如今, 满足客户期望不再是足够的. 蓬勃发展, 企业必须超越这些期望, 利用以客户为中心的人工智能是实现这一目标的关键.
将人工智能融入客户关系管理 (客户关系管理) 增强追加销售和交叉销售策略, 允许企业分析广泛的客户数据以提供个性化建议.
Keep reading to discover how customer-centric AI elevates CRM strategies, offers personalized insights and real-time decision-making, and ultimately delivers more satisfying customer journeys.
Leveraging AI for Customer Insights
AI can reveal invaluable patterns and trends by analyzing huge amounts of data. It enables you to understand customer tendencies, habits, 和偏好.
Before we discuss how AI can enhance customer relationship management, let’s dive into how AI algorithms analyze customer behavior and data.
人工智能算法如何分析客户行为
人工智能正在改变企业分析消费者行为的方式，并改变消费者与企业互动的方式.
企业主可以使用多种工具通过人工智能处理客户数据, 但总的来说, 这个过程是这样的:
- 数据采集: 该电子商务平台收集有关客户互动的大量数据, 包括浏览历史记录, 购买行为, 查看的产品, 产品调查, 在页面上花费的时间, 和人口统计信息. 纳入 客户的反馈意见 into this data collection enriches AI’s understanding of customer satisfaction and service expectations.
- AI algorithms implementation: AI algorithms process and analyze this wealth of data. Machine learning in sales, such as collaborative filtering or content-based recommendation systems, is used to identify patterns and correlations among customer behaviors.
- Pattern recognition: The AI algorithms identify patterns, such as common product combinations frequently purchased together (cross-selling patterns) 或客户在购买前经常查看的产品 (表明偏好).
- 个性化推荐: 人工智能驱动的推荐引擎利用这些见解. 当客户访问平台时, 根据浏览历史实时生成个性化产品推荐, 过去的购买, 以及类似的用户行为.
- 持续学习和改进: 人工智能算法不断从新的数据输入和客户交互中学习. 随着收集更多数据, 模型不断发展并完善其建议, 确保它们保持相关性和准确性.
先进的预测分析工具，例如 IBM 的 SPSS Statistics, 阿尔泰克斯, 微软的 Azure 机器学习处理这些数据, 识别模式, 相关性, 以及表明未来潜在行为或需求的趋势.
根据分析, 开发预测模型是为了预测可能的客户行为或需求. 这些模型使用统计算法来预测结果, such as the likelihood of a customer making a certain purchase, churn probability, or preferred product categories.
AI-Infused Upselling & Cross-Selling Strategies
AI-infused upselling strategies leverage artificial intelligence to enhance sales by encouraging customers to purchase additional or upgraded products or services.
Here’s an overview of key AI-driven upselling tactics:
AI-Powered Product Recommendations and Customization
人工智能驱动的客户分析是 现代营销策略, using advanced algorithms to create detailed and dynamic profiles of individual customers.
通过收集和分析广泛的客户数据，例如购买历史记录, 浏览行为, 人口统计, 以及与企业的互动——人工智能精确定位不同的行为模式, 喜好, 以及个人特质.
This enables sellers to offer tailored product recommendations based on individual customer behaviors and preferences to suggest complementary or upgraded products.
例如, Amazon’s AI algorithms analyze extensive customer data, 包括浏览历史记录, items viewed, items purchased, and search queries.
“Customers Who Bought This Also Bought” recommendations on Amazon
Based on this analysis, Amazon’s recommendation engine employs machine learning models to predict and suggest products that align with each customer’s interests and preferences.
When a customer explores a specific product, Amazon’s AI generates “Frequently Bought Together” 或者 “Customers Who Bought This Also Bought” recommendations, showcasing complementary or upgraded products. These suggestions encourage customers to consider additional purchases beyond their initial choice—and suggest items they may be interested in.
