Building use cases
Learn how to build an OfferFit use case, so you can automate personalized experimentation and optimize outcomes like conversions, retention, or revenue—without manual A/B testing.
While OfferFit works best with Braze, a variety of other platforms are already supported. We’ll continue updating our documentation so you’ll have everything you need—even if you’re not using Braze.
About use cases
A use case is a custom configuration for OfferFit’s AI decisioning engine that’s tailor-made to meet a specific business goal.
For example, you could build a repeat purchase use case to increase follow-up conversions after an initial sale. You define the audience and message in Braze, while OfferFit runs daily experiments and automatically tests different combinations of product offers, message timing, and frequency for each customer. Over time, OfferFit learns what works best and orchestrates personalized sends through Braze to maximize repurchase rates.
To build a good use case, you’ll:
- Choose a success metric for OfferFit to optimize for, such as revenue, conversions, or ARPU.
- Define which dimensions to test, such as offer, subject line, creative, channel, or send time.
- Select the options for each dimension, such as email versus SMS, or daily versus weekly frequency.
Sample use cases
Here are some examples of use cases that you can build with OfferFit. Your AI decisioning agents will learn from every customer interaction and apply those insights to the next day’s actions.
| Use case | Business goal | Using typical methods | Using OfferFit by Braze |
|---|---|---|---|
| Cross-Sell or Upsell | Maximize average revenue per user (ARPU) from internet subscriptions. | Run annual campaigns offering every customer the next-highest tier plan. | Empirically discover the best message, sending time, discount, and plan to offer for each customer, learning which customers are susceptible to leapfrog offers and which customers require discounts or other incentives to upgrade. |
| Renewal & Retention | Secure contract renewals, maximizing both contract length and net present value (NPV). | A/B test manually, and offer significant discounts to secure renewals. | Use automated experimentation to find the best renewal offer for each customer, and identify customers who are less price sensitive and need less significant discounts to renew. |
| Repeat Purchase | Maximize purchase and repurchase rates. | All customers receive the same journey after making a website account (such as the same email sequence with the same cadence). | Automate experimentation to find the best menu item to offer each customer, as well as the most effective subject line, sending time, and frequency of communication. |
| Winback | Increase reactivation by encouraging past subscribers to resubscribe. | Sophisticated A/B testing and segmentation. | Leverage automated experimentation to test thousands of variables at once, discovering the best creative, message, channel and cadence for each individual. |
| Referral | Maximize new accounts opened through business credit card referrals from existing customers. | Fixed email sequence for all customers, with extensive A/B testing to determine the best sending times, cadence, etc. for the customer population. | Automate experimentation to determine ideal email, creative, sending time, and credit card to offer specific customers. |
| Lead Nurturing & Conversion | Drive incremental revenue and pay the right amount for each customer. | As privacy policies change at Facebook and other platforms, prior approaches to personalized paid ads become last effective. | Leverage robust first-party data to automatically experiment on customer segments, biding methodology, bid levels, and creative. |
| Loyalty & Engagement | Maximize purchases by new enrollees in a customer loyalty program. | Customers received a fixed sequence of emails in response to their actions. For example, all new enrollees in the loyalty program receive the same journey. | Experiment automatically with different email offers, sending times, and frequencies to maximize purchase and repurchase for each customer. |
Building a use case
Prerequisites
Before you can build a use case, you’ll need to integrate OfferFit by Braze.
Step 1: Contact OfferFit
OfferFit’s AI Expert Services team will work closely with you to scope, design, and build your OfferFit use case. If you haven’t already, reach out to get started.
You’ll complete the following steps together to build a custom use case that’s right for you.
Step 2: Design your use case
Alongside OfferFit’s AI Expert Services team, you’ll define:
- a target audience,
- the business metric to optimize,
- the actions for OfferFit’s AI decisioning agent, and
- any first-party customer data the agent should leverage to drive your business outcomes.
With the design in hand, the team will work with you to identify and complete any additional integration requirements.
Step 3: Set up your delivery platform
Next, the AI Expert Service team will help you set up your marketing automation platform. While OfferFit works best with Braze, a variety of other platforms are supported—contact your AI Expert Service team for additional resources.
To set up Braze:
- Create a campaign or Canvas. OfferFit will use this delivery method to send 1:1 personalized activation events to the users in your defined audience.
- Be sure you don’t include a Braze control group, so OfferFit can be the dedicated control group instead.
- Depending on your dimensions, you can configure Liquid tags in your creative content to dynamically populate your messaging with OfferFit recommendations. OfferFit will pass customer-specific content to the Liquid tags in your templates using the Braze API.
Step 4: Launch and monitor
After launching your use case, your AI Expert Services team will continue to monitor and tune it to your agreed-upon design. They’ll also help you make any adjustments, expansions, or modifications to the use case, if needed.
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