Personalization is the modern-day marketer’s best friend. Artificial intelligence has already become a vital tool for the marketing strategies of many popular brands. Machine learning algorithms are taking on what were once tedious and time-consuming day-to-day tasks, allowing marketing teams the freedom to focus on more business-imperative thoughts and initiatives.
While many uses for artificial intelligence marketing (AIM) and machine learning are still being explored, one that recently came to light is the ability to provide personalized incentive recommendations. This innovative technology allows marketers to deliver the best advantages and opportunities for each customer based on individual shopping history, browsing and buying behavior, and past engagement.
In this article, we take an in-depth look at what incentive recommendations are, and how personalized marketing technology can help marketers solve the mystery of which incentives work, as well as how, when, and where to offer them to each customer for maximum effectiveness.
What’s the Problem with Incentives?
Marketers should already know that incentives are highly effective marketing tactics. From discounts, to free shipping, to buy-one-get-one offers, marketers use this sort of enticement to engage and encourage their customers to buy or to take an action of some sort. However, marketers frequently use a one-size-fits-all approach to these offers. They throw a single offer to thousands, or even hundreds of thousands, of customers without the detailed understanding of which incentives will be most effective for each customer, or if a customer even needs an incentive at all. There is also the problem of offering too many incentives, lowering the perception of the brand’s prestige in the eyes of their valued shoppers.
Nonetheless, the amount of manual work required to sift through all the data and determine which incentive will work best for each individual is far too overwhelming for the vast majority of marketing teams, not to mention an incredibly inefficient use of time. However, by using AI marketing and machine learning technology, marketers can deliver personalized incentives to each customer, quickly and easily.
What Are Incentive Recommendations?
At Emarsys, incentive recommendations are part of our approach to AIM, helping marketers deliver customers truly 1-to-1 personalized incentives. By analyzing campaign launch lists, the incentive recommendation feature can identify which customers are more likely to purchase when offered an incentive, and then assign the most efficient incentive to each one.
In other words, instead of sending the same incentive to the whole launch list, or grouping them into broad categories, marketers are able to simply define the range of incentives they desire. The AI can then make the best possible decision, given the information available, as to which one will resonate best with each customer on a case-by-case (or you could say, cart-by-cart basis).
How Does It Work? What Are The Benefits?
Incentive Recommendation is a simple feature, but one that can add value and increase ROI for every marketer, and every campaign. Marketers simply upload a series of incentives (in the form of images or code snippets) to their automation platform, assigning a value to each one. Through machine learning and artificial intelligence, the Emarsys platform then uses behavior data to predict a customer’s most likely response.
For example, will 5% off be enough to influence a purchasing decision? Or will they require as much as 25% off before making a purchase? This behavior history, combined with additional settings defined by the marketer, is applied to the smart incentives, and the program decides which incentive to present to each customer.
You may be saying yourself: “but what about the customers that don’t need any incentive?”. There’s a chance they will buy regardless, or contrarily, maybe no amount of incentive will push them to convert. The incentive recommendation technology leverages machine learning to predict these circumstances as well, and simply prevents them from receiving any incentives. Brands can also use a control group to measure the effectiveness of this feature by selecting a percentage of contacts at random and sending them all a fixed, default incentive instead of a smart incentive.
This level of personalization is impossible for marketing teams to achieve by manually trying to predict and customize incentives for customers. Time just simply doesn’t permit this sort of activity at scale. However, by using a marketing strategy that automatically offers truly personalized incentives, each customer receives the offer most likely to convince them to make a purchase. This customized approach not only drives sales, but also improves the customer experience, thus strengthening the relationship between customer and brand.
Marketers today are under constant pressure to perform, do more with fewer resources, and to continue providing a positive ROI for the brand. By working together, AI and machine learning tools such as personalized marketing incentive recommendations mean marketers can provide personalization in ways never seen before. It is the artificial intelligence era, and smart marketers can certainly find ways to benefit.
Learn more about how Emarsys’ personalized incentive recommendations can help your marketing program.