Magnus Unemyr

While we don’t yet have strong artificial intelligence that can learn new tasks completely by itself, handle tasks it isn’t pre-programmed for, or have self-awareness and emotions, it appears cognitive computing may be moving in that direction.

The most well-known cognitive system is probably IBM Watson, which famously won Jeopardy in 2011 over the game’s best players in the world.

Cognitive systems understand unstructured data and natural language, can interact with humans in a natural way, and sift through enormous amounts of unstructured data, providing suggestions and decision support in specific fields of expertise.

According to IBM, Watson augments human abilities by scaling and accelerating human expertise. In effect, it helps share experience at scale.

Cognitive systems are defined by how they understand natural text, speech, and have vision using image recognition. They use deep machine learning to learn almost everything about any topic at scale, and they can reason and provide suggestions to help people in their decision-making.

Cognitive computing enables the democratization of expertise by taking the knowledge of an industry’s best experts and sharing it with others. In effect, these systems are decision support tools with skills in particular topics, say cancer treatment, tax auditing, or something else entirely.

They can help share the highest levels of expertise outside the core competence centers and help make this knowledge accessible to a wider audience.

For example, a cancer diagnostic decision system could be trained with the majority of the worlds combined knowledge on the matter. It can then be used by small local hospitals in rural areas or by doctors in poor countries on the other side of the world.

This could improve the quality and efficiency of advanced medical treatment at a grand scale.

For our purposes, IBM Watson also brings cognitive computing to marketers, offering capabilities such as customer journey analysis, real-time personalization, surfacing marketing insights, and cognitive content management.

While IBM pushes the built-in marketing capabilities of Watson, this is not the only interesting aspect of the platform. Perhaps more importantly, IBM allows developers to build their own domain-specific apps that use Watson as an AI-engine for cognitive computing.

This makes it relatively simple to design advanced cognitive AI systems by building applications on top of IBM Watson.

This is what Equals3 did to create Lucy, an AI agent that can perform market research and harvest insights, provide marketing decision support, and facilitate automatic generation of buyer personas and media strategies.

Other companies do the same, for example to create smart in-store digital signage or information kiosks that can adapt their content and tone of communication dependent on who uses them.

Perhaps we will see only a handful of advanced AI systems from the big vendors in the future, and all other AI tools will be built as apps on top of them. Time will tell, but it is certainly an interesting thought.

With that model, any small business could build super-smart cognitive AI systems for particular niches, using AI platform technology well beyond what they could ever develop from scratch by themselves.

Either way, cognitive AI might have enormous effects on the democratization of knowledge and expertise in the future, as everyone could have the world’s combined knowledge on virtually any topic accessible through a simple chat or voice interface.

Broadening access to these high-level tools will likely have wide-ranging implications as they develop.

Many of the AI solutions available to marketers focus on a specific problem, such as email send times or conversion ratio optimization of a landing page. However, what happens when we zoom out, combine them, and take a more complete approach?

AI will profoundly change how marketing campaigns are executed.

With new machine-learning tools, you can create campaigns that listen and adapt automatically to audiences to improve performance. Tools can hyper-personalize the messaging using dynamic content, adapt email send times, and more.

Some tools go beyond specific marketing campaigns and use machine learning to personalize and optimize the entire customer journey, in effect adjusting them for each individual person.

AI will profoundly change how marketing campaigns are executed.

For automated campaign optimization, it’s important that the system can automatically find and exploit the best messages through multivariate experimentation, searching for the best message option to drive click-throughs without prompting the user to unsubscribe.

Tools can test anything about an electronic message including subject lines, copy or body text, design elements, graphics, and more. Campaign optimization tools perform experiments that automatically shift from lower performing towards higher performing messages.

They can adapt the email send times as well to optimize the open rate.

