Podium to SurveySparrow Ticket Management Migration Guide

Migrating from Podium to SurveySparrow Ticket Management means transforming a messaging-first customer interaction platform into a feedback-driven ticket system. There is no built-in importer, no pre-built connector, and no CSV upload that handles the full scope. Every migration requires extracting data through Podium's REST API (v4) or FTP export, transforming it to match SurveySparrow's ticket and contact schema, and loading it through SurveySparrow's REST API (v3). (docs.podium.com)

Podium stores data as continuous conversations across SMS, webchat, and phone channels, organized by contacts and locations. SurveySparrow Ticket Management is built around discrete tickets with subject, description, priority, status, assignee, and comments — typically generated from survey responses, NPS detractors, or form submissions. The structural gap between these platforms is substantial, and every migration decision requires deliberate mapping.

This guide covers the full technical path: data model differences, object and field-level mapping, viable migration methods, API constraints on both sides, architecture decisions, compliance considerations, and the edge cases that cause silent data loss.

For migrations heading the other direction or evaluating SurveySparrow's data model against other platforms, see our SurveySparrow Ticket Management to Front Migration Guide and LiveChat to SurveySparrow Ticket Management Migration Guide.

Why Teams Move from Podium to SurveySparrow Ticket Management

This migration is a platform category shift, not an upgrade within the same category. Teams migrate for these reasons:

• Feedback loop closure. Podium captures customer interactions but doesn't structure them into trackable, resolvable tickets. SurveySparrow creates actionable tickets from survey responses and routes them through SLA-tracked workflows.
• Cost consolidation. Teams already using SurveySparrow for NPS, CSAT, or employee surveys want a unified platform for follow-up actions, rather than paying separately for Podium's messaging stack.
• Structured support operations. Podium conversations are open-ended messaging threads. SurveySparrow tickets have discrete lifecycle stages (Open → In Progress → Resolved → Closed), priority levels, assignees, and due dates — giving operations leads visibility that Podium's inbox does not provide.
• Survey-driven ticket creation. SurveySparrow's workflow engine auto-creates tickets from detractor scores or specific survey responses. Teams moving toward a feedback-first operating model find this a better fit than Podium's text-first approach.

The core architectural difference: in Podium, a customer has a single continuous conversation thread per channel. In SurveySparrow, a customer can have multiple isolated tickets open simultaneously for different issues.

Architecture Differences: Podium vs SurveySparrow Ticket Management

Before mapping fields, understand how these two data models differ.

Podium is a customer interaction platform built around SMS, webchat, reviews, payments, and marketing campaigns for local businesses. Its core data objects are:

• Contacts — customer records with name, phone, email, address, tags, and up to 8 custom fields
• Conversations — threaded message exchanges across channels (SMS, webchat, email, phone), each assigned to a user at a location
• Locations — physical business locations that organize conversations, reviews, and campaigns
• Reviews — Google/Facebook review data collected through review invites
• Payments — transaction records processed through Podium Payments
• Campaigns — bulk messaging sequences

SurveySparrow Ticket Management is a feedback-driven ticketing layer inside SurveySparrow's survey platform. Its core objects are:

• Tickets — discrete units of work with subject, description, priority, status, assignee, due date, and custom fields
• Contacts — audience records with email, phone, name, and custom properties
• Contact Lists — segmented groups of contacts for survey targeting
• Ticket Comments — threaded responses on tickets (internal notes and customer-facing replies)
• Users/Teams — agents and agent groups that handle ticket assignments

Data Model and Object Mapping

This is the most critical phase. Podium and SurveySparrow do not speak the same language.

Object-Level Mapping

Field-Level Mapping: Contacts

Field-Level Mapping: Conversations → Tickets

For a reusable worksheet for field mapping and status normalization, see our data mapping guide.

Migration Approaches

CSV Export + API Import (Hybrid)

How it works: Export contacts from Podium as CSV via the Contacts tab. Clean the file. Import contacts into SurveySparrow via CSV import or Contacts API. Create tickets via SurveySparrow's Tickets API (v3) — there is no documented self-serve CSV import for tickets. (podium.com)

When to use: Small datasets (under 5K contacts). You only need contacts and a subset of conversation history. Limited engineering bandwidth.

Pros: Low engineering effort for contacts. SurveySparrow supports CSV contact import natively.

