This article substantiates the necessity of implementing individualized approaches in the digital retail of photographic equipment. The relevance of the study is driven by the rapid growth in volumes of behavioral and transactional data and the high competitiveness of the online market, where up to 80% of consumers expect personalized offers from brands and are willing to share their data to improve service quality. The objectives of the work are a systematic review of the theoretical foundations of one-to-one marketing, an analysis of the scale of CRM and CDP platform usage, and an assessment of the economic effect of applying algorithmic recommendation systems in the photographic equipment segment. The novelty of the research lies in the comprehensive combination of industry statistics analysis with concrete personalization techniques for photographic equipment, including differentiated content for novices and professionals. For the first time, the author integrates data on the multiplicative effect of personalized scenarios—a 288% increase in conversion and a 369% rise in average order value when interacting with dynamic recommendations—with market development forecasts for accessories and ROI metrics of email campaigns. The main conclusions confirm that the implementation of an end-to-end data → model → offer architecture based on the CRM + CDP linkage and machine learning not only nearly triples purchase likelihood and quadruples average order value but also reduces customer acquisition costs by up to 50%, while simultaneously increasing customer lifetime value and loyalty. Successful strategy execution requires the consolidation of transactional, behavioral, and demographic data, continuous A/B testing, and optimization of content chains according to consumer experience level. This article will be useful for marketers, product managers, and e-commerce executives specializing in photographic equipment sales.

personalization, marketing communications, CRM, CDP, machine learning, e-commerce, photographic equipment

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