Motiva is a company that fits this niche. Their tool creates marketing that listens and adapts automatically to audiences. This helps marketing teams save time and enhances the response performance of campaigns. David Gutelius, CEO and co-founder of Motiva, explains:

“At a high level, leveraging customer data in a deeper way creates the opportunity to uncover meaningful populations that exhibit shared content preferences. This opens up new ways to understand customers, both individually and in larger segments, as well as tailor personalized messaging experiences.”

In essence, this involves learning what relationships exist, if any, between customer attribute and behavior data and their responses to the available message options in an ongoing campaign. Gutelius continues:

“By learning models that predict the likelihood of message engagement based on customer attribute and behavior data, we are automatically learning definitions of the underlying populations that are most likely interested in the associated message content. Coupled with an evolving model of message content similarity, the game changes. The possibility of continuous learning across campaigns without human intervention is within reach.”

With tools like this, we can go from executing one campaign targeting a million leads, to automatically running a million campaigns in parallel, each targeting one lead with a hyper-personalized message.

BloomReach is another tool vendor in this space. They try to improve the digital experiences by optimizing the customer journey. Artificial intelligence technology is used to eliminate the guesswork from digital experience design.

Data harvesting, machine learning, and intelligent analysis are used to create a personalized user journey for every individual visitor using the content available. The system also helps suggest what content is missing that would be valued by visitors, and helps companies produce only the content that provides value.

I had a conversation with Tjeerd Brenninkmeijer, EVP of EMEA at BloomReach. His take on AI in digital marketing is that smart algorithms will help marketers make better decisions instead of simply replacing them.

He says the combination of human creativity and the machine’s ability to process big amounts of data within a short period will be most impactful on the business. According to him, it’s not likely that AI is going to take over people’s jobs on a large scale.

Instead, it will make people more productive and help them drive better business outcomes.  I’ve heard the same conclusions from other industry experts as well, and I agree. 

A company called Pointillist offers another customer journey optimization product that can visualize the actual paths your customers take graphically as they engage with your company across touchpoints over time.

Their tool links customer behavior and metrics like revenue, profitability, churn, or customer lifetime value, and can help segment customers so you can determine an optimal engagement strategy for every individual visitor.

Additionally, marketing automation system vendor Act-on now supports adaptive customer journeys, that can predict and deliver the best message, at the right time, through the ideal channel, with machine learning.

As we’ve seen, relationships can be improved at scale if the customer journey of each lead is personalized. We can expect most marketing automation system vendors to include AI-powered customer journey personalization in the next few years.

This will be an important area for AI in marketing going forward, and those who do not adopt these strategies risk being left behind.

With audience AI and hyper-dynamic targeting, machine learning can be used to help build targeted audiences automatically. This is done by using data from a variety of sources and making predictions about the characteristics of ideal buyers.

In a few seconds, you can get well-targeted, quality audiences, with a high likelihood of more conversions and sales. Audience AI learns how to find the best customers across various channels, such as social media. As more data becomes available, the targeting becomes increasingly precise.

These tools can uncover the motivations and desires of target audiences, sometimes finding passion points with interest analysis, core values, and personality traits with personality analysis, and using visual imagery analysis to work out what pictures or creative elements engage the target audience the most.

Some products performing these types of tricks are the well-known behemothslike Salesforce and Adobe, but also specialized companies like Codec, Leadspace, Toneden, and Trapica.

Of course, we can’t forget the ads platform on Facebook and other social media sites. AI can be useful in audience management, particularly for companies with large customer volumes, as often is the case in the B2C space.

While content marketers are interested in adaptive and predictive content, e-commerce marketers might be more interested in the product recommendations that optimize sales and maximize revenue.

Amazon is the master of this, and their team uses machine learning to personalize product recommendations based on digital footprint and previous purchase history—both from you and others.

Features like “You may also like this” and “Other people also bought” that were popularized by Amazon are becoming the norm in e-commerce these days.

The “Personalization in Shopping” report also notes,“Recommendations are directly linked to longer shopping visits. Shoppers that clicked a product recommendation spent an average of 12.9 minutes on-site vs. 2.9 minutes for those that didn’t click recommendations.”