Cons: Podium's contact CSV export only includes name, phone, email, status, address, tags, and date added — no conversation history. Tickets must still go through the API. Loses all relational context between contacts and conversations. Attachments and inline images are lost.

Complexity: Medium | Scalability: Low

Full API-Based Migration

How it works: Authenticate with Podium's REST API via OAuth (developer.podium.com). Extract contacts, conversations, and messages programmatically. Transform in a staging database. Create contacts in SurveySparrow via POST /v3/contacts, tickets via POST /v3/tickets or the batch endpoint, and add comments via the Ticket Comments API. Rebuild relationships using ID mapping tables. (docs.podium.com)

When to use: Mid-size to large datasets. You need full conversation history with comments. You want to preserve relational context.

Pros: Full control over data transformation. Preserves conversation threading as ticket comments. Handles custom fields, tags, and metadata. SurveySparrow's batch ticket creation endpoint reduces API calls.

Cons: Requires Podium developer account approval (allow 2–5 business days). Rate limits on both sides constrain throughput. Significant engineering effort — budget 80–160 hours for 10K–50K conversations.

Complexity: High | Scalability: High

FTP Export + Custom ETL Pipeline

How it works: Request FTP access from Podium (requires Account Owner). Download raw data tables. Based on observed FTP exports, typical tables include:

Load into a staging database (PostgreSQL, BigQuery). Write transformation queries to map Podium's schema to SurveySparrow's. Push transformed data to SurveySparrow via API.

When to use: Large datasets (50K+ conversations). Teams with data engineering resources. When you need a full audit trail or the data must also feed analytics, archives, or a CRM.

Pros: Access to complete raw data including fields not available via API. SQL-based transformations scale well. Staging database provides a validation checkpoint. Repeatable for incremental migrations.

Cons: FTP access requires manual Podium support request (typical turnaround: 1–3 business days). Column names in FTP files may differ from API field names — validate against actual exports. Still constrained by SurveySparrow's API rate limits on the load side.

Complexity: High | Scalability: Very High

Middleware Platforms (Zapier, Make)

How it works: Set up triggers in Podium (e.g., new conversation, new contact). Map fields in the middleware platform. Push to SurveySparrow as new contacts or tickets. Zapier lists both Podium and SurveySparrow apps, and Make's HTTP module can call arbitrary APIs. (zapier.com)

When to use: Ongoing sync of new data after the primary migration is complete. Not suitable for historical migration.

Pros: No code required. Fast to set up for forward-looking sync.

Cons: Cannot migrate historical data. Limited field mapping flexibility. Task-based pricing adds up fast at scale (Zapier charges per task; a single conversation with 10 messages = 11 tasks). No batch operations. Weak error handling and auditability.

Complexity: Low | Scalability: Very Low

Managed Migration Service

How it works: A migration partner scopes the project, handles extraction, transformation, dry runs, cutover, and validation. You review and approve.

When to use: When you can't allocate engineering time. Complex datasets with multi-location data, deduplication needs, or compliance requirements. When accuracy matters more than cost.

Pros: No internal engineering burden. Provider handles API constraints, edge cases, and error recovery. Typically includes validation and rollback planning.

Cons: Higher direct cost (typical range for this migration path: $2,000–$15,000 depending on volume, complexity, and attachment handling). Requires trust in the provider's technical capability. Ask how source IDs, retries, validation reports, and rollback manifests are stored.

Complexity: Low (for your team) | Scalability: High

Approach Comparison

Recommendations by scenario:

• Small business (<5K contacts, history not critical): Preload contacts via CSV and start fresh in SurveySparrow.
• Mid-market (5K–50K conversations): Full API migration. Budget 80–160 engineering hours.
• Enterprise (50K+ conversations, multi-location): FTP + ETL pipeline or managed service. You need manifests, retries, dry runs, and relationship rebuilds.
• Ongoing sync: Run the bulk migration first, then add webhook or middleware-based delta sync.
• Low engineering bandwidth: Use a managed service.

Pre-Migration Planning

Do not write code or map fields until you have audited the source data.

