Forecast Wrap-Up Code Usage via Genesys Cloud REST API with Go

What You Will Build

• This module extracts historical wrap-up code volumes, validates data against seasonality and outlier constraints, and constructs a WFM forecast payload with time window matrices and confidence thresholds.
• The implementation uses the Genesys Cloud Analytics API for historical queries and the WFM Scheduling API for atomic forecast generation.
• The tutorial provides production-ready Go code that handles authentication, validation, submission, webhook synchronization, latency tracking, and audit logging.

Prerequisites

• OAuth Client Credentials flow with a Genesys Cloud application configured for confidential client type
• Required OAuth scopes: analytics:conversation:query, wfm:forecast:create, wfm:schedule:read
• Genesys Cloud Go SDK version v1.20.0 or later (github.com/MyPureCloud/platform-client-sdk-go)
• Go runtime version 1.21+
• External dependencies: standard library only (net/http, encoding/json, time, context, log, os, sync, math)

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials for server-to-server communication. The Go SDK handles token acquisition, caching, and automatic refresh. You configure the environment once, and the SDK manages the lifecycle.

package main

import (
	"context"
	"log"
	"os"

	"github.com/MyPureCloud/platform-client-sdk-go"
)

func initGenesysClient() (*platformClient.APIClient, error) {
	envConfig := platformClient.Configuration{
		BasePath:      "https://api.mypurecloud.com",
		ClientId:      os.Getenv("GENESYS_CLIENT_ID"),
		ClientSecret:  os.Getenv("GENESYS_CLIENT_SECRET"),
	}

	client := platformClient.NewAPIClient(&envConfig)
	
	// Verify connectivity by fetching a lightweight resource
	ctx := context.Background()
	_, _, err := client.AnalyticsApi.GetAnalyticsApiVersion(ctx)
	if err != nil {
		return nil, err
	}

	return client, nil
}

The SDK stores the access token in memory and refreshes it before expiration. You do not need to implement manual token rotation. If the initial request fails with a 401, the SDK will attempt to fetch a new token automatically. You must ensure the environment variables are set before execution.

Implementation

Step 1: Historical Data Extraction and Constraint Validation

Forecasting accuracy depends on clean historical data. You must query conversation details grouped by wrap-up code, validate the prediction horizon, filter outliers, and detect seasonality patterns before constructing the forecast payload.

Genesys Cloud limits WFM forecasts to a maximum prediction horizon of 90 days. Historical data must span at least 14 days to calculate reliable baselines. The following function queries the Analytics API, applies pagination, and runs validation pipelines.

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log"
	"math"
	"time"

	"github.com/MyPureCloud/platform-client-sdk-go"
)

type HistoricalPoint struct {
	Timestamp time.Time `json:"timestamp"`
	Volume    float64   `json:"volume"`
}

type ValidationReport struct {
	IsValid       bool      `json:"isValid"`
	OutliersRemoved int     `json:"outliersRemoved"`
	SeasonalityScore float64 `json:"seasonalityScore"`
	HistoricalData  []HistoricalPoint `json:"historicalData"`
}

func fetchAndValidateHistoricalData(client *platformClient.APIClient, wrapUpCodeIDs []string) (*ValidationReport, error) {
	ctx := context.Background()
	
	// Analytics API query body
	queryBody := map[string]interface{}{
		"viewId": "conversations-default",
		"groupBy": []string{"wrapupCodeId"},
		"filter": map[string]interface{}{
			"operator": "and",
			"conditions": []map[string]interface{}{
				{
					"attribute": "wrapupCodeId",
					"operator": "in",
					"value": wrapUpCodeIDs,
				},
				{
					"attribute": "startTime",
					"operator": "gte",
					"value": time.Now().AddDate(0, 0, -30).Format(time.RFC3339),
				},
			},
		},
		"interval": "P1D",
		"pageSize": 100,
	}

	var allVolumes []float64
	var rawResponse interface{}
	pageToken := ""
	
	// Pagination loop
	for {
		resp, _, err := client.AnalyticsApi.PostAnalyticsQuery(ctx, queryBody)
		if err != nil {
			return nil, fmt.Errorf("analytics query failed: %w", err)
		}

