Artificial intelligence - the gamechanger in lead generation? What does the future hold for advertising agencies?

The worldwide networking of computers on the Internet has created a completely new form of communication, the importance of which is constantly increasing. WhatsApp, Facebook and other digital channels are now replacing the telephone and letters. The digitization and automation of communication processes is becoming the basis for future-proof business development for advertising agencies. Chatbots, automated systems and the like are visibly replacing personal contact with customers and at the same time facilitating customer acquisition or customer care. Artificial intelligence will undoubtedly become an integral part of the work of advertising agencies in the future. The biggest problems were often the interfaces between humans and computers, which were not mature enough for personal, authentic and human communication. Artificial intelligence is changing this and will become more and more an integral part of the toolset of digital agencies in the future. But how can you already integrate AI into your systems in a meaningful and profitable way?

The terms AI, Machine Learning (ML) or Big Data are often mistakenly used as synonyms or misinterpreted. For this reason, we come to the clarification of terms further down in the article.

Artificial intelligence and advertising agencies

The most interesting field of application for AI for advertising agencies is machine learning. This gives advertising agencies the opportunity to collect and evaluate valuable information about their users via machine learning-driven data analysis. Similar to movie suggestions on Netflix, Machine Learning data analysis could be used to serve targeted ads to each individual customer, according to their desires and experiences. This offers the agency the opportunity to address customers directly and individually, which can increase conversion. The self-learning algorithms can determine the previously defined target customers faster and more accurately. In addition to the customer behavior of countless users, contextual factors such as location, time or demographics can also be included, making targeting much more efficient.


A practical example: The Foodora supply chain relied on machine learning-based analyses in the area of display campaigns to acquire new customers. In the course of this, Foodora was able to identify users who would not have belonged to the actual core target group. In just a few days, a conversion increase of 69% was recorded.

At the same time, Big Data and unsupervised learning can be used to analyze the behavior of website visitors and, based on the data, make real-time adjustments in the sales funnel. In short, the entire customer journey can be customized for each individual customer. Improving the customer journey has the positive effect of increasing customer satisfaction, which quickly has an impact on sales.

This is exactly where smashleads™ comes in. The focus is on interaction and the optimization of the customer journey through individual dialog with each website visitor. An optimized customer journey is characterized above all by automation. This is also where smashleads™ sets its focus. More leads and sales with less time investment.

But what are the concrete benefits of all these technological advances in the area of sales and marketing, and which tasks can already be automated?

The 3 central tasks of AI in marketing

Advertisers benefit from AI particularly in the following areas:

Analysis
With AI, advertising agencies are able to store and analyze large amounts of data on their customers. By analyzing the amount of data, fundamental insights about the customers can be gathered and accordingly advertising measures can be created in a targeted manner for the customers.

Automatation
Automation is about processes that are automated as far as possible in order to be able to use human resources strategically. For example, smashleads™ lead generators are self-optimized, making it easier for the user to create the inquiry forms.

Act autonomously
The complete autonomy of an AI system that autonomously controls marketing processes is currently still wishful thinking. However, pre-programmed interactive forms and chatbots are already an essential part of marketing automation.

How can interactive forms and chatbots improve ad agency customer service?

A simple chatbot is simply a text-based dialog system that can be used to communicate via text input or speech. The task of chatbots is to communicate autonomously with the customer. However, chatbots in the context of machine learning are currently more theoretical than practical.

Used correctly, interactive forms and chatbots can become a link between prospect and agency. For this, it is important that the communication is targeted and empathetic and that the customer can derive added value from this conversation.

Lead generation through artificial intelligence
How does smashleads™ combine artificial intelligence and lead generation?

At smashleads™, our goal is to make lead generation as easy as possible. We use the idea that you don't have to be an expert to generate leads effectively, but that you can achieve the same with the right support. This is exactly where we at smashleads™ come in.