Data Audit Checklist

• Contacts: Total count. How many have email addresses? How many are phone-only?
• Conversations: Total count by channel (SMS, webchat, email, phone). Average messages per conversation.
• Locations: How many? Are conversations spread evenly or concentrated?
• Tags: What tags exist? Are they used consistently?
• Custom fields: Which of the 8 custom contact fields are populated? What data types?
• Reviews: Count. These have no SurveySparrow equivalent — archive separately.
• Payments: Count. Transaction history must be archived outside SurveySparrow.
• Attachments: Do conversations contain images, PDFs, or files? Podium attachment URLs expire after 7 days — this affects extraction timing. (docs.podium.com)
• Data residency: Which Podium data center holds your data? Which SurveySparrow data center will you use? (See Compliance Considerations below.)

Define Migration Scope

Not everything in Podium should migrate:

• Migrate: Active contacts with email, conversations from the last 12–24 months, open/unresolved conversations.
• Archive: Closed conversations older than 24 months, all reviews, all payment records, inactive contacts without email.
• Discard: Test contacts, spam conversations, duplicate records.

Choose a Cutover Strategy

For most Podium-to-SurveySparrow migrations, a phased approach by location works well. Migrate one location's data first, validate, then proceed. A big-bang weekend cutover is viable for smaller datasets where you want to avoid split-brain scenarios with agents working in both systems.

Compliance Considerations

Migrations between platforms involve data in transit. Address these before extracting:

• Data residency: SurveySparrow operates multiple data centers (US, EU, AP). Your SurveySparrow account is assigned to a specific data center, which determines both your API base URL and where customer data is stored. Confirm your data center assignment with SurveySparrow support before beginning extraction. If your Podium data is in the US and your SurveySparrow instance is in the EU (or vice versa), you're performing a cross-border data transfer.
• GDPR: If you hold EU customer data, the migration constitutes data processing. Ensure your Data Processing Agreements (DPAs) with both Podium and SurveySparrow cover migration activities. Staging databases and intermediate storage (S3, local disk) are additional processing locations that must be documented.
• Data minimization: Migrate only data you have a legitimate business purpose to retain. Conversations older than your retention policy should be archived or deleted, not migrated to a new platform.
• Right to erasure: If any contacts have pending deletion requests under GDPR Article 17 or CCPA, exclude them from the migration. Do not create new records in SurveySparrow for contacts you're obligated to delete.
• Encryption in transit: Both Podium and SurveySparrow APIs use HTTPS/TLS. Ensure your staging database and any intermediate file storage are encrypted at rest. FTP exports from Podium should be requested over SFTP where available.

Migration Architecture

Data Flow: Extract → Transform → Load

┌─────────────┐     ┌──────────────────┐     ┌─────────────────────┐
│   Podium    │────▶│  Staging Layer   │────▶│    SurveySparrow    │
│  REST API   │     │  (Transform +    │     │    Tickets API v3   │
│  or FTP     │     │   Validate)      │     │    Contacts API     │
└─────────────┘     └──────────────────┘     └─────────────────────┘

Do not pipe extracted data directly to SurveySparrow. If the load fails, you don't want to re-extract from Podium. Stage raw JSON and downloaded attachments in a local database or secure storage (S3, Blob).

Podium API: Extraction Constraints

• Authentication: OAuth 2.0 via developer.podium.com. Developer account requires approval (allow 2–5 business days). (docs.podium.com)
• Base URL: https://api.podium.com/v4/
• Rate limits: Per-endpoint rate limits apply. Podium returns 429 with a Retry-After header (supports both integer seconds and HTTP-date format). A 24-hour envelope quota also applies silently — Podium does not return a warning header before you hit it, and does not document the exact threshold publicly. Monitor your daily call count.
• Pagination: Cursor-based. Podium does not return a total record count in paginated responses — you cannot calculate a progress percentage during extraction. Track pages processed instead.
• Key endpoints: GET /v4/contacts, GET /v4/conversations, and conversation messages per conversation UID.

SurveySparrow API: Loading Constraints

• Authentication: OAuth 2.0 or Bearer token (generated in Settings → Apps & Integrations).
• Base URL: Depends on data center. Known base URLs include https://api.surveysparrow.com (US), https://eu-api.surveysparrow.com (EU), and region-specific variants. Confirm your data center with SurveySparrow support — using the wrong base URL will return authentication errors.
• Rate limits by plan tier:

These are observed values, not officially published tier-by-tier limits. SurveySparrow's API documentation does not enumerate plan-specific limits — confirm with SurveySparrow support before planning your migration timeline.

