How Has Machine Learning Impacted Marketing?

How Has Machine Learning Impacted Marketing?

Machine learning is helping marketers to go beyond existing marketing strategies and make precise decisions based on data. 

The collection, processing, and use of data have become strategically imperative that drives better businesses. This data has allowed machine learning to minimise the gap between strategic models and the actual outcomes of marketing campaigns.

According to Forrester’s Global State of AI Online survey, the most prominent strategic growth was observed in customer experience and support (57%). This benefit was followed by the company’s ability to deliver better products and services. When companies meet the consumer’s expectations, there is the added challenge to stay ahead of the curve. This compels enterprises to revise their business models and push their services further to bring disruptive change in the market. The impact of machine learning on industrial disruption is proven to be the third key benefit. However, efforts in minimising the customer churn (10%) still need consideration.

Also Read: Machine Learning, Why This is a Good Investment for CMOs

Disruption and Progress Through Machine Learning

Another study by Capgemini found that the implementation of machine learning in three out of four companies helped to achieve a 10 per cent hike in the sales of new products and services. Despite being in its nascent stage, machine learning has helped companies to gather structured and unstructured data to leverage computational efficiency and customer interaction.

IBM Watson’s artificial acumen is accounted to process 80 teraflops operations per second. Similarly, LinkedIn has employed Kafka Apache, whose machine learning model handles over 1.4 trillion messages every day.

In 2015, RankBrain, a machine learning initiative from Google, proposed a smarter way to rank the websites over traditional SEO tools. Since then, many e-commerce sites have incorporated techniques such as semantic search and Natural Language Processing (NLPs) into their systems to improve search engine results page. It also promises to improve customer experience in searching for relevant information with accurate results based on language and visual recognition. For instance, Pinterest enables visual search which allows users to search for information they need, based on non-verbal cues.

Also Read: Big Mac to Big Data: McDonald’s Forays into ML for Better Customer Experience

Role of Analytics in Machine Learning

Four types of analytics play a crucial role in defining the end-result through machine learning. These include,

Descriptive analytics is the first step in the process that is necessary for the machine learner model before diving into advanced analytics. It defines what is

  1. happening with the data in the real-time. This helps marketers visualise the data and prepare the roadmap accordingly.
  2. Diagnostic analytics is crucial in defining why is this happening and what is driving things up or down. This is the most abstract basis of analysis, among others that depend on high or low, is the correlation in the collected data.
  3. Predictive analytics resolves the curiosity of what is likely to happen. It collects the data that occurred in the past, and the algorithm then helps the marketers to use that information to predict the likelihood of something that might happen in the future.
  4. Prescriptive analytics helps define the best possible actions based on the outcome of predictive analytics. This process is quite analogous to the exit poll during election campaigns. The marketers now know the probability of something that will take place in the future. Eventually, it presents theoretical outline that enforces them with the ways to deal with the stats.

How would these four analytics help the marketers to serve key perspectives?

  • By understanding the customer segments and having strategic preparedness about the markets they are going to serve.
  • By prioritising the lead-scoring ideology that raises their chances of lead generation and manifests a fair amount of revenue for the business.
  • By planning revenue modelling that can help the marketers with the quantitative data about the revenue generation.
  • By focusing on customer retention and the factors that may cause customer attrition, marketers can plan their strategies on how to deal with both the circumstances.

Also Read: Implementing Machine Learning for Data Analysis

Sentiment Analysis, an Invaluable Part of Machine Learning

Sentiment analysis helps marketers collect detailed customer data, similar to what an ECG does for the cardiologist. Sentiment analysis presents an extensive customer report and their overall attitude towards any product, service, marketing campaign or even a person.

For example, a movie production company could observe the opinion of the audience towards the movie based on their Tweets right after the premiere. Also known as opinion mining, sentiment analysis helps marketers make sense of the customer’s opinions, moods, and reactions. In this case, all the Tweets of moviegoers are extracted, mentioning the keywords surrounding the movie. Those texts are then parsed and compared to a list of words in positive and negative contexts to determine the overall opinion.

The analysis can also be used to study the intention of customers on a particular subject, like mobile phones. For example, a potential buyer would publish a social media post asks people to help them with a decision to purchase a smartphone from rivals like Samsung and iPhone. Mining this information would help a marketer from either brand to set advertising strategies, discounts and other offers that will help target potential consumers.

Also Read: A Handbook For Marketers: All You Need to Know About Customer Data Platforms

Machine learning today has paved the way to bring fruitful decisions with its ability to observe insights. Moreover, for marketers, those decisions have significantly helped them to move their organisations ahead.

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