Build Model

Moveo One makes it possible to build predictive models automatically — without writing code or managing training pipelines.

You simply define what you want to predict (the Target Event) and under what conditions (the Conditional Events), and the Moveo One backend handles data preparation, feature extraction, training, validation, and deployment.

OverviewDirect link to Overview

Each predictive model in Moveo One answers a question like:

Goal	Example
Predict user conversion	“Will the user complete checkout?”
Detect churn	“Will this user return after 7 days?”
Identify confusion	“Is the user likely to abandon onboarding?”

Models are powered by anonymized behavioral data captured from SDKs — event sequences, durations, transitions, and motion features.

Building via DashboardDirect link to Building via Dashboard

Step 1. Open the Model BuilderDirect link to Step 1. Open the Model Builder

In the Moveo One Dashboard, go to:
AI → Predictive Models → Build New

Step 2. Choose a Target EventDirect link to Step 2. Choose a Target Event

Select the main event you want to predict — for example:

checkout_complete
session_end
form_submit

Step 3. Add Conditional EventsDirect link to Step 3. Add Conditional Events

Pick 1–3 events that precede the target event, for example:

add_to_cart
view_product
checkout_start

These act as input signals for the model.

Step 4. Define Observation WindowDirect link to Step 4. Define Observation Window

Set a time window (e.g., 30 minutes or 24 hours) that determines how far back in user activity the model looks for context.

Step 5. Click Build ModelDirect link to step-5-click-build-model

Moveo One automatically:

Selects relevant features
Balances datasets
Trains multiple algorithms (e.g., gradient boosting, logistic regression)
Chooses the best performer
Deploys the model for real-time prediction

Example Model Configuration (JSON)Direct link to Example Model Configuration (JSON)

{
  "targetEvent": "checkout_complete",
  "conditionalEvents": ["add_to_cart", "checkout_start"],
  "observationWindow": "30m",
  "split": {
    "train": 0.8,
    "test": 0.2
  }
}

You can submit similar configurations via the API (see below).

Model Training ProcessDirect link to Model Training Process

Feature Extraction — builds event sequences and statistical signals
Training Phase — multiple models are evaluated and tuned
Validation — best model selected based on accuracy/F1-score
Deployment — endpoint automatically becomes active
Monitoring — metrics appear in dashboard (latency, accuracy, drift)

Example Metrics OutputDirect link to Example Metrics Output

{
    "standard_metrics": {
      "accuracy": 0.5501930501930502,
      "precision": 1.0,
      "recall": 0.20205479452054795,
      "f1": 0.33618233618233617,
      "auc": 0.9184901200145471
    },
    "optimal_threshold": 0.05
  }