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How Machine Learning Improves Marketing Strategies – IoT For All

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Did you know that an average marketer spends about 16 hours a week on repetitive tasks, according to a HubSpot survey? The most time-consuming activities are content creation and collection and organizing and analyzing marketing data from different sources. In this article, we’ll talk about how to delegate some marketing tasks to machines that can learn. 
Dealing with gigabytes of information is an arduous task for the human brain but it can easily be handled by machines. For example, ML algorithms of the SocialMiningAI tool search in real time through millions of public social posts to identify prospective customers with high intent to make a purchase. With the help of natural language processing (NLP) and image recognition algorithms, it’s easy to identify a potential lead who is interested in a specific service or a product. 
Also, using machine learning algorithms for social listening is a powerful tool for reputation management and brand awareness campaigns, saving hours of work for brand managers. Modern algorithms are not only able to quickly process huge amounts of data – monitoring mentions of your organization or keywords that are relevant to your business – but are also able to understand patterns, providing valuable insights for marketers.
For example, ML-based social listening can help marketing managers find brand ambassadors, study customer feedback and their experience in using the product to improve it, or create effective marketing campaigns.
Dividing your audience into groups with common needs and interests allows you to target more effective messages for each group of customers. Machine learning algorithms for marketing not only allow businesses to automate this process but also to find hidden patterns in the data that elude the human eye.
Having an ML algorithm find the best segmentation strategy automatically is not only more efficient, but it can also lead to better conversions. As businesses reach their target audience with greater focus, they can be much more persuasive and effective at converting a potential customer into a regular buyer.
Clustering machine learning algorithms are unsupervised machine learning algorithms which means they use untagged data and can be used to discover natural patterns in the dataset. For example, K-clustering works on the principle of finding similarity or closeness between the data points and clustering data based on that. K here means the number of possible clusters.
To describe the essence of clustering algorithms, let’s consider the following example. Let’s assume you have three friends: Alice, Bob, and Emma. All of you have different preferences for movies and snacks. For example, you and Emma both like westerns and popcorn, but Alice and Bob both like chips and musical movies. So, you and Emma tend to spend time together and you are more close to each other than to Alice and Bob. The same goes for Alice and Bob; they tend to be closer to each other than to you and Emma. So, you and your friends form two clusters based on your preferences.
In clustering analysis we can accurately calculate the distance between people in groups, we can estimate the number of groups, and, with the help of domain experts, describe those groups.
Machine learning can be used to optimize and automate marketing campaigns. This opens up opportunities not only for improving the efficiency of launching and operating these campaigns but also for saving time and money that could be spent on other projects. Usually, marketing specialists spend hours finding ways to increase conversions or optimize campaign budgets. Machines can do this much faster and more efficiently.
For example, advanced machine learning is what makes Google Ads a leader among advertising services. Features such as smart bidding, data-driven attribution, and responsive display ads allow marketers to rely on the system to select the most optimal ad settings based on campaign objectives. These self-learning capabilities of the algorithms apply to other marketing tools as well, allowing you to test different headlines, email subject lines, images, CTAs, and other variables.
Marketing campaigns can be optimized without machine learning using A/B testing. However, this is usually feasible when using only one or two variables. Machine learning, on the other hand, can use many more variables at once when testing the effectiveness of different types of campaigns. After performing this testing, it can report the results back to the marketer. The most effective variation of the campaign can then be used.
Some ML-based marketing tools can automatically select the marketing channel based on your marketing strategy. For example, Mercato uses smart algorithms to attract customers to their website using geo-optimized promotion across several digital channels. Cost-effective cross-channel execution is undoubtedly something that can optimize the entire marketing process of a company, and ML is the main tool for achieving this.
Marketers need to look at their analytics daily. It can often be difficult to see complex patterns in the data that can have profound impacts on a business. It also can be challenging to find potential opportunities within this data. Spending the time necessary to recognize these potential opportunities could be costly. However, a machine learning algorithm can easily do it for you.
