We live today in the era of avoidance. Advertising and marketing reach us through billboards, newspapers, magazines, television, web banners, sponsored posts on social media, the list is endless. On an average, humans see close to 5000 ads in a single day, and that number is only increasing. How individuals and customers react to this bombardment of promotional material is double-edged. Some engage, and some go out of their way to avoid such material. In the past two years, the number of web users who installed ‘ad-blocker’ plugin has risen by over 50 million.
What does this mean for marketers?
When customers aren’t willing to look at ads online in exchange for access to free content or services anymore, it is a problem for websites that offer ads, as marketers who have placed ads on their websites do not see the results they need. Media Publishers are left between the devil and the deep sea as they customers are unhappy looking at ads and marketers are unhappy when ads are blocked. Recently, many online publishing companies blocked access to free content requesting users to disable their adblocker plugins to view content, in a clear move to please advertisers and marketers. Marketers like Rob Leathern of Optimal.com have called this move ‘a big mistake’ for a number of reasons.
Why do we block ads?
Users block ads primarily because they are impersonal, intrusive and irrelevant.
Being forced to engage with something you weren’t looking for often leaves users feeling cheated. Beyond this, ads today are also so deceptive that just clicking on some result in a torrent of pop ups or messages about your computer being infected that confuse a novice user and create a gateway for malware. Users want to see clever, engaging, informative messages when they use the internet, not be goaded to buy products and use services they aren’t interested in.
If not ads, what works?
Customers want seamless experiences, they want to don’t want experiences that are in your face. This is why brands around the world now invest in content. Content is the opposite of online advertising. It is informative, builds curiosity, desire, opinions, shared goals, and a whole lot more. It is a repository you can get answers, and a valuable knowledge reserve.
See how Forbes personalizes the content, giving you stats on how many views an article has had and what social platform it is currently popular on? That is engaging and informative, and it uses artificial intelligence software. Good content and content recommendations build trust in publishers and brands. Good content helps ecommerce platforms create relevant connections with customers. Customers feel appreciated or understood when they see something familiar and relevant.
So how does AI in marketing work?
As marketers and advertisers, it is best on the right side of the ad block war, and earn the trust of customers and publishers by providing personalized, quality content and fine tuning all communication to be relevant to a customer. How can you use artificial intelligence software to do this?
Artificial Intelligence Software for Marketers uses pieces of information that users provide as they use brands, try products, navigate ecommerce platforms, read content, and understand not just what they like and dislike, but when they like to receive communication and when they don’t, what mediums they prefer to receive them on and more. These hints and clues can be analysed as large pattern filled datasets by artificial intelligence networks to arrive at meanings that make a difference.
Machine learning helps marketers create personal connections by understanding patterns in images, content, user behavior and more. Google’s Bubble Zoom tool for comic books, uses artificial intelligence software to train the tool to identify speech bubbles in comics.
Google Bubble Zoom Tool
This means that users can pinch and zoom into speech bubbles with ease while reading comic books on mobile and tablet devices – which improves their entire reading experience. Yup, machine learning for comics is officially a thing.
Collaborative Filtering for Predictive Recommendations
For collaborative filtering to work right, marketers need to have the right data set of diverse elements. Likes on facebook or follows on twitter could help artificial intelligence software find other people with similar tastes and make recommendations for content and products this way based on what they like. Soundcloud and Apple music use this to give their customers new music recommendations and help them discover new artists and music everyday. This is a great way for marketers to engage customers with content they love without using intrusive ads.
Boomtrain – Personalized 1:1 recommendations for content and products catered to every unique customer.
Predictive Analytics are another great way to understand what might hamper a customer’s experience and take preemptive steps to correct this. This could be something as colossal as a car predicting when its tyres need to be changed to prevent accidents and mishaps on the road, to a sensor in your milk jar that adds milk to your online grocery shopping cart as you run out. Sensors and connected devices using IoT could make this happen in the future.
Amazon’s ‘Dash’ button needs users to just push, and guarantees a speedy delivery of regularly ordered items like toilet paper, coffee and food staples. Artificial intelligence software will have the power to understand these consumer needs and alert both customers and marketers so they can take the next steps.
Alteryx – A self-service analytics platform for business analysts.
Dataiku DSS – Turn raw data into predictions.
For a marketer looking to use artificial intelligence software, semantic analysis is a great place to start. The great part about brands on social channels isn’t growing likes and shares, but understanding what customers have to say about your brand, product or service. Applying a semantic analysis program to do this can help you understand at a larger scale their mood and pain points, and respond appropriately. Samsung’s Note7 Recall is a classic example of an instance where brands could use social listening, to understand customers’ moods after such an incident and respond appropriately,
Netbase – Social Listening for enterprise level brands to understand their customers better.
Clarabridge – Clarabridge processes written feedback and other sources to understand what customers are saying in emails, call transcripts, call center notes, CRM data, online reviews, tweets and more.
Have more tools to add to this list? Let us know, and we’ll get you up here.
This post is part of a series that talks about artificial intelligence today and how it is changing the future as we imagine it. Read our previous post artificial intelligence tools to increase your efficiency and disrupt your business here.