Artificial intelligence (AI) can seem scary and far away. It may bring to mind a future dominated by robots, even though so many consumers are using it already without realizing it. Too often, people do not see where businesses apply AI in everyday life.
 
In this post, you’ll gain a better understanding of what AI is and how marketing uses it for more effective, engaging experiences.
 
Marketing has employed AI for at least ten years, mainly via machine learning. Very often, people mix up the two, so let’s start by understanding the differences between these two and a couple of other related terms.
 
Artificial intelligence (AI): This is a broad discipline to create intelligent machines that emulate and exceed the full range of human cognition.
 
Machine learning (ML): This is a subset of AI that often uses statistical techniques to give machines the ability to “learn” from data without being explicitly given the instructions for how to do so.
 
Reinforcement learning (RL): This is an area of ML that uses software agents to learn goal-oriented behavior by trial and error in an environment that provides rewards or penalties in response to the agent’s actions (called a “policy”).
 
Deep learning (DL): This is an ML area that attempts to mimic the activity in layers of neurons in the brain to learn how to recognize intricate data patterns. The “deep” in deep learning refers to the many layers in contemporary ML models that help learn rich data representations to achieve better performance gains.
 
When referring to algorithms and recommendations many years ago, we were already into the AI field. In recent years, AI has evolved significantly and now involves multiple techniques to make machines more intelligent and generate better results.

Most AI challenges are common across the board.

First, we have input. AI is no good without the right human input. If we do not feed it clean, formatted data – and data diverse enough to mitigate bias – the machine cannot provide satisfactory results.
 
Next, we need to give the machines time to learn algorithms. Each time the system captures more data, it refines its calculations and renders more accurate results.
 
Finally, people need to know how and what to add or refine (rules or filters). If not applied correctly, the results can be very off. On one project, for example, a team had put so many rules and filters into the systems that they prevented the system from doing what it was supposed to do. We had to reduce the rules to a bare minimum and let the machine relearn before steering it in the right direction by re-injecting the rules that would get the expected results. 
 
People need to be present to readjust a machine when it malfunctions. AI is therefore only as smart as the person behind it. Indeed, a machine can achieve unwanted goals if wrongly managed. We should not be afraid of machines, but of the people working them; as with most technologies, we can get the best or the worst out of it.
 
Now, how can AI be applied to marketing? Let’s organize the applications into four groups based on the marketing objectives they support.

Improve the user experience.

Improve search thanks to image recognition and language processing. The customer can take a picture of a product they are looking for, enter it into a search engine, and get matching results. They use images instead of text. When people use Siri or Alexa, language and voice recognition help them find what they need. Recent research has shown that many current voice and image-recognition algorithms show significant race and/or gender bias, so make sure to investigate these issues before incorporating this technology. 
 
Chatbots & concierge. Businesses use chatbots to provide automated assistance to customers. It helps shoppers navigate through customer service, reduces wait times, and provides immediate assistance. If a business configures chatbots correctly, they can provide helpful, detailed answers. There are many chatbots on the market. For Shopify, I like Tidio.
 
Analyze user experience and help website optimizations. Tools like Dynamic Yield enable you to test all areas of a site and define which message or design is the most efficient for a specific customer. You can then define multiple interfaces per audience. Try B12 or Bookmark for creating website templates and landing pages.
 
Predict churn and create smart customer engagement. Machine learning algorithms gather data about disengaged customers and apply predictive models to determine which accounts are at high risk of churn.
 
Improve imagery. AI makes the design process more manageable. We recommend Adobe Sensei for this function.

Improve marketing’s relevancy.

Improve social media. This is what Twitter and Facebook do by targeting an audience with specific messages.
 
Tag products to be more relevant. Organize and categorize product catalogs based on images, creating faster product descriptions. Catchoom provides this functionality.
 
Demand forecasting for inventory management. Automate replenishment and supply chain optimization with tools such as Remi AI.
 
Predictive analysis. Analyze inventory, customer behavior, buying patterns, churn, etc.
 
Market analysis and data mining: Define virtual panels to test products. Response AI provides test automation for market research, for example.
 
Optimization of display ads by creating micro-moments. Target customers using native ads.
 