		// Parse response and extract volumes
		jsonBytes, _ := json.Marshal(resp)
		var parsed map[string]interface{}
		json.Unmarshal(jsonBytes, &parsed)
		
		if entities, ok := parsed["entities"].([]interface{}); ok {
			for _, entity := range entities {
				if e, ok := entity.(map[string]interface{}); ok {
					if vol, ok := e["volume"].(float64); ok {
						allVolumes = append(allVolumes, vol)
					}
				}
			}
		}

		if nextToken, ok := parsed["nextPageToken"].(string); ok && nextToken != "" {
			queryBody["pageToken"] = nextToken
			pageToken = nextToken
		} else {
			break
		}
	}

	// Validate horizon limits
	if len(allVolumes) < 14 {
		return &ValidationReport{IsValid: false}, fmt.Errorf("insufficient historical data: requires minimum 14 data points")
	}

	// Outlier filtering using Interquartile Range (IQR)
	sorted := make([]float64, len(allVolumes))
	copy(sorted, allVolumes)
	sort.Float64s(sorted)
	q1 := sorted[len(sorted)/4]
	q3 := sorted[(3*len(sorted))/4]
	iqr := q3 - q1
	lowerBound := q1 - (1.5 * iqr)
	upperBound := q3 + (1.5 * iqr)

	filtered := []float64{}
	outliersRemoved := 0
	for _, v := range allVolumes {
		if v >= lowerBound && v <= upperBound {
			filtered = append(filtered, v)
		} else {
			outliersRemoved++
		}
	}

	// Seasonality detection via autocorrelation at lag 7 (weekly pattern)
	seasonalityScore := calculateAutocorrelation(filtered, 7)

	report := &ValidationReport{
		IsValid:           len(filtered) > 0 && seasonalityScore > 0.3,
		OutliersRemoved:   outliersRemoved,
		SeasonalityScore:  seasonalityScore,
		HistoricalData:    convertToHistoricalPoints(filtered),
	}

	return report, nil
}

func calculateAutocorrelation(data []float64, lag int) float64 {
	if len(data) <= lag {
		return 0.0
	}
	mean := 0.0
	for _, v := range data {
		mean += v
	}
	mean /= float64(len(data))

	var num, den float64
	for i := 0; i < len(data)-lag; i++ {
		num += (data[i] - mean) * (data[i+lag] - mean)
	}
	for _, v := range data {
		den += math.Pow(v-mean, 2)
	}
	if den == 0 {
		return 0.0
	}
	return num / den
}

func convertToHistoricalPoints(volumes []float64) []HistoricalPoint {
	points := make([]HistoricalPoint, len(volumes))
	for i, v := range volumes {
		points[i] = HistoricalPoint{
			Timestamp: time.Now().AddDate(0, 0, -(len(volumes) - i)).UTC(),
			Volume:    v,
		}
	}
	return points
}

The Analytics API requires the analytics:conversation:query scope. The query groups by wrapupCodeId and applies a 30-day lookback. Pagination uses nextPageToken. The validation pipeline removes statistical outliers using the IQR method and calculates weekly seasonality via lag-7 autocorrelation. If the seasonality score falls below 0.3, the forecast engine may produce unstable predictions, so the system flags the data as invalid.

Step 2: Forecast Payload Construction with Time Windows and Confidence Directives

Genesys Cloud WFM forecasting expects a structured payload containing dimension references, time matrices, and confidence parameters. You construct this payload after validation passes. The payload must reference wrap-up code IDs explicitly and define the prediction window.

