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The Multiplier: BCG X’s Official Blog

Join us at the intersection of innovation and experience with our blog, where our seasoned experts in tech, design, and entrepreneurship provide deep dives into emerging technology topics. Equip yourself with cutting edge insights and stay at the forefront of the tech wave with us.
2023-10-25
Marketing is no stranger to having to do more with less, especially in today’s economic climate. But even during periods of growth, businesses need to carefully allocate their marketing dollars. Exploring experimental frameworks for traditional marketing strategies is one way marketers can drive efficiencies and, at the same time, improve marketing performance.
2023-10-03
By Kunal Kumar and Miao He
Manufacturing companies are facing a conundrum: In the face of greater economic uncertainty and rising interest rates, they must maintain high productivity and improved efficiency without increasing CapEx spending. BCG clients have found EBITDA impact as high as +8% through improvements in decision-making related to productivity drivers such as machine setups and changeovers, operator-skill level, unplanned production downtime, and idle time. Recently, many clients have found that they can improve decision making through the use of optimization-based production-scheduling tools.
2023-10-03
By Audrey Bryce
In a world filled with visual stimuli and digital distractions, one medium has, ironically, quietly been making its mark: audio.
2023-10-03
By Jerry Wang and Jiachen Huang
As practitioners of Recommender Systems (RecSys) in different industries and fields, we often find situations where explicit feedback is a rarity. Imagine a scenario in which the only information available is product transaction history data: products a customer purchased and when they purchased them. What is lacking is information on products the customer perused, but rejected. In this implicit feedback dataset, we have positive feedback only (purchases), and all non-purchases are a mix of unknown (products customers haven’t seen) and true negative feedback (products customers have seen but didn’t like). This poses a challenge when building a recommender model that predicts a customer’s propensity to purchase other products.

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