2023年是学习机行业巨变的一年，这一年X教育公司、科大讯飞、猿辅导等教育科技公司先后入局学习机市场，并取得了亮眼的成绩。新品牌的强势加入占据了线上渠道，老牌学习机企业依靠线下渠道的护城河稳固住线下渠道。学习机市场已经是一片红海，如何突破当前的学习机直播营销模式带来新的营收增长，是X教育公司学习机业务发展面临的关键问题，也是本文研究的主题。

在研究过程中，本文先通过问卷调查和深度访谈系统了解学习机的直播营销现状，然后对问卷调查得到的数据进行分析，通过深度访谈获得的多方信息，得出了学习机的直播营销的问题及其背后的原因。最后，通过SICAS消费者行为分析模型结合消费者购买决策过程，提出了X教育公司学习机的直播营销策略优化。

研究发现，在X公司消费者购买决策过程的五个环节均存在不同程度的问题：一是品牌感知不全面，二是直播吸引力不足，三是交互沟通不够，四是购买行为转化率不高，五是用户分享意愿低。针对这些问题，本文基于SICAS模型提出了X公司学习机的直播营销优化策略：第一，扩建直播矩阵账号，调动达人发布分享，增加触达机会；第二，增加内容输出，提高主播综合素质，开拓更多直播形式，提高观众兴趣度；第三，有奖互动问答，调动观众积极性；第四，增加促销方式，做好蓄水等待转化；第五，丰富分享活动形式，提高分享激励价值。这些措施将有助于提高现有直播营销模式的效果，也将为其他公司学习机的直播营销策略提供借鉴。

2023 is a year of great change in the learning machine industry. In this year, X Company, iFlytek, Monkey Tutor and other educational technology companies have entered the learning machine market and achieved outstanding results. The strong participation of new brands has occupied the online channel, and the established learning machine enterprises have steadily established the offline channel by relying on the moat of the offline channel. The learning machine market is already a red sea. How to break through the current live marketing mode of learning machines to bring new revenue growth is the problem studied by the author.

In the process of research, this paper first understands the current situation of live marketing of learning machines through questionnaire survey and in-depth interview system, then analyzes the data obtained from the questionnaire survey, and obtains the problems of live marketing of learning machines and the reasons behind them through the information obtained from in-depth interviews. Finally, SICAS consumer behavior analysis model combined with consumer purchase decision-making process, put forward X Education company learning machine live marketing strategy optimization.

The research found that there are problems of varying degrees in the five links of the consumer purchasing decision-making process of  X company:  first, the brand perception is not comprehensive;  second, the live broadcast attractiveness is insufficient;  third, the interactive communication is not enough;  fourth, the conversion rate of purchase behavior is not high;   fifth, the user's willingness to share is low.  In view of these problems,  this paper proposes the live broadcast marketing optimization strategy of X company's learning machine based on the SICAS model:  first, expand the live broadcast matrix account, mobilize the master to release and share, and increase the opportunity to reach;  second, increase the content output, improve the comprehensive quality of the anchor, develop more live broadcast forms, and improve the audience's interest;  third, the interactive question and answer with awards, mobilize the enthusiasm of the audience;  fourth, increase the promotion methods, do a good job of water storage and waiting for conversion;  fifth, enrich the forms of sharing activities, and improve the value of sharing incentives.  These measures will help improve the effect of the existing live broadcast marketing model, and will also provide reference for the live broadcast marketing strategy of other companies' learning machines.