Use of Artificial Intelligence [AI] in Digital Marketing is Beneficial

Artificial intelligence, or AI, is already transforming the face of marketing as we know it. AI technology can help to optimize and speed up many different marketing tasks, improving customer experiences and driving conversions. If you’re involved in enterprise marketing, there’s a good chance you’re already using some type of AI-powered solution in your martech stack. But many marketers still do not understand the benefits of AI and machine learning over traditional “non-intelligent” marketing software. If you’re not fully on the bandwagon yet or you’re just considering dipping your toes in the water, you’re not the only one. Investing in new technology is a big commitment and it can be intimidating when it’s underpinned by complex concepts like machine learning algorithms.

Quick Takeaways

The way that consumers respond to and interact with marketing messages is changing. Traditional marketing methods like media advertising and direct mail are no longer as effective as they once were. One of the reasons for this is, that today’s consumers expect brands to tailor messages to their location, demographics, or interests. Many will not engage with or even may ignore non-personalized marketing.

A report by management consulting firm Accenture found that over 40% of consumers switched brands due to a lack of trust and poor personalization in 2017. 43% are more likely to make purchases from companies that personalize the customer experience.

Consumers are more likely to interact with personalized marketing messages. Data from Experian shows emails are 26% more likely to be opened when they have personalized subject lines. Further, 79% of consumers in a global poll conducted by Marketo said they are only likely to use brand promotions if they’re specifically tailored to past interactions. AI enables marketers to personalize their communications on an individual level rather than the generic target groups that marketers relied on in the past.

This technology works by predicting customer behaviour based on intelligence learned from previous brand interactions. This means that marketers can send content and marketing communications that are most likely to convert the lead into a sale, at the best possible times to drive conversions. Most people will already be familiar with the tailored recommendations that are offered when you log into a site like Amazon or Netflix.

These recommendation engines have become increasingly sophisticated over the years and can be startlingly accurate, particularly for users who have had an account for several years so the service has been able to collect lots of data. For example, Amazon has a record of:

• Every purchase you’ve ever made

• Your product browsing history

• The addresses you’ve lived and worked at

• Items you’ve wished for

• TV shows and music you’ve played

• Apps you’ve downloaded

• Product ratings you’ve made and reviews you’ve left

• Devices you’ve used to watch movies or download ebooks

• Everything you’ve asked Alexa if you have an Echo

It can use this information to deliver product recommendations based on your interests, past purchases, and what other people have purchased who also bought the same items as you. Say you’ve previously bought a printer then Amazon is quite likely to recommend you print cartridges and paper. If you’re expecting a baby and you’ve ordered stretch mark cream and pre-natal vitamins, don’t be surprised if baby clothes and toys start popping up in your recommended products.

All this is powered by an AI framework called DSSTNE that has been released as open-source software to improve its deep learning capabilities. At the same time, Gartner predicts that while 90% of brands will use some form of marketing personalization by 2020, most will fail to produce optimally personalized content.

The answer to both improving personalization and producing more and better content is in AI. By analyzing customer data, machine-learning algorithms enable marketers to offer a hyper-personalized customer experience.

2. Dynamic Pricing

Providing discounts is a surefire way to accelerate sales, but some customers will buy with a smaller discount, or if there is no discount at all. AI can be used to set the price of products dynamically depending on demand, availability, customer profiles, and other factors to maximize both sales and profits.

You can see dynamic pricing in action using the website camelcamelcamel.com, which tracks the price of Amazon products over time. Each product has a graph showing just how much the pricing fluctuates depending on the season, popularity, and other factors. If you’ve ever searched for a flight and then gone back to buy it a couple of days later only to find it’s gone up a few hundred dollars, this is also a good example of dynamic pricing at work.

3. Customer Service Chatbots

Facebook Messenger, WhatsApp, and other messaging apps have become popular and convenient way for customers to contact companies, but ensuring the accounts are constantly staffed with customer service agents can be expensive.

To reduce the workload and provide a faster response to customers, some organizations are now using chatbots to deal with common customer queries and provide instant replies at any time of the day or night. Chatbots can be programmed to provide set replies to frequently asked questions and to direct the conversation to a human agent if the question is too complex. This means that customer service time is reduced and the workload lifted, leaving the agents free to deal with conversations that need a more personal response.

With virtual assistants like Siri, Google Assistant, Alexa, and Cortana, we’re getting more comfortable with chatbots and in some cases even prefer them to a real person. AI language processing algorithms have become incredibly advanced in recent years, making it possible for machines to replace human agents in customer service and sales roles.

Chatbots are not only more cost-effective than hiring more team members to deal with inquiries, but they can also do it in a more efficient and sometimes even more “human” way. Machines never have a bad day unlike humans so they can be relied on to always be polite, engaging, and likeable.

