For clarity and for context within this article, I find the following definitions helpful:
- Artificial intelligence is a broad field, covering a range of machine applications to carry out tasks that typically require human intelligence. Human intelligence encompasses a broad range of behaviors, so it should be no surprise that the umbrella term “artificial intelligence” can be used to categorize natural language processing, chess playing, driverless cars and millions of examples in between.
- Machine learning is often conflated with AI, but it is actually an application (and therefore a subfield) of artificial intelligence. In Stanford University’s definition, “Machine learning is the science of getting computers to act without being explicitly programmed.” We can consider algorithms to be the building blocks of any machine learning system.
- Deep learning is a machine learning method, loosely based on how the human brain works, that uses neural networks to solve complex problems. It has been behind a host of breakthroughs in the fields of speech recognition and image processing, for example.
Today’s technology giants are all heavily invested in the potential of these AI methods to deliver better products and services, as they provide scale and computational power that humans alone could never offer.
Of course, this technology has risen to prominence in the age of big data. To make the “big data” concept a bit more tangible, in 2017, people took 46,000 Uber trips, made 4 million Google searches and shared 456,000 Tweets — according to an aggregation of data performed by Domo.
Behind every one of these interactions lies a person, an intention and a context. Moreover, every one of these interactions produces the data points necessary to understand consumers at a deeper level than ever before. That sort of knowledge is invaluable to marketers, with many now viewing data as the most important currency we have.
However, if data is indeed the new oil, we are still in the process of inventing the combustion engine. Without the right tools at our disposal, all of the insights our customers are revealing will simply go unnoticed.
AI makes search marketers more productive
In such a data-rich, time-poor environment, modern-day SEOs have an unenviable task. Some of the most common challenges faced on a daily basis include:
- Monitoring website performance in analytics platforms to discover insights.
- Understanding audience behaviors to help deliver personalized experiences.
- Generating content ideas that will deliver traffic and revenue.
- Managing budget to stay within target margins.
- Multitasking across the many aspects of an organic search campaign.
Too often, these restricting factors can lead SEOs to make sacrifices in order to hit their targets. The resources just aren’t available to tap into the power of our consumer data, manage our budgets and delegate tasks all at the same time.
However, all of these objectives can be managed much more effectively and profitably with the assistance of artificial intelligence.
If we refer back to each of those challenges, AI can provide a fast, effective solution for each:
- Monitoring performance: AI can process data, alert the user to any anomalies and highlight quick wins to action immediately.
- Delivering personalized experiences: Machine learning can automatically tailor messaging based on a user’s historical and predicted behavior.
- Generating content ideas: Deep learning can identify trending topics and spot gaps in the market, then suggest new content titles.
- Budget management: Artificial intelligence automatically recommends the areas where we can spend our time and money most profitably.
- Multitasking: One of AI’s great selling points is its ability to juggle millions of balls at once. We can delegate multitasking to an AI-driven technology so we can get on with the more creative and strategic work.
The power of AI to deliver insights
The unifying thread through all of this is the fact that AI can deliver highly relevant insights automatically, at huge scale, and in a manner we can easily share with other departments in our organization. Without the right technology, we could only achieve this with the support of hundreds of analysts and an infinite budget.
It is worth noting that the difference between a valuable insight and a simple observation is incredibly significant for any business. A true insight illuminates something new and guides future action based on the moments and metrics that matter. For example, the knowledge that consumers are struggling to access our content could lead us to make technical changes to the URLs in question. The impact of this can be monitored very clearly, and we can attribute a dollar value to the original insight.
Conversely, much of what arises through manual research falls into the category of observation. The knowledge that mobile traffic from users in California has increased since last week may be interesting, but without context or clarity, it does not produce any guidance.
Search marketers should seek out a platform that employs deep learning technology to sift through search, social and content marketing data from a range of analytics platforms to produce these insights. This should be achieved across all territories, devices, and demographics, allowing new information to surface that would typically slip through the cracks.
When evaluating technology for these purposes, marketers should ask these questions:
- What is the benefit? How does it save time and build efficiency?
- What data sources and data sets are involved in all calculations, including search, social and local?
- How does it index URLs? Is data fresh, accurate and collected frequently to keep track of the SEO landscape?
- How sophisticated is the AI? What are the machine learning and deep learning applications used to identify patterns in consumer data?
- How does it change our business operation capabilities?
- What clear business problems does it solve?
- Does it contain intuitive dashboards that display all findings in a digestible manner that can be shared with non-technical audiences and across the digital organization?
Conclusion
In all walks of life, including search marketing, machine learning can lead to better results — if we know how to use it to our advantage. That starts with understanding where our data comes from and what it could be used for, then deciding which business problems we want to use these assets to solve.
The proliferation of data should open up a new era of opportunity for all marketers, but only those who understand the potential of artificial intelligence will tap into these resources fully. By incorporating the power of AI and deep learning, search marketers can move beyond simple observations and find new patterns in user behavior. The result is faster, more accurate and actionable insights to deliver on the metrics that matter.
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by Jim Yu
Source: searchengineland.com