Decrypt the Secret Weapon Behind Persado: Machine Learning How to Create Marketing Content

So far, the individualized functions based on machine learning still focus on behaviors and preferences, namely finding the right person, the right place, and the right time. New machine learning techniques now incorporate emotional factors into computational equations to achieve better information links. Through this research, we began to understand how practitioners use machine learning technology to optimize the final link of marketing, that is, how to solve the â€œcorrect newsâ€ and what value these early adopters get from it.

In today's market, marketers must pass custom experiences to customers through digital channels. Machine learning and forecasting technologies are rapidly emerging in content changes. E-mail marketing, web page optimization, and ad-pushing are just a few practices, but they have all achieved huge returns on investment. Through this technology, marketers can intelligently generate corresponding content based on different emotions, which greatly enhances personalization.

According to a survey of 301 practitioners, the vast majority of content developers who have not yet used machine learning are ready to start. And those who have already used machine learning to report on content did not spend too much energy and improved performance by 22% - 266%. The survey results show that marketers and advertisers are becoming more familiar with the content generated automatically by machine learning technology, and the efficiency and effectiveness of communication with customers have also been significantly improved.

Machine Learning Takes the Lead in Emerging Advertising Marketing

38% of practitioners have incorporated machine learning and forecasting technologies into their advertising data management platform (DMP). The other 35% are also ready to implement the relevant deployment within 1 year (Figure 1).

The machine learning-based marketing management program is a common practice to enhance the ability of marketers to segment audiences. For example, the platform can use behavioral data to discover that the customer may cancel the subscription, so taking action in time can avoid losing customers.

Many tech giants including Amazon, Facebook and Google are also using machine learning to recommend related products or content. Machine learning-based recommendation systems have undoubtedly been widely used in Internet services.

More cutting-edge applications include Acquisio's development of a complete set of quoting budget management services through machine learning. The platform monitors bid search, prices pricing in a timely manner, and ultimately invests the minimum amount of manpower to obtain maximum value at the lowest cost.

Generate marketing content based on machine learning

Natural language processing (NLP) technology has been used to improve search engines and e-mail classification. Recently, this technology has also produced results in artificial intelligence (AI) assistants. Representative products include Siri and Amazon's Alexa. Finally, NLP technology now also begins to play a role in marketing.

In our survey, we found that 80% of all people surveyed who have not yet used machine learning for content development are ready to use, 55% indicate that they will be in the testing phase, and 25% indicate that they will immediately start trials (Figure 2). 12 % of people said there was no budget problem, only 8% said they did not trust machine learning technology.

This technology has been proven to optimize all types of digital advertising marketing, such as email content and ad display. Machine-generated message quality has always been better than artificially generated messages. For this reason, we are prepared to adopt automated digital marketing techniques. If we do not keep pace with this technology upgrade, it is very likely that we will lag behind in the future competition because the gap between new machine-based learning methods and traditional methods will become more and more. Big.

Machine Learning for Personalized Marketing Services

Machine learning optimizes marketing content through a series of information elements, including product descriptions, buttons, formats, and positioning.

Marketers promote publicity through personalized positioning, repositioning, promotions, purchases, loyalty, and activities.

Not only that, but it also helps accelerate innovation. By continuing to learn and use individual behavior patterns, the machine learning platform can be transformed from extensive marketing to a more personal approach because no one can achieve such a large scale.

Advantage:

Get customer feedback: Machine learning can generate the best marketing content by capturing customer preferences.

Increase revenue: The relevant groups have proven in many studies that related marketing generates 3 to 5 times more income than non-relevant marketing, and the optimization of emotional language can improve relevance.

Shorten the time to market: The machine learning method basically avoids all sorts of useless and rework possible, and accelerates the entire operating cycle through highly influential and inflammatory language.

ï‚· Increase customer interaction: The content created by machine learning is more conducive to benign interaction, increase user loyalty to the brand, and make it easier for new users to accept and recognize the brand.

Value-in-depth analysis: The emotions that the users express and the language that the customer buys are the essence of the business insight into traffic transformation.

Personalized three elements: behavior, preference, emotion

Until now, personalization technology still relies heavily on user behavior, including the use of data such as clicks, tags, and real-time events. The new machine learning technology takes the user's preferences and emotions into consideration, allowing marketers to create attractive, in-view audience emotions, and true and effective content, which can effectively improve the personalization effect.

One such example is Persado's cognitive content platform. Persado uses natural language processing and machine learning to automatically generate behavior and attitude-based data.

After conducting the same survey of 301 practitioners, it was found that marketers are already using behavioral prediction techniques that include emotional language science (Figure 3). Personalized predictions are becoming more common, with 52% of marketers using behavioral data, 49% using real-time data, and 17% using machine learning to develop specific languages â€‹â€‹for their target customers.

You need to understand the machine learning technology

Due to the natural black box properties of machine learning technology, marketers need to master the basic principles and methods of optimizing marketing content.

Here are some key NLP query methods:

ï‚· Algorithm: Pay attention to the learning characteristics of machine learning algorithms. For the NLP platform, users should be aware of how the system ensures that the machine can effectively use language expressions and automatically determine its usage based on business behavior.

ï‚· Behavioral triggering: Determine how the system interprets and understands the behavior of the audience. With an appropriate language classification system, the platform can slowly determine specific trigger behaviors and learning iterations based on past performance so that it can be effective in the future.

ï‚· Segmentation discrepancies: Make it clear how categories, including industry, channel, geography, and type of participation (one-time or subscription), scroll through the data. The difference information can further subdivide and enrich the language classification. For example, the same message has different effects in different contexts.

ï‚· Language categorization: You should understand how to break down the specific language of marketing into different categories so that the platform can test unrelated elements in the message. Such categorization may include functional languages â€‹â€‹(such as calling operations) or descriptive languages â€‹â€‹(such as product lists). ).

ï‚· Emotional Rankings: Effective communication full of emotions that become individual-specific emotions can be used as data to better understand and speak to your audience. A basic type of sentiment analysis is to divide information into positive or negative ones. More subtle information can reveal more diverse emotional categories. The more specific the platform is for emotional analysis, the more likely it is to reach the target audience level.

Machine learning has a huge effect on marketing

In the online department store advertisements, the styles that include achievements, special and security emotions contributed 77% of the response rate, and the click-through rate increased by 57%.

In the online shop's advertisements, the style containing emotional excitement contributed 51% of the response rate, and the click rate increased by 42%.

Top 100 e-tailersâ€™ successful emails, including achievements and anxiety, contributed 72% of the response rate, and the click-through rate increased by 130%.

In the success message of the discount store, the excitement and gratitude included contributed 44% of the response rate, and the CTR increased by 266%.

Top 100 e-tailersâ€™ title bar, including achievement sentiment contributed 53% of the response rate, and the conversion rate was 278%.

The title bar of the top 100 apparel retailers contributed 45% of the response rate to the eagerness and gratitude, and the conversion rate increased by 402%.

The future of machine learning in marketing

The application of machine learning technology allows marketers to make unprecedented attempts and practices. Marketers have always been through the "cognitive computing revolution" to determine the "right person", "the right place" and "the right time." Until recently, machine learning began to deal with "right information."

The content created by marketers through machine learning can use emotional triggers to resonate strongly with customers. This approach helps to create deeper connections with customers and truly one-on-one personalization.

Those who do not yet have this technology will find it difficult to compete with them. In the next few years, the content generated automatically by the machine will begin to compete with the content written manually, and the marketing content based on data feedback will also be developed. Ultimately will surpass the level of human peers.

Via Persado