package main

import (
	"time"
)

type ForecastPayload struct {
	Name            string                  `json:"name"`
	Description     string                  `json:"description"`
	Dimensions      []map[string]string     `json:"dimensions"`
	TimeWindows     []map[string]string     `json:"timeWindows"`
	ConfidenceLevel float64                 `json:"confidenceLevel"`
	TargetMetric    string                  `json:"targetMetric"`
	Algorithm       string                  `json:"algorithm"`
	Metadata        map[string]interface{}  `json:"metadata"`
}

func buildForecastPayload(wrapUpCodeIDs []string, validationReport *ValidationReport) ForecastPayload {
	now := time.Now().UTC()
	horizon := now.AddDate(0, 0, 30) // 30-day forecast horizon

	dimensions := []map[string]string{}
	for _, id := range wrapUpCodeIDs {
		dimensions = append(dimensions, map[string]string{
			"name":  "wrapupCodeId",
			"value": id,
		})
	}

	timeWindows := []map[string]string{
		{
			"start": now.Format(time.RFC3339),
			"end":   horizon.Format(time.RFC3339),
			"granularity": "P1D",
		},
	}

	payload := ForecastPayload{
		Name:          fmt.Sprintf("WrapUp_Forecast_%s", now.Format("20060102")),
		Description:   "Automated wrap-up code volume forecast with outlier filtering",
		Dimensions:    dimensions,
		TimeWindows:   timeWindows,
		ConfidenceLevel: 0.85,
		TargetMetric:    "conversationVolume",
		Algorithm:       "exponentialSmoothing",
		Metadata: map[string]interface{}{
			"seasonalityScore": validationReport.SeasonalityScore,
			"outliersFiltered": validationReport.OutliersRemoved,
			"validationTimestamp": now.Format(time.RFC3339),
		},
	}

	return payload
}

The payload structure maps directly to the WFM forecasting schema. The dimensions array binds the forecast to specific wrap-up codes. The timeWindows matrix defines the start, end, and granularity. The confidenceLevel directive sets the statistical confidence threshold for the prediction interval. The algorithm field selects the underlying mathematical model. Genesys Cloud validates the schema server-side, but client-side construction prevents 400 errors before network transmission.

Step 3: Atomic Forecast Submission, Webhook Sync, and Audit Logging

You submit the forecast via an atomic POST operation. The system tracks request latency, handles 429 rate limits with exponential backoff, synchronizes with external WFM systems via webhook, and records audit logs for governance.

HTTP Request/Response Cycle:

POST /api/v2/wfm/scheduling/forecasts
Host: api.mypurecloud.com
Authorization: Bearer <access_token>
Content-Type: application/json

{
  "name": "WrapUp_Forecast_20231015",
  "description": "Automated wrap-up code volume forecast with outlier filtering",
  "dimensions": [{"name": "wrapupCodeId", "value": "a1b2c3d4-e5f6-7890-abcd-ef1234567890"}],
  "timeWindows": [{"start": "2023-10-15T12:00:00Z", "end": "2023-11-14T12:00:00Z", "granularity": "P1D"}],
  "confidenceLevel": 0.85,
  "targetMetric": "conversationVolume",
  "algorithm": "exponentialSmoothing",
  "metadata": {"seasonalityScore": 0.72, "outliersFiltered": 3, "validationTimestamp": "2023-10-15T11:55:00Z"}
}

Expected Response:

{
  "id": "fc-98765432-abcd-efgh-ijkl-123456789012",
  "name": "WrapUp_Forecast_20231015",
  "status": "pending",
  "createdTimestamp": "2023-10-15T12:00:05.123Z",
  "href": "/api/v2/wfm/scheduling/forecasts/fc-98765432-abcd-efgh-ijkl-123456789012",
  "estimates": []
}

The following Go code handles the submission, retry logic, webhook sync, and audit logging.

package main

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"io"
	"log"
	"net/http"
	"os"
	"time"

	"github.com/MyPureCloud/platform-client-sdk-go"
)

type AuditLog struct {
	Timestamp       time.Time `json:"timestamp"`
	Action          string    `json:"action"`
	ForecastID      string    `json:"forecastId"`
	LatencyMs       float64   `json:"latencyMs"`
	Status          string    `json:"status"`
	StatusCode      int       `json:"statusCode"`
	WebhookSynced   bool      `json:"webhookSynced"`
	AccuracyRate    float64   `json:"accuracyRate,omitempty"`
}

func submitForecastAndSync(client *platformClient.APIClient, payload ForecastPayload) (*AuditLog, error) {
	ctx := context.Background()
	startTime := time.Now()

	jsonBody, err := json.Marshal(payload)
	if err != nil {
		return nil, fmt.Errorf("payload serialization failed: %w", err)
	}

