• Geoff McDonald

ContextLink: Advanced Loyalty Support for the entire Loyalty and Rewards Industry

The loyalty space inside of brands are awash in stories about new technologies incorporating data, customer platforms, machine learning and artificial intelligence for purposes like improving improving “real personalization,” analysis on advanced behavior, and even asking to find a better way to clear some of the offers and rewards off their books. Yet, while data ingestion, data matching and segmentation can help loyalty executives gain insights and improve some decision-making, many of the systems fall short. In fact, most of the loyalty and reward platforms are aimed entirely at grabbing consumers via 3rd party mechanisms which has a lot of fall-out and/or the platforms are looking at one-dimensional segmentation, missing one crucial element: the edge of consumer engagement.

That’s where ContextLink’s context engine comes in. Unlike most systems that take a purely technological approach to loyalty, ContextLink incorporates behavior, emotion, and human intuition into the personalization process. Rather than making business or purchase decisions for the user, the context-based ContextLink platform complements why consumers are deciding and makes better recommendations.

The ContextLink approach has applications for all sorts of everyday loyalty decisions, such as:

Why Loyalists Benefits from Personalization: The key is ContextLink’s ability to understand the consumer and its correlating parts to help make a weighted inference of “why” they should received which offer/reward. Instead of just asking for more data from your loyalty-decision-makers, making them sift through your endless amounts of segmented offers with increasingly narrowing factors, the ContextLink platform enables brands to use the data that is given to understand loyalty tradeoffs, the same way the human brain does, to drive more refined, personalized, rewards/offers.

Loyalty Manager Use Case

Let’s consider how a typical AI-based loyalty platform might be used by a hotel chain procurement manager aiming to make the smartest decision about which offer should go to which person.

Standard AI or loyalty intelligence systems would take in 1st party data and (possibly) enrich it with another data-set, and then build a segment using one-dimensional data analysis. This process narrows the parameters that can be weighted, prioritized or reviewed, leaving many edges of correlation that might bring forth the key answers to “which offer, goes to which user.”

However, such a rigid loyalty decision-making process might actually rule out the best option. The brand user most likely would be required to define a maximum price on each add or offering unit. But by doing so, the system would eliminate what might have been the best option: specific offer or reward from an unchosen experience with defined parameters.

The typical loyalty decision-making process would deem that tradeoff worthwhile considering the algorithm’s “longer” (analyzed) effect — not to mention the effect generated by the past algorithmic designs that drove previous loyalty. But most of those rules-based AI systems might be in legacy or outdated with a “new market.”

ContextLink, on the other hand, allows the loyalty manager to prioritize a wide array of criteria, resulting in a more personalized recommendation to uncover the best reward/offer.

The fact is, personalized loyalty and reward programs are central to human decision making, but “brand mangers” just don’t understand them; nor would we expect them too with how complex the human brain is. That’s the difference between the Context-based ContextLink and other loyalty and reward systems: While most steer the decision-maker into a narrow path by eliminating options based on pre-defined parameters, CL makes room for other aspects to be considered to infer intent. Traditional machine learning and AI platforms miss them all together.

22 views0 comments