4. Search Engine Optimization

Search algorithms are improving all the time in every aspect from small database product searches on e-commerce sites to search engines like Google that are used by millions of people every day. Integrating AI into search can pick up misspellings and suggest alternatives (“did you mean…”) and may be influenced by your past browsing or shopping behaviour.

Google is becoming increasingly sophisticated at working out searcher “intent” For example if someone searches for “Apple” are they looking for information about the fruit, the technology company, or the record label? Most search engines know if a user is on their mobile phone and searching for “coffee shops” they’re looking for a coffee shop within a few miles, rather than researching coffee shops in general.

Special results such as shopping and Google My Business results are also providing a better user experience for searchers, and voice search is becoming more commonplace as the number of AI-powered devices and assistants continues to grow.

Further, with the growth of mobile internet usage and smart home speakers, voice search is increasing all the time and is expected to continue doing so. AI is necessary to interpret complex patterns in speech and to recognize meaning from spoken search queries, which are very different from traditional typed searches. Marketers can also use AI to optimize their content for voice search, helping to improve SEO and site traffic as we move increasingly into a voice-operated digital world.

5. PPC Ad Optimization

A/B testing is the traditional approach to optimizing marketing messages and display ads, but it’s a painstaking process with an infinite number of variables to try out, and therefore takes up a lot of time and resources. With AI algorithms you can continually and automatically optimize your ads depending on conversions and interactions.

That said, are becoming more immune to ads. The rise of apps like Ghostery, to detect and block tracking technology, has made things more challenging for publishers and advertisers alike. The impact on the publishing industry is staggering: By the end of this year, revenue losses at $35 billion are estimated assuming the rate of adoption continues.

In the past, brands like Unilever and agencies like Havas chose to freeze Google and YouTube spending because of ad placement beside “undesirable or unsafe content”. This, on top of the questionable reporting on viewability, and the rising incidences of ad fraud are making brands and agencies alike become more cautious about how they spend.

Here’s the thing: the customer journey begins from the moment of interest. How we engage with that customer to put the most relevant information in front of them, at the time they would have the highest likelihood to respond is the holy grail. The last decade has witnessed practitioners in this young digital landscape testing, implementing and succeeding in applying techniques to maximize performance.

Google has realized that knowing what ads work can’t be done by measuring performance in aggregate. The reason they’ve moved to conversion metrics (CV)  is that the Click-through rate (CTR) is a misnomer. It’s no longer a measure of true intent. How you measure intent is not an aggregation of behaviours by ad format (yes, I’m simplifying). Rather, it’s by understanding the events in the buying funnel that attribute to the buying behaviour. And here’s our introduction to Artificial Intelligence and why it will be the next evolution in the journey for the CMO.

AI ad optimization is also in use on social networks such as Instagram. Algorithms analyze the accounts that a particular user is following and will show the ads most likely to be relevant to this user. This provides a better experience to the user and a better ROI for the advertiser as fewer ads are shown to people who aren’t interested in them.

6. Content Creation and Curation at Scale

Content marketing offers an impressive return on investment. But it can also be resource intensive. As mentioned in the Gartner predictions, most brands struggle, not with collecting sufficient data, but with producing enough content to ensure a personalized experience for everyone.

Machine-generated content has been around for quite a while but the first unsophisticated attempts were pretty unreadable – they may have fooled the search engines (temporarily) but not humans.

AI for content creation has now become incredibly sophisticated to the point where Stylist magazine published three automatically generated articles created by Articoolo in its special “Robots” edition. AI can help to speed up and optimize your content marketing in several ways. Automated content software is now able to generate news stories and reports in a matter of seconds that would take human writers hours or days to create.

Even if you don’t trust machines to take over your content creation process entirely, they’re still useful for smaller tasks like generating your social media posts. The Washington Post uses in-house reporting technology called Heliograf to write basic social media posts and news stories. Computers are also pretty good at coming up with formulaic headlines, particularly those that can be classed as “clickbait”.

You may not be thinking about replacing your copywriter with AI software just yet but we may be closer to this than you think. Several global brands, including Forbes, are now publishing content that’s at least partly generated by AI. This use of AI makes content production much faster and more efficient and enables marketers to scale up their content marketing – something that 47% of marketers say is their biggest challenge.

Benefits of AI in Digital Marketing

AI is the computer equivalent of an extremely powerful human brain. It uses algorithms to collect, analyze, apply and even learn from data inputs. Human marketers can only do so much with all the data that is now available from digital channels. AI is much more efficient, and it is only growing more productive.

Using AI in digital marketing helps:

• Maximize data collection

• Visualize customer journey

• Research customers

• Personalize content

• Identify trends in data more quickly

• Provide a more convenient customer experience

• Free up humans for more creative, critical thinking tasks