The relationship between machine learning and marketing has been flourishing in the past years, giving birth to a new set of strategies and tools that optimize the process. The modern marketer has no choice but to jump on the bandwagon in order to stay competitive and maintain a desirable skill-set in the industry.
On the flip side, we have all been asking for this revolutionary way of performing marketing tasks. “The customer is at the heart of the business” is such a familiar statement and now we can finally deliver on it by using machine-learning to our advantage. As Brian Solis, Principal Analyst and futurist at Altimeter said:
“AI and machine learning will have the most profound impact on marketing in 2018 because it will fundamentally make ‘marketing’ more human…Which is ironic in and of itself”.
By tracking and analyzing data with the purpose of driving customer engagement, here are the top 10 applications of machine-learning in marketing.
1. Predict customer lifetime value
The acquisition of a new customer costs way more than keeping satisfied those that are already around. Smart budgeting and effort distribution are a must for marketers if they want to strike a balance between costs and results.
Using RFM (recency, frequency and monetary) analysis we can predict a customer’s lifetime value and focus on those that will bring the most revenue in the future.
2. Predict customer churn
Customer churn prediction is connected to customer lifetime value. Predictive analytics uses data on customer behavior to forecast whether a customer is about to leave a business. Such information is extremely useful for marketers as they can detect risks at an early stage and act in a timely fashion to prevent that from happening.
Let’s say a customer visits your website weekly for the past 6 months, but the next one he only visits it once, leading perhaps to him not coming back altogether. If you anticipate this, you can take a step forward and propose a new deal or offer him something valuable like a discount or free delivery.
This builds a proactive mindset for the marketer, helping both parties build a loyal, trustworthy relationship.
3. Improving the customer journey
Back in the days, customer experience was left to the person who was selling the company goods. The interaction happened one-on-one. In the digital era this is no longer happening, nor is it possible to cater a unique experience to every person interested in your product. But technology advancements have set to solve this problem. Machine learning algorithms can perform, dare I say, an even better job than humans at understanding customer needs and moving them through the buyer’s journey with ease.
Customer support is also seeing improvements thanks to Natural Language Processing. Customers can state their issues and machine learning technology will decipher the meaning, further delivering the standardized message content to the customer support team. Solving complaints fast and effective is key in keeping up with the industry and customer demands.
From seamless, personalized navigation through all touchpoints to fast and efficient customer support, we can improve customer retention and build lasting loyalty
4. Lead scoring
Machine Learning can generate propensity models that use data to score leads based on their probability to make a purchase. This comes in handy when dealing with many leads as it saves time and increases efficiency, freeing the sales team to perform other important tasks.
Lead scoring also helps in defining the ideal buyer persona. Looking at the profiles of the customers who purchase the most, marketers can identify common traits for which to look in the future.
“Without personalization, we are losing relevancy, and fast” Marvin Chow – VP of Marketing at Google
The main purpose of machine learning marketing is to offer a personalized customer journey that is specifically tailored to fit individual needs at that point in time. On-site personalization, as well as email and ad personalization, can go a long way in achieving your objectives.
With personalization powered by machine learning, you can foresee where the customer is in the buying journey and serve him the right content. If the customer is a frequent visitor to your website, there is no point in delivering redundant content when you could offer him a discount or free shipping to build loyalty.
6. Product recommendations
Up-sell and cross-sell can witness much better engagement by using machine-learning to accelerate and optimize the product recommendation process. By predicting demand and analyzing past customer behavior marketers can make targeted offers that have a better chance of converting.
Appropriate product recommendations also help to keep things fresh and avoid redundancy. Why would you offer a customer a credit card deal if he already has one when you can offer him house insurance since he just bought his dream home?
7. Dynamic pricing
To discount or not to discount? That is the burning question for many marketers out there. Sales can be the ace in the hole sometimes but constantly cutting the price can be more detrimental to the company’s profit than not having discounts at all.
Machine learning algorithms can identify the people who don’t need a discount to buy the product, as well as those who could use this nudge to finally make the purchase.
Using dynamic pricing can maximize sales and keep your business profitable. But this tactic should be approached carefully depending on your industry and customers.
8. Ad targeting
The quest for ad optimization is never-ending for the marketer. A/B testing is the foundation for a well-oiled campaign, but it can never surpass machine learning. Data analysis fused by constant learning and adjustment is a bulletproof strategy for reaching the right audience, at the right time in the buying lifecycle.
9. Marketing automation
Automation allows marketers to set up workflows and perform frequent experiments to optimize the process. But the process requires a lot of trial and error without really understanding the patterns that shape your results. With machine-learning, you can ditch that tedious process and let it run tests based on deep insights into your customers’ habits. Marketing automation becomes far more powerful this way.
10. Understand and predict ROI
No wonder marketing ROI increases when the customer experience and data-driven sales techniques are part of your strategy. And we have many examples of companies who are leading their industries with machine-learning as their strong suit. But understanding and predicting ROI helps you build a data-driven culture within your company which is far more valuable in the long run.
We can’t turn a blind eye to all the applications and benefits of machine learning in marketing. More accuracy, more time to focus on creative tasks and more data to harness. The stats are there, people swear by it and companies all over the world are preparing the ground for top-notch customer experiences and skyrocketing sales.
Machine learning is the missing piece of the puzzle. Now’s the time to step back, look at the bigger picture and start strategizing for success.
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