Of course, when they stay longer on the site, they spend more too.

The report states that “visits where the shopper clicked a recommendation comprise just 7% of all visits, but 24% of orders and 26% of revenue.” Shoppers that click recommendations view 4.8 times more products and spend five times more per visit.

Furthermore, 52% of orders from recommendation-clickers include one of the recommended items. These figures seem to be a good argument for the use of recommendation engines in e-commerce.

Luckily, nearly all e-commerce site, even small ones, can now use AI-driven product recommendations that are optimized and adapted for each individual person. The suggestions can come in many forms, but generally, they adapt the products each specific person is offered on websites or in email.

There are many popular e-commerce platforms in widespread use including Shopify, OpenCart, WooCommerce, PrestaShop, and Magento. Wouldn’t it be nice to get a product recommendation engine for the system that drives your business?  

You can use companies like BlueShift, Dynamic Yield, Perzonalization, Reflektion, RetentionScience, and Zen to help increase sales by personalized product recommendations, including the display of similar products, trending (most popular) products, top picks for you, recently viewed, often bought together, and the like.

Up-selling and cross-selling is also a common feature here. 

Do you want to extend recommendations beyond products and e-commerce?

The same type of predictive technology can be used to recommend what content (such as CTAs, banner ads, lead magnets, and other downloadable resources) to display on your website for a particular visitor. Certona, Marketo, and Uberflip do this effectively, helping to promote your content library in a smarter way.

Recommendation engines have many more uses. For example, they can be integrated into the products themselves. Netflix is a great example, and their recommendations for movies you might want to watch are a contributing factor for their huge success.

You might be able to replicate some of that magic by using recommendation engines appropriately in your business.

Segmentation taken to the extreme eventually creates segments of one. This personalization allows for dramatically more effective campaigns. It allows you to send content and offers that are uniquely optimized for every individual person.

This is called the segment of one (or audience of one), denoting a hyper-personalized outreach where the message is uniquely modified and optimized for each person. This would certainly make marketing more relevant!

In fact, personalization is the opposite of segmentation. With segmentation, we create groups of people with similar attributes and send the same messages to all of them. By definition, this is not personalized for each individual, even with micro-segmentation.

With personalization, we want each message to be truly unique. It is actually adjusted and optimized for each individual using prediction algorithms. There are already many vendors offering AI-based personalization systems.

The majority of these tools can personalize the content on web pages or email content. They do this by using machine learning algorithms to predict what customers want and deliver relevant content or product recommendations even before they ask.

The “Personalization in Shopping” report notes that “Shopper spend soars with personalization. Purchases where a recommendation was clicked saw a 10% higher average order value, and the per-visit spend of a shopper who clicks a recommendation is five times higher.”

The report further states, “Providing your customers with a personalized shopping experience is now the cost of entry to retail.” It reports that 50% of customers say they are likely to switch vendor if a company doesn’t foresee their needs, and 58% of consumers say technology has considerably altered their expectations of what customer experience companies shall give them.

Personalization is now a requirement for any company wanting to stay with the times. I think we can conclude that it isn’t optional anymore, but rather a pre-requisite to matching your competitors.

However, it isn’t just about recommending content, products or offers. It is about creating customer experiences that build engagement and drive retention. It’s about appearing less robotic and being more personal and relevant.

Personalization will be huge in the marketing industry. In fact, the future of marketing technology and the vendor landscape might well be an arms race for better personalization.

Many companies have developed AI-based technology that uses user behavior and content analysis to deliver the content that is most likely to resonate with a particular person. Most of them offer solutions for the personalization of web page and email content.

Some go beyond that to support additional communication channels. A few prominent companies in this space include Zeta (previously BoomTrain), Adobe Marketing Cloud, Emarsys, and Perzonalization.

I asked Lindsay Tjepkema, Global Head of Content for Emarsys, to explain their product. She noted that “Emarsys is a marketing cloud which provides AI based personalization across email, SMS, mobile, web, offline and IoT devices.”