• Batch endpoints: POST /v3/tickets/batch for tickets and POST /v3/contacts/batch for contacts. In testing, batch endpoints accepted up to 50 records per request reliably; larger batches returned intermittent 413 errors. Start with batches of 25 and increase incrementally. Comments have no batch endpoint; comment replay is typically the longest pole of the migration. (developers.surveysparrow.com)
• Custom fields: Must be created in SurveySparrow before migration. Custom ticket fields can be created through the UI (Settings → Ticket Management → Ticket Fields) but the API does not expose a custom field creation endpoint — this is a manual pre-migration step. API calls referencing non-existent fields return a 422 error. (developers.surveysparrow.com)
• Error response format: SurveySparrow returns JSON errors in this structure:

{
  "error": {
    "status": 422,
    "message": "Validation failed",
    "details": [
      {"field": "subject", "message": "is required"},
      {"field": "custom_fields.channel_type", "message": "field does not exist"}
    ]
  }
}

Log the full error response body for every failed request. The details array identifies which specific fields caused the rejection.

• Related tickets: SurveySparrow allows one parent per child ticket, multiple children per parent, and no child-of-child trees. (support.surveysparrow.com)
• Webhook events: SurveySparrow fires webhook events on ticket creation, update, and status change. If you have webhooks configured during migration, they will fire for every migrated ticket. Disable webhooks before bulk loading and re-enable after migration completes to avoid flooding downstream systems.

Handling Rate Limits at Scale

With SurveySparrow capped at roughly 120 calls/hour on Business plans, a dataset of 20K contacts and 30K conversations will take significant calendar time without batching.

Throughput estimation for a Business-tier plan:

Comment replay dominates the timeline. For large datasets, consider: migrating only the most recent N comments per conversation, concatenating older messages into the ticket description, or requesting a temporary rate limit increase from SurveySparrow.

Build your pipeline with exponential backoff and jitter:

import random
import time
 
def backoff_retry(func, max_retries=5):
    for attempt in range(max_retries):
        result = func()
        if result.status_code != 429:
            return result
        base_delay = min(2 ** attempt, 120)
        jitter = random.uniform(0, base_delay * 0.5)
        time.sleep(base_delay + jitter)
    raise Exception("Max retries exceeded")

Never retry on a fixed delay. Throttle conservatively at 70–80% of documented limits. Hitting the ceiling during a migration is worse than being 20% slower.

Step-by-Step Migration Process

Step 1: Extract Data from Podium

Contacts:

import requests
import time
 
PODIUM_BASE = "https://api.podium.com/v4"
HEADERS = {"Authorization": "Bearer <podium_access_token>"}
 
def extract_contacts():
    contacts = []
    cursor = None
    page = 0
    while True:
        params = {"limit": 100}
        if cursor:
            params["cursor"] = cursor
        resp = requests.get(f"{PODIUM_BASE}/contacts", headers=HEADERS, params=params)
        if resp.status_code == 429:
            retry_after = resp.headers.get("Retry-After", "60")
            # Handle both integer seconds and HTTP-date format
            try:
                wait = int(retry_after)
            except ValueError:
                wait = 60  # Fallback for HTTP-date format
            time.sleep(wait)
            continue
        resp.raise_for_status()
        data = resp.json()
        contacts.extend(data.get("data", []))
        page += 1
        print(f"Extracted page {page}, total contacts so far: {len(contacts)}")
        cursor = data.get("metadata", {}).get("cursor")
        if not cursor:
            break
        time.sleep(4)  # Throttle conservatively
    return contacts

Conversations:

def extract_conversations(location_uid):
    conversations = []
    cursor = None
    while True:
        params = {"locationUid": location_uid, "limit": 50}
        if cursor:
            params["cursor"] = cursor
        resp = requests.get(f"{PODIUM_BASE}/conversations", headers=HEADERS, params=params)
        if resp.status_code == 429:
            retry_after = resp.headers.get("Retry-After", "60")
            try:
                wait = int(retry_after)
            except ValueError:
                wait = 60
            time.sleep(wait)
            continue
        resp.raise_for_status()
        data = resp.json()
        conversations.extend(data.get("data", []))
        cursor = data.get("metadata", {}).get("cursor")
        if not cursor:
            break
        time.sleep(3)
    return conversations

Download attachments immediately. Podium message attachment URLs expire after 7 days. If you delay extraction, you lose the files permanently. Download to local storage or S3 during the extraction phase and replace URLs with local file paths in your staging data. (docs.podium.com)

Alternative: FTP export. Request FTP access from Podium (Account Owner required). You'll receive flat files with tables including contacts, conversations, conversation_items, and feedback_interactions. See the FTP schema table above for observed column structures.

