How Artificial Intelligence is changing Digital Marketing
10 ways AI is changing Digital Marketing
Digital Marketers have to understand current technology, commerce trends, and — above all — human psychology: what drives individuals as well as broader social trends. They have to intuit how to leverage new technology on both the micro and macro levels, all while constantly revising for improved performance. Marketing is pop psychology mixed with consumer technology, with a little bit of fortune-telling thrown in, too. Today’s emergent “third-platform” technologies, such as data analytics, mobile devices, automation, and artificial intelligence (AI), are changing the way society and individuals interact on a fundamental level. One has to look no further than the use of marketing to find a microcosm of how new software solutions, machine learning, and big-data analytics are changing the game.
Artificial Intelligence (AI) is a branch of computer science that deals with building intelligent machines that can think and respond like humans. The history of artificial intelligence dates back to the 1950s. In Ivy League research labs and back rooms of government installations, some of the brightest minds in science were hammering out a concept called “neural networks.” Their aim was to create computer programs that mimicked the complex, interconnected nervous systems that run the human brain and allow for nuanced decision-making processes. These scientists wanted to create systems that could identify patterns, recognize significant data, and categorize information based on its importance. The Turing Test, proposed by English Mathematician Alan M. Turing in 1950, was a test that determined the intelligence of computers and was taken in order to identify whether the computer could achieve human-level performance in all cognitive tasks sufficient enough to fool an interrogator.
- Machine learning is a subset of AI that enables machines to automatically learn and improve from experience. Specialised systems are created for this purpose and no explicit programming is needed to add new definitions to the database. The machines can learn on their own.
- Deep learning is a subset of machine learning comprised of extremely large neural networks and a massive collection of algorithms that can mimic human intelligence.
The next generation of AI will be able to collect, process and understand an enormous amount of data. The result will be new levels of automation and personalization. Here some examples how Artifical Intelligence is changing Digital Marketing.
1) Predict the Behavior of your Customer with Propensity Modeling and Predictive Analytics
Propensity models are statistical scorecards that are built to identify prospects who are more likely to respond to an offer. It correlates customer characteristics with anticipated behaviors. For AdWords professionals, it is crucial to use Google’s DoubleClick Bid Manager, in which you define your target audience and campaign objectives and the tool automatically recommends strategies to achieve the desired goals. Moreover, predictive analytics allows marketers to extract information from data and uses it to predict purchase trends and user behavior patterns. The Adobe predictive analytics tool analyses large volumes of data and helps to uncover the most impactful insights.
It works via the steps as explained here:
- Objective identification and data extraction: In this step, you need to identify the business objectives and analyse the available source data to determine patterns that match your needs. Thereafter, data is extracted to create models.
- Model creation and validation: In this step, data mining is used to refine and select a final model. The models are validated based on the set goals.
- Apply results and manage models: The final step is the application of the model results into business decisions and constantly refining the models for better outcomes.
2) Use AI-Powered Chatbots to Improve User Experience
Most businesses are already aware of live chatbots, or Artificial Intelligence systems that you chat with in an instant messaging format, and many are already using this feature on their site. But have you thought of upgrading the traditional live chats with AI-powered chatbots? The time is just ripe to do so.
In the words of Google: “Think about what makes your website unique, valuable, or engaging. Make your website stand out from others in your field.”
Chatbots are a feature that can really make your website stand apart from the rest. Here are some advantages that chatbots have over live chat:
- Chatbots can assist customers 24×7 and they can retain customer data. In other words, customers don’t have to repeat themselves with every interaction. This makes the customer experience more enjoyable.
- They are friendly and never lose patience. Your customers can be angry but the messenger bot will always treat your customers politely.
- They can handle several customer requests simultaneously, so the waiting time is reduced to nil.
- Chatbots are useful in creating personalized advertisements for users which can boost sales.
- They have the power to track customer purchase patterns. This allows the business to revamp their existing marketing strategy and increase sales.
3) Use AMP and Reduce Load Time
In October 2015, Google announced AMP web pages, which are a lighter version of the traditional web pages and aim to drastically improve the performance of the mobile web, such as reducing page load time to improve the user experience.
With the help of RankBrain, Google is getting better and better at knowing what is best for the user. AMP content automatically shows up in priority placement areas like the News Carousel. Having an AMP page means that you increase your chances of ranking on the top three search results of Google.
