France has traditionally marketed its wine with a ““Château”” mindset, promoting itself as the world’s foremost winemaker. The sale of the “Château” Latour-Laguens in the Bourdeaux area to a Chinese buyer aroused many eyebrows, since China does not have a strong winemaking tradition. What about custom? What about genuineness? But
What consumers truly value can be difficult to pin down and psychologically complicated. But universal building blocks of value do exist, creating opportunities for companies to improve their performance in existing markets or break into new markets. In the right combinations, the authors’ analysis shows, those elements will pay off
As companies have struggled to make use of datasets and AI, many have started to create data products – reusable datasets that can be analyzed in different ways by different users over time to solve a particular business problem. Data products can be a powerful tool, especially for large, legacy
Do highly creative ads really inspire people to buy products? Studies have found that creative messages get more attention and lead to positive attitudes about the products, but there’s little evidence linking those messages to purchase behavior. To address this gap, Reinartz and Saffert developed a consumer survey approach that
With consumers nowadays preferring personalized content, marketers are finding success in targeted messaging: providing value to attract potential customers based on the customers’ interests and readiness to buy. It’s an important consideration for how to create search-friendly content throughout the consumer journey. The stages a buyer goes through – from
Many businesses are managing a sharp decline in sales during the ongoing coronavirus crisis. An instinctive reaction may be to cut low-performing products from their menu of offerings – but this isn’t always the best way forward. The authors lay out a case for adding an ultra-expensive product to their
Guy Rosen has served as the vice president of product management at Facebook since 2013. This is the transcript of an interview with Frontline’s James Jacoby conducted on September 5, 2018. It has been edited in parts for clarity and length. Bring me back to the moment when you’re basically
Your business is preparing to launch a brand new product into the market. The product has been built and refined through many iterations, and now you are ready to ship it to customers. Only one question remains: what price should you charge for your product?
In this article, we analyze Van Westendorp’s Price Sensitivity Model, a data-driven pricing model that uses survey data to determine customers’ willingness to pay for your product. This article details how the Van Westendorp model works, why it addresses shortcomings in current pricing conventions that many businesses use, and what its limitations are.
As a small business or entrepreneur, you know how much work it takes to market your products and services to the right people. Digital marketing, content marketing, and social media marketing are all huge parts of the process. But none of them will work to your advantage without the right dose of product positioning.
Manufacturing CEOs I’ve spoken with are in unanimous agreement that the best way to drive new revenue growth is by transitioning to more services-based revenue models based on next-generation products. Their product roadmaps include configurable, customized products capable of delivering data back to manufacturers they can monetize as services. Manufacturers are looking to get beyond relying on transaction revenue alone. They’re most focused on how they can use configurable products to launch higher-margin outcome-based business services, and their 2020 roadmaps reflect this goal. The following graphic from McKinsey’s Leveraging Industrial Software Stack Advancement For Digital Transformation (50 pp., PDF, no opt-in) explains why manufacturers’ 2020 roadmaps are dominated by more configurable products capable of driving new services-based business models.
Manufacturers’ most valuable data is generated on shop floors daily, bringing with it the challenge of analyzing it to find prescriptive insights fast – and an ideal problem for machine learning to solve.