A Salesforce migration is less about moving rows and more about keeping revenue reporting continuous while the team changes tools. This guide walks through the full build: planning the cutover, exporting cleanly from Salesforce, mapping objects to Strkr, rebuilding custom fields and automations, running parallel validation, training the team, and decommissioning the old org on a schedule you control.
Time: 3 to 6 weeks of elapsed time, roughly 60 to 100 focused hours of work
Before any export runs, write down what success looks like in one page. Name the objects moving over, the reports that must still work on day one, the automations that must not drop, and the user roles the new system has to support. Pick a cutover date that gives you at least two weeks of parallel running. Avoid month-end, quarter-end, and any week with a board meeting. Share the page with sales leadership, revenue operations, and finance, and get written sign-off. Scope creep during a migration is the single biggest reason projects slip past three months. A frozen scope with a dated cutover forces every mid-flight request into a post-launch backlog, which is where most of them belong anyway.
A migration is the one time every team gets to delete the garbage with full air cover. Use that air cover. Pull a report of duplicate Accounts by name and website, duplicate Contacts by email, and Opportunities with no activity in six months. Merge duplicates in Salesforce before export using the standard merge tool for Accounts and Contacts, or Data Loader with external IDs for bulk cleanup. Close out stale Opportunities as closed-lost with a reason code so the historical conversion math still reads correctly. Normalize picklist values that drifted over the years, especially Lead Source, Industry, and Opportunity Stage. If you skip this step, you are paying to move bad data into a new system where it will embarrass you faster because every surface is cleaner.
Before any data lands, build the receiving structure. In Strkr, create the roles, teams, and user accounts that match your Salesforce profiles and territories. Decide early how you will handle the ownership mapping: Salesforce stores Owner as a user ID, and that ID has no meaning in Strkr. The cleanest path is to export the Salesforce user list, build a two-column CSV of Salesforce user IDs to Strkr user IDs, and run every record through that lookup during import. If a Salesforce owner has left the company, map them to a holding user named something like Former Owner so the records import cleanly without inheriting a working rep they do not belong to. Resist the urge to flatten the role tree during migration; a hierarchy rewrite in flight hides data loss. Rewrite the hierarchy after go-live if you need to.
Open a spreadsheet with three columns: Salesforce object and field, Strkr object and field, and transformation rule. Walk every object in scope row by row. Standard objects map cleanly in most cases: Account to Account, Contact to Contact, Lead to Lead, Opportunity to Deal. Custom objects need a judgment call. Decide whether each one belongs as a new Strkr custom object, a related list on an existing object, or a set of custom fields. Flag anything that depends on a Salesforce feature you have not confirmed exists on the Strkr side, such as record types, person accounts, or formula fields with complex cross-object references. For every field, decide the transformation: direct copy, picklist remap, concatenation, split, or drop. A field with no owner on the receiving side is a field that gets dropped. Write the dropped list down and get sign-off from the business owner so there are no surprises on day one.
With the map in hand, build the receiving schema in Strkr. Create custom fields on Account, Contact, Lead, and Deal to match the mapping sheet. Use the right field types from the start: picklists where Salesforce had picklists, multi-select where Salesforce had multi-select, date where Salesforce had date. A text field holding a date is a problem you will chase for months. Set up field-level permissions so sensitive fields such as revenue, close date, and executive sponsor notes carry the same visibility they did in Salesforce. Create any custom objects decided in the mapping step and link them to parents with the right relationship types. Freeze the schema before you run the first full import. Changing a field type after import means re-importing that column, and re-imports compound risk.
Salesforce gives you two reliable export paths, and the right migration uses both. Weekly Export, available in Setup under Data Export, produces a full backup zip of every standard and custom object as CSV plus attachments. Request it at the start of your export week; it can take hours to generate for a large org. Weekly Export is the authoritative snapshot you build the migration from. Data Loader is the surgical tool for pulling specific objects on demand, running queries with SOQL, and extracting record IDs you will need for the ID lookup tables. Export in this order so parent records exist before children: Users, Accounts, Contacts, Leads, Opportunities, OpportunityLineItems, Products, Pricebooks, Tasks, Events, Notes, Attachments, Chatter feed items if you are keeping them. Keep a copy of every export file in a locked folder with the export timestamp in the filename. If anything goes sideways during import you will want the exact source of truth to diff against.