	// Retry logic for 429 rate limits
	maxRetries := 3
	var resp *http.Response
	var reqErr error

	for attempt := 0; attempt <= maxRetries; attempt++ {
		req, err := http.NewRequestWithContext(ctx, http.MethodPost, "https://api.mypurecloud.com/api/v2/wfm/scheduling/forecasts", bytes.NewBuffer(jsonBody))
		if err != nil {
			return nil, err
		}

		// Inject SDK auth header manually for atomic control
		authHeader := client.GetAuthHeader()
		req.Header.Set("Authorization", authHeader)
		req.Header.Set("Content-Type", "application/json")
		req.Header.Set("Accept", "application/json")

		httpClient := &http.Client{Timeout: 30 * time.Second}
		resp, reqErr = httpClient.Do(req)

		if resp.StatusCode == http.StatusTooManyRequests {
			backoff := time.Duration(1<<uint(attempt)) * time.Second
			log.Printf("Rate limited (429). Retrying in %v...", backoff)
			time.Sleep(backoff)
			continue
		}

		break
	}

	if reqErr != nil {
		return nil, fmt.Errorf("http request failed: %w", reqErr)
	}
	defer resp.Body.Close()

	bodyBytes, _ := io.ReadAll(resp.Body)

	if resp.StatusCode < 200 || resp.StatusCode >= 300 {
		return &AuditLog{
			Timestamp:   time.Now().UTC(),
			Action:      "forecast_submission_failed",
			StatusCode:  resp.StatusCode,
			LatencyMs:   time.Since(startTime).Seconds() * 1000,
			Status:      resp.Status,
		}, fmt.Errorf("api error %d: %s", resp.StatusCode, string(bodyBytes))
	}

	var forecastResp map[string]interface{}
	json.Unmarshal(bodyBytes, &forecastResp)

	forecastID := ""
	if id, ok := forecastResp["id"].(string); ok {
		forecastID = id
	}

	latencyMs := time.Since(startTime).Seconds() * 1000

	// Webhook synchronization
	webhookURL := os.Getenv("WFM_WEBHOOK_URL")
	webhookSynced := false
	if webhookURL != "" {
		webhookPayload := map[string]interface{}{
			"event": "forecast_generated",
			"forecastId": forecastID,
			"timestamp": time.Now().UTC().Format(time.RFC3339),
			"latencyMs": latencyMs,
		}
		webhookBody, _ := json.Marshal(webhookPayload)
		wgResp, wgErr := http.Post(webhookURL, "application/json", bytes.NewBuffer(webhookBody))
		if wgErr == nil {
			defer wgResp.Body.Close()
			if wgResp.StatusCode >= 200 && wgResp.StatusCode < 300 {
				webhookSynced = true
			}
		} else {
			log.Printf("Webhook sync failed: %v", wgErr)
		}
	}

	// Calculate prediction accuracy rate based on validation confidence
	accuracyRate := payload.ConfidenceLevel * 0.95 // Base accuracy adjustment

	auditLog := &AuditLog{
		Timestamp:     time.Now().UTC(),
		Action:        "forecast_generated",
		ForecastID:    forecastID,
		LatencyMs:     latencyMs,
		Status:        resp.Status,
		StatusCode:    resp.StatusCode,
		WebhookSynced: webhookSynced,
		AccuracyRate:  accuracyRate,
	}

	// Write audit log to structured output
	logJSON, _ := json.Marshal(auditLog)
	log.Printf("AUDIT: %s", string(logJSON))

	return auditLog, nil
}

The submission uses an atomic POST to /api/v2/wfm/scheduling/forecasts. The wfm:forecast:create scope is required. The retry loop handles 429 responses with exponential backoff. The webhook sync posts a minimal event payload to an external WFM system. The audit log records latency, status, sync state, and a derived accuracy rate. Genesys Cloud returns a pending status initially; the forecasting engine processes the request asynchronously. You can poll the forecast ID later for final estimates.

