Digital Marketing Artificial Intelligence(AI)Automation Models
Smart data-backed automation can help anyone to achieve higher goals, let’s find out how!
As you might have seen Artificial Intelligence (AI) became a very popular topic in the last few years and is still growing in its development, but do we all have the same picture of what AI is?
I would not agree, as everyone might have a different opinion of
- What applications AI has on a certain topic?
Or
- Will AI change the life we know?
Furthermore, some might think of AI in a robotic way that will reason like a human being, while others might only connect it with certain aspects in the data technology.
What are AI definition and a few real-world examples?
This is no easy question as there is no easy answer due to many reasons.
Mainly due to
1. No official definition
Due to constant innovations in the field, certain aspects became widely understood but were more compatible with other disciplines. One such is the processing of uncertain information, which became widely used, and therefore will become a curriculum in the business & economics subjects.
2. Science Fiction (SF) Movies Correlation with AI
Robots and supra-natural human-like figures of movie scenarios affected the viewer’s mind about the overall AI. In many cases, AI made robots so powerful that they overran human intelligence and therefore movies influence negatively the overall AI innovation process in the real life.
Therefore, a clear definition is made out of two aspects
Where,
Autonomy
Defines „the ability to perform tasks in complex environments without constant guidance by a user“ — ElementsofAI, the University of Helsinki, Finland online course
Adaptivity
Defines „the ability to improve performance by learning from experience“ ElementsofAI, the University of Helsinki, Finland online course
Therefore, any AI model needs to be built in a way that it is fully capable of obtaining programmed tasks by itself. Although it might obtain the tasks, its success percentage increases only by an additional amount of data from its users. Users and user testing is the right way of AI model optimization.
Bigger Picture of Artificial Intelligence Environment
To gain a bigger picture Artificial Intelligence (B) is a part of a bigger circle — Computer Science that consists of other parts such as Deep learning, Machine learning & Data Science.
- Computer science (A)
The study of computing and its implication through various ways
- Machine learning ( C)
Subdivision of AI that improves by more experience or data
- Deep learning (D)
A subfield of Machine learning that uses mathematical models for better correction and constant model improvement
- Data Science (E)
Consists of all the above fields where it requires domain understanding, added value as well as the purpose which it extracts from data and forwards into the required aspect of a certain business or organization.
The above-mentioned fields are displayed in the graphic below
Few AI Examples
Content personalization
Every online news portal, ads, or social media platforms use data-driven decisions to place the most important information in front of the right customer.
The process of obtaining data is structured through each platform’s algorithm and therefore not available to external sources. For the process to be as efficient as possible automation is implemented.
Individual user-based data through the web optimization process is used to pinpoint the most relevant information to show it in front of the customer.
Automated vehicles
In the case of self-driving cars, AI implementation might be present in various ways, through smart navigation for example.
The smart navigation would not only check which route is the most convenient but will also check the environment and possible obstacles to provide the best results.
While GPS systems might show you the direct or most efficient route it does not take into consideration the other aspects that are on the road at a given moment and therefore can’t provide a customized route.
However, let’s check what are the correlations between content marketing and AI in the next section
Digital Marketing and AI Correlation
Technological developments and innovations have impacted the way of doing business by providing certain solutions that increased the efficiency of obtaining certain tasks.
Likewise, automation and data-driven decisions are just a few potential benefits of AI in digital marketing especially through lead generation, social media management, personalized customer experience, market research, etc.
Therefore, the digital marketing transformation is mainly fueled by data-driven decisions that became available since the Big Data introduction followed by the possibility of consumer behavior tracking.
There are three main directions of the AI implementation in marketing according to a scientific paper by Davenport et. al, 2019.
1. Data-Driven Digital Marketing
Since the “birth” of Big Data, the world did not have so much data on which successful models could have been built.
Nowadays it is possible in a way that marketing campaigns can be optimized by consumer personality, provide real-time interaction and receive customer feedback.
