When it comes to channel incentive and loyalty programmes, personalisation is key. The more personalised, the greater the outcomes, both in terms of partner satisfaction and business performance.

This blog explores how personalisation can be built into channel loyalty programmes from the ground up, and how businesses can evolve their strategies using AI-powered targeting.

The Power of Personalisation

Channel loyalty programmes thrive when they feel relevant and rewarding to the individual partner. Because of that, we start thinking about personalisation right from the beginning, during programme design.

Leveraging a concept known as idiosyncratic fit from behavioural economics plays a central role here. It refers to the feeling that a programme is uniquely suited to a partner’s needs and strengths. When partners perceive this fit, they’re more likely to engage consistently and pursue maximum rewards.

Designing channel loyalty with layers of personalisation

Effective personalisation is a layered strategy. Here’s how it can be built into every aspect of a loyalty programme:

1. Programme Rules and Earning Schedules

At the foundation, both the core-programme rule structure and earning schedule should be based on partner-specific purchase characteristics. For example, rewarding channel partner’s for year-over-year progress is much more effective than rewarding for an average or one-size-fits-all target.

2. Targeted Promotions

Next, promotions should target and trigger based on, ideally, the “next best action” for each individual channel partner. For example, targeting promotions based on voids in a channel partner’s purchase history or complementary products tied to initial purchases.

3. Personalised Communications

Finally, content and communications should target and trigger based on partner-specific profile information such as business type, location, or lifecycle stage to ensure relevance and resonance.

How AI Supercharges Loyalty Personalisation

Knowing that personalisation is critical to a programme’s success, it’s not surprising that many manufacturers are now interested in exploring artificial intelligence to help optimise and automate elements of personalisation throughout loyalty campaigns. However, because AI relies on partner data, many companies are not in a position to effectively use it. This is especially true in the channel loyalty space. The good news is there are other options that allow you to take the first steps and build a path to AI.

AI is used in many ways, so it’s important to note that the AI we’re talking about here is the application of advanced computer analysis using techniques that mimic human intelligence to interpret data, determine outcomes, learn from previous interactions, and execute actions. With the ability to scale at massive rates and quickly distil complex variables into simple, applicable formats, AI is an ideal tool for generating fast, accurate personalisation.

Example: A Loyalty Programme for Restaurant Owners

Here’s an example of a channel loyalty programme for restaurant owners sponsored by a food service company that shows how to integrate AI into a loyalty programme. In this scenario, AI would identify the “next best action” or promotion for each individual restaurant based on consideration of a number of different variables:

• Partner-specific data including purchase history, share of wallet, size of business, purchase potential, partner lifecycle stage, and location.
  
• Product-specific data including up-sell/cross-sell matrices, complementary product relationships, seasonality and weather factors, and product margin.
  
• Data on the behaviour of similar partners.

Essentially, AI would identify the ideal promotion for each partner individually — the ideal product with the ideal incentive at the ideal time — based on a virtually infinite number of variables.

The path to AI

Let’s look at the options along the path to AI. Targeted marketing and personalisation can occur at various degrees of sophistication ranging from having next to no data, to a mid-level amount using data extraction, to deeply complex applications that begin with access to rich amounts of data. Opening the field to thousands of combinations, the choices may overwhelm any marketer.

Level 1: Zero-party data

In situations where there is little to no data on partners, personalisation is possible by leveraging a zero-party data strategy: data that partners intentionally and proactively share. In this scenario, we provide an opportunity for partners to share their preferences early in the process. For instance, partners are offered a chance to select from a set of options or share their preferences. The data collected then becomes the basis for future personalised marketing.

Level 2: Data extraction or retrieval

When you have a partner database to work with, data extraction is the easiest method to leverage for personalisation. At the most basic level, an analyst manually identifies and selects target audiences aligned with the desired behaviour. After the manual approach is proven, the process can be automated. A software engineer or analyst writes code that is run on demand to identify and select target audiences. With automation, data extraction becomes scalable. Costs are reduced. Speed to market is accelerated. Plus, the number of input (target audience)/output (desired behaviour) combinations can be increased.

Level 3: Traditional application development

Traditional application development involves creating algorithms (sets of rules) that analyse multiple data points to identify target audiences that align with desired behaviours based on predetermined criteria. Each potential scenario and outcome is planned, developed, and accounted for within the source code. And every change or addition to the process must be manually added by a human in the form of additional data points or additional source code. Traditional application development can also involve the development of a predictive model where an analyst leverages data mining and statistical analysis to reveal patterns and trends. The insights are then used to predict future behaviours and outcomes.

Some examples of inputs and outputs are:

Inputs:

• Partner transactional data
• Partner characteristics
• Partner profile information
• Partner engagement activity or lack of activity
• Similar and ideal partner characteristics
• Partner characteristics tied to retention and attrition
• Product characteristics and relationships
• Geographic factors like seasonality and weather patterns

Outputs:

• Specific behaviour or series of behaviours
• Optimal promotional offer
• Optimal timing

Level 4: AI

At this point we have reached the final step in the continuum: AI. Targeting and personalisation leverages the predictive branch of AI versus the generative branch. Predictive AI uses machine learning to make predictions based on historical data whereas generative AI uses machine learning to create content like text, images, and sounds from large language models. Predictive AI models find patterns, develop insights, and analyse data to predict future events. The accuracy of predictive AI depends on the quality and amount of data the model is trained on. Through trial and error, the algorithm becomes better at predicting the future.¹

Data considerations

As previously mentioned, data is at the foundation of targeting and personalisation, regardless of where you are on the continuum. There are a number of factors to consider as you assess your readiness and develop your strategy:

As you move up the continuum, you can significantly increase the number, complexity, and granularity of the variables used in your targeting and personalisation efforts. You can also increase the timeliness of deployment up to real time. However, data hygiene, integrity, and governance also becomes more critical because there are not as many manual checks and the potential negative impact increases exponentially.

Personalisation Is a Journey

Whether you’re just starting with zero-party data or ready to deploy predictive AI, the journey to personalisation is a strategic one. Each step brings you closer to delivering loyalty experiences that feel truly tailored, and truly valuable.

The future of channel loyalty is personalised. The question is – how far along the path are you?

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Sources:

1. Smith, Robert F. “Generative AI vs. Predictive AI: Distinguishing the Difference.” (2024)