Introduction
In the age of personalized marketing, extreme segmentation, and the recent advent of deep-learning models for customer behavior, it’s surprising to see that rewards platforms are not fully leveraging these cutting-edge technologies.
Indeed, human behavior is often predictable, with past actions serving as a reliable indicator of future behaviors. This is why recommendation systems are so effective in marketing and predicting customer preferences. On a granular level, these systems analyze a wealth of historical interaction data to predict the most suitable product or loyalty program to present to users, thereby maximizing acquisition and engagement.
In the following sections, we will delve into the eight reasons why any platform aiming to build trust, foster connections, and enhance acquisition, engagement, and retention for their brands should implement a recommendation system.
1
Get a revenue boost without additional marketing spend
Attracting targeted traffic to your platform is already a challenging task. Failing to present users with items that perfectly match their interests is always a missed opportunity. Much like in e-commerce, recommendation systems can significantly enhance your conversion rates. By simply displaying the right items, you can experience a direct increase in revenue, without the need to boost incoming traffic.
2
Enhanced User Experience through Personalization
Your users’ time on your platform is an invaluable resource. Utilizing recommendation systems can streamline the process of finding the perfect loyalty program or product on your platform. This not only improves the user experience but also boosts engagement and, most likely, user retention on your platform.
3
Integration of demographics and external data for even more personalization
Recommendation systems leverage the historical interaction data of users with the platform to suggest the most suitable items in real time. However, these algorithms are also capable of integrating a variety of other data sources, including demographics and other relevant data.
Incorporating this data proves to be highly effective if it is related or correlated to user preferences.
4
Save your time
Many rewards platform owners manage to manually change the items they display to the users. By automating this task in an optimized manner, you’ll save a significant amount of time that would otherwise be spent on manual operations.
5
Keep control over the marketing goals
Your marketing goals can fluctuate over time. There may be instances where you prioritize conversions over revenue. Alternatively, there may be times when you aim to increase the variety of products purchased, rather than focusing on revenue.
By integrating a recommendation system, you gain the ability to steer these marketing goals. You can effortlessly modify the way products are showcased to optimize your specific marketing goal for the day or week.
6
Leverage Business Intelligence and Data-Driven Insights
In the process of training the recommendation model, the AI engineer embarks on an initial phase of data exploration. This stage serves as a golden opportunity to glean valuable business insights, marking the commencement of a robust business intelligence process.
7
Position yourself as a leading technology expert among brands
This will bolster your standing as a tech-savvy platform that is consistently at the cutting edge of marketing technology innovations. It will facilitate your collaborations with other brands, thereby expanding the B2B aspect of your business.
8
Obvious ROI
Ultimately, the return on investment (ROI) for these projects is undeniably straightforward. With careful evaluation before initiation, the revenue impact can offset the deployment costs of such projects within a matter of months.
Conclusion
Delivering the appropriate item to every customer – that’s the essence of marketing. In today’s digital age, we possess all the necessary technology to construct highly customized marketing strategies on a large scale, maximizing the potential of your inbound web traffic.