As customers interact with the platform, the AI continuously learns from their behaviors and refines its recommendations. The system adapts to individual preferences, ensuring increasingly accurate and relevant suggestions.
An example of how Amazon leverages user preferences data to create product recommendations. (资源: Rejoiner)
Amazon’s AI-driven product recommendations contribute significantly to the platform’s success in upselling. Customers are more inclined to explore and potentially purchase additional products, 增加销售额并提高客户满意度.
顺便一提, 如果您使用 Ecwid by Lightspeed 在线销售, 你可以 显示相关产品 与 “你也许也喜欢” 产品详细信息页面和结帐时显示的部分.
动态定价策略和报价优化
人工智能通过分析市场趋势实现动态定价策略, 竞争对手定价, 和实时客户行为. 这使得企业能够优化追加销售的定价策略, 提供个性化折扣, 或与个人客户产生共鸣的捆绑交易.
优步, 叫车服务, uses AI-driven dynamic pricing, known as “surge pricing,” to optimize pricing strategies based on real-time demand, supply, and other factors.
Here’s how Uber implemented their dynamic pricing strategy with the help of AI.
Uber’s AI algorithms continuously analyze data in real-time, including factors like ride demand, traffic conditions, weather, time of day, and historical rider behavior.
Based on this analysis, Uber’s AI adjusts fares dynamically. During peak times or high demand, surge pricing is activated, 提高票价以激励更多司机, 确保更快的取货并满足不断增长的需求.
此外, 优步可能会根据个人乘客的乘车历史记录为其提供个性化折扣或促销, 使用频率, 或特定场合. 例如, 可以向经常使用的用户或在需求低迷时期提供有针对性的促销活动，以鼓励更多乘车.
这些策略最大限度地提高了司机的收入并鼓励乘客继续使用它们.
增强客户体验
通过在 CRM 中利用人工智能, 企业可以通过个性化服务增强客户体验.
例如, Spotify 使用 AI 算法分析用户偏好, 聆听习惯, 和历史数据来创建个性化播放列表, recommendations, 以及每个用户的每日组合.
Spotify 个性化播放列表的示例
这种个性化方法通过根据每个听众的独特喜好定制音乐来增强整体用户体验, 让聆听和发现符合他们口味的新音乐的时间变得更加愉快.
交叉销售策略
Cross-selling tactics integrated into AI-enhanced CRM systems leverage artificial intelligence to identify and capitalize on opportunities to offer complementary products or services to customers aligned with customer buying behaviors.
例如, Netflix effectively tailors its marketing campaigns for cross-selling by recommending TV series or movies to users based on their viewing history.
Netflix makes recommendations based on a user’s viewing history
If a user likes to watch science fiction shows, Netflix’s algorithm suggests similar content or promotes a newly released series within that genre, encouraging the user to explore and watch more content.
Further enhancing these personalized marketing efforts, AI chatbots provide immediate, personalized recommendations to customers. This not only improves the shopping experience but also significantly increases sales opportunities by making every customer interaction an opportunity for targeted marketing and upselling.
Examples of AI-Enhanced CRM Systems
将追加销售策略集成到人工智能增强型 CRM 系统中涉及利用预测分析来识别理想的追加销售机会. 人工智能驱动的 CRM 系统在客户互动期间向销售代表提示相关的追加销售建议, 提高成功追加销售的机会.
Salesforce 的爱因斯坦分析
销售队伍, 领先的 CRM 平台, incorporates AI-powered tools like Einstein Analytics to assist sales representatives in identifying and capitalizing on upselling opportunities during customer interactions.
Salesforce’s Einstein Analytics leverages predictive analytics to analyze vast datasets within the CRM. It evaluates customer data, purchase history, interactions, and other relevant information to predict potential upselling opportunities.
Einstein Analytics spots patterns hinting at upselling opportunities. 例如, detecting increased product usage may signal interest in upgrades or add-ons.
