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The Impact of iPhone 15 on the Secondary Mobile Market: Understanding Through Predictive Analytics

Apple’s latest iPhone made its debut early September and, as usual, started shipping shortly after the announcement. This time around, the release included a standard and Pro version, in two different sizes, and various memory configurations. While the move to USB-C was highly anticipated, the iPhone 15 does not bring major improvements in terms of performance or features. Consumers view these advancements as largely iterative, and the upgrade cycle is driven more by contractual and retailer incentives than by hype.

The launch prices for these new models are not significantly different than in past years, with prices ranging from $800 to $1600 for these premium devices. Many consumers are looking for ways to bring the price down, often by trading in their old devices for credit towards the new one. Securing these high-quality trade-ins has become a priority for carriers and retailers.

The secondary market for pre-owned devices continues to grow, with the pandemic driving many consumers to purchase additional devices, leading to increased demand for pre-owned phones. Trade-ins also contribute to the growth of the pre-owned market. Large-scale macro-level factors, such as growing demand for refurbished iPhones, supply chain issues, market share, inventory levels, consumer spending and inflation, and competitive discounts, all impact the B2B market for pre-owned iPhones.

Small-scale micro-level factors also play a role in determining an individual device’s value, including age, model, condition, carrier locked states, and seasonality. B-Stock utilizes advanced machine learning algorithms to accurately predict market prices for iPhones, empowering organizations to make smart data-backed decisions and optimize their margins.

With years of market data and resale expertise, B-Stock has the capability to predict B2B mobile market prices as far as 16-20 weeks in the future. The company uses advanced statistical techniques, including outlier detection, interpolation, and machine learning, to provide deep insight into the secondary mobile market pricing. B-Stock’s extensive data set enables outlier detection to identify unusual or abnormal data, interpolation to fill in missing data, and machine learning to predict B2B market prices.

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