Create dynamic audiences, customer segments, and find the most profitable audiences. Salesforce can do this in their CRM.
 
Display targeted offers and content. Push content and offers based on customers’ interests.
 
Optimize all email elements based on customers’ reactions (title, colors, buttons, content, etc.) and sentiment analysis. Tools like Persado deliver stunning results.

Help create marketing assets.

User-generated content (UGC) curation and syndication. Identify UGC content or topics to use on one’s platform. Customer reviews can be syndicated by product and compiled for use on another platform. This is very useful for large retailers selling different brands and products. Upcontent is an excellent example of a curation tool.
 
Influencers identification. The influencer marketing industry global market spend is expected to be $5 -10 billion by the end of 2021. The market is vaste and companies are facing the challenge of identifying the right influencer for the right campaign. AI solutions like Social Bakers help in the process in reaching out the right influencer.
 
Content creation. Create blog articles in seconds using SEO keywords. We tried Articoolo, but it needs more work to be efficient. 
 
OpenAI and DeepMind are pushing the research in the AI space and are coming up with models such as GPT-3 that seem very promising. The quality of the results rendered are getting better and better every day. 

Help manage marketing operations.

Monitor social media. Follow all the conversations on social media in your field of activity.
 
Improve customer service data. This helps agents be more efficient on the phone by providing them with customer data or creating scripts/answers based on customers’ individual needs.
 
Fraud detection. Detect suspicious behaviors based on location, IP, or products that reduce chargebacks.
 
Robotics for pick packing in warehouses. The robot goes to the correct aisle and shelf to pick the product ordered.
 
Push notifications. AI can help in two ways: 1) by pushing the right notification to the right customer based on their preferences, 2) by optimizing the notification message and highlighting the words that will have the most conversion impact with the targeted customer. 
 
Automatic pay-per-click (PPC) budget adjustments and expenses. AI allows to adjust the PPC budget towards the criteria that bring the best conversion results (keywords, time of day, target segment, best ad format, ad group and campaign group, channel, location and negative keywords). A better adjustment means a better campaign and better Return on Ad Spend (ROAS) for the brand.
 

Realizing the full value of AI in marketing.

 
Besides the automation of some tasks, the most significant value of AI is the relevancy marketers can bring to their customers. Customers are drowning in an ocean of content and offers. They are demanding more and becoming less patient with brands. 
 
If you do not chase them and pop up at the exact instant when they are receptive to a piece of information that brings them value, you lose them. They can find everything on the internet but do not know what to look for anymore. They cannot distinguish truth from misinformation either, so marketers need to be accurate and relevant to get the highest ROI and efficiency.
 
Better decision making equals more efficiency, better return on investment (ROI), and more revenues. The equation is simple. Solving the equation is way more complicated.
 
Personalization has shown its efficiency and ROI. In some experiments we ran, we saw conversion results increase five times over when using personalization. Being relevant clearly pays off. Recommendations can generate more than 30% of the total sales on a site, becoming a way to help customers find products that might interest them. 
 
Customers require an ever more seamless experience and less disruptive advertising so they can be in and out in a few clicks. Hyper-personalization can have some drawbacks, however. By repeatedly targeting and refining, you reduce the funnel of information you present to the end-user. By reducing the users’ window, you control their thinking. In this way, you begin molding customers’ minds about a piece of specific information. They will see this information repeatedly because it is part of their interests.
 
But this technique does not help open a person’s mind. People become swayed to a single line of thinking and do not challenge it because it is the only thing they see. You can create a sort of commercial monopoly using information and as a brand, if you are not part of this pool, you can see how you miss out on some of the opportunity.
 
Such hyper-personalization also prevents discovery and makes it more difficult for a new entrant. This is why when we do hyper-segmentation, we need to add some components for discovery in the algorithm, so the customer does not end up with the same recommendations over and over. You need to find a good ratio between relevancy and discovery and expand from there. Customers might want to learn about other products that they would miss otherwise. In general, a ratio of 80/20 or 90/10 between relevancy and discovery is necessary.
 
AI helps marketers be more relevant and get more efficient results. This is no longer futuristic but is happening right now, in organizations of all sizes. Not leveraging AI in a strategy, whether for a small business or a larger corporation, is now more than ever a missed opportunity for your product and users.