Use AI to Personalize and Optimize Interactions With Customers

2023-04-02 15:00:01
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Illustration: © IoT For All

Artificial Intelligence (AI) is revolutionizing how marketers and customer-facing business areas are interacting and engaging with customers. In fact, in today’s fiercely competitive world, data science is helping rewrite the dynamics of the business, as it enables precision in the personalization of the customer journey, which was not possible before. Today, every company’s future is inexorably tied to the journey of its customers. Studies have shown that 88 percent of U.S. marketers reported seeing measurable improvements due to personalization, and 44 percent of consumers said they would become repeat buyers after a personalized shopping experience with a company. What’s more, businesses saw an average increase of 20 percent in sales when using personalized AI experiences.

In fact, customer personalization does not end at selling a product or service to a customer. It has to extend beyond. Highly personalized customer service can help a brand exceed customer expectations resulting in a higher Net Promoter Score (NPS). This will help reduce churn and upsell/cross-sell opportunities. For personalization to be effective, it requires a systemic and sustained effort and the involvement of all of the team members. Investment in data, tech, and people is required to make it a success.

“For personalization to be effective, it requires a systemic and sustained effort and the involvement of all of the team members.”

-DAIN Studios

DAIN Studios

How AI Can Help

Personalization AI can help businesses improve the customer experience, increase sales and revenue, and improve their marketing efforts. We recommend you focus on four major initiatives for the deployment of AI and data science to personalization:

#1: Customer Onboarding

By setting the customers up for long-term use of your product or service from the initial stages with the help of algorithms, you can increase retention rate, boosting referrals and reducing abandonment rate.

#2: Next-Best-Action Calculation

By using a dynamic decision strategy that uses all customer data to find the best next action for (potential) customers, you can increase customers’ satisfaction which will lead to higher conversion rates and revenues.

#3: Cross-Sell and Upsell of Products/Services

By recommending products or services that are tailored to a user’s interests, you can increase the likelihood that a user will make a purchase, which will lead to an increase in revenue.

#4: Churn Prediction & Prevention

Based on a dynamic calculation of the percentage of dropped-out customers within a predefined time interval and deploying prevention strategies to prevent churn, you can ensure a long-term relationship with the customer and revenues.

The impact of the deployment of personalization AI can be measured in the:

  • Increased overall revenue and revenue per customer – by up to 25 percent.
  • Higher conversion rates for products and services – by up to 20 percent.
  • Higher ROI for marketing Investments – 2x to 3x.
  • Higher customer satisfaction – significant.
  • Lower churn rate – by up to 30 percent.
  • Improved customer experience and brand experience.

*Please note that the benchmarks and numbers mentioned in the article are based on internal research and clients projects of DAIN Studios.

Industries that Benefit from Personalization AI

While Personalization AI can be beneficial to a wide range of industries, including e-commerce, manufacturing of consumer and industrial goods, retail, finance, healthcare, and more, specific applications will vary depending on the needs and goals of the individual business.

For example, manufacturers and retailer can engage in a direct-to-consumer interaction and use AI to understand customer needs, recommend the products based on their browsing and purchase history, and hence increase the overall basket value.

In the healthcare industry, personalization AI can be used to provide personalized service, such as by providing information or assistance that is tailored to a customer’s needs. In the finance industry, personalization AI can be used to provide personalized financial advice and recommendations, such as by analyzing a customer’s financial history and providing advice on investment or savings options.

Getting Started

Getting started with the Personalization AI journey means getting the business ready to become data-driven. While all of the following steps will be important, without data, none of it will work.

Getting the data to be able to build machine-learning models means centralizing and activating the data. Centralizing data will help get all the data in a high-quality manner into one location, like a CDP. Activating the data means acting on the outputs of the machine-learning model to derive real, tangible value for the customer and business. There are also a number of activities that the business needs to focus on:

  • Identify the specific goals and objectives that the company hopes to achieve with personalization AI. This can include goals such as improving the customer experience, increasing sales and revenue, or improving marketing efforts.

  • Collect and activate data about the company’s customers. This can include data about their preferences, behavior, and interests. This data can be used to train the personalization AI and to provide personalized experiences for individual customers.

  • Select and implement a personalization AI platform that is suitable for the company’s needs and goals. Specific platforms or tools will depend on the company’s needs and goals while integrating the personalization AI with the company’s existing systems and processes, such as customer relationship management (CRM) systems or marketing automation tools will be the key to success.

  • Monitor and evaluate the performance of the personalization AI to ensure that it is achieving the desired goals and objectives. This can involve tracking key metrics, such as customer satisfaction or sales revenue, and making adjustments as necessary to improve the performance of the personalization AI.

Making the Impossible Possible

Overall, the real benefit of using AI and machine learning in marketing, sales, and customer care for the personalization of customer engagement, is making the impossible possible: being faster in calculating the best outcomes within a complex environment, detecting patterns, and optimizing granular behavior which would be invisible for the human eye. Personalization AI is a game-changer and a competitive necessity for any business nowadays.