Speed equals revenue, especially on mobile. In fact, studies have shown even a 100-millisecond delay in page load time correlates with lower conversion rates.
To better understand the business impact AMP provides mobile sites, Google commissioned Forrester Consulting to conduct a Total Economic Impact™ study across publishers and e-commerce websites using AMP.
You can easily create an AMP page by following the instructions provided here.
4) Leverage the Power of Audience Insights and improve Customer Experiences on social media using sentiment analysis.
You can now target your ads by audience interests
This is possible with the help of in-market audiences. In the words of Google: “For example, if you’re a car dealership, you can increase your reach among users who have already searched for ‘SUVs with best gas mileage’ and ‘spacious SUVs.’ In-market audiences uses the power of machine learning to better understand purchase intent. It analyzes trillions of search queries and activity across millions of websites to help figure out when people are close to buying and surface ads that will be more relevant and interesting to them.”
In other words, in-market audiences uses the power of machine learning to unlock new opportunities for marketers.
Top rated AI-powered social media listening and monitoring tools offer advanced sentiment analysis to help you make sense of the millions of conversations that happen related to your brand every week.
The secret of converting a potentially negative situation on social media into a neutral or positive situation is speed. With AI tools becoming more and more accurate at identifying the sentiment associated with a social media post, digital marketers now have the artillery to respond to customer comments and queries in near real-time. Sentiment analysis can quickly flag posts that are potential negative comments about your brand, customer complaints, or an ugly jibe by a competitor. This enables you to blanket down the fire, and promote your speed-to-answer as a positive brand trait.
Feedback mechanisms, let’s face it, are limited. As soon as you hand over a feedback form to a customer, you either get disinterested 4/5 ratings, or comments that are mere over-burnt frustration that would only go if you offer designer ice cream tubs for free to insatiable customers!
AI-powered sentiment analysis is smart enough to identify social media mentions of your brand and pick up comments and posts that could carry actionable insight on what your customers really want. The CoffeeCup example we covered earlier is also a testimony of how the café could have come to know of its long service times had it been ‘listening’ to social media sentiments earlier.
Not long back, digital marketers only had the options of using structured surveys and questionnaires to elicit useful feedback from customers on social media. Topped with AI-based sentiment analysis, these feedback campaigns can be easily monitored to collect all user-generated content that could offer insight. Because the percentage of users that actually respond to direct queries by brands is diminishing (in Q2, 2015, only 30% of 7 million brand questions on Twitter were answered), sentiment analysis is bound to be the replacement for social media surveys in the near future.
With every business jumping on the social media bandwagon, it’s becoming increasingly difficult for brands to deliver their messages with the right impact. Proper usage of hashtags and in-depth sentiment analysis can help here.
Powered by ever-improving algorithms, sentiment analysis tools can:
- Report events such as sudden spikes in brand mentions
- Help you compile lists of influencers talking about your brand
- Make you aware of trends to reveal the right demographics interested in your products and brands.
This insight helps you create high impact messages, and helps you target them at audiences that are likely to respond to them.
Tools like Trackur can quickly figure out the data and provide sentiment analysis and influencer scoring. By mapping reputation improvement spikes with the social campaigns that brought them about, and by conducting demographics level reputation tracking, brands can significantly improve customer experiences by offering them more of what they already liked.
Too many times, however, brands commit the mistake of treating only ‘share of voice’ as a representation of how well they’re doing on social media. What if 80% of the mentions are negative? Here’s an example:
This post is a ‘mention’. But it’s negative. Chipotle would not want any of it. Surprise, the brand used sentiment analysis to fight off the crisis.
Competition analysis can be the biggest enabler for brands to prevent brand switching amongst customers, and even get back lost customers from competing brands.
AI fuelled sentiment analysis has a lot to give here. Not only can such tools help you remain aware of the kind of social media buzz your strongest competitors are making, but also help you compare your social media campaigns against theirs.
Look to leverage negative mentions of your competitors to suggest your products as alternatives, and take their positive mentions as inspirations. Here’s a killer example of how Close.io sneaked into a conversation to create a well-greased gateway for a potential customer.
Sentiment analysis can be the enabler of runaway success for social media brands; use it while it’s still a lever of competitive advantage.
The vehicle of digital marketing is at the cusp of milestone now, with AI empowered social media listening and sentiment analysis ready to re-transform social media as a goldmine for brands.