Step 2: Transform Data

Contact transformation:

def transform_contact(podium_contact):
    email = podium_contact.get("email")
    if not email:
        phone = podium_contact.get("phone", "unknown")
        email = f"{phone.replace('+', '')}@placeholder.migration"
    
    return {
        "email": email,
        "full_name": podium_contact.get("name", ""),
        "phone": podium_contact.get("phone", ""),
        "custom_properties": {
            "podium_id": podium_contact.get("uid"),
            "podium_status": podium_contact.get("contactStatus"),
            "podium_tags": ",".join(podium_contact.get("tags", [])),
            "podium_address": podium_contact.get("address", ""),
        }
    }

Conversation → Ticket transformation:

def transform_conversation_to_ticket(conversation, messages, user_map):
    first_message = messages[0] if messages else {}
    body = first_message.get("body", "")
    contact_name = conversation.get("contactName", "Unknown")
    channel = conversation.get("channelType", "unknown")
    date = conversation.get("insertedAt", "")[:10]
    
    # Generate a meaningful subject since Podium has none
    subject = body[:200] if body else f"{channel.upper()} - {contact_name} - {date}"
    
    podium_assignee = conversation.get("assignedUserUid")
    ss_assignee = user_map.get(podium_assignee)  # Pre-built lookup table
    
    return {
        "subject": subject,
        "description": body or "No message body",
        "priority": "Medium",  # Podium has no priority — set default
        "status": map_status(conversation),
        "assignee_id": ss_assignee,
        "created_at": conversation.get("insertedAt"),  # Preserve original timestamp
        "custom_fields": {
            "channel_type": channel,
            "podium_conversation_id": conversation.get("uid"),
            "location": conversation.get("locationName"),
            "inbound_initiated": conversation.get("isInboundInitiated"),
        }
    }
 
def map_status(conversation):
    # Podium has no formal status — infer from state flags.
    # Validate this mapping against your actual data before running at scale.
    if conversation.get("isArchived"):
        return "Closed"
    if conversation.get("isUnread"):
        return "Open"
    return "Resolved"

Step 3: Load into SurveySparrow

Load contacts first, then tickets, then comments. This order matters — tickets need contact IDs, and comments need ticket IDs.

SS_BASE = "https://api.surveysparrow.com/v3"  # Confirm data center
SS_HEADERS = {"Authorization": "Bearer <surveysparrow_token>", "Content-Type": "application/json"}
 
def create_ss_contact(contact_data, id_map, podium_uid):
    # Idempotency check: skip if already created
    if podium_uid in id_map:
        return id_map[podium_uid]
    
    resp = requests.post(f"{SS_BASE}/contacts", headers=SS_HEADERS, json=contact_data)
    if resp.status_code == 429:
        time.sleep(60)
        return create_ss_contact(contact_data, id_map, podium_uid)
    if resp.status_code == 422:
        # Log validation error with details
        print(f"Validation failed for {podium_uid}: {resp.json()}")
        return None
    resp.raise_for_status()
    ss_id = resp.json().get("id")
    id_map[podium_uid] = ss_id  # Store mapping for rollback and linking
    return ss_id

Create tickets in batch where possible to reduce API call count:

def create_tickets_batch(tickets, batch_size=25):
    resp = requests.post(
        f"{SS_BASE}/tickets/batch",
        headers=SS_HEADERS,
        json={"tickets": tickets[:batch_size]}
    )
    if resp.status_code == 200:
        return resp.json().get("batch_token")
    if resp.status_code == 413:
        # Batch too large — halve and retry
        mid = batch_size // 2
        if mid < 1:
            raise Exception("Single ticket too large for API")
        token1 = create_tickets_batch(tickets[:mid], mid)
        token2 = create_tickets_batch(tickets[mid:batch_size], mid)
        return [token1, token2]
    return None
 
def check_batch_status(token):
    resp = requests.get(f"{SS_BASE}/tickets/batch/status/{token}", headers=SS_HEADERS)
    return resp.json()

Store the returned SurveySparrow contact and ticket IDs in a mapping table as you go. This is your rollback safety net and your validation tool.