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  • Artificial Intelligence

  • Artificial Intelligence
  • IoT Business Strategy

参考译文
使用人工智能来个性化和优化与客户的互动
插图:© IoT For All --> 人工智能(AI)正在彻底改变营销人员和面向客户的业务部门与客户互动和参与的方式。事实上,在当今竞争激烈的市场中,数据科学正在帮助重塑商业格局,因为它使客户旅程的个性化更加精准,而这是以前无法实现的。如今,每一家公司的未来都不可避免地与客户旅程息息相关。研究表明,88% 的美国营销人员报告称,由于个性化策略,他们看到了可衡量的改进;而 44% 的消费者则表示,在与公司进行个性化购物体验后,他们会成为回头客。更重要的是,企业在使用个性化 AI 体验后,销售额平均提高了 20%。实际上,客户个性化并不仅仅止步于向客户销售产品或服务。它还应延伸到更广阔的领域。高度个性化的客户服务可以帮助品牌超越客户期望,从而提高净推荐值(NPS)。这将有助于降低客户流失率,并创造升级销售和交叉销售的机会。为了实现有效的个性化,它需要系统性的、持续的努力,并需要所有团队成员的参与。要使其成功,还需在数据、技术和人员方面进行投资。“要实现有效的个性化,它需要系统性的、持续的努力,并需要所有团队成员的参与。”-DAIN Studios DAIN Studios 人工智能如何助力个性化 人工智能可以帮助企业提升客户体验、增加销售和收入,并优化营销工作。我们建议在部署人工智能和数据科学以实现个性化方面,重点关注以下四个主要举措: #1:客户引入 通过在客户使用产品或服务的初期阶段利用算法为他们设定长期使用的基础,可以提高客户留存率,促进推荐,并降低客户流失率。 #2:最佳下一步行动计算 通过使用动态决策策略,利用所有客户数据找出(潜在)客户的最佳下一步行动,可以提高客户满意度,从而提高转化率和收入。 #3:交叉销售与升级销售 通过推荐与用户兴趣相符的产品或服务,可以增加用户购买的可能性,从而提高收入。 #4:客户流失预测与预防 通过动态计算在预定义时间段内客户流失的百分比,并部署预防策略以减少流失,可以确保与客户的长期关系和收入的稳定性。 个性化 AI 部署的影响可以通过以下指标来衡量: 总体收入和每位客户的收入增长——最高达 25%。 产品和服务的转化率提高——最高达 20%。 营销投资的 ROI 提高——最高达 3 倍。 客户满意度提升——显著。 客户流失率降低——最高达 30%。 客户体验和品牌形象的提升。 *请注意,本文中提到的基准和数字基于 DAIN Studios 的内部研究和客户项目。 受益于个性化 AI 的行业 虽然个性化 AI 可以对许多行业带来益处,包括电子商务、消费品和工业产品制造、零售、金融、医疗保健等,但具体的应用会根据企业的具体需求和目标而有所不同。例如,制造商和零售商可以进行直接面向消费者的互动,并使用人工智能来了解客户需求,根据其浏览和购买历史推荐产品,从而提高整体订单价值。在医疗保健行业,个性化 AI 可以用于提供个性化服务,如根据客户需求提供定制的信息或帮助。在金融行业,个性化 AI 可以用于提供个性化的财务建议和推荐,例如通过分析客户的财务历史,为其提供投资或储蓄建议。 开始行动 要开始个性化 AI 之旅,意味着企业需要为成为数据驱动型做好准备。尽管以下所有步骤都很重要,但如果没有数据,这一切都无法实现。要构建机器学习模型,首先要集中并激活数据。集中数据有助于以高质量的方式将所有数据汇聚到一个位置,例如客户数据平台(CDP)。激活数据意味着根据机器学习模型的输出采取行动,从而为客户和企业带来切实的、可见的价值。企业还需要关注以下几项活动: 确定公司希望通过个性化 AI 实现的具体目标和目的。这可能包括提高客户体验、增加销售和收入或优化营销工作的目标。 收集并激活有关公司客户的数据。这可以包括他们的偏好、行为和兴趣数据。这些数据可用于训练个性化 AI,并为个别客户提供个性化体验。 选择并实施一个适合公司需求和目标的个性化 AI 平台。具体平台或工具将取决于公司的需求和目标,同时将个性化 AI 与公司现有的系统和流程(如客户关系管理(CRM)系统或营销自动化工具)整合是成功的关键。 监控和评估个性化 AI 的性能,以确保其实现预期的目标和目的。这可以通过跟踪关键指标(如客户满意度或销售收入)并根据需要进行调整,以提高个性化 AI 的性能。 让不可能成为可能 总的来说,将 AI 和机器学习用于营销、销售和客户服务中的客户互动个性化,其真正的优势在于让不可能成为可能:在复杂环境中快速计算出最佳结果、发现模式并优化微粒级行为,而这些对人眼来说是不可见的。个性化 AI 是当今任何企业的游戏规则改变者和竞争必需品。TweetShareShareEmail 人工智能 --> 人工智能IoT 商业战略
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