What is Salesforce optimization?
Salesforce optimization is the work of improving an existing org so people use it and trust its data. It usually covers simplifying page layouts and processes, cleaning duplicate and incomplete data, consolidating automation, rebuilding reports and dashboards, and training users on the changes. It differs from rescue, which deals with projects that never worked.
Signs you need optimization
- Reps keep their real pipeline in spreadsheets.
- Leaders do not trust dashboard numbers.
- Duplicate accounts and contacts make reporting unreliable.
- Several admins have added fields and automation over the years and nobody is sure what still runs.
What we deliver (6)
Adoption review
Login, usage and field-completion data plus interviews to see where people avoid the system and why.
Simplification
Leaner page layouts, fewer required fields and processes that match how teams actually work.
Data cleanup
Deduplication, standardization and validation rules to keep data clean going forward.
Automation consolidation
Old workflow rules and Process Builder moved into maintainable Flows, with conflicts removed.
Reporting rebuild
Reports and dashboards rebuilt around the numbers leaders actually use.
Enablement
Short, role-based training on what changed.
Optimization, phase by phase (4)
What happens in each phase, what you get at the end of it, and what we need from your team.
- 01
Measure
We establish a baseline before touching anything. That means pulling login history, record creation by user, field fill rates on key objects and the age of open opportunities or cases. We sit with a few reps, service agents or managers and watch them work a real record, noting every workaround and every tab they open outside Salesforce. Meanwhile an architect catalogs automation by object and checks which reports feed the dashboards leadership reviews each week.
- 02
Prioritize
Findings get scored on two axes: how much friction or reporting error each one causes, and how hard it is to change safely. Hiding unused fields or fixing a picklist is cheap and visible, so those go early. Merging duplicate accounts or retiring a legacy trigger needs planning, so those get sequenced with testing time. We also agree on a small set of metric definitions, like what counts as an active opportunity, before any report gets rebuilt.
- 03
Fix
Changes are built in a full or partial copy sandbox so testing uses realistic data. Layouts get rebuilt per role with Dynamic Forms where it helps, stages and picklists get consolidated, and duplicate records are merged with rules that keep the right owner and history. Overlapping workflow rules and Process Builders are migrated into Flows one object at a time, with regression tests after each so nothing that quietly worked before stops working.
- 04
Re-launch
Changes reach users in planned batches with a short explanation of what is different and why. Training is by role and uses their own records, so a rep sees their pipeline in the new layout rather than a demo. For several weeks after each release we watch the same usage signals captured at the start and compare them to the baseline, then adjust anything that still causes people to leave Salesforce for a spreadsheet.
What drives the effort in optimization (6)
| Factor | Keeps effort down | Raises effort |
|---|---|---|
| Duplicate and dirty data | A few thousand records with obvious duplicates that matching rules can merge cleanly. | Hundreds of thousands of records, conflicting owners and duplicates spread across accounts, contacts and leads. |
| Layers of legacy automation | A few workflow rules on one or two objects that map neatly to a Flow. | Workflow rules, Process Builders, triggers and Flows all firing on the same object in unknown order. |
| Agreement on definitions | Leadership already agrees on stages, lead status and what counts as pipeline. | Each region or team defines a qualified lead differently, and every report reflects a different version. |
| Number of user roles | One sales team with a single sales process and shared page layouts. | Inside sales, field reps, channel partners and service agents each needing distinct layouts and training. |
| Reports tied to other systems | Dashboards built only on Salesforce data with standard report types. | Metrics that blend Salesforce with ERP or billing data through integrations or Tableau. |
| Appetite for removing things | Leaders are willing to retire fields, stages and reports nobody has touched recently. | Every field has a defender, so each removal needs evidence, sign-off and an archive plan. |
Rates typically range from $185 to $225 per hour depending on scope and expertise required, and you get a written estimate after discovery, so these factors are what we ask about first.
Related client work (9)
More optimization client work
- A packaging-automation manufacturer replaced Outlook scheduling with Salesforce Field Service across 37 work typesManufacturing
- A cybersecurity company unified sales and support on Salesforce and migrated 2,300 accounts in 6-8 weeksCybersecurity
- An event-production company consolidated six tools into one custom Sales Cloud workflowMedia & Event Production
- A fast-growing mortgage lender activated Marketing Cloud across 30,000+ recordsMortgage & Lending
- An automotive-compliance company consolidated onto Salesforce Marketing Cloud in two weeksAutomotive Compliance
Common optimization mistakes (4)
- 01
Adding fields to fix adoption
When reps skip Salesforce, the instinct is to add required fields or validation rules to force data in. That usually produces junk values and more resentment. Adoption improves when the system asks for less and gives more back, such as a pipeline view reps would build themselves or a call log that saves them a step later.