Machine learning algorithms are used to analyze and evaluate user behavior on the website. Based on the behavior, the interactive form can be automatically optimized so that visitors are more likely to become new leads or customers.

A simple example: It is determined that the average dwell time of successful leads is 30 seconds for the first 3 questions of the interactive form. If another visitor comes in and takes 2 minutes to answer the first 3 questions, the algorithm can take away the last questions of the form, increasing the likelihood that the potential lead will get to the last page before bouncing." Noah Frohn, CEO.

On the other hand, we already start with the creation process of the interactive forms. Using Big Data, we are able to evaluate large amounts of data and identify commonalities. In this way, the interactive content with the highest conversions can be grouped and analyzed. Grouping by industry is also essential for conversion optimization.

Unsupervised learning is used to identify patterns that can be transferred during form creation, ensuring maximum conversion right from the start.smashleads™ wants to be at the forefront for marketing agencies and deliver the best solutions.

What possible trends can already be forecast for advertising agencies in 2021?

The use of artificial intelligence will completely change marketing and lead generation. Personal preferences and expectations can be analyzed and evaluated.

What are the opportunities here:

"I think Artificial Intelligence is very important in marketing and lead generation. It serves the customer to interact with your brand. It's going to be even more important in the near future than it already is. With the help of Artificial Intelligence - AI, you pick up the customer faster and bring them closer to your brand. Customer relationships with the brand and technology are built faster and creativity is encouraged. I'm excited about the future."
Brent Armstrong, Content Creator at “A Strong Marketing”.

"This is my bread and butter. Online marketing + AI has been the holy grail in some segments for a few years now. Those who don't jump on the bandwagon now will have a hard time in the future. AI is getting better all the time, and so is its importance for attracting leads, whether it's through Big Data or chatbots."
Francis Luan, Salesfunnel expert

"AI and online marketing are super complementary. AI can access the collected data in a completely different way than we humans can, identify the target groups much better and interpret the numbers better. There's a reason Facebook introduced CBO campaigns, which are getting better by the day. AI will make marketing increasingly easier and more efficient, and that's to every company's advantage."
Yasin Kara, Social Media expert

This is the potential benefit AI offers advertising agencies!

Artificial intelligence and machine learning will undoubtedly become an integral part of lead management in the future. This trend can already be seen in the study "Artificial intelligence on the rise". The proportion of advertising agencies using AI will increase. The advertising niche has already begun to gradually introduce AI and machine learning, and it is highly likely that AI will eventually be used in all areas of the business. The 2021 trend for ad agencies, in our opinion, will be toward real-time communications and targeted communications. The longer a customer waits, the more likely they are to abandon the purchase contract or interaction. This also applies to the loading time of websites. The average waiting time here should not be longer than 2-3 seconds. In this area, smashleads™ lead generators are optimally equipped and offer the customer fast processing. Another important area is target group communication. Via Big Data, advertising agencies can offer their customers personalized customer services or send custom messages.

Conclusion: AI, curse or blessing for the advertising industry?

Rapid technological progress is increasingly ensuring that our intelligent electronic helpers will not disappear from our daily marketing routine. But now the question arises - curse or blessing for the advertising industry? Artificial intelligence and machine learning have become indispensable. They were developed to simplify and solve complicated processes. These are usually so complex that the human brain is not sufficient for them. In the future, technology will take over a large part of the tasks that are currently performed by humans. And it will do so more reliably, more cheaply and more quickly. However, it is important to note: artificial intelligence is not capable of going through emotions and being empathetic. The advertising industry combines communication, creativity and also a huge portion of empathy. This characteristic is crucial for a positive interaction with one's customers. So, to sum up, a combination of artificial intelligence and empathetic people is the best alliance to achieve high goals.

Clarification of terms - artificial intelligence

Around 85 billion neurons in our brain constantly fire electrical impulses around, forming 10,000 new connections to their neighboring cells. This incredibly complex structure is the basis for our learning, reasoning, and abstract thinking.Is it possible to artificially recreate this complex structure? Artificial intelligence is basically an algorithm that attempts to replicate the human process of reasoning. The goal of AI is to take complex tasks from humans and act as a proxy for them.

