Thanks to a growing number of customer-centric tools, brands can generate personalized marketing experiences that engage users far more effectively than a one-size-fits-all approach.
Today’s marketers need to excel at delivering consistent, harmonious branded experiences to prospects and customers. Some believe the average person sees 4,000 to 10,000 advertisements per day — and that’s likely a conservative estimate. Unfortunately, the sheer quantity of ads, emails, and social media messages everyone sees can make it difficult to resonate with potential customers. To that end, brands are increasingly turning to dynamic marketing solutions: platforms that personalize brand experiences to build stronger customer relationships while maximizing revenue, engagement, and lifetime value (LTV).
At its core, dynamic marketing aims to identify and respond to customer needs or interests in real-time. Thanks to machine learning technology, it’s possible to analyze user data, generate an ideal promotional offer, and assemble custom experiences within moments. This process forges stronger connections with customers and increases their lifetime value (LTV). In this article, we will explore some of the features and capabilities modern dynamic marketing solutions offer.
Dynamic marketing solutions
Dynamic marketing solutions are like shopping at a local boutique. The owner of the boutique can create custom deals for loyal patrons or design bargains to entice new customers. The big box store treats every visitor the same, regardless of what they’ve purchased previously or are looking for now.
Mobile markets aren’t quite the same, but dynamic solutions do allow for custom deals by estimating the price sensitivity of any user. Promotional pricing is a great example — with a machine learning platform, marketers can predict how likely any individual is to convert into a payer and complete a purchase. These algorithms will then automatically generate offers in the form of a personalized promotion based on likelihood to purchase, such as bonus points, first purchase discounts or other promotional areas of the game. When implemented effectively, dynamic solutions can increase conversion rates, enhance engagement, and maximize in-app revenue while also delivering optimal experience for the user.
And that’s just one benefit of pricing optimization. Marketers can scale these insights up to a global level by analyzing country-specific economic data, international game markets, and other contextual trends. This process allows machine learning models to generate global pricing estimates constructed horizontally by country — setting optimal price points for any in-app item by taking price sensitivity into account. Whatever your scale, dynamic pricing makes it possible to make the best possible offers to your highest-value users.
Dynamic loyalty automation
Loyalty marketing is already an essential part of any brand’s retention strategy, but it too can be dynamic, making loyalty programs more engaging. By taking in-app event data and analyzing how users engage with content, machine learning algorithms calculate a loyalty points-and-reward system that will be effective for a given audience
Loyalty automation techniques prove effective on an individual scale as well, allowing marketers to deploy personalized loyalty tactics. A mobile game, for example, might entice players to join a rewards program by highlighting promotional point reward activities depending on their engagement level. Likewise, in a shopping app, automated systems can recommend available products for purchase with reward points. In all cases, personalized loyalty rewards tend to be valuable to your most valuable users, which increases LTV in turn.
Every advertisement uses a combination of offer, copy, tone, and imagery to make an impression on its audience. Each ingredient will resonate with different audiences in some way, but static campaigns are time-consuming to create and difficult to personalize, making it more challenging to reach every user. That’s why automated ad creative is now an essential part of dynamic marketing solutions.
As with any dynamic solution, a machine learning engine analyzes available user data to determine ideal creative traits, as well as ideal timing. The algorithms assemble this information into a behavioral profile and then choose the most relevant copy, background image, promotional offer, and call to action. In short: it’s remarkably easy for such a platform to automatically assemble and deploy the right ad to the right user.
Using wappier’s Next Best Action service as an example, our algorithms can generate and assess practically infinite value combinations and identify in real-time the optimal creative and a personalized offer that resonates with each user at the ideal moment. By calculating which offer and creative will convert each user, brands can more effectively drive brand engagement and increase LTV.
Dynamic marketing solutions are necessary because they let mobile marketers strike a balance between global and personal scales. In wappier’s case, our machine learning engine and Next Best Action capabilities make it possible to dynamically target individuals with the right ad at the right moment. If you’re ready to create personalized experiences with dynamic messaging, promotions, and background images, get in touch for more details!