5) Scale Up Your Content Marketing with AI-Generated Content
Wordsmith, a natural language generation (NLG) engine “that lets you turn data into text at any scale and in any format or language,” was able to create around 1.5 billion human-sounding articles in 2016. AI isn’t able to write natural-sounding content for every topic, but it is useful for some types of data-focused content such as “quarterly earnings reports, sports matches, and market data.”
Another AI-powered tool called Acrolinx, which “helps you produce great content with the only AI platform for enterprise content creation,” regularly creates content for major brands like Facebook, IBM, Microsoft, Nestle and Caterpillar.
As per an article published on Marketing Artificial Intelligence Institute: “Acrolinx uses a variety of techniques in its multilingual natural language processing (NLP) engine, including machine learning and knowledge-based approaches to ensure the best combination of scalability and precision.”
Once you are able to produce content on scale, the next step is reaching your target audiences. You must always combine the power of text and images, as socially shared content that is accompanied by images have the highest chances of reader engagement. You can easily download free stock photos from platforms like Burst to get started.
Moreover, marketers are already aware of the fact that the fastest way to reach customers is through influencers. With platforms like Influence.co you can easily reach relevant influencers at scale.
AI cannot replace niche content experts, but they can certainly boost the production of content based on sports matches, financial reports and market data.
6) Deliver a Highly Personalised Website Experience to Every User
People love content, offers, products and services that are personalised for them. The 2017 Trends in Personalization Evergage report suggests that around 33% of the surveyed marketers use AI as a means to deliver personalized experiences to the user.
With AI, analyzation of data points has become easier. You can display personalized content and offers to each and every individual prospect by analyzing their location, device, past interaction, demographics, etc.
You also have the power to automate e-mail marketing and send regular push notifications to the prospects based on the micro moments or their current interaction with your business.
Rare Carat uses IBM Watson technology that allows prospects compare diamond prices across various online retailers so that the buyers are able to find the right diamond at the right price.
The New York-based startup and e-commerce platform for buying diamonds does this with the help of an AI-powered robot called “Rocky.” The robot is able to answer all the queries associated with diamonds and also assists buyers with purchasing a ring at the best price.
7) Optimise for Voice Search Queries
With the rise in voice search queries, it is becoming more than necessary for marketers to optimize for natural language long-tailed voice queries. Voice search will change future SEO strategies, and brands need to keep up. A brand that nails voice search can leverage big gains in organic traffic with high purchase intent thanks to increased voice search traffic due to AI driven virtual personal assistants.
Google has disclosed in its blog that around 70% of queries that the Google Assistant receives consists of natural, conversational language and not the typical keywords that are used in a typed Google search.
It is crucial to identify the intent behind the conversations that prospects are having with your brand. Create pages that provide a direct answer to the questions asked by searchers. Questions normally start with “who,” “what,” “where,” “when,” “why” and “how,” so try optimizing your web pages accordingly.
Create local landing pages for every location that you are targeting. Develop local website content that would spark the interest of the people in your area and make use of structured markup to make it easier for the search engines to understand the context of the page.
8) Attract Visitors with AI-powered content curation
AI can improve Digital Marketing techniques such as content marketing, SEO and other ‘earned media’ to bring visitors to your site and start them on the buyer’s journey. AI & applied propensity models can be used at this stage to attract more visitors and provide those that do reach your site with a more engaging experience.
AI generated content is an interesting area for AI. AI can’t write a political opinion column or a blog post on industry-specific best practice advice, but there are certain areas where AI generated content can be useful and help draw visitors to your site. For certain functions AI content writing programs are able to pick elements from a dataset and structure a ‘human sounding’ article. An AI writing program called ‘WordSmith’ produced 1.5 billion pieces of content in 2016, and is expected to grow further in popularity in the coming years.
AI writers are useful for reporting on regular, data-focused events. Examples include quarterly earnings reports, sports matches, and market data. If you operate in a relevant niche such as financial services, then AI generated content could form a useful component of your content marketing strategy. The good news is that automated insights, the firm behind Wordsmith, has announced a free beta version of its AI writing application, so you can try out the technology and see if it could be useful to your brand.
AI powered content curation allows you to better engage visitors on your site by showing them content relevant to them. This technique is most commonly found in the ‘customers who bought X also bought Y’ section on many sites, but can also be applied to blog content and personalizing site messaging more widely. It’s also a great technique for subscription businesses, where the more someone uses the service, more data the machine learning algorithm has to use and the better the recommendations of content become. Think of Netflix’s recommendation system being able to consistently recommend you shows you’d be interested it.