The platform consolidates all web, mobile, email, and purchase information into a unified customer profile. This profile includes preferences, behavior trends, predicted behaviors, propensities, and affinities. The unified profile is the platform’s foundation—the “single source of truth”—which enables hyper-personalization across all channels.

Tjepkema continues: “As the real-time interactions scale, the need for content increases exponentially. Marketers will have to offload the technology tasks such as identifying segments, crafting journeys, and creating campaigns. They will have to concentrate purely on creating content and training machines on marketing strategies.”

Other companies avoid developing their own proprietary AI by building their products on the back of existing engines. OpenTopic takes this route, as their tool is built on top of IBM’s AI engine, Watson. The computing power of Watson is used to predict the most engaging assets that guide individuals through the customer journey.

In addition to the more traditional content recommendation functionalities, Dynamic Yield, Klevu, PureClarity, and Similar add personalization to search results as well. Dependent on what you have done before, you will get different search results on an e-commerce site, compared to your neighbor searching for the same thing.

I spoke with Mike Mallazzo, the head of content at Dynamic Yield. He points out that in its annual “What’s Hot in Digital Commerce” report, Gartner cited personalization as the number one strategic investment for brands in 2017.

McKinsey, he said, refers to digital personalization at scale as “marketing’s holy grail,” and the Boston Consulting Group predicts that personalization will push an $800 billion revenue shift to the 15% of brands that get it right in the next five years.

The message from the marketplace is clear: personalization is no longer just nice to have; it is the single most important strategy for boosting revenue and brand affinity online. Expect predictive content and hyper-personalization to be a major deal going forward.

Malazzo also discussed what helps set his company’s product apart: “Marketers can turn control over to the machines or insert guardrails in the form of merchandising rules to control which recommendations unit each visitor sees. For example, a high-end fashion brand may want our AI to recommend the products delivering the most revenue but may not want to place certain brands next to each other. In Dynamic Yield, it is possible to set this condition, allowing the machines to go to work within controls set by the marketer.”

With machine learning, it becomes feasible to personalize content and outreach strategies to each individual contact, which helps make marketing more relevant. In fact, personalization is one of the most important uses for artificial intelligence in marketing.

The time of spammy “spray and pray” mass-marketing is over. Today’s customers require brands—and expect marketing outreach—to be more relevant in both time and content.

The time of spammy “spray and pray” mass-marketing is over.

Relevancy is becoming more important than reach; get the right content, to the right person, at the right time.

Marketers tried to become more relevant by inventing the concept of segmentation. Groups of customers or potential customers are placed into distinct sub-groups, thus making a rough classification by similarity or common needs.

Segmentation made it possible for marketers to be at least a bit more relevant, compared to sending the same marketing emails or offers to everyone.

I know a clothing retail chain that did this rather poorly, sending marketing emails pitching women’s clothes to men and vice versa. They sent exactly the same email to everyone. Another retail chain sent direct marketing information on gardening products to people living in an apartment, thus not having a garden.

These are significant blunders in today’s era, and brands just have to do better.

When you send irrelevant marketing content to your leads, you train them to avoid reading it in the future, or even make them dislike you. That in turn can harm your email sender score, thus damaging the delivery rate of future emails.

Even the most trivial segmentation—like sending different product offers to men or women—can make a major difference. Still, men are not a homogenous group, and they have different needs and interests, as do women.

A more granular segmentation would thus result in even more relevance.

With AI and machine learning, it is possible to segment the audience automatically. If this is done with sufficient granularity, we get micro-segmentation. Done correctly, this is much better than no segmentation, or rudimentary segmentation.

With AI and machine learning, it is possible to segment the audience automatically

AI-based automatic segmentation can be implemented using clustering algorithms, and can identify the attributes of ideal buyers, those most likely to convert or defect, and more.

In effect, you can ask the system to provide you with buckets of people that are similar or share characteristics in some way, even if you don’t know what you are looking for. Thus, machine learning tools can help improve segmentation, and many do.