 2.5 quintillion bytes of data, and that number is only growing more extensive with the arrival of the Internet of Things.
More importantly, this data doesn’t just stand still. It moves, accumulates, and evolves.
If you know how to leverage it correctly, data can help you to make better business decisions and increase profits.
On the other hand, if you fail to use data correctly, you’ll miss out on some vital opportunities.
One of the most valuable areas where data can potentially make its mark right now is in the Product Lifecycle Management environment.
Product Lifecycle Management (PLM) involves paying close attention to the stages that a product goes through when it enters the market. The more we know about the product lifecycle, the easier it is to make predictions about where to invest in new products and how to boost profitability.
Here’s how big data plays a significant role in virtually every stage of the Lifecycle Management process.
Big Data in the Introduction Stage
The introduction stage of the Product Lifecycle is when you first introduce a new item to the market. Researchers tell us that around 70 to 90% of all lifecycle costs build up here.
During the introductory stage, your sales are often slowest, because you haven’t begun to develop product awareness and reach. However, there are also some great opportunities for reducing waste. Looking back over your data from previous introduction stages for similar products allows you to focus only on those ideas in your pipeline that are most likely to generate success. Your market data will show you which kinds of products generate the biggest return on investment and what kind of pricing structure you’ll need to choose to generate a profit.
The right data about your marketplace will also allow you to determine whether there’s too much competition in your current landscape to thrive at this stage.
As you move through the design process to introduce your new project, your data will also provide some insight into the suppliers, materials, and machines you can use to reduce resource use.
Data in the Growth Period
The growth stage in a Product Lifecycle is the most potentially lucrative time. This is when you can generate more attention for your product and services and begin to develop better returns.
Once again, data has a significant role to play.
You can look back at your previous audience testing metrics to determine which kinds of customers you should target first with new products. There’s also the potential to use existing data about your previous products to determine whether it’s best to start with a beta rollout for some of your most loyal customers before you start supplying demand to consumers all over the world.
As you move through the growth period, you’ll be gradually collecting more data about changing customer preferences, which you can add to your decision-making strategy. The information you collect about your marketplace might gradually push you towards advertising your products to a new audience.
Alternatively, you might discover that people are more likely to buy at a slightly different price point.
Using Data Through Maturity
In the maturity stage of the Product Lifecycle, your new offering has usually already reached its saturation point.
This means that customers already know about it, but you might not be getting as much attention or profits as you got throughout the growth period. That’s because the audience you were already targeting probably has your product by now.
You can use your data to determine how to keep profits rolling in.
For instance, you might decide to search through your customer data to see if you have any aligned customer bases that you haven’t reached out to yet. Maybe you could even think about bringing some existing customers on as advocates for your brand, giving them rewards for referrals? You can also look at your previous information to determine whether you’re more likely to boost declining sales with access to more discounts, free shipping, or marketing events.
Historical data can easily boost your sales right now.
Reducing Decline With Data
Finally, no matter how amazing your product is, there’ll always be a period of decline in its popularity. This is when sales begin to decrease, and companies need to figure out what to do next to ensure its continued success.
A great option is to use your data insights to determine whether you can create a closed loop for materials. Dell’s takeback service is a great example of a fantastic closed-loop strategy, which takes computers manufactured by the company and turns them into new products.
You can use a recycling strategy like this to reduce your carbon emissions, improve your brand reputation, and lower supply costs during the decline stage.
At the end of life stage, data will give you a better insight into when the best time is to recycle a product, retire it, or simply add new features to make it more appealing all over again.
Your information from previous products will help you lower your risk of wasting an item that still has value.
Overcoming PLM Challenges With Data
Product Lifecycle Management is a critical tool for companies who want to strengthen their outcomes, improve their profitability, and make the most of customer demand.
However, in the past, managing the lifecycle hasn’t been easy. Misaligned software in the business environment makes it harder to collect data that leads to a unified and single point of truth. Employees manually entering information also introduces new risks for inaccuracies.
To truly understand the lifecycle in full, companies need an environment that’s built to collect and use data as efficiently as possible. This means creating an aligned system for Product Management that brings all of the software and data processes in the business together in one environment, from customer relationship management tools, to sales forecasting software. Tracking and analyzing data throughout the customer and product journey is at the core of the emerging technology driving the transformation of the fourth industrial revolution. The more data companies collect through this journey, the more advanced the business environment will likely become.
An aligned PLM strategy could lead to automated systems, AI and machine-learning driven development, and so much more.
Data is at the Heart of Everything
In an environment where companies have more access to information than ever before, there’s no excuse for decision making that isn’t driven by data.
The information you collect through the Product Lifecycle will inform you about how to impress your customers, where you can cut costs, and even how you can improve efficiency with automation and machines. The more data you collect, the easier it will be to see the trends that can increase your product profitability faster, and identify the roadblocks that are preventing you from reaching your full potential.
Data should be a crucial factor for all of the four stages of the standard Product Lifecycle. Remember to regularly check and update your PLM framework based on what you learn from regular data analysis and feedback.