Programmatic Media buying can use propensity models generated by machine learning algorithms to more effectively target ads at the most relevant customers. Programmatic ads need to get smarter in the wake of Google’s recent brand safety scandal. It was revealed ads placed programmatically through Google’s ad network were appearing on terrorist’s websites. AI can help here by recognizing questionable sites and removing them from the list of sites ad’s can be placed on.
AI is being used to create marketing content and interpret users’ reactions to it. No computer can yet rival Philip Roth or Maya Angelou, but there are programs that can whip up everything from personalized content suggestions to ad copy, subject headlines to calls to action. In fact, Gartner predicts that machines will create 20% of commercial content by 2018.
9) Personalised advertising becomes truly personal
The creation of better advertisements is one of the biggest ways artificial intelligence is impact marketing. The ability for brands to use AI to research and develop crucial marketing aspects, such as keyword searches, empowers marketers to build smarter, more effective ads that should lead to more conversions. For years, companies have focused on who to show ads to and when to show the ads. AI allows marketers to, instead, focus on what messages to show the audience so brands can create powerful ads specific to the target audience. With programmatic accounting for 67% of all global display ads in 2017, AI is needed more than ever to ensure the increased volume of ads doesn’t affect the quality of ads.
One form of AI that is showing significant promise in this area is Natural Language Processing (NLP). NLP is a cognitive machine learning technology that can find trends in behavior and traffic the same way a human brain does. Using NLP in this way will match ads with individuals based on context, compared to just keywords in the past, therefore significantly increasing click rates and conversions.
One thing to know is that consumers today have large piles of data about them at marketers’ fingertips as a result of what they do on Twitter, Facebook, and other digital channels. This makes people wonder why — despite the data available about today’s consumer — do we still see ads that don’t pertain to the individual? The problem won’t continue for long; individual advertising is an ongoing project and one that is worth the effort. Traditional audience segmentation is dying. People want and need to receive personalized treatment, and AI supports the customer preference and will continue to generate and serve people’s needs in the future with the help of artificial intelligence.
Machine learning algorithms can run through vast amounts of historical data to establish which ads perform best on which people and at what stage in the buying process. Using this data they can serve them with the most effective content at the right time. By using machine learning to constantly optimise thousands of variables you can achieve more effective ad placement and content than traditional methods. However, you’ll still need humans to do the creative parts!
When it comes to programmatic ad buying, the current process is largely dependent on a machine’s ability to make decisions. Variables like location, previous search history and search text syntax are taken into account when selecting the ad, but these are still largely impersonal generic ads. With AI, ads can be personalized to a far greater degree and instantly delivered to users based on a qualified profile shot of their entire browsing history. AI can ‘learn’ to avoid users who have never interacted with a specific type of advertisement. Each time a similar campaign is run it will know who is likely to engage and who isn’t, only targeting the most relevant users. Of course, the technology behind this kind of ultra-personalization is still being refined, but so far all signs point to a new era in personalized marketing.
10) New AI Software for Digital Marketers
New AI applications increasingly narrow the technological gap between the data collection and deployment stages to provide solutions that help in the strategic decision-making process of marketing. The beauty of AI is that in many cases it’s self-teaching, or cognitive, meaning the longer it’s in use, the more accurate and beneficial its decisions. These applications are programmed not only to replicate how the human brain works, but also to continually evolve to better mimic intelligent processes and automate them.
Advances in the area of big data have helped in the early stages of the marketing cycle by collecting and aggregating the data points that marketers need to develop strategy. At the opposite end of the cycle, automated technologies can deploy campaigns to laser-targeted demographics in a multitude of ways at near instant speeds.
But artificial neural networks can take this magic a step further by using unstructured data to draw conclusions about causes and effects within data. Because of its ability to detect and extrapolate upon patterns, AI can identify opportunities and automatically act upon them. And the more a neural network goes through this process, the faster and more accurate it becomes. These cognitive approaches are already being applied to numerous aspects of the marketing sciences, with new applications emerging every day.
Every day, companies are using artificial intelligence software to optimise their own processes, reduce overhead, decrease turnaround time, and improve output. Technology is evolving at an unprecedented rate, and teams already making the move to marketing AI software are at a distinct advantage to jump on the next innovation.