Step 4: Add Ticket Comments (Message History)

For each migrated ticket, replay the conversation messages as comments:

def add_ticket_comments(ticket_id, messages, dead_letter_queue):
    for msg in messages[1:]:  # Skip first message — already used as description
        body = msg.get("body", "")
        if not body or not body.strip():
            dead_letter_queue.append({
                "ticket_id": ticket_id,
                "message_uid": msg.get("uid"),
                "reason": "empty_body"
            })
            continue
        
        comment = {
            "body": body,
            "is_private": msg.get("isInternal", False),
        }
        resp = requests.post(
            f"{SS_BASE}/tickets/{ticket_id}/comments",
            headers=SS_HEADERS, json=comment
        )
        if resp.status_code == 429:
            time.sleep(60)
            # Retry only the current message
            resp = requests.post(
                f"{SS_BASE}/tickets/{ticket_id}/comments",
                headers=SS_HEADERS, json=comment
            )
        if resp.status_code >= 400:
            dead_letter_queue.append({
                "ticket_id": ticket_id,
                "message_uid": msg.get("uid"),
                "reason": f"http_{resp.status_code}",
                "response": resp.text
            })
            continue
        time.sleep(1)  # Throttle within rate limits

Comments have no batch endpoint. This is the slowest part of the migration. For a 30K-conversation dataset with an average of 5 messages each, expect 120K+ individual API calls for comments alone. Budget accordingly — see the throughput estimation table above.

Step 5: Validate

Run validation checks after each batch, not just at the end. See Validation and Testing below.

Edge Cases and Challenges

Phone-Only Contacts

Podium is SMS-first. Many contacts have only a phone number — no email. SurveySparrow's CSV importer requires email; the API may allow email-less contacts in specific configurations, but do not rely on this. (support.surveysparrow.com) Options:

• Enrich pre-migration using a data enrichment service (Clearbit, Apollo, ZoomInfo). Typical match rates on US phone numbers: 30–50%.
• Generate placeholder emails (e.g., 15551234567@placeholder.migration) — mark with a custom property email_placeholder: true for cleanup post-migration.
• Skip contacts without email if they don't need tickets in SurveySparrow.

Multi-Location Data

Podium organizes everything by Location. SurveySparrow has no Location concept. If you need location-based segmentation:

• Create a custom ticket field called "Location" (type: dropdown) and populate it during migration.
• Create separate Contact Lists per location for survey targeting.
• Map Podium Locations to SurveySparrow Teams if your operating model assigns agents by location.

Conversation Threading → Flat Comments

Podium conversations are continuous threads that may span weeks or months. Long conversations (50+ messages) can become unwieldy as ticket comment threads. Consider:

• Migrating only the last N messages per conversation (e.g., last 20).
• Chunking long conversations by time gaps (e.g., >7 days of silence creates a new ticket). This preserves context while keeping individual tickets manageable.
• Migrating the entire thread as one closed ticket for archival purposes.
• Concatenating all messages into the ticket description with timestamps for a compact record:

def concatenate_messages(messages):
    """For archival tickets, combine all messages into a single description."""
    lines = []
    for msg in messages:
        direction = "→" if msg.get("direction") == "outbound" else "←"
        timestamp = msg.get("insertedAt", "")[:19]
        body = msg.get("body", "[no content]")
        lines.append(f"[{timestamp}] {direction} {body}")
    return "\n\n".join(lines)

Duplicate Records

Podium contacts can exist at multiple locations. Reused phone numbers and merged contacts can create requester drift if you key only on email. (docs.podium.com) SurveySparrow deduplicates by email. Run deduplication before loading:

• Group contacts by email.
• Merge tags and custom fields from duplicates (concatenate, don't overwrite).
• Keep the most recently active record as primary.
• Log all merge decisions for audit purposes.

Attachments

Podium conversations may contain images, PDFs, or files sent via MMS or webchat.

• Attachment URLs expire after 7 days. Download files during extraction, not later. (docs.podium.com)
• SurveySparrow accepts only pdf, png, jpeg, mp3, csv, and wav attachments, up to 15 MB. Files outside these types or over this limit will be rejected. Common unsupported types from Podium: .gif, .heic (iPhone photos), .mp4 (video MMS).
• MMS images are often low-resolution — evaluate whether they're worth migrating.
• For rejected attachments, append a plain-text note to the ticket comment: " [Attachment not migrated: {filename}, {size}MB, type: {filetype}]".

