The enterprise interest in Artificial Intelligence is surging. In an era of cut-throat competition where it’s either do or die, businesses have realized the transformative value of AI to gain an upper hand over their rivals. Given its direct contribution to business revenue, it comes as no surprise that marketing has become one of the major application areas of machine learning.
- Per Capgemini, 84% of marketing organizations are implementing Artificial Intelligence in 2018, in some capacity
- 3 out of the 4 organizations implementing AI techniques have managed to increase the sales of their products and services by 10% or more.
In this article, we look at 5 innovative ways in which machine learning is being used to enhance digital marketing.
Efficient lead generation and customer acquisition
One of the major keys to drive business revenue is getting more customers on board who will buy your products or services repeatedly.
Machine learning comes in handy to identify potential leads and convert those leads into customers. With the help of the pattern recognition techniques, it is possible to understand a particular lead’s behavioral and purchase trends. Through predictive analytics, it is then possible to predict if a particular lead will buy the product or not. Then, that lead is put into the marketing sales funnel to perform targeted marketing campaigns which may ultimately result into a purchase.
A cautionary note here – with GDPR (General Data Protection Regulation) in place across the EU (European Union), there are restrictions in the manner AI algorithms can be used to make automated decisions based on the consumer data. This will make it imperative for the businesses to strictly follow the regulation and operate under its purview, or they could face heavy penalties.
As long as businesses respect privacy and follow basic human decency such as asking for permission to use a person’s data or informing them about how their data will be used, marketers can reap the benefits of data driven marketing like never before. It all boils down to applying common sense while handling personal data, as one GDPR expert put it. But we all know how uncommon, that sense is!
Customer churn prediction is now possible
‘Customer churn rate’ is a popular marketing term referring to the number of customers who opt out of a particular service offered by the company over a given time period. The churn time is calculated based on the customer’s last interaction with the service or the website. It is crucial to track the churn rate as it is a clear indicator of the progress – or the lack of it – that a business is making.
Predicting the customer churn rate is difficult – especially for e-commerce businesses selling a product – but it is not impossible thanks to machine learning. By understanding the historical data and the user’s past website usage patterns, these techniques can help a business identify the customers who are most likely to churn out soon and when that is expected to happen. Appropriate measures can then be taken to retain such customers – by giving special offers and discounts, timely follow-up emails, and so on – without any human intervention.
American entertainment giants Netflix make perfect use of churn prediction to keep the churn rate at just 9%, lower than any of the subscription streaming services out there today. Not just that, they also manage to market their services to drive more customer subscriptions.
Dynamic pricing made easy
In today’s competitive world, products need to be priced optimally. It has become imperative that companies define an extremely competitive and relevant pricing for their products, or else the customers might not buy them. On top of this, there are fluctuations in the demand and supply of the product, which can affect the product’s pricing strategy.
With the use of machine learning algorithms, it is now possible to forecast the price elasticity by considering various factors such as the channel on which the product is sold. Other factors taken into consideration could be the sales period, the product’s positioning strategy or the customer demand.
For example, eCommerce giants Amazon and eBay tweak their product prices on a daily basis. Their pricing algorithms take into account factors such as the product’s popularity among the customers, maximum discount that can be offered, and how often the customer has purchased from the website.
This strategy of dynamic pricing is now being adopted by almost all the big retail companies even in their physical stores. There are specialized software available which are able to leverage machine learning techniques to set dynamic prices to the products. Competera is one such pricing platform which transforms retail through ongoing, timely, and error-free pricing for category revenue growth and improvements in customer loyalty tiers.
To know more about how dynamic pricing actually works, check out this Competitoor article.
Customer segmentation and radical personalization
Every individual is different, and has unique preferences, likes and dislikes. With machine learning, marketers can segment users into different buyer groups based on a variety of factors such as their product preferences, social media activities, their Google search history and much more.
For instance, there are machine learning techniques that can segment users based on who loves to blog about food, or loves to travel, or even which show they are most likely to watch on Netflix! The website can then recommend or market products to these customers accordingly. Affinio is one such platform used for segmenting customers based on their interests.
Content and campaign personalization is another widely-recognized use-case of machine learning for marketing. Machine learning algorithms are used to build recommendation systems that take into consideration the user’s online behavior and website usage to analyse and recommend products that he/she is likely to buy. A prime example of this is Google’s remarketing strategy, which tries to reconnect with the customers who leave the website without buying anything by showing them relevant ads across different devices.
The best part about recommendation systems is that they are able to recommend two completely different products to two customers with a different usage pattern. Incorporating them within the website has turned out to be a valuable strategy to increase the customer’s loyalty and the overall lifetime value.
Improving customer experience
Gone are the days when the customer who visited a website had to use the ‘Contact Me’ form in case of any query, and an executive would get back with the answer. These days, chatbots are integrated in almost every ecommerce website to answer ad-hoc customer queries, and even suggest them products that fit their criteria. There are live-chat features included in these chatbots as well, which allow the customers to interact with the chatbots and understand the product features before they buy any product.
For example, IBM Watson has a really cool feature called the Tone Analyzer. It parses the feedback given by the customer and identifies the tone of the feedback – if it’s angry, resentful, disappointed, or happy. It is then possible to take appropriate measures to ensure that the disgruntled customer is satisfied, or to appreciate the customer’s positive feedback – whatever may be the case.
Marketing will only get better with machine learning
Highly accurate machine learning algorithms, better processing capabilities and cloud-based solutions are now making it possible for companies to get the most out of AI for their marketing needs. Many companies have already adopted machine learning to boost their marketing strategy, with major players such as Google and Facebook already leading the way. Safe to say many more companies – especially small and medium-sized businesses – are expected to follow suit in the near future.
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