Setting up NBA is a five-step exercise: define recommendations, build the strategy, drop the component on a page, test, and measure. The order matters. Recommendations are the units the strategy works with, so they have to exist first.
- Define the action set as Recommendation records
Create Recommendation records: name, description, image, accept and reject labels, target flow. Each recommendation is reusable across strategies, so think generic and parameterizable.
- Build the strategy in Strategy Builder
Setup, Next Best Action, New Strategy. Use Load to bring recommendations in, Branch to filter by record fields or predictive scores, Map to set per-recommendation context, and Prioritize to return the top three.
- Place the Einstein Next Best Action component
In App Builder, drop the Einstein Next Best Action component on the target Lightning page. Bind it to the strategy. Configure the layout (cards versus list) and the max recommendations displayed.
- Test with a range of record states
Open records covering high score, low score, missing data, and edge cases. Verify the recommendations match the strategy intent. Click Accept and confirm the flow fires.
- Build a Recommendation Reaction report
Report on the Recommendation Reaction object grouped by recommendation. Track acceptance rate weekly. Use the data to refine branches in the next strategy version.
The reusable action units. Each has a name, description, image, target flow, and labels for accept and reject.
Load, Branch, Map, Filter, Prioritize, Recommendation Limit. The composition produces the final ordered list.
Calls to predictive models from inside the strategy. Supports Einstein Discovery and CRM Analytics scores as inputs to branching.
The Einstein Next Best Action component goes on a Lightning page. One per surface; multiple strategies can run on different pages.
The Recommendation Reaction object captures acceptance and rejection. The basis for measuring strategy effectiveness.
- Over-recommendation kills adoption. Surfacing ten recommendations per record trains users to ignore the component. Cap at three and prioritize hard.
- Recommendation collisions between strategies on the same page confuse users. Scope strategies cleanly by audience and surface.
- Strategy Builder is its own canvas. Teams expecting flow-style logic find it unfamiliar. Plan ramp-up time for the first strategy.
- Recommendation Reactions are the only feedback signal. Without a recurring report on acceptance rate, the strategy stagnates.
- Predict nodes call models synchronously. Heavy predictive scoring inside a strategy can slow the page load. Keep predict nodes lean.