Agent Attribution

If an agent no longer works at your company, their SurveySparrow account might not exist. Map inactive Podium agents to a generic "Legacy System" user in SurveySparrow to prevent API validation errors. Create this user before migration begins.

Timestamp Preservation

SurveySparrow's API may default the ticket creation date to the moment the API call is made. Explicitly pass the historical created_at timestamp in your payload. Verify in a test migration that backdating works for your SurveySparrow plan and tier — some plans may not support setting historical creation dates via API.

API Failures and Retries

• Podium returns 429 with a Retry-After header in both integer-seconds and HTTP-date format — parse both. When the 24-hour envelope quota is hit, the Retry-After value may be very large (thousands of seconds).
• Build an idempotency layer: store Podium UID → SurveySparrow ID mappings as you go, so you can resume without duplicating records.
• Use a dead-letter queue for failed records so one bad attachment doesn't poison an entire ticket batch.
• Implement checkpoint files: after every N records (e.g., every 100), write the current cursor/offset to disk so the pipeline can resume from the last checkpoint after a crash.

Limitations and Constraints

Be direct about what will not map:

SurveySparrow's ticket schema is more rigid than Podium's flexible messaging objects. Expect to flatten certain metadata into custom fields. SurveySparrow does not expose a general custom-object framework for Ticket Management — custom ticket fields and contact properties are your only extension points. (developers.surveysparrow.com)

Validation and Testing

Record Count Reconciliation

Run count comparisons for every batch:

Podium contacts exported:      12,450
SurveySparrow contacts created: 11,830  (620 skipped — no email, logged to dead_letter.json)

Podium conversations exported:  8,200
SurveySparrow tickets created:  8,200   ✓

Total messages extracted:       45,000
Ticket comments created:        44,850  (150 failed — 142 empty body, 8 HTTP 500, logged)

Log every skipped or failed record with the reason. Acceptable loss thresholds: <1% for contacts (excluding intentional skips), <0.5% for tickets, <2% for comments.

Field-Level Validation

• Sample 5% of records (minimum 100) and compare field-by-field between source and target.
• Verify custom fields populated correctly (especially multiselect and dropdown values — check for case sensitivity mismatches).
• Check that ticket assignees match the intended user mapping.
• Confirm timestamps are in the correct timezone (Podium stores UTC; verify SurveySparrow's handling).
• Verify subject lines aren't truncated mid-word (truncate at word boundaries, not character count).

User Acceptance Testing

1. Migrate a single location's data first as a pilot.
2. Have 2–3 agents review their assigned tickets in SurveySparrow.
3. Verify conversation history is readable as ticket comments.
4. Confirm ticket workflows and SLAs trigger correctly on migrated data.
5. Test that search works for migrated tickets (custom field values, contact names).
6. Sign off before proceeding to full migration.

Rollback Planning

SurveySparrow's API supports DELETE /v3/tickets/{id} and DELETE /v3/contacts/{id}. If validation fails:

• Delete all created tickets using the ID mapping table.
• Delete created contacts.
• Fix the transformation logic.
• Re-run.

Store all ID mappings (Podium UID → SurveySparrow ID) in a persistent store throughout the migration. This is non-negotiable. Use SQLite for simplicity or PostgreSQL for concurrent access:

CREATE TABLE migration_manifest (
    podium_uid TEXT PRIMARY KEY,
    ss_id INTEGER,
    object_type TEXT,        -- 'contact', 'ticket', 'comment'
    payload_hash TEXT,       -- SHA-256 of the request payload
    http_status INTEGER,
    retry_count INTEGER DEFAULT 0,
    attachment_checksums TEXT, -- JSON array of MD5 checksums
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    error_detail TEXT
);

SurveySparrow can export ticket data as XLSX, JSON, or filtered CSV for reconciliation. Oversized CSV exports may split into multiple files. (support.surveysparrow.com)

For a reusable QA checklist, see our post-migration QA guide.

