Editor’s Note: We think this piece is important because AI is transforming all industries and our author Lomit Patel is the foremost expert in how AI can transform growth marketing. In this short excerpt, Patel walks you through a scale for the purpose of evaluating autonomous marketing and marketing automation solutions. We’d love to hear from you about what you think about this piece.

编者注： 我们认为这很重要，因为AI正在改变所有行业，而我们的作者Lomit Patel是AI如何改变增长营销的最主要专家。 在此简短的摘录中，Patel会逐步指导您进行规模评估，以评估自主营销和营销自动化解决方案。 我们很乐意听到您对您对此作品的看法。

There are many exciting ways you can apply the power of AI and ML to streamline marketing processes across the entire customer marketing funnel to help growth teams work smarter by automating in the following areas to help them stand apart from the competition:

您可以通过多种激动人心的方法来利用AI和ML的功能来简化整个客户营销渠道中的营销流程，从而通过以下方面的自动化来帮助增长团队更聪明地工作，以帮助他们在竞争中脱颖而出：

• Segmentation分割
• Personalization个性化
• Media buying媒体购买
• Campaign optimization广告活动最佳化
• Predicting customer behavior预测客户行为
• Data analysis and reporting数据分析与报告
• Customer support客户支持
• Better cross-platform attribution更好的跨平台归因
• Fraud prevention预防诈骗
• Creative development and iteration创意开发与迭代

I’ve found plenty of examples of ways that AI is transforming growth marketing to allow us to achieve things that would never have been possible without it. With AI, you can work smarter and gain a holistic, real-time view of your customers and their relevant interactions throughout the entire journey. AI lets you act quickly on your data and makes it easier to focus on the higher value work by getting fast, actionable insights.

我已经找到了很多例子，说明人工智能正在改变增长营销的方式，使我们能够实现没有它就不可能实现的目标。 借助AI，您可以更智能地工作，并在整个旅程中获得客户及其相关交互的整体实时视图。 人工智能使您可以快速处理数据，并通过快速，可行的见解更轻松地专注于更高价值的工作。

However, while the data to support AI is critical, data is nothing without a clearly defined business problem focused on cost reduction, risk reduction, and profit. Perhaps the most interesting thing about AI is that, while it can automate and do “work” at greater efficiency, it uses machine learning to “think” and “learn” over time, strategizing, designing, recognizing patterns, and making decisions. If that sounds a lot like a human brain, it’s because deep learning, one of the most important methods of machine learning, is based on the idea of a neural network, modeling the structure and function of the human brain.

然而，尽管支持AI的数据至关重要，但如果没有明确定义的业务问题(专注于降低成本，降低风险和盈利)，数据就什么都不是。 也许关于AI的最有趣的事情是，尽管它可以自动化并以更高的效率完成“工作”，但它会使用机器学习随着时间的推移“思考”和“学习”，制定战略，设计，识别模式并制定决策。 如果这听起来很像人类的大脑，那是因为深度学习是机器学习最重要的方法之一，它是基于神经网络的思想，对人类大脑的结构和功能进行建模。

评估自主营销的成熟度(在自动驾驶汽车族的帮助下) (Assessing the Maturity of Autonomous Marketing (with Help from the Self-Driving Car Folks))

With ambitions to launch self-driving cars to the public in 2020, Tesla gets a lot of attention in the autonomous vehicle industry. But big automobile companies, startups, and tech giants are all working to deliver safe, self-driving vehicles to the masses.

鉴于2020年向公众推出自动驾驶汽车的雄心，特斯拉在自动驾驶汽车行业引起了很多关注。 但是大型汽车公司，初创公司和科技巨头都在努力向大众提供安全的自动驾驶汽车。

To make sense of where artificial intelligence and automation is at and where it’s going, the industry trade association the Society of Automobile Engineers (SAE) introduced its autonomy scale. It helps the industry determine and classify different levels of autonomous capabilities for vehicles.

为了弄清人工智能和自动化的现状以及未来发展的方向，行业贸易协会汽车工程师协会(SAE)引入了其自治规模。 它可以帮助行业确定和分类车辆的自动驾驶能力的不同级别。

Level 0

0级

No automation. The driver controls steering and speed (both acceleration and deceleration) at all times, with no assistance at all. This includes systems that only provide warnings to the driver without taking any action.

没有自动化。 驾驶员始终无需任何帮助即可控制转向和速度(包括加速和减速)。 这包括仅向驾驶员提供警告而不采取任何措施的系统。

Level 1

1级

Limited driver assistance. This includes systems that can control steering and acceleration/deceleration under specific circumstances, but not both at the same time.

有限的驾驶员协助。 这包括可以在特定情况下但不能同时控制转向和加速/减速的系统。

Level 2

2级

Driver-assist systems that control both steering and acceleration/deceleration. These systems shift some of the workload away from the human driver, but still require that person to be attentive at all times.

同时控制转向和加速/减速的驾驶员辅助系统。 这些系统将某些工作负荷从驾驶员身上转移了下来，但仍然要求该人始终保持专注。

Level 3

3级

Vehicles that can drive themselves in certain situations, such as in traffic on divided highways. When in autonomous mode, human intervention is not needed. But a human driver must be ready to take over when the vehicle encounters a situation that exceeds its limits.