Open each exported CSV in a spreadsheet or a lightweight ETL tool and apply the transformation rules from the mapping sheet. The most common transforms: remap Salesforce user IDs to Strkr user IDs using the lookup CSV, remap Salesforce record IDs to a staging ID column so related records can be linked after import, convert date and datetime strings to the format Strkr expects, split or combine name fields where the models differ, and translate picklist values that changed names. Import in the same parent-first order used for export so related records find their parents on the first pass. Strkr accepts CSV import on every core object; import a small test batch first, verify a handful of records by hand, then run the full file. Keep an import log for each run with row counts in, row counts out, and the IDs of any records that failed. A clean import has fewer than one percent errors; above that, pause and investigate before continuing.
Salesforce automations rarely survive a lift and shift. Workflow Rules, Process Builder flows, Flow Builder flows, and Apex triggers each have Strkr equivalents in Flows, but the trigger model and the action set differ. Walk the automation inventory from Step One and recreate each one in Strkr Flows. Common patterns translate directly: notify the owner when a Deal stage changes, create a Task when a Contact is created, update a field when another field changes. Complex Apex logic is usually a sign the business rule was never written down; use the rebuild as a chance to document the intent in plain language first, then build the Flow against that intent. Test every Flow end to end with real data in a sandbox before enabling it in production. Pay special attention to automations that touch billing, forecasting, or external systems. A Flow that silently fails to update a close-date field will not surface until month-end forecast drift makes it obvious.
Do not flip the switch and shut Salesforce down the same day. Run both systems in parallel for at least two weeks. During parallel, reps continue to work primarily in Salesforce but spot-check their records in Strkr daily. Revenue operations runs the top five forecast reports in both systems every Monday and compares. Any delta above two percent gets investigated the same day. In parallel, run the training. Keep sessions short, forty-five minutes maximum, and role-based: one session for reps, one for managers, one for revenue operations. Record every session. Set up a dedicated Strkr channel in your internal chat tool for migration questions and staff it with the admin and one experienced rep for the full parallel window. Training that happens in the week before go-live is better than training delivered in a dense day-long bootcamp, which nobody remembers.
The last mile is the one most migrations skip, and it is the one that costs the most if you skip it. Build a dated decommission plan the week before cutover. On cutover day, put Salesforce into read-only mode by downgrading user profiles so nobody can create or edit records; a frozen source of truth stays trustworthy. Thirty days after cutover, pull a final Weekly Export and archive it to cold storage along with the mapping sheet, import logs, and automation documentation. Sixty days after cutover, remove paid user licenses to stop the burn. Ninety days after cutover, cancel the Salesforce contract or formally renew only the minimum licensing needed for historical audit access, which is usually one or two admin seats. Document the archive location and retention policy. Finance and legal will ask for it, and a decommission plan with named owners and dates is the artifact that lets them say yes.
Three to six weeks of elapsed time is realistic for a mid-sized sales org with standard objects and modest customization. Larger orgs with heavy Apex, custom objects, and tight integrations with billing or marketing systems can stretch to eight weeks. The focused work is roughly sixty to one hundred hours and should be scheduled around existing quota cycles, not jammed into the quiet weeks before quarter-end.
Yes if you plan a parallel-running window of at least two weeks. During parallel, reps keep working in Salesforce and spot-check Strkr daily, while revenue operations compares forecast numbers weekly. The cutover itself takes less than an hour because the data has already been imported and validated. True zero downtime for the end user is normal with this approach.
Each custom object gets a design decision during the mapping step. Objects that genuinely represent a distinct entity become Strkr custom objects. Objects that are really a related list under an existing record become a related list in Strkr. Objects that are a de facto field extension become custom fields. Walking that decision tree deliberately usually cuts the custom object count by half, which makes the new system faster to learn and cheaper to maintain.
No, provided you keep a frozen Salesforce environment as a historical reference for at least a year and you migrate closed deals with their original close dates. Day-one Strkr reports should match day-one Salesforce reports to within two percent for the trailing twelve months. Anything beyond twelve months is usually safer to query from the archived Salesforce export than to recreate inside the new system.
Do not try to port the formula logic. Document what each formula was meant to show as a plain-language description, then recreate the output in Strkr either as a calculated field, a Flow that updates a value when inputs change, or a report that derives the number at query time. Strkr AI can help translate the intent into a working Flow once the plain-language description exists.
Tasks, Events, Calls, and Notes all migrate cleanly as Strkr activities. Chatter feed items are a judgment call. If your team uses Chatter heavily for deal context, export the feeds and import them as activity notes on the parent record. If Chatter is mostly empty, skip it and archive the raw export instead. Reading a two-year-old Chatter thread is rare enough that keeping it in the archive is usually enough.
Strkr gives you the schema flexibility, Flow automation, and parallel-reporting views you need to move off Salesforce on your own schedule. Build the mapping once, validate for two weeks, and cut over without downtime.
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