Your overarching goal as a retail marketer is simple: build high-value, long-lasting relationships with your customers. Getting there isn’t quite so simple; even the most sophisticated and data-rich online retailers, like Amazon, don’t nail their personalized recommendations 100% of the time.
If you have thousands (or millions) of customers, it seems impossible to know each person individually and market to them as an audience of one, speaking to them as you would a friend. Machine learning can help.
While it may seem like a paradox, technology can foster more human relationships between your brand and your customers. Using predictive analytics and software powered by machine learning, you can autonomously transform data into action and scale your personalized relationship marketing efforts.
Advertisement
Imagine you’re a marketer for an athletic clothing brand, charged with converting as many new customers as possible into repeat purchasers. You have data about your customers’ product preferences and purchases, but need to figure out which action will encourage additional sales. You know from experience that free shipping and 20% off coupons are effective incentives, but your customers don’t respond in the same way; each person has his or her preference for which offer to receive, and when and how to receive it.
It’s not realistic to manually evaluate how each customer responds to different deals and test the different permutations that lead to the best results. This is where automation comes in. Machine learning allows you to process customer behavior and data at a massive scale, while predictive analytics capabilities help you anticipate what that behavior will look like in the future.
So, in this scenario, you could set up a campaign to target different groups of customers with a 20% discount offer and measure each group’s results against a control. Eventually, the group that responds best to discounts will get 20% off. In the immediate term, you gain clarity into the effectiveness of each marketing action; longer term, the software makes more effective recommendations for specific customers.
Implementing machine learning can also aid you in identifying, and nurturing, your most valuable customers. As the Amazons of the world make the competition fiercer for all retailers, it’s the customers that come back to you again and again that are most critical to the continued success of your brand. 60% of revenue in e-Commerce is generated by the top 25% of customers; you need to make sure that 25% is taken care of.
Software can help you identify customers by “life stage” — for instance, distinguishing new customers, high-rollers, customers who are about to churn and those who have already churned. The ability to predict that a particular customer is at a high risk of churning, and actually do something about it, represents a huge opportunity to salvage, and ultimately strengthen, a customer relationship.
It makes business sense to keep a churning customer; acquiring a new customer is 5X more expensive than retaining an existing one. What’s more, that customer initially came to your brand for a reason. If you can bring them back into the fold by demonstrating that you understand their needs — with an email sharing a sale on products they’ve enjoyed in the past, or a promotional code for that pair of running sneakers they are likely to buy given their previous purchase preferences — you have the potential to not only win them back for one sale, but for life.
With the analytics in place to delineate your biggest fans, you can take action to make them feel special, whether that’s sending them pre-order offers for upcoming products, offering exclusive invitations to parties or pop-up shops, reminding them to stock up at just the right time, or sending them a special discount code on their birthday.
Marketers who are able to deliver highly relevant content to their customers, leveraging technology to better understand when, where and how they want to be communicated with, will be that much closer to earning their trust — and keeping their business — for the long run.
Pini Yakuel co-founded Optimove in 2009 and has led the company, as its CEO, since its inception. With two decades of experience in analytics-driven customer marketing, business consulting and sales, he is the driving force behind Optimove. His passion for innovative and empowering technologies is what keeps Optimove ahead of the curve. He holds an MSc in Industrial Engineering and Management from Tel Aviv University.
