Have you ever looked at a website or an email and thought how easy some marketers have it with their “product recommendation” sections? Some combinations are so obvious, that a “recommendation” seems unnecessary. For instance, if you’re selling a printer, the customer obviously needs paper, ink and toner – it’s common sense. But for many retailers, their merchandise isn’t purchased in such neat and obvious ways. In order to create these product recommendations, marketers are forced to either make educated guesses, which compromise accuracy, or spend precious time reviewing advanced analytics. Not every company can afford a team of data scientists specifically dedicated to marketing, but that doesn’t mean those companies should be at a disadvantage. There’s got to be a smarter and faster way for marketers to gain these insights.
The solution? Machine Learning. It may just sound like a buzzword, but the reality is that it can – and should – be a mid-market retailer’s secret weapon. At its most basic, machine learning is the science of using algorithms that can learn from data to make determinations or predictions about something in the world.
How Do Product Recommendations Work?
In the case of product recommendations, machine learning models use your customer and order data, run it through the specialized model and output various product recommendations based on each customer’s demographics, marketing interactions and past purchases. For instance, if a customer purchased a brown shoe and brown belt, the model may recommend shoe care, a shoe in the same color and perhaps a belt to match. However, another customer who made the same shoe and belt purchase may receive a different recommendation if their past behavior or demographics are different. A marketer can leverage this data to target customers with their specific recommendations through dynamic content. Dynamic content allows you to deploy unique emails to each of your customers with relevant information and recommendations. This is 1-to-1 marketing at scale.
In additional to providing recommendations at the individual customer level, machine learning models can be used to identify product recommendations for groups of look-a-like customers. With a click of a button, marketers gain insights into the products that are typically purchased together, allowing them to make smarter decisions such as bundle offers during special periods or sales.
Machine Learning Applications Beyond Product Recommendation
The ability to receive individual and cluster product recommendations is major priority for many of our customers and prospects, but it’s really just the tip of the iceberg in terms of how machine learning can benefit your business.
For example, one question marketers are constantly tasked with is, “How do we retain our customers?” As most marketers know, it’s much cheaper to retain customers than acquire new ones. Furthermore, by retaining customers, you have the ability to create your own brand ambassadors without paying for it! QuickPivot helps you answer this question, and analyzes all relevant data points and identifies the specific customers that are most likely to churn within your predetermined time period (ex. 30, 60 or 90 days). This customer list is pure gold for marketers. Imagine using this list in conjunction with a product recommendation or special discount offer to keep them from churning/leaving.
Regardless of your title, your business, or the size of your team, we’re all short on-time and working through a never-ending to-do list. Marketers are sitting on top of more data than ever, and the expectations are to gain strategic insights that have a real business impact – but not every team is equipped with the team or resources to be successful. Machine Learning may sound fancy or out of reach, but it’s really just a means to an end. It’s the most time-efficient and intelligent way for marketers to get answer to their most pressing questions.
Do you have a pressing marketing question related to your customer data ? Let’s schedule some time to chat. Then we’ll get the robots involved.