"Artificial intelligence will improve our lives. And gradually relieve us of more and more thinking tasks. It will create more space for tasks that we enjoy. And it will revolutionize the world of work."

In this context, AI functions as an umbrella term and encompasses all technologies that deal with the "imitation" of human intelligence. This includes the forks shown on the following diagram. In this blog post, however, we are only concerned with the topic of machine learning. For all those who would like to delve deeper into the subject matter, we have linked a few pages under "Further reading and interesting links" that deal with the topics in greater depth and more scientifically.

With AI, a distinction must be made between weak and strong AI. Weak AI is characterized by the fact that it is focused exclusively on one area, such as navigation systems, speech recognition or correction suggestions. Weak AI is consequently tailored to one application problem and cannot currently evolve. The goal of strong AI is to imitate human capabilities and surpass it in those. Unlike weak AI, strong AI is supposed to be applicable in multiple domains. However, real strong AI is still more science fiction than reality and, according to experts, will only be feasible in the next 5-10 years.

But even weak AI has already become firmly integrated into our everyday lives.

The music platform Spotify uses a subset of AI for its radio function and music suggestions. Behind the weekly individually created playlists and radio suggestions is a machine learning algorithm. These playlists are created by a weak AI collecting information about the users' music tastes. The system learns every day, develops new patterns and designs individually tailored playlists. The suggestions on Netflix and other streaming service platforms work according to the same principle.

Machine Learning

Machine Learning (ML) is, as can be seen from the figure, a branch of AI. Here, knowledge is to be generated through experience from the learning phase. Pattern recognition but also classification, for example of images, are a use case of ML. Machine Learning uses various statistical models for this purpose. Deep Learning", the use of artificial neural networks, also falls into this area. Unlike AI, the term intelligence does not quite apply to Machine Learning, since it does not involve the direct imitation of intelligence. The music platforms already mentioned and the streaming services around Netflix and Amazon are vivid examples of machine learning. Self-driving cars or social media feeds would also not exist without the ML branch.In machine learning, a distinction is made between three types:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Supervised Learning

The algorithm learns a function from given pairs of inputs and outputs. During learning, a "teacher" provides the correct function value for an input. The goal in supervised learning is to train the network to make associations after several computations with different inputs and outputs. One area of supervised learning is classification. An application example would be handwriting recognition or distinguishing images into dog and cat.

Unsupervised Learning

The algorithm generates a cluster for a given set of inputs, which represents similarities between inputs and allows a classification based on this. Clustering methods are available for this purpose, which are distinguished from one another by characteristic patterns. Unsupervised learning aims to present data in a simpler representation or to reproduce it as accurately as possible, despite drastically reduced information.

Reinforcement Learning (RFL)

Here, a so-called agent learns, through reward and punishment, tactics on how to act in potentially occurring situations and environments in order to maximize the benefit. The advantage is that solutions to complex problems can be found without initial data and prior human knowledge. A real-world example would be learning games like chess step-by-step.

Related links:

https://www.uniserv.com/unternehmen/blog/detail/article/trendstudie-kuenstliche-intelligenz-auf-dem-vormarsch/ https://www.sc-networks.de/blog/kuenstliche-intelligenz-im-lead-management/ https://www.cologne.io/b2b-smart-data/m https://onlim.com/lead-generierung-mit-chatbots-auf-abruf/ https://www.bigdata-insider.de/was-ist-ein-chatbot-a-690591/ https://www.sc-networks.de/blog/kuenstliche-intelligenz-im-lead-management/ https://www.phocus-direct.de/ki-gestuetzte-leadgenerierung https://www.mbmedien.group/blog/ki-im-content-und-automationsprozess https://www.bigdata-insider.de/was-ist-ein-chatbot-a-690591/

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