As an example, an ML algorithm can identify unusual spikes in data and alert a marketing manager of interesting findings such as the time of day at which the most conversions are carried out, and spend the budget exactly when it will bring the maximum result. If an ML algorithm can process and analyze campaign data automatically, this leaves time for marketing managers to spend more time on other aspects of their work.
Beyond automating and optimizing content, machine learning can go so far as to enable a computer to create content automatically that looks as if it was created by a human. This can be done through generative adversarial networks, or GANs, for images. A generative network generates content, while an adversarial network detects and eliminates unwanted results. After many iterations, the result will include new image content that can be used to enhance marketing campaigns. 
For automatic text generation, BERT or GPT approach is usually used. For example, BERT can generate the whole article on any topic with just a few sentence inputs. The framework has large informational datasets trained on Wikipedia and Google’s BooksCorpus. Even if marketers don’t use the generated text in its purest form, they can take new ideas from it or edit it, which still speeds up the content creation process compared to if you had to do it from scratch.
By using these ML-based marketing tools, businesses can automate more of their content creation processes and divert their focus toward strategy and data analysis. 
Machine learning algorithms are great at recognizing patterns, especially in e-commerce contexts. When users visit e-commerce storefronts and purchase items, all the information about their activity and purchases can be recorded and anonymized to be used for marketing campaigns. One of the most useful applications of this example is to increase customer loyalty with personalized suggestions also known as recommendation systems. 
Creating a machine learning recommendation engine is usually based on one of two methods: collaborative filtering or content-based filtering. The first model uses data about the preferences of users with certain similarities and, based on them, issues recommendations to the user. The content-based approach uses product features in which the user was interested to recommend other products similar to what the user likes. For some systems, a combination of the two approaches can also be used, as Netflix did, for example.
When it comes to managing product supply as well as dynamic pricing, demand forecasting powered by machine learning can help immensely. By using past data to predict when consumers will be more likely to buy certain products, businesses can stock up on products early to meet the demand. They can also prepare marketing campaigns to inform customers of sales that best compete with other companies during those times. If you know that consumers will be more likely to purchase items at a certain time of the year, you can increase conversions during those times to net even more gains. 
By using past data, machine learning can better understand how much value each customer contributes to the business. This can be used to predict the lifetime value of customers to a business. This information is useful not only for investors but also for the company’s long-term decision-making. 
Conversely, when a consumer leaves the system by unsubscribing from a newsletter or another program, they contribute to a business’s churn rate and can negatively impact their metrics. Machine learning can better analyze trends in a business’s churn rate and help marketers understand what went wrong and when. By understanding the demographics and behaviors of users who leave the system, marketers can come up with strategies to reduce their losses. 
AI assistants can play the role of online consultants increasing the level of customer engagement with the brand. Offering product recommendations, discount coupons, order information, or the nearest store address are just a few tasks that AI assistants can do. Modern AI assistants are getting smarter and can perform a wide range of tasks, giving the impression that the buyer is communicating with a real person.
For example, Samsung introduced an AI-powered virtual avatar called Neon a few years ago. This is an AI assistant that looks like a human hologram, can communicate with you, and act like a real companion. Neon avatars are customized per the client’s requirements and can be used in a variety of applications.
Since not every business can master the creation of its own AI avatars, their simpler counterpart can be ML-based chatbots. Due to advances in natural language processing, chatbots can make certain aspects of a business’ customer service much more efficient. Chatbots can help customers with tasks like troubleshooting and even purchases. In situations that a chatbot can’t handle, the program can automatically escalate it to a human customer service representative.
Machine learning is improving by the day, but the amount of data that we collect is also ever-growing. The more high-quality data that we have, the better our predictions and pattern analyses will be. However, circumstances are always changing, so businesses need to stay on top of the curve to maintain their relevance in the market. Machine learning will also affect businesses in many ways other than just marketing and you’ll need to prepare for in order to keep up to date.

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