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.
1. Improved Personalization & Recommendations
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, 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 behavior 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 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 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 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 preferring 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 likable.
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 suggesting alternatives (“did you mean…”) and may be influenced by your past browsing or shopping behavior.
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 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 become 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 is that knowing what ads works 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 behaviors by ad format (yes, I’m simplifying). Rather, it’s by understanding the events in the buying funnel that attribute to the buying behavior. 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 a human writer 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.
Curated content is yet another way to scale up without using your own resources. AI is highly efficient at finding and selecting the right content for your audience, enabling you to automate the curation process.
7. Optimizing the “When” and “Where” of Digital Promotion
The growth of digital marketing has opened up many new options for marketing, but all these new possibilities also mean that the sheer number of choices can be difficult to manage.
There are many different channels available to content marketers in the enterprise but not all these channels will perform equally well for each lead. While it’s possible to work out the best channels through thorough experimentation, this process takes time and is highly resource-intensive.
AI removes this burden of manually selecting the best channels for each marketing campaign to target specific leads. AI-powered software can automatically find the channels with the highest chance of success in real-time, based on each interaction with the brand.
Timing is also vital when it comes to making the most of your marketing campaigns. Again, AI eliminates the need for guesswork, experimentation, or relying on industry averages such as “the best time to post on LinkedIn is Wednesday between 10am and 2pm”. AI scheduling software can automatically calculate the best times for posting promotions on each marketing channel, for each individual customer.
8. Automated Marketing Processes
Marketing automation has been around for quite some time. You don’t copy and paste content into thousands of emails, manually changing the name each time – email marketing software can do this for you in seconds.
AI-powered email or automation software enables you to ramp things up a notch and takes away some of the burden of decision making. AI is highly efficient at performing repetitive tasks, meaning machines can take away the majority of this work from human marketers. This frees up time and resources for tasks involving the “human element” such as following up on leads and communicating directly with customers.
Some examples of AI-powered marketing automation include personalizing customer experiences, responding to customer interactions, and contacting leads at optimal times using the channels with the highest chance of success.
You can use AI to help you to decide not only what content to create, but also when, how, and where to publish and distribute it. The whole process can be automated with a single click.
By turning over these repetitive tasks to marketing software, you can increase your productivity and focus your efforts on strategic marketing planning, talking face to face with customers, and other areas where humans excel over computers.
9. Processing Big Data
Humans are better than machines at doing many things but they are also prone to making errors. This is particularly true when it comes to using data, especially large quantities of data. You can use AI to reduce errors due to duplicated or out-of-date data. Software can parse and merge several databases, combining intelligence from many different sources without resulting in duplicate data.
Most enterprise organizations are already collecting a vast amount of valuable data about their customers and industry, but the majority are failing at using the data they collect.
A survey of North American and European business leaders, including enterprise-level organizations with over 2,500 employees, found that only 4% of companies are making the most of their data.
There are several reasons for this including a lack of skills and technology and not employing a data analyst. Many businesses are understandably overwhelmed at the sheer volume of their data sets. This is where AI offers a huge advantage for processing and understanding data.
Artificial intelligence excels at processing large data sets and spotting trends and patterns in data. It can be, therefore, used to gain valuable insights from data and deliver this information in a way that’s easy to understand and use for employees across all levels of the marketing and wider management team.
10. Understanding and Predicting Customer Behavior
Did you know that AI can predict your personality traits better than your spouse of partner, your friend, even your family? That’s one of the key insights I learned a few years ago when I attended Pegaworld at the MGM Grand in Las Vegas, NV.
More than 4,000 Business and IT leaders came together to understand the role of technology and AI in the future of sales, marketing, customer service. This event included some really smart people, if a bit more technical than the audience I typically see at Marketing conferences.
But an audience who is still trying to solve the same problems I think we all are:
- Marketers trying to understand how to effectively reach target customers when ad blockers, changing media consumption habits, and ad performance are impeding us.
- Customer Service people looking improve customer satisfaction, net promoter scores all while holding down costs.
- And sales is looking to sell more in a world that is largely tuning out of promotional messages and avoiding business development approaches of old
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