Custom Formula Ranking Strategy

Custom Formula is a ranking strategy available in Decision Policies that replaces a single-variable ranking signal with a weighted combination of multiple parameters. You choose which parameters matter and assign a percentage weight to each. The engine multiplies each parameter score by its weight, sums the results, and ranks all eligible Offerings for every customer accordingly.
The result is a ranking that reflects both what is strategically important to your business and what is personally relevant to each customer — recalculated in real time, without manual intervention.

Custom Score  = (Attribute 1 Score × Weight %) + (Attribute 2 Score × Weight %) + … + (Attribute N Score × Weight %) + (Performance Score × Weight %) + (Priority Score × Weight %)

How it compares to other ranking strategies

MoEngage offers three ranking strategies in Decision Policies. Understanding when each one fits helps you choose the right approach or combine them across different policies.

	Manual priority	Merlin AI	Custom formula
How ranking works	Author assigns a rank (100, 90, 80…) to each offering manually	AI learns from clicks and conversions to find the best-performing offering for each user	Marketer defines a weighted formula combining Offering attributes, performance, and priority
Who sets the score	Author	Algorithm (learns over time)	Marketer defines weights; engine calculates score per user at runtime
Personalisation	None — same rank for all users	Implicit — learns aggregate patterns across users	Explicit — score changes per user based on their profile (via dynamic attributes)
Business goal control	High — author fully controls rank	Low — optimises for engagement/conversion, not specific business goals	High — marketer decides how much weight each goal carries
Responds to strategy shifts	Manually — re-rank all offerings	Gradually — model needs time to relearn	Immediately — adjust weight in the Decision Policy; all Offerings re-rank instantly
Best for	Small, stable catalogs with clear priority	Large catalogs where engagement is the primary goal	When business value AND user relevance both need to influence ranking
Requires Offering Attributes	No	No	Yes
Limitation	Does not account for customer intent or business value trade-offs	Cannot protect high-value, low-CTR Offerings from being buried	Requires upfront configuration of attributes and weights

Information

Manual Priority does not account for the customer. Merlin AI does not protect high-value, low-CTR offerings. Custom Formula is the only strategy that lets you simultaneously express what an Offering is worth to the business and how relevant it is to this specific customer.

Parameters available in Custom Formula

A Custom Formula can include up to 5 dynamic parameters in addition to static parameters. Parameters fall into two categories.

Dynamic parameters — Offering Attributes

These are attributes you configure under Settings > Offering Decisioning > Offering Attributes. You can include up to 5 offering attributes — Fixed Value, Dynamic Value, or a mix — as parameters in a formula.

Fixed Value attributes capture the strategic importance of the Offering itself. The score is set by the author at Offering creation time and is the same for every customer. Examples: business objective, margin tier, campaign urgency.

Dynamic Value attributes capture relevance to the individual customer. The score is calculated at decisioning time by matching the offer’s associated value against the customer’s live MoEngage profile. Examples: category affinity, churn propensity, purchase recency.

Static parameters

Two built-in parameters are always available regardless of attribute configuration:

• Offering Performance — the historical Click-Through Rate (CTR) or Conversion Rate (CVR) of the offer.
• Offering Priority — the manual priority rank assigned to the Offering (1 to 100). The priority number itself becomes the base score.

All weights across all selected parameters must sum to exactly 100%.

How to set up Custom Formula ranking

Before you begin

If you have not yet set up attributes, navigate to Settings > Offering Decisioning > Offering Attributes and create at least one attribute before proceeding. Refer to the Create offering attributes guide for instructions.

Step 1 — Create or edit a Decision Policy

Navigate to the Decision Policy you want to configure, or create a new one. In the Ranking Strategy section, select Custom Formula from the available options.

Step 2 — Add parameters

Click Add offering attribute (0/5) in the Custom Formula builder. You can add:

• Any offering attributes you have configured (Fixed Value or Dynamic Value)
  • For Fixed Value attributes, you’ll have to choose one of the options from the dropdown.
• Offering Performance (Click-Through-Rate or Conversion Rate)
• Offering Priority score

You can add up to 5 parameters in total. If the Add Parameter button is disabled, you have reached the maximum.

Step 3 — Assign weights

For each parameter, enter a weight percentage. Weights represent how much each parameter contributes to the final score. All weights must sum to exactly 100%.

