Because AI ( Artificial Intelligence ) affects our daily lives and AI will have billions of dollars in impact on companies every year. Also on yours.
There are literally dozens of consumer applications for AI on a daily basis, from Netflix recommending you to watch a program and Google Maps redirecting you around a road accident, to Amazon predicting what else to buy or Gmail giving you finished sentences. Still, most people have no idea what it is and how it works because they don’t realize that machine learning is performing these tasks.
AI makes your life better by making things easier and more personal. Exactly the same happens in marketing and sales. The software we use will only get smarter over time. This will allow marketers to personalize scale business and do things in a way that is currently limited by the human factor.
Al will transform marketing
AI can help your business make better marketing decisions and drive more customers to your brand. New technology will change the marketing approach, just as everything else in our lives is already changing.
Many entrepreneurs will be amazed at the possibilities within 3 or 5 years, but only a limited segment of entrepreneurs already understand the potential of intelligent software and know-how to find these tools today. By doing this, they give themselves a multi-year edge over colleagues who are still afraid to explore AI. Perhaps this matter seems too abstract or overwhelming to be easily understood. Whatever the reason would be, brands that don’t find smarter plans to do their marketing will be left in the dust by their competitors who do.
Consumers already expect the benefits of AI
Over the past decade, consumers have become accustomed to giving up data and privacy in exchange for personalization and ease of use. People now expect a degree of personalization in the way companies market their products, even in the B2B environment. Anything less than a seamless or frictionless business buying experience is frustrating. People want the convenience that AI can provide.
What is AI actually in marketing?
From a marketing perspective, there are some fundamental things to learn about machine learning and deep learning and how each of these work in practice. The first is that the software that powers the computers is inherently stupid. Machines cannot see, hear, understand or speak. AI is simply an umbrella term to make machines ‘ smarter’ and give them human-like possibilities.
Alexa, Siri or any other voice assistant doesn’t understand anything right away. These tools are trained to hear the human language, understand what you say, process it and generate the best answer to your question.
Predictions about future AI results
The primary sub-branch of AI is called machine learning. In the simplest terms, it makes predictions about future results based on historical data. This is comparable to data science, which has been doing the same for decades. The difference is that machine learning improves as more and newer data becomes available and predictions are made based on this new information.
For example, Google Maps or Waze is not powered by a human who types directions to redirect people when taking exits, encountering accidents, or doing other things on the road. It uses data points from multiple sources to predict a better path… while you drive.
In the daily tasks of your entrepreneurship, you will find that you just make a series of predictions at any time of your day. Marketers make a series of predictions every day about what to write; where and when to publish it; how much to spend on ads; the colors, illustrations and design; and much more based on past human behavior. Then they just hope for the best. Each of these predictions is an example where machine learning can be applied.
Exploring AI for your business
Many agencies claim to use AI when in fact they don’t. This has actually become a major problem in the sector. Many entities will say they use machine learning, deep learning, or neural networks. It is up to you as a potential customer to determine if it is true.
Ask specific questions about exactly how this AI technology is used in the process and how it will make your business more efficient and smarter. Challenge the sellers of the technology to see if they really understand the technology.
Applications of AI in marketing and sales
Chatbots: A common misconception about chatbots is that the word ‘ bot ‘ is an AI in the name of the chatbots alone. This is not the case. Most chatbots currently available, whether used for customer service, autoresponders or sales, rely heavily on human logic and arrays of ‘ if-then ‘ statements to function.
Chatbot technology with AI-powered capabilities can eliminate the need for people to set up and update these rules. To find out if a chatbot technology supports these capabilities, ask questions about how the company uses natural language processing and machine learning to make things more efficient for you.
Natural language processing means that the technology processes the meaning of the question or statement as it is naturally spoken or typed, and then generates a correct response and recommendation. Machine learning is when the technology can determine which recommendations are actually useful and automatically eliminate those that are not. The chatbot then adjusts any future recommendations based on this information. The key is that the chatbot technology fully processes, learns and adapts without a human having to follow every interaction and manually adjust a rule.
Facebook Ad Algorithm: The Facebook ad algorithm works with an auction and is a big black box that few people understand. Facebook uses the ad algorithm to determine the best ads, show them to the best audience, and create a good user experience at the same time.
In the meantime, the auction is the Facebook ad algorithm. The auction identifies the order in which your ad appears in the news feed relative to other ads. If you target an audience of 1.5 million people, thousands of other advertisers are potentially targeting people in the same audience. An ad wins the auction and is therefore shown first, second or third if it has the highest total value.
Value is not only about money, but also about the relevance of the ad. Facebook wants to show ads that users want to see and engage with.
The total value of an ad consists of three components: bids, estimated action rate, quality and relevance (also known as user value).
When you advertise on Facebook, you can bid three ways: lowest cost, lowest cost with bid cap and target cost. The lowest cost is the standard bid and what advertisers are likely to use 99% of the time.
Several factors contribute to the estimated action rate. First, the recent activity of the ad (such as share, comment, watch a video ad, click to your website or back to the page, etc.).
The user value component of the Facebook advertising algorithm used to be known as ad quality and relevance. The experience of a user after clicking a post has a major impact on this factor in the Facebook ad algorithm.
Google Ads also capitalizes on AI in marketing: ads that automatically adjust to better match user searches. Google introduced responsive search ads, which test different versions of a brand ad and automatically identify the best-performing ad creation for certain searches. With this AI tool, Google saves brands time they would have spent manually optimizing ads before. And responsive search ads help improve results – brands using Google’s machine learning ad optimization see up to 15% more clicks.
An optimization tool that will help brands direct pedestrians to their physical stores. Google introduced local campaigns that help brands deliver ads in search, Google Maps, and YouTube after a company gave Google its ad budget, business locations, and ad creativity. Brick store brands may be more willing to try the tool to take advantage of the growing number of consumers who rely on mobile phones for personal buying decisions: mobile searches for ‘ near me ‘, for example, have grown three times in recent years.
The fastest way to lose money in Google Ads is to keep bidding on an ad that doesn’t generate ROI. When the clicks come in but the sales don’t, it can be a disaster. Likewise, when an ad gets the bids but not the clicks, your Quality Score will suffer, and eventually, your ROI will automatically follow. A well-built machine learning algorithm will understand when it is necessary to pause an ad to avoid damaging your ROI or Quality Score.
Google Ads’ Dynamic Search Ads are part of the machine learning technology currently built into the platform so that anyone using Google Ads can take advantage of it. Dynamic Search Ads automatically generate headlines to grab a searcher’s attention. After uploading a list of landing pages that you want Google to generate dynamic ads for, Google will identify searches that match your landing pages and then automatically generate ad content using phrases from your pages. Google also generates ad suggestions based on machine learning. These recommendations use past performance models to suggest changes to your ads that should improve your results.