The Role of AI in Digital Marketing
Artificial Intelligence is everywhere and is now predominant in digital marketing. We have compiled these articles to help explain the effect of AI in digital marketing.
What is Artificial Intelligence (AI)?
Artificial Intelligence provides machines with the ability to analyze data and perform cognitive tasks. It observes and thinks like a human to take relevant decisions, making the work of planning and execution quicker and simpler.
Although most people might argue that this will replace humans, AI helps them by streamlining the process. Once the requirements are set, and the inputs are given, AI automates the whole mundane process. This ensures that humans are not burdened with any tedious work.
Like mentioned earlier, AI increases efficiency through automation. This leaves people with more time to focus on other tasks. But that is not the only advantage offered by AI. Here are some other benefits of using AI in Digital Marketing.
Using AI in Digital Marketing raises productivity since the processes are automated based on the instructions given. AI is also capable of understanding which content performs better and helps with content curation. As a result, the right content reaches the right audience, thereby increasing the ROI.
A study conducted by MarketingProfs showed results that supported this fact. While surveying Businesses that implemented AI in their Marketing Strategies, they found that these Businesses had
- 59% Better Closing Rates
- 58% Increased Revenue
- 52% More Conversions
- 54% More Traffic and Engagement
These numbers convey the importance and necessity of using AI in Marketing.
Artificial Intelligence uses the concept of Machine Learning (ML) to learn about its users. It can study their patterns to show content according to their preferences, keeping the audience interested and engaged. When people have a good user experience, they are more likely to buy the products or services.
Here are some examples of AI enhancing user experience.
- ‘Suggestions’ Tab in the Right-hand side panel on Youtube
- ‘People also buy’ Section on Online E-Commerce sites like Amazon
- ‘People also ask’ Section in Google Search Results
Top Benefits & Uses of AI in Digital Marketing
The way that consumers respond to and interact with marketing messages is changing. As a result, 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, today’s consumers expect brands to tailor messages to their location, demographics, or interests. As a result, many will not engage with or even may ignore non-personalized marketing.
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. For example, 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 expected to use brand promotions if they’re specifically tailored to past interactions.
AI enables marketers to personalize their communications individually rather than the generic target groups that marketers relied on on 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 offered when you log into a site like Amazon or Netflix.
These recommendation engines have become increasingly sophisticated over the years. They 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. Also, 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.
This is powered by an AI framework called DSSTNE, 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.
Providing discounts is a surefire way to accelerate sales, but some customers will buy with a smaller value or no discount at all.
AI can set the price of products dynamically depending on demand, availability, customer profiles, and other factors to maximize 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 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 an excellent example of dynamic pricing at work.
Facebook Messenger, WhatsApp, and other messaging apps have become a 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 offer set responses to frequently asked questions and 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 preferring them to a natural person. In addition, 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. In addition, machines never have a terrible day, unlike humans, so they can be relied on always to be polite, engaging, and likable.
Search algorithms are improving in every aspect, from small database product searches on e-commerce sites to search engines like Google used by millions of people every day.
Integrating AI into search can pick up misspellings and suggesting 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.
Impressive results such as shopping and Google My Business results also provide a better user experience for searchers. In addition, voice search is becoming more commonplace as the number of AI-powered devices and assistants grows.
Further, with mobile internet usage and intelligent home speakers, voice search increases and is expected to continue doing so.
AI is necessary to interpret complex speech patterns and 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.
A/B testing is the traditional approach to optimizing marketing messages and display ads. Still, it’s a painstaking process with an infinite number of variables to try out, and therefore takes up a lot of time and resources. However, you can continually and automatically optimize your ads with AI algorithms depending on conversions and interactions.
That said, they have become more immune to ads. Moreover, the rise of apps like Ghostery to detect and block tracking technology has made things 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, is 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. The holy grail is how we engage with that customer to put the most relevant information in front of them when they are most likely to respond. The last decade has witnessed practitioners in this young digital landscape testing, implementing and succeeding in applying techniques to maximize performance.
Google has realized is that knowing what ads works can’t be done by measuring performance in aggregate. They’ve moved to conversion metrics (CV) because the Click-through rate (CTR) is a misnomer. It’s no longer a measure of actual intent. How you measure sense is not an aggregation of behaviours by ad format (yes, I’m simplifying). Instead, 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 show the most likely ads relevant to this user. This provides a better experience to the user and a better ROI for the advertiser as fewer ads are displayed to people who aren’t interested in them.
We hope that these articles have helped you in your search for marketing techniques and tools. If you have any questions or are in need of marketing assistance, do not hesitate to call hughesagency.ca
Article compiled by hughesagency.ca
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