Companies spend millions of dollars in market research before coming up with any product or service. However, it is important to remember that for the final product to sell, the right marketing tools are crucial.
With stakes being higher than ever, businesses can’t afford to take a hit-or-miss approach to marketing. This is where predictive analytics and machine learning come in.
Predictive analytics or predictive marketing lets marketers drive decisions in a big way. In fact, according to Salesforce, 91% of marketers have or are in the process of implementing predictive marketing. They explain the marketing connection:
“Predictive marketing uses data science to accurately predict which marketing actions and strategies are the most likely to succeed. In short, predictive intelligence drives marketing decisions.”
Moreover, a vast majority of executives who have been overseeing predictive marketing efforts for at least two years report increased return on investment (ROI) as a result of their predictive marketing.
Let’s take a look at how machine learning—that goes hand-in-hand with predictive analytics—can help add more clarity to your marketing decisions:
Identifying High-Value Opportunities & Customer Retention
Sophisticated predictive marketing models are helping businesses make their customer acquisition efforts more efficient. In fact, a report by the Aberdeen Group states that companies using predictive analytics are twice as likely to identify high-value prospects.
Businesses that build their marketing efforts around predictive analytics and machine learning, tend to have a higher conversion rate.
Moreover, predictive marketing models also provide insights into the future of customer behavior. These can be used for retaining customers and for upselling and cross-selling to them.
Blanket assumptions on the conditions that make customers leave have never really worked. When a business knows when a specific segment of customers is going to leave and the factors that contributed to this, they can plan their retention strategy better. A mere 5% increase in customer retention can increase a company’s profitability by 75%.
Upsell and Cross-sell
With machine learning, marketers can ensure product recommendations reach the right customer hrough the right channel at the right time. All of this in a way that enhances the customer experience and enhances the chance of success.
Marketing Collateral Optimization with Reinforcement Learning
Rather than predicting the current best move, reinforcement learning rewards systems for long-term wins. By employing a calculative reward function—to calculate the outcome of each combination—RL can help make the right call.
A simple example of reinforcement learning is A/B testing. With reinforcement learning, you can achieve the same goals with better results and reduced costs. Let’s talk about this with an example.
Let’s say you have five versions of a landing page. To choose the best out of these, one option is to run an A/B test with a large enough sample. This is going to be expensive and time consuming because you will spend time and money to promote bad versions of your landing page and will lose potential customers.
Another option is to use reinforcement learning and make the machine figure it out for you. By being rewarded each time a version of a landing page converts, the machine can find out the best version that should be used. So that means you can generate higher ROI by displaying only those versions of your landing pages or ads that convert the most number of visitors.
Conversational AI for Personalized Experiences
Promising an array of opportunities to marketers, conversational AI enables brands to utilize messaging apps, chatbots, and digital assistants. With customer experience reigning supreme, conversational AI can even hold highly personalized conversations with a number of prospects or customers at the same time.
One of the biggest ways conversational AI is making an impact on marketing is through chatbots. The transformation of artificial intelligence has seen chatbots become ubiquitous. Not only are they cost-effective, but chatbots can also improve engagement and the conversion rate via personalization.
Moreover, messaging apps and digital assistants also see the application of conversational AI. Both these platforms present an opportunity for marketers to use conversational AI and connect with prospects and customers throughout their buyer journey.
Understanding Customer Behavior
Machine learning based prediction algorithms analyze customer data to envision future user paths. With these data-driven insights, marketers can prevent customer attrition and run targeted campaigns that yield better ROI.
There are three principal predictive models:
This model segments target groups on the basis of numerous variables. Mostly used for customer segmentation, some of the cluster models include behavioral clustering, brand-based clustering, and product-based clustering.
This model is used for giving true predictions about customer behavior. Common models include lifetime value, propensity to buy, propensity to convert, the likelihood of engagement, and propensity to churn.
Recommendation Engine Models
This model is used for recommending products, services, and ads to customers based on a variety of variables. Common models include upsell, cross-sell, and next-sell (like used by Amazon and Netflix).
The potential of machine learning and predictive analytics is beyond comprehension. With the insights received from predictive analytics and the application of machine learning to marketing, businesses can improve the customer experience, attract and retain more customers, provide a personalized experience and increase revenue.