Using AI to accelerate your marketing plan: content creation – Bestgamingpro

E-commerce has grown in popularity with private consumers and businesses, but the epidemic has unleashed an unprecedented wave of activity in areas that had never used e-commerce before.

With this rapid development and increased customer expectations for speed and delivery, direct-to-consumer brands must improve their marketing capabilities to meet these new demands.

The desire for content, which must now be disseminated on many platforms and audiences quickly and intelligently, is at the heart of this movement. However, without the proper artificial intelligence (AI) and machine learning (ML) infrastructure, creating effective content for many platforms, including e-commerce, is extremely difficult.

When AI is implemented, so too is content and content production.

To persuade people, companies need to communicate something smart and relevant to the consumer. Great content connects, adds relevance, and impacts behavior. Creating this type of material requires researching data from various platforms, assessing reaction rates to different materials, and exploring customer sentiment and engagement. It all takes time – a lot.

AI and ML have the potential to speed up this process, provided the data science is applied correctly. AI has the ability to analyze a huge amount of data and provide suggestions for content that would likely elicit the desired response. Businesses can use automated analytics to create more relevant content by automating the process.

Traditionally, direct-to-consumer businesses have used AI and ML for social listening and news. While some social media platforms now offer in-app purchases, the majority of customers shop through conventional methods, using their social media activities only for product research purposes.

LinkedIn is one of the most popular social networking sites with over 500 million users. It is extremely important that businesses use it to influence consumer behavior, collect data and analyze trends.

AI and ML combine information from these platforms; Context, relevance, sentiment and user feedback are all taken into account to determine what motivates customers and recommend the best performing content for each situation.

AI / ML is used to improve e-commerce through AI / ML.

AI and ML can be critical to the success of e-commerce content development. With more and more consumer shopping online, new methods have emerged to meet demand.

This has created further challenges for content marketers, who now have to deal with direct-to-consumer businesses looking to expand their presence on additional business platforms and channels. Businesses can solve these problems using AI and ML while increasing their visibility across platforms and generating insights that drive growth. Consider the scenario of an international chocolate company.

In 2019, the company had a commercial presence both on its own website and on a leading e-commerce website, where it had many product pages to meet various categories and test different keywords and images. .

The marketing department used the platform to review the most popular sites and determine which components customers found most useful. In addition, they had to identify which research data was also important.

The company wanted to expand its online business presence to other retail websites and social media platforms. While this expansion promises to be beneficial, it would essentially cage consumer behavior and sentiment data from every point of sale within the app.

The challenge now was how to effectively study what attracted audiences to each platform and create more “feel-good” material that differentiated the company from its competition.

The chocolate company was able to capture and combine data from its e-commerce channels, own product sites, and all new platforms using AI and ML.

The ability, through AI, to explore and analyze the content of each product, segment and platform has allowed the company to grow rapidly without losing relevance. In addition, the improved efficiency accelerated the production of material that addressed target consumers, while increasing the number of page visits and sales.

Apart from dry data analysis, artificial intelligence and machine learning can be used to fuel creativity. These technologies allow companies to analyze branded content from a variety of sources, create connections between platforms, improve content production, and enable their marketing teams to develop and distribute the most relevant material. on multiple platforms.

Direct-to-consumer businesses can use cognitive marketing AI to identify and promote content that connects, makes sense, and influences behavior faster than conventional technologies. All of these activities allow businesses to adapt and react to changes in sentiment in real time.

E-commerce is growing in popularity with private consumers and businesses, but the epidemic has sparked a previously unknown frenzy of activity.

With this rapid development and changing consumer expectations for speed and delivery, direct-to-consumer businesses must increase their marketing capabilities to meet demand. At the heart of this movement is the need for content, which must now be disseminated across many platforms and audiences in a fast and intelligent way.

However, without an advanced artificial intelligence (AI) and machine learning (ML) infrastructure, producing good content for many platforms, including e-commerce, is extremely difficult.

When AI is applied, so will content and content production.

To affect customers, companies need to communicate something compelling and relevant to them. Great content resonates with people; it establishes relevance and influences behavior.

Creating this type of material requires researching data across various platforms, assessing response rates to different types of material, and exploring customer sentiment and engagement. Unfortunately, all of this takes time and resources.

AI and ML have the ability to accelerate this process. AI can analyze a large amount of data and come up with suggestions on what content is most likely to elicit the desired response. This automated analysis enables businesses to generate useful content while increasing content creation to be suitable for a variety of platforms and market segments.

Direct-to-consumer businesses have used AI and ML primarily for social listening and news in the past. While some social media sites now offer in-app purchases, the majority of customers still shop outside of apps and their use of social media is geared towards product research.

Using artificial intelligence and machine learning to sift through huge amounts of data from social media platforms, analyze it for context, relevance, sentiments, and feedback, and then determine what motivates the consumer and what content is most likely to be successful for each situation.

Using artificial intelligence and machine learning to grow e-commerce

The development of e-commerce content is also aided by AI and ML. With the increase in online shopping, new methods of meeting demand have emerged.

These developments have introduced new challenges for content marketers as direct-to-consumer businesses seek to expand their presence across commerce platforms and channels. Businesses can overcome these issues using AI and ML while increasing their visibility across platforms and capturing information that will drive growth.

Consider the scenario of a global chocolate business. The company had a commercial presence both on its own website and on a leading e-commerce website in early 2019, where it hosted numerous product pages to cater for various segments and test different keywords. and images.

The marketing team used the platform to examine the most successful pages and determine which items consumers found most important. In addition, the team had to assess which research data was also important.

As a result, the company needed a new plan that would expose it to a larger online audience. This expansion, while appealing, would “cage” consumer behavior and sentiment data from each platform within the respective system.

The next issue will be how to effectively analyze what resonated with each audience and how to continue to produce useful “feel-good” content that sets the business apart from the competition.

Using artificial intelligence (AI) and machine learning, the chocolate company was able to retrieve data from its online outlets, own product sites and all new platforms.

The company’s AI-powered ability to collect and rate content for every element, segment and platform has allowed it to grow rapidly while producing the most appropriate content for each digital property.

Additionally, improved efficiency has spurred the creation of material related to target consumers while driving increased page visits and sales. Artificial intelligence and machine learning are often seen as a technology with limited applications outside of dry data analysis, but they can also be used to fuel creativity.

These solutions allow businesses to review branded content from a variety of sources, make connections across platforms, improve content production, and empower their marketing teams to create and distribute the most relevant material. on multiple platforms.

Incorporating artificial intelligence into a marketing plan allows businesses that speak directly to consumers to quickly determine what content connects, makes sense, and influences behavior. All of these features allow businesses to react in real time to changes in sentiment.