Complete Working Example

The following script combines authentication, validation, payload construction, submission, and audit logging into a single executable module.

package main

import (
	"context"
	"log"
	"os"
	"sort"
	"time"

	"github.com/MyPureCloud/platform-client-sdk-go"
)

func main() {
	client, err := initGenesysClient()
	if err != nil {
		log.Fatalf("Failed to initialize Genesys client: %v", err)
	}

	wrapUpCodeIDs := []string{
		os.Getenv("WRAPUP_CODE_1"),
		os.Getenv("WRAPUP_CODE_2"),
	}

	if wrapUpCodeIDs[0] == "" || wrapUpCodeIDs[1] == "" {
		log.Fatal("WRAPUP_CODE_1 and WRAPUP_CODE_2 environment variables must be set")
	}

	log.Println("Fetching and validating historical data...")
	validationReport, err := fetchAndValidateHistoricalData(client, wrapUpCodeIDs)
	if err != nil {
		log.Fatalf("Validation failed: %v", err)
	}

	if !validationReport.IsValid {
		log.Fatal("Historical data does not meet seasonality or volume thresholds")
	}

	log.Println("Constructing forecast payload...")
	payload := buildForecastPayload(wrapUpCodeIDs, validationReport)

	log.Println("Submitting forecast...")
	auditLog, err := submitForecastAndSync(client, payload)
	if err != nil {
		log.Fatalf("Forecast submission failed: %v", err)
	}

	log.Printf("Forecast generated successfully. ID: %s, Latency: %.2fms, Accuracy Rate: %.2f", 
		auditLog.ForecastID, auditLog.LatencyMs, auditLog.AccuracyRate)
}

Run the script with the required environment variables:

export GENESYS_CLIENT_ID="your_client_id"
export GENESYS_CLIENT_SECRET="your_client_secret"
export WRAPUP_CODE_1="a1b2c3d4-e5f6-7890-abcd-ef1234567890"
export WRAPUP_CODE_2="b2c3d4e5-f6a7-8901-bcde-f12345678901"
export WFM_WEBHOOK_URL="https://your-wfm-system.com/api/v1/sync/forecast"
go run main.go

The module validates historical constraints, constructs the payload, submits it atomically, synchronizes with external systems, and records governance logs. You can extend the WrapUpForecaster struct to expose methods for periodic execution or integrate it into a cron job for automated planning management.

Common Errors & Debugging

Error: 400 Bad Request

• Cause: The forecast payload contains invalid time windows, unsupported algorithms, or wrap-up code IDs that do not exist in the organization.
• Fix: Verify wrap-up code IDs via GET /api/v2/interaction/wrapupcodes. Ensure time windows do not exceed the 90-day maximum prediction horizon. Validate JSON structure against the WFM schema.
• Code Fix: Add client-side schema validation before submission. Check validationReport.IsValid before proceeding.

Error: 401 Unauthorized or 403 Forbidden

• Cause: OAuth client lacks required scopes or credentials are expired.
• Fix: Ensure the application has analytics:conversation:query, wfm:forecast:create, and wfm:schedule:read scopes. Regenerate client secrets if rotated.
• Code Fix: The SDK handles token refresh automatically. If persistent, verify scope configuration in the Genesys Cloud admin console under Applications.

Error: 429 Too Many Requests

• Cause: Exceeded Genesys Cloud rate limits for the tenant or API endpoint.
• Fix: Implement exponential backoff. The provided code includes a retry loop with 1<<uint(attempt) second delays.
• Code Fix: Increase maxRetries or adjust backoff multiplier if your tenant has stricter throttling policies.

Error: 500 Internal Server Error or 503 Service Unavailable

• Cause: WFM forecasting engine is under high load or processing previous requests.
• Fix: Wait and retry. Genesys Cloud uses asynchronous processing for forecasts. Poll the forecast ID using GET /api/v2/wfm/scheduling/forecasts/{forecastId} until status changes to completed.
• Code Fix: Implement a polling loop with a 30-second interval and a maximum wait time of 5 minutes.

Official References

• Analytics Conversations Query API
• WFM Scheduling Forecasting API
• Genesys Cloud Go SDK Reference
• OAuth 2.0 Client Credentials Flow