数据驱动:唤醒商业机会与优化运营策略

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"从数据中发现商业机会,主讲人是TalkingData的首席布道师鲍忠铁。讲座强调了数据对于企业决策和人类生活的影响,指出数据资产的重要性以及数据运营在业务中的关键角色。讨论了如何通过数据运营唤醒沉睡客户、提高单客价值、降低获客成本等,提出了3A3R运营指标体系,并强调了深度分析数据以拨开奢华指标的重要性。此外,还提到了数据运营团队的资源配置,包括至少配备数据分析师和数据挖掘工程师,并需要定期进行运营活动的分析和优化。" 在当今数字化时代,数据已经成为了企业发掘商业机会的核心工具。鲍忠铁的讲座指出,很多企业的数据资产仍然没有得到充分利用,而有效的数据运营能够显著改变企业决策,提高客户价值,以及优化业务流程。 首先,数据可以帮助企业识别并唤醒那些沉睡的客户,将休眠客户比例从35%降至理想状态的20%左右,以此提升客户活跃度。同时,通过对单个客户的深入了解,可以提升信用卡和证券客户的单客价值,如将信用卡单客价值提升至1000元,证券客户提升至700元。 其次,数据运营不仅仅是写文案或做直播,而是应该积极参与到业务中,通过数据驱动策略来降低获客成本。例如,在证券行业,通过数据运营可以寻求2-3倍的投资回报率(ROI),并提高信用卡借贷人群比例,以及提升证券交易金额和频次。 在资源配置方面,一个App团队应至少配备6名运营人员,其中包含数据分析师和数据挖掘工程师,以确保每天都能进行运营活动的分析、实施和优化。这样的结构有助于企业快速响应市场变化,通过数据洞察做出决策。 此外,3A3R运营指标体系( Acquisition、Activation、Retention、Revenue、Referral)提供了全面评估业务表现的框架,包括DAU、MAU、ARPU、转化率等关键指标,以及对不同客户群体的深入分析,如老客户比例、年轻客户比例、高价值客户比例等,这有助于企业更精确地定位目标客户,优化产品收入趋势,提升ROI,以及提高各个阶段的转化效率。 最后,讲座提醒企业要设定明确的目标,比如从数据中发现新的增长点,不断调整策略,持续改进数据运营的效果,从而实现商业价值的最大化。通过对各项数据指标的深度分析,企业能够更好地理解市场动态,发现潜在机会,实现可持续的业务增长。
2016-09-08 上传
Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.