在某些情况下可以自行驾驶的车辆，例如在分开的高速公路上行驶时。 在自主模式下，不需要人工干预。 但是，当车辆遇到超出其限制的情况时，驾驶员必须准备好接管。

Level 4

4级

Vehicles that can drive themselves most of the time, but may need a human driver to take over in certain situations.

大部分时间可以自行驾驶的车辆，但在某些情况下可能需要人工接管。

Level 5

5级

Fully autonomous. Level 5 vehicles can drive themselves at all times, under all circumstances. They have no need for manual controls.

完全自主。 5级车辆在任何情况下都可以随时驾驶。 他们不需要手动控制。

I propose a similar scale for the purpose of evaluating autonomous marketing and marketing automation solutions.

为了评估自主营销和营销自动化解决方案，我提出了一个类似的规模。

Level 0

0级

No automation. Marketers manage all tasks with basic tools and CRM systems that provide no real automation, but act as storage repositories for marketing data and results reporting (dashboards or “business intelligence” systems).

没有自动化。 营销人员使用基本工具和CRM系统管理所有任务，这些工具和CRM系统不提供真正的自动化功能，但充当营销数据和结果报告(仪表板或“商业智能”系统)的存储库。

Level 1

1级

Recommendation automation. Marketers leverage systems capable of following business rules (defined by the marketer) to make business recommendations for optimizing marketing outcomes. Examples include dashboards with recommendation systems for adjusting marketing spend by channel. The user must take the final step of making the recommended adjustments.

推荐自动化。 营销人员利用能够遵循业务规则(由营销人员定义)的系统提出业务建议，以优化营销结果。 示例包括带有推荐系统的仪表板，用于按渠道调整营销支出。 用户必须采取最后的步骤进行建议的调整。

Level 2

2级

Rules-based automation. Building on business rules set by marketers in Level 1, Level 2 rules-based automation goes the next step and adjusts marketing campaigns automatically (generally via an application or API) without user intervention or approval. Such systems rely on the user to create the rules. Dynamic market conditions shift on a daily, hourly, or even minute-by-minute basis; rules-based systems are rigid and not very flexible to adapt to market changes.

基于规则的自动化。 基于市场营销人员在第1级中设置的业务规则，基于第2级规则的自动化是下一步，无需用户干预或批准即可自动调整营销活动(通常通过应用程序或API)。 这样的系统依靠用户来创建规则。 动态的市场条件每天，每小时甚至每分钟都在变化； 基于规则的系统是僵化的，不能适应市场变化。

Level 3

3级

Computational autonomy. Systems that use machine learning to observe, learn, and improve outcomes based on statistical analysis combined with marketing automation. No intervention is required by the user, apart from setting goals or broad-based parameters such as campaign dates or geographies for digital campaigns.

计算自主权。 使用机器学习来观察，学习和改善结果的系统，该系统基于统计分析和市场营销自动化相结合。 除了设置目标或广泛的参数(例如活动日期或数字活动的地理位置)之外，用户无需干预。

Level 4

4级

Insightful autonomy. Systems that understand the contextual meaning of user interactions, content, behavior, performance data, and more to personalize 1:1 marketing messages across various channels and drive optimal performance for operators.

有见识的自治。 系统了解用户交互，内容，行为，性能数据等的上下文含义，以跨各种渠道个性化1：1营销消息并为运营商带来最佳性能。

Level 5

5级

Fully autonomous. Level 5 systems build insightful autonomy capabilities but generate their own unsupervised tests, creative variations, targeting parameters, and more with no ongoing intervention from the marketing team.

完全自主。 5级系统构建了具有洞察力的自治功能，但无需市场营销团队的持续干预即可生成自己的无监督测试，创意变化，目标参数等。

Most growth teams are in the process of figuring out how to reach a level of proficiency to move from Level 0 to Level 2. However, the biggest challenge and opportunity is to advance from Level 2 to Level 5, to completely reap the benefits of the full superpowers of artificial intelligence to scale up your efforts into the world of Customer Acquisition 3.0.

大多数成长团队都在研究如何达到从0级到2级的熟练水平。但是，最大的挑战和机遇是从2级升级到5级，以完全获得该团队的收益。全面的人工智能超级能力，可将您的工作扩展到客户获取3.0的世界。

学得更快。 深入挖掘。 看得更远。 (Learn faster. Dig deeper. See farther.)

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加入O'Reilly在线学习平台。 立即获得免费试用版，即时找到答案，或者掌握一些新的有用的知识。

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Lomit Patel is the Vice President of Growth at IMVU, responsible for user acquisition, retention, and monetization. Prior to IMVU, Lomit managed growth at early stage startups including Roku (IPO), TrustedID (acquired by Equifax), Texture (acquired by Apple), and Earthlink. Lomit is a public speaker, author, advisor, and recognized as a Mobile Hero by Liftoff. To learn more or to get in touch with Lomit, visit lomitpatel.com.

Lomit Patel是IMVU的增长副总裁，负责用户获取，保留和货币化。 在加入IMVU之前，Lomit负责早期创业公司的增长，包括Roku(IPO)，TrustedID(由Equifax收购)，Texture(由Apple收购)和Earthlink。 Lomit是一位公开演讲者，作家，顾问，并被Liftoff评为“移动英雄”。 要了解更多信息或与Lomit联系，请访问lomitpatel.com。