Salesforce’s AI system also provides sales reps with actionable insights. It offers upselling suggestions and talking points based on opportunities identified.
Sales reps leverage AI-driven suggestions to customize conversations, addressing customers’ needs with relevant upselling offers. 例如, they may suggest an upgraded subscription or additional features based on usage patterns.
顺便一提, if you sell online with Ecwid, 你可以 将您的在线商店连接到 Salesforce 通过扎皮尔. 这边走, 新客户将根据新的 Ecwid 订单在 Salesforce 中自动创建.
亚马逊个性化
亚马逊个性化, 亚马逊提供的机器学习服务, 旨在解决创建个性化推荐时常见的挑战, 包括新用户数据的问题, 流行偏见, 以及不断变化的用户意图.
与传统推荐引擎不同, 亚马逊个性化 擅长处理用户数据有限或不断变化的场景. This proves especially beneficial for identifying upselling opportunities, even with new users or when user preferences change over time.
Several well-known companies, such as Domino’s, Subway, and Yamaha, have recognized the significance of AI in understanding and catering to customer needs.
How to Tailor Marketing Campaigns for Upselling and Cross-Selling
You can tailor marketing campaigns for upselling and cross-selling with the help of strategic approaches even if you don’t use AI-powered tools.
For the best outcomes, you need customer data and targeted messaging. Here’s a breakdown of the process:
Perform Customer Segmentation
Use CRM data to segment customers based on their purchase history, 喜好, and behavior. Categorize them into groups with similar buying patterns or interests.
如果您使用 Ecwid 在线销售, you can view, 寻找, and edit all the customer information you need on the 顾客 页. 从那里, you can filter your customer base using various parameters and export the segment to work with it in a different service (例如, 通过您选择的电子邮件服务发送有针对性的电子邮件。)
Ecwid 中的客户页面还提供对客户订单历史记录的访问, 促进分割过程. 通过了解您的客户’ 购买习惯和偏好, 您可以更有效地针对每个细分受众群定制消息传递.
Ecwid 管理中的客户页面
识别机会
分析购买历史和行为数据，以确定追加销售和交叉销售的机会. Determine which products or services complement previous purchases or align with customers’ 利益.
例如, when selling online through Ecwid, you have the option to configure 自动化营销电子邮件 showcasing related products or top sellers.
Related products in an order confirmation email
Make Personalized Recommendations
Create personalized recommendations based on customer segments. Use AI algorithms to suggest related or upgraded products in marketing materials, email newsletters, or on a website. 例如, Amazon’s “Frequently Bought Together” 或者 “你也许也喜欢” 部分.
努力实现有针对性的消息传递
精心设计有针对性的信息，突出互补产品或服务的价值. 展示附加产品如何增强客户体验或解决特定问题.
获得真正优化的消息, 考虑 翻译内容 与不同的受众和语言产生有效的共鸣.
提供奖励或捆绑
提供折扣等激励措施, 捆绑交易, 或忠诚度奖励，以鼓励客户探索更多产品. 使价值主张有吸引力且清晰.
与 Lightspeed 的 Ecwid, 您可以在以下产品的帮助下销售产品捆绑包 追加销售 & 交叉销售产品包, 产品组合, 和 宝哥 应用.
应用多渠道方法
实施多渠道营销策略，通过各种接触点接触客户. Use emails, 社交媒体内容, 网站弹出窗口, 以及个性化平台推荐.
展现个性化推荐的力量
在客户关系的动态格局中, 个性化推荐和有针对性的营销是成功的支柱. 通过利用 CRM 数据, 您可以释放定制追加销售和交叉销售活动的潜力.
当细调时, 这些策略引起个人客户的共鸣, 驾驶参与度, 增加销售额, 并培养品牌忠诚度.
拥抱来自 CRM 系统的见解, 创建自定义营销活动, 并了解如何会见您的客户’ 独特的偏好和需求可以创造奇迹.
