What do P&G, Jaguar, Land Rover, Unilever, Johnson & Johnson, or Rolls Royce have in common? Yes, they are all leading product companies of the world. But apart from that, they all vouch for the transformative power of having a cutting-edge product lifecycle management (PLM) approach in place to facilitate top-notch customer experience, boosts R&D, manufactures new goods, and fuels efficiency.
According to Forrester, by 2020, 85 percent of customers expect companies to automatically personalize deliverables and proactively take care of its needs. So, PLM is primarily about ensuring differentiated, superior customer experience by driving innovation, achieving faster time-to-market, delivering quality, while keeping costs in line. While IoT (Internet of Things), AI, ML, NLP and other such innovative technologies offer real-time information, challenges (detecting failures, predicting loss, calculating correlations and prioritizing solutions with cost limits) remain.
Generate more value from data and enable insightful decision making
While organizations worry about the cost of new products and ROI, customers care about the product’s price/value ratio and quality. Advanced analytics can help both the stakeholders to adapt to new business opportunities. Incorporating advanced analytics into the process can reap many benefits: companies can fine-tune their market forecasts, predict failures and estimate downtime, creating more value for the business and their customers.
Road-blocks in product lifecycle management
Executives managing the product development process must think through some critical decision-making points when strategizing for the digital future. A few of them are:
- How to overcome constant technological disruptions to deliver customer experience that excels?
- Which internal/external factors will influence the product’s performance?
- How to leverage the available skill sets, technology and knowledge to drive organizational efficiencies through smarter operations?
Many companies still lack the arsenal of digital tools required for smooth functioning and must rely on guesswork or trial and error.
How does analytics come to the rescue?
Historically, organizations have long relied on traditional product development methodologies such as FMEA (failure mode and effect analysis), DOE (design of experiment), mean time between failure analysis and value stream analysis.
With ever-increasing volume of data coming in today, conventional technologies fall short and disruptions are widespread. But innovative companies know that data-driven insights play a role across all functions of the product lifecycle and strategize accordingly to maximize the value derived from the investment.
For example, Netflix’s sustained success comes as no surprise for companies that understand the value of leveraging advanced analytics, machine learning and algorithms to drive powerful customer conversations. Netflix has something that is more valuable than money: Contextual Information. Using this data, their recommendation algorithm suggests the most relevant content to its users based on their preferences. The resulting customer experience is exceptional.
Plugging Advanced Analytics in PLM
- Ideation for new product development: Social media, predictive analytics, crowdsourcing, AI and other technologies are used by companies to optimize features that users will pay for.
- Engineering and design: Product data derived from statistical analytics models helps companies identify the right components for product design and tackle challenges along the process. BOM (bill of materials) analysis, regression model, predictive modeling help account for changes in the market price and give a more accurate measure to set the right price for the product.
- Development and validation: Companies can now predict what customers will respond to and plan for the ‘next best action’ or the ‘next best conversation.’ For example, IBM’s The Weather Company, is changing the future with deep weather data analytics and industry-leading AI. Their OPRO (outage prediction resource optimization) system utilizes analytics and big data to study historical data points. With improved predictability, the number of power outages can be determined, which helps them estimate the impact of the storm beforehand. This provides a 2-3-day buffer time, thereby, driving more strategic decision making that improves safety, reduces cost and drives ROI.
- Pre-production and marketing optimization: Companies can refine existing product features and outline certain specifications for new variants by leveraging customer data analytics. Also, companies can predict the promotional channels that can be used to reach the target customer.
- Customer retention: Predictive modeling can be used to determine and decrease churn rate. In a B2B scenario, this is most useful for subscription services. For example, in the telecommunications industry, companies offer free storage space for being a loyal customer.
Vivian Gomes is Vice President-Marketing at CSS CORP.
Disclaimer: This article is published as part of the IDG Contributor Network. The views expressed in this article are solely those of the contributing authors and not of IDG Media and its editor(s).