Marketing has always been tough. In current times, it’s getting tougher by the day. With the evolution of technologies, data has increased in an explosive volume and finicky consumers have found countless ways to search for and interact with brands. In this scenario, the marketer’s job has become complex and extremely challenging.
It’s in this interesting backdrop that marketers are adopting artificial intelligence (AI) and machine learning (ML) technologies to help understand consumer behavioural patterns. They need to understand how consumers learn about certain brands that they eventually become loyal to, wade through the buying funnel, and make the decision of purchasing from that brand. In this age of personalization, they are also required to capture, assess and utilize the available data mined from consumer actions so they can tailor messages as per the demand of each consumer.
How Machine Learning is Beneficial for Brands
Success in marketing depends on multiple factors. Marketers need consumer research to build their branding strategy, enticing content to engage their audience, a firm hold over behavioural economics, and an ability to gauge consumer sentiments. The long and short of it all is that in this digital age, marketers cannot do without mastering automation, data and analytics.
This is where ML technologies come into play. ML can effectively improve marketer performance on common tasks like customer segmentation, extracting relevant content, communication with customers, generating branded collateral and overall outcomes and productivity. Operating a marketing unit without a machine learning mechanism is a serious handicap that can prove detrimental.
However, it’s necessary to understand how ML works before one can truly adopt it in one digital marketing strategy. It needs to configure the solutions required basis the long-term goals.
Application of Machine Learning in Marketing
ML has already been implemented in various digital marketing departments across the globe. It involves using metrics like data, online channels and content to improve productivity and better understand the prospective audience.
Here are some ways in which machine learning is being used in digital marketing strategies:
- Customer Segmentation & Discovery
Not all customers can be the same. Back in the day, even when direct mail dominated the landscape, list segmentation did matter. Since every customer is different from the other, therefore not everyone can get the same message. As a result, marketers need to segment their lists to make sure that the right message reaches the right person. As we have vast amounts of data today, segmentation becomes a lot easier and in-depth. We can know where a certain group of people live, the age group they fall in, whether they own a home or a car, income range, family size, and much more.
Machine Learning can turn all this data into valuable insights that can lead to directed marketing messages. ML also has the potential to delve much deeper to distinguish between high-value and low-value consumers. Consequently, it can find out the best customers that a particular brand can target. It can help determine which products to promote and what the buying behaviour amongst the audience will be going forward.
- Lead Scoring
In the B2B space, marketers do not just need to know their prospects and customers on a deeper level but also need to know how to score leads to proceed further. Machine Learning can help businesses do the same. It can help with the original collection of information from lead generation and analysis of unstructured data such as emails, phone call records and social posts to determine patterns and find good prospects. This information is essential for effective marketing campaigns.
- Pay-per-click Advertising
Successful pay-per-click (PPC) is ideally started with a human-centred strategy but then bids are to be changed basis the data available. Machines tend to dominate the domain of bidding. Since the process of bidding relies heavily on pattern recognition and statistics, it is hence the best uses of Machine Learning in marketing. Machines can even determine how a consumer might interact with a specific ad based on their previous behaviour and stats for a particular keyword combination, landing page and advertisements.
- Dynamic Pricing
ML can also assist in dynamic pricing online. Prices tend to change instantly based on factors such as demand, supply, competitor pricing, consumer’s level of interest and prior engagements with the brand through marketing. For instance, ride-sharing and airline companies are two case scenarios where dynamic pricing is used to maximise revenue.
- Text Classification for User Insights
With natural language processing (NLP), ML system can probe text or voice-based content and then classify each content type based on metrics like tone, topic or sentiment to generate insights on consumers or for curating relevant materials. This further helps determine the general tone of users who are reviewing a product or service.
- Text Extraction &Summarization for Trending News
Marketers can use ML to extract useful and relevant content from news articles and other data sources online to understand how people view their brand or react to their products. Certain platforms enable companies to acquire full visibility into their customer’s values and beliefs and know-how these attributes eventually impact their buying decisions. Tech-savvy marketing teams can leverage ML to build their own algorithm with the help of APIs meant for news aggregation, monitoring social media sentiment and other similar purposes.
- Machine Translation Via Attentional Neural Networks
Attention mechanisms embedded in deep learning assist in improving machine translation empowering marketers to prepare for the global stage. Translation helps a brand make entry into a new, uncharted, and linguistically different market through advances in AI that enable machine translation to achieve human-like precision. In order to rationalize costs and speed up the whole process, several companies opt to have a human translator review and sign off the output received from machine translation.
- Dialog Systems for Chatbots & Customer Service
One of the most ubiquitous applications of AI and ML are bots and chatbots. However, the most common ones used marketing bots that we see are scripted and use minimal natural language processing and ML. The highly sophisticated dialogue systems can reference external knowledge database, adapt to unusual questions, and escalate issues to human agents whenever required. Many companies have adopted chatbots for engaging and interacting with customers throughout their lifecycle – right from the time they learn about a certain brand to the time when they have made purchases and now need customer support.
- Automated Data Visualization
AI is much faster and much more efficient at transforming data into useful visual insights as compared to any human expert. Usually, analysts use tools such as Excel to manually create visualizations, but automated analytics solutions for enterprises can centralize data sources to generate necessary dashboards and reports for the marketing teams. Several platforms also use data analytics and advanced ML algorithms to vividly present market trends and consumer behaviour patterns, which are otherwise hidden from plain view and cannot be easily converted to practical insight.
- AI-enabled Ad Copywriting
Reports have shown that nearly 35% of email recipients open an email based on the subject line alone. Also, emails carrying a personalized subject line tend to achieve higher click-through rates as compared to those that are devoid of one. This is the reason why brands need to focus on these small but essential elements that can deliver better results. AI-enabled tools can help curate personalised email subject lines along with emoji feature that can be added based on a specific situation.
To streamline procedures and raise productivity, digital marketers need to start using ML tools for automating processes and effectively using data. As an industry, digital marketing is ripe for new challenges and opportunities. If increasing engagement and brand awareness is what marketers are aiming for with leads, then it is inevitable for them to understand their customers and their demands. ML will help expand the capabilities of businesses to reach out to a larger audience.
In the foreseeable future, machines and people will likely work together and take marketing initiatives to the next level. Marketing products will not be a time-consuming and tireless campaign to curate, create and share quality information. Instead, digital marketers will be able to generate more brand awareness and do it more effectively and personally than ever before.
This article is authored by Amol Roy, Founder – Shutter Cast.