Example weight configuration

• Business Goal: Increase gross margin (Fixed Value attribute) — 40%
  • Business goal is the name of the Fixed value attribute. Increase gross margin is one of the options.
• User preferred category (Dynamic Value attribute) — 30%
• Offering Performance (CTR) — 20%
• Offering Priority — 10%

Step 4 — Save and publish

Once all weights sum to 100%, save the Decision Policy. The Custom Formula becomes active immediately for all Offerings within that policy. You do not need to edit individual Offerings — the engine uses the formula to score and rank them automatically for each customer.

How the score calculation works

When a customer qualifies for multiple Offerings in a Decision Policy, the engine calculates a Custom Score for each eligible Offering and ranks them in descending order. The highest-scoring Offering is shown to the customer.

Calculation formula

Custom Score  = (Attribute 1 Score × Weight %) + (Attribute 2 Score × Weight %) + … + (Attribute N Score × Weight %) + (Performance Score × Weight %) + (Priority Score × Weight %)

Worked example

• A Decision Policy contains two offerings: Offering A (New iPhone launch) and Offering B (Discount on Apparel). The policy is configured with the attributes and weights listed above in Step 3.
• Offering configuration
  • Offering A
    • 1 Fixed value: Business goal with 2 options:
      • Option 1: Increase gross margin ; Score: 90
      • Option 2: Reduce customer churn ; Score: 10
    • 1 Dynamic value attribute: User preferred category
      • Option: Electronics
  • Offering B
    • 1 Fixed value: Business goal with 2 options:
      • Option 1: Increase gross margin ; Score: 75
      • Option 2: Reduce customer churn ; Score: 25
    • 1 Dynamic value attribute: User preferred category
      • Option: Fashion
• A user “Ross” has “Electronics” as their primary category affinity; User “Rachel” has “Fashion” as their primary category affinity.

Scores for Ross:

Parameter	Weight	Offering A score	Offering B score	Weighted contribution
Business goal - Increase gross margin (Fixed)	40%	90	75	36.00 (90 × 0.40)  |  30.00 (75 × 0.40)
User preferred category (Dynamic)	30%	100	0	30.00 (100 × 0.30)  |  0.00 (0 × 0.30)
Offering Performance (CTR)	20%	60	80	12.00 (60 × 0.20)  |  16.00 (80 × 0.20)
Offering Priority	10%	70	70	7.00 (70 × 0.10)  |  7.00 (70 × 0.10)
Total Custom Score	100%			Offering A: 85.00  |  Offering B: 53.00

Result for Ross

Offering A scores 85.00, Offering B scores 53.00. Offering A is ranked first — the iPhone launch is shown to Customer X despite having a lower CTR than the Apparel offer.

Scores for Rachel:

Parameter	Weight	Offering A score	Offering B score	Weighted contribution
Business goal - Increase gross margin (Fixed)	40%	90	75	36.00 (90 × 0.40)  |  30.00 (75 × 0.40)
User preferred category (Dynamic)	30%	100	0	0.00 (0 × 0.30)  |  30.00 (100 × 0.30)
Offering Performance (CTR)	20%	60	80	12.00 (60 × 0.20)  |  16.00 (80 × 0.20)
Offering Priority	10%	70	70	7.00 (70 × 0.10)  |  7.00 (70 × 0.10)
Total Custom Score	100%			Offering A: 55.00  |  Offering B: 83.00

Result for Customer Y

Offering A scores 55.00, Offering B scores 83.00. Offering B is ranked first — the Discount on Apparel is shown to Customer Y.

What happens when two offerings have the same score?

If two or more Offerings in a Decision Policy calculate to the same Custom Score for a given customer, the engine applies a sequential tie-breaking logic to determine which Offering is ranked first:

Step	Condition	Logic
1	Earliest end date & time	The Offering expiring soonest wins — it is more time-critical.
2	Earliest start date & time (if end dates match)	Among equally expiring offers, the one that went live first wins.
3	Random selection (if start & end dates match)	One Offering is chosen at random.

Next Steps

• To configure the Offering Attributes used by this strategy, refer to Managing Offering Attributes.
• To learn how to create a Decision Policy, refer to Create Decision Policy.
• To assign Offering Attributes to your offerings, refer to Create Offering Attribute.