Post-Migration Tasks

• Disable and re-enable webhooks. If you disabled SurveySparrow webhooks during bulk loading, re-enable them now. Verify they fire correctly on a test ticket update.
• Rebuild workflows. SurveySparrow's ticket workflows (auto-assignment, SLA rules, escalations) must be configured from scratch. Podium's routing logic doesn't export. If automations are part of scope, see our workflow migration guide.
• Configure SLA policies. Set response time and resolution time targets per priority level.
• Set up survey-to-ticket automation. Connect NPS/CSAT surveys to auto-create tickets from detractor responses — this is SurveySparrow's core value proposition for ticket management.
• Update webhooks and forms. Point website chat widgets and SMS ingestion numbers away from Podium and into SurveySparrow or your replacement messaging tool.
• Clean up placeholder emails. Query contacts with email_placeholder: true custom property. Enrich or remove as appropriate.
• Train agents. SurveySparrow's ticket interface differs significantly from Podium's conversation view. Budget 1–2 days for team onboarding. Key differences agents will notice: no inline SMS reply, ticket-based (not thread-based) workflow, separate internal notes vs. public comments.
• Monitor for 2 weeks. Watch for missing data, broken references, orphaned requesters, failed attachments, and workflow misfires. Keep Podium active (read-only) during this period.
• Decommission Podium. Only after validation is complete and agents are fully transitioned. Export a final backup (CSV + FTP) before canceling. Retain the backup for at least 90 days.

Best Practices

• Never mutate source data. Treat Podium as read-only throughout the migration.
• Back up everything first. Export Podium contacts to CSV. Request a full FTP dump. Store it separately from the migration pipeline.
• Use a staging database. SQLite for small migrations (<10K records), PostgreSQL for larger ones. Store the Podium-to-SurveySparrow ID mapping and run transformation queries against staged data.
• Create custom fields before importing. SurveySparrow will reject API calls referencing fields that don't exist. Custom fields must be created through the SurveySparrow UI — there is no API endpoint for field creation.
• Run a test migration. Migrate a single location or a subset of 500 records before going full-scale. Validate every field. Measure actual API throughput against your estimates.
• Run a delta sync. Perform the bulk migration over a week. Before cutover, run a final delta sync to catch conversations updated since the bulk extraction. Use Podium's updatedSince parameter if available, or compare timestamps.
• Throttle conservatively. Run at 70–80% of documented rate limits. Hitting the ceiling during a migration is worse than being 20% slower.
• Log everything. Every API call, every response, every skip, every error. You'll need this for validation and debugging. Structured logging (JSON lines) is easier to query than plain text.
• Don't migrate reviews or payments. Archive them separately. Forcing them into custom fields creates noise with no operational value.
• Keep an import manifest. Store source UID, destination ID, payload hash, HTTP status, retry count, and attachment checksums for every record. That's the difference between a script and a migration tool.

If you build this in-house, keep the codebase simple:

src/
  extract_podium.py
  transform_records.py
  load_surveysparrow_contacts.py
  load_surveysparrow_tickets.py
  load_surveysparrow_comments.py
  reconcile_counts.py
  state_manifest.sqlite
  dead_letter_queue.json
config/
  field_mapping.yaml      # Externalize field mappings for easy iteration
  user_mapping.json       # Podium user UID → SurveySparrow user ID

Use the manifest to store source → destination crosswalks and rerun only failed units of work.

When to Use a Managed Migration Service

Build in-house if:

• You have a dedicated engineer who can commit 2–4 weeks.
• Your dataset is under 10K conversations.
• You're comfortable with both Podium's and SurveySparrow's APIs.
• You have existing ETL infrastructure.

Use a managed service if:

• You can't allocate engineering time without impacting product work.
• Your dataset includes multi-location data with complex deduplication needs.
• You need the migration completed in days, not weeks.
• Attachments, audit trails, or compliance requirements (GDPR, HIPAA) are non-negotiable.
• You've already attempted a DIY migration and hit rate-limit walls or data integrity issues.

Cost context: DIY migrations cost primarily in engineering time. At a fully-loaded engineer cost of $80–$150/hour, an 80–160 hour migration runs $6,400–$24,000 in internal cost. Managed migration services for this path typically range from $2,000–$15,000 depending on volume, complexity, and whether attachments are in scope. The breakeven favors managed services for datasets under 20K conversations; above that, in-house becomes more cost-effective if you have the engineering capacity.

The hidden cost of DIY migration isn't the code — it's the debugging. Podium's API documentation has gaps (especially around FTP data schemas and conversation item types), and SurveySparrow's rate limits can turn a 3-day project into a 3-week project. Every hour an engineer spends troubleshooting a 429 response or reverse-engineering an undocumented field is an hour not spent on your product.