The Holy Trinity of Digital Marketing in 2019 and Beyond

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The success of digital marketing strategies heavily depends on how to optimize the use of available data. For example, an overwhelming amount of data is available on social media. However, data is of no use if we don’t optimize them and use them in the best ways possible. Take a look at the following examples:

Example 1: Data Optimization for Email Marketing

To ensure that recipients read emails, digital marketers must select the best combinations of subject lines, content, images, CTAs, etc. Also, digital marketers must personalize emails according to the interests of recipients. Both of these tasks require lots of time, financial resources and trained human resources for data analysis etc. AI and machine learning can help in data analysis and creation of personalized emails. Before we discuss how AI and machine learning can help in data optimization for email marketing, let’s have a rundown on AI and machine learning.

What’s AI?

According to John McCarthy, one of the pioneers of AI, it is “ the science of making machines that can perform tasks that are characteristic of human intelligence.” Tasks might include recognizing images, understanding human languages, or making decisions.

AI in Digital Marketing- Here’s What the Future Has in Store for Us

What’s Machine Learning?

Machine learning is an application of AI. We insert huge data sets to computer systems until they start recognizing patterns in data and learn over time to give more accurate answers. Machine learning has spared programmers from manually coding instructions to obtain optimum outputs.

How can AI and Machine Learning Optimize Data for Email Marketing?

According to an email marketing statistics report published by HubSpot, 86% of B2B professionals rely on emails to communicate for business purposes. B2C marketers agree that every welcome email records 320% more revenue than rest of other promotional emails. AI and machine learning can help marketers create more effective emails and improve the outcomes of email marketing efforts.

1- Hyper-personalization

The success of email campaigns depends on email open rate. The average number of promotional emails is on the rise and email open rate is also showing a slow but steady increase. In 2018, email open rate reached at 24.88% which was a 0.3% increase than 2017. Take a look at the bar chart below:

Open Rate Benchmark by Year (2006-2018)

Image Source: https://bit.ly/2HuuRmL

AI and machine learning can help digital marketers create hyper-personalized emails to increase email open rates. By analyzing past conversation records, AI and machine learning can recognize matching patterns and predict the interests of email recipients so that marketers can recommend products or services to them, accordingly.

2- Intelligent Segmentation

Lists of email subscribers and prospective customers are usually very big. Hence, it’s impractical to think that we can personalize every email. AI and machine learning can help us segmentize customers and create emails for different segments of customers. For example, by data analysis, AI can segmentize customers on the basis of purchase behaviors, demographics, geographic locations etc.

3- Optimization of Email Subject Lines

Email subject lines play a crucial role in increasing or decreasing email open rates. As per an email marketing stats report for 2018, 56% of brands said that they got better responses by sending emails with emojis in the subject line. The report also stated that having the word ‘Donate’ in the email subject line can reduce open rates by more than 50%.

Apart from multiple other works, AI-powered ZOE email bot can help digital marketers write and test email subject lines, email body copies and CTAs. To know more about ZOE email bot, click the link above.

Example 2: Data Optimization for Social Media Marketing

Social media analysis depends on big data or extremely large data sets. Given the huge amount of unstructured and variable data available on social media platforms, to come to exact conclusions, analysts need large sets of data for analysis.

Fortunately, the amount of data available on social media platforms is huge. For example, in 2016, Instagram announced that on average, users share 95 million photos and videos every day. According to data released by Facebook in 2015, on average, its active users send 31.25 million messages, every day. And on average, Twitter users send 500 million tweets per day (based on data released by Twitter in 2016).

But big data are of little use if we don’t have resources and tech tools to analyze them and optimize them to our advantage. Deep learning can help in the analysis of big data to give results with high accuracy and reliability. Let’s know more about deep learning.

What’s Deep Learning?

Deep learning is another approach to machine learning. Deep learning is based on complex systems that include layers of networks, comprising of artificial neurons, resembling a human brain at a rudimentary state. Layers of artificial neuron networks give depth to machines and that’s how the name ‘deep learning’ originated.

The main objective of deep learning is to recognize patterns from huge sets of data (also known as big data) so that, machines can predict customer interests etc., with higher accuracy and reliability.

How can Deep Learning Help in Data Optimization for Social Media Marketing?

According to social media stats and facts report, 88% of brands are at present marketing on social media. The report also states that consumers who receive good customer services on social media are expected to spend 21% more money on products of respective companies. Deep learning can help in predicting purchase behaviors, interests, and even spending capacities of consumers.

1- Extract information from unstructured data

Conversations on social media are unstructured. As social media conversations are highly variable and complex, digital marketers often find it difficult to analyze, sort and segmentize them. Deep learning can help marketers to analyze big data and keep tabs on consumer opinions and rising trends.

2- Gather insights from images

Social media posts are increasingly becoming visual. Social media platforms like Instagram encourage people to share images and videos. With limited textual content to rely upon, before the advent of AI and deep learning, it was virtually impossible to detect common patterns in visual posts.

Deep learning can help digital marketers find out when people are sharing images or videos of products and services of brands on social media. AI and deep learning can now recognize objects, logos, and faces in images as well as in videos. For example, when we upload images of our friends or relatives on Facebook, the latter suggests the names of people on our uploaded images, for the purpose of tagging. AI and deep learning make this possible.

3- Understand voice of consumers

Take a look at the bar chart below to have a glimpse of how brands upload annoying posts on social media without understanding consumer opinions:

Annoying Actions Brands Take on Social MediaImage Source: https://bit.ly/2Y2846E

Unlike humans, machines don’t understand the sense of texts. Computers can’t tell for sure whether texts convey sarcasm or double meanings. AI and deep learning can help computers comprehend the sense of texts. AI-powered ZOE chatbot can identify tones of conversations, analyze human sentiments, and modify conversations to suit the emotions of human respondents.

To know more about how to leverage AI, machine learning and deep learning for digital marketing, click here. You can also meet our Director, Business Solutions, Varun Kashiv, and others at Social Media Marketing World 2019 in San Diego, California, from March 20 to 22, and talk to them personally. Otherwise, you can comment below and type your queries there.

Check our next blogs for more updates on AI bots, enterprise mobility, digital marketing, etc. Make informed decisions.

 

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