Online marketers always need more data. The ways that users engage with online content is always changing, driven by new technology, a fluid search engine landscape, and unexpected shifts in consumer behaviour. Sometimes, the data that marketers need to make truly meaningful decisions about their budgets just isn’t there.
In recent years, tech companies have been experimenting with ways to use machine learning to fill in glaring gaps and dig deeper into the information that already gets gathered.
Tracking Customer Journeys Across Devices
Many common forms of digital marketing attribution and campaign testing use cookies to identify users. This approach is fundamentally limited in that it assumes a user goes through the marketing funnel in a single session on a single device. The flawed notion produces bad data. Cross-device analytics is a still developing field that:
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First, recognizes that users engage with brands across multiple devices, often using two or three to perform a single task.
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Second, reframes the analytics process to be consumer-focused, rather than device-focused.
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Third, helps companies develop an understanding of more complex, lateral, web-like user behavior across multiple devices. A linear journey through the funnel is becoming an outdated idea.
When a user clicks “buy,” where did they come from? Did their engagement with your brand begin on a tablet? A smartphone? Which device do they conduct research on, and which do they purchase on? These questions present a number of user behaviour analysis challenges.
Companies like Google are releasing innovations in machine learning analytics that allow marketers to track a user’s journey through different devices. From the physical to the digital world, across platforms and browsers, marketers can track a user’s entire journey from the top of the funnel down to the final call to action.
Technologies like IBM’s Watson supercomputer have set the stage for automated marketing platforms that allow companies to customize the user experience and buying process depending on more in-depth, responsive data.
Examples of machine learning tools that interact directly with users include Amazon’s Echo and Google’s Siri. These are the most modern forms of conversational commerce, which can gather data about a user’s preferences through everyday casual interactions.
Assessing the True Value of Web Traffic
Not all traffic is equal! The SEO industry has known this for a while, which is why bounce rate has become such a prominent ranking factor. A click doesn’t necessarily mean the user was expecting, or wanting to end up on the target site. If a user doesn’t engage, they were a lower value conversion. They might be the complete wrong demographic. So paying the same amount for every click on an ad doesn’t make sense. Indeed, strategies that don’t target the right users can end up harming your organic visibility.
Google is working to help marketers more effectively prioritize their ad spending by evaluating the true return on investment of certain kinds of traffic. Their machine learning tools will help marketers identify the most valuable demographics to target and spend on. It’s important for marketers to use these tools to craft more engaging, specialized content, not just for conversion rates but in order to make the best use of search engine optimization strategies.
User Satisfaction vs. SEO Ranking
Machine learning is giving Google more and more insight into how users respond to certain types of content and links. This is allowing them to identify which SEO tactics are at odds with UX, and which marry with it nicely. Any search engine strategy needs to take into account the mood of the user and the relevance of the content.
The more machine learning can be leveraged to ensure that online advertising is timely and relevant to the user, the better. Remember that as the marketer’s tools become more advanced, so too do Google’s algorithms for ranking websites. For SEO, machine learning means a greater focus on user experience between links. Google is putting primary importance on user satisfaction. That means your ad campaigns need to take UX into account at every point of interaction.
The twist at the end of this story is that taking full advantage of the data made available by machine learning means putting a careful human touch on your digital marketing content. You need to use the data to make your resources higher quality, predict the intent of users visiting your pages, and keep them engaged. Your digital strategy is no longer just about getting more visibility than your competition in order to perform better; it’s about performing better first, and then being rewarded with visibility.