- 02
Rebuilding dashboards before cleaning data
A new dashboard on top of duplicates and inconsistent stages simply displays the same wrong numbers in a nicer layout. Leaders notice quickly and trust drops further. Clean and standardize the underlying records first, lock in definitions, and only then build the charts, so the first impression of the new reporting is one of accuracy.
- 03
Migrating automation one-to-one
Converting each old workflow rule or Process Builder into its own Flow keeps all the original overlap, just in a newer tool. Several automations still fire on the same save and still fight each other. Consolidating means reading what each one does, merging logic per object and deleting what no longer serves a purpose.
- 04
Declaring success at go-live
Optimization changes behavior, and behavior slips back when nobody is watching. If usage is not measured against the original baseline after release, old spreadsheets quietly return within a quarter. Keep tracking the same signals for a while after re-launch, and assign someone to act on what those signals show.
Questions to ask before you sign (5)
Optimization sits between a light admin refresh and a full rebuild, so partners pitch it very differently. Some propose a long assessment with no fixes, others start changing things on day one. Asking how they measure, decide and prove results reveals whether you are buying an improvement or just activity.
- 1
How will you measure adoption before and after?
Listen for specific signals such as logins by role, records created or updated, field completion and activity logged against opportunities. A partner who only mentions surveys or anecdotes will struggle to show whether the work changed anything, and you will have no baseline to compare against later.
- 2
How do you decide what to remove versus keep?
Good partners check field usage, report dependencies and automation references before recommending deletions, and they archive rather than destroy. They should also expect you to make the final call on anything business-critical and bring evidence to that conversation rather than opinions.
- 3
What is your approach to legacy automation?
You want to hear about inventorying automation by object, consolidating logic and testing each change in a sandbox. Watch out for a plan to convert everything mechanically, which preserves the overlap that causes errors, or for vague promises to modernize without describing how.
- 4
Who on our side needs to be involved, and how much?
An honest partner tells you that leaders must resolve definition disputes and that power users need time to test. If a proposal suggests the work can happen with no one from your team, expect results that do not match how people actually sell or serve.
- 5
Can you show an org you simplified and what changed?
Ask for a real example with specifics: what the org looked like, what was removed or merged and how usage moved afterward. Partners with genuine experience describe tradeoffs and things they would do differently, not only a clean success story.
Products, industries and markets
Salesforce products we work with here
Optimization by industry
Industries where we have delivered optimization
Markets where optimization is a common need
Guides on salesforce optimization (7)
- Salesforce Change Management: Getting Users to SwitchOptimization · 8 min read
- How to Measure Salesforce Adoption: Metrics That MatterOptimization · 8 min read
- Salesforce Training Plan: Role-Based Training That SticksOptimization · 6 min read
- Record Types vs Page Layouts vs Dynamic FormsOptimization · 8 min read
- Salesforce Mobile App for Field Sales Reps: Setup GuideSales Cloud · 9 min read
- Salesforce Outlook and Gmail Integration GuideSales Cloud · 8 min read
- Why Salesforce adoption is low, and how to fix itOptimization · 3 min read
Salesforce Optimization questions (9)
Why is Salesforce adoption low?
The usual causes are a system that asks for more data entry than it gives back, layouts built for managers rather than users, and dashboards that do not match what reps see. One manufacturer we worked with had overcomplicated its org through several admins; after streamlining it and integrating NetSuite, all 99 reps were active within 30 days.
Do we need to start over to fix a messy org?
Usually not. Most orgs can be simplified in place. A rebuild only makes sense when the data model itself is wrong for the business, which a health check will tell you.
How do you fix dashboards nobody trusts?
Start with the data underneath: duplicates, missing values and inconsistent stages. Then rebuild reports around agreed definitions. A staffing firm we worked with went from broken dashboards to accurate per-rep activity visibility this way.
How is optimization different from managed services?
Optimization is a defined project with a start and end. Managed services is ongoing. Many clients do an optimization project first and then keep improving through managed services.
What should we measure to know if optimization worked?
Pick a few signals tied to the original problem and capture them before any changes. Common ones are active users by role, opportunities updated in the last week, completion of the fields leaders rely on and the number of duplicate records created. Pair those with a simple check: are leadership meetings running from Salesforce dashboards or from exported spreadsheets? If the numbers improve but the spreadsheets persist, there is more work to do.
Can