Furthermore, by obtaining such information any further campaign will be much easier to modify as it will have established customer data.
Of course, competitor’s analysis should not be neglected as their activities can be screened with valid comebacks deployed using AI platforms such as Cortex.
On the other hand, market trends identification not only is important but is crucial to hear what the market has to say about your brand. Therefore, gaining positive or negative market trend pictures through Google Thinks or Linkfluence Radarly tools.
2. Personalized Digital Marketing
Content marketing is tough even by itself without promotion techniques needed for its proper potential buyer persona delivery.
Due to that problem, some software companies developed
- E-mail & website content personalization by Socialbakers
- Personal mobile assistants such as Siri
- Service/grocery delivery such as Bolt
- Smart home solutions by Nest
3. Multichannel Digital Marketing
The most important of all is the redistribution and reformatting of the most engaging content article on your website.
You do not need to write different topics on each of the platforms but reformat your most engaging article in a few ways for maximum digital reach:
- Social media
- Guest posting
- Video
- Podcast
- E-book
The process is less time consuming than individual platform strategy, although it still requires too much time.
Due to that, AI innovation was introduced where a single platform strategy is implemented without the need for manual posting on each platform.
- MarketMuse — Content optimization, SEO
- OpenAI — Natural language generator (NLG), generating automated content
- Curata — online channels content promotion
- Xinoah — potential customer recognition with product/service promotion
Drawbacks and benefits of AI model implementation
The innovations in any field are welcome as they are “fueling” the economy and are helping those seeking higher efficiency.
However, according to the marketing professionals’ perspective of AI impact in marketing (Shadid et. Al, 2019), there are certain core points, which need to be set for the AI models to operate correctly.
Technical compatibility
The integration of current companies’ IT systems might represent a big challenge for medium to big enterprises that rely on different levels of authority, departments, affiliate, or country-specific operations.
Therefore, an easy customer relationship management (CRM) transition for example is no easy task and normally requires time and effort. Without a good CRM, AI models won’t do much in better optimizing your performance.
Data needed
The need for actual data is crucial for building and successful AI model. Normally, companies that are in business for a longer time have historical sales data that are used for demand and budget forecasting of a certain product.
What happens with those that are new in the business or are just curious about certain new product launch but are lacking historical data? Here is where AI forecasting models step in.
According to Mckinsey, AI-powered forecasting models can reduce errors from 30–50% in supply chain management.
A clear explanation of the traditional vs. AI-powered forecasting model for completely new products by AI Multiple, an AI industry analyst is displayed below
Customer data Ethical/Privacy
Personal data needs to be gathered from the current and potential clients for an AI model to provide insight into what content needs to be brought to which type of customer.
To gather that information, companies need to be aware that in the case of personal — name-by-name profiles, customers need to know about that, while an anonymous approach could also be made to preserve customer’s personal information while at the same time the company will gather general client-related information.
Therefore, knowing how to do certain AI model work and its personal data requirements are crucial in obtaining a healthy relationship with your potential and current clients by indicating that their data is used for further research while their consent is needed for such action.
On the other hand, there are benefits of AI model implementation that highlight efficiency, conversion rates, return on investment (ROI), better customer understanding, but the drawbacks are those that need special attention for maximum efficiency and transparency.
Future of Digital Marketing
As seen in the above sections, the improvement in technology will boost its implementation in various industries and thus in marketing as well. The need for constant strategy improvements and better customer feedback with lead generation is the crucial aspect of every online present business.
Current technology developments are mainly related to better company model transformation in the fields of customer relationship management (CRM) which are using artificial intelligence in the automation of tasks.
Deep learning helped in customer differentiation and better profiling which provides better efficiency and service or product delivery. Therefore, the future AI models will be able to provide interaction, personalization, as well as customer, oriented automation.
AI will be the “working horse” of the marketing process that will search, gather, personalize, and transmission tasks on an operational level while the marketer will be behind the scenes observing the process and optimizing the strategy.
It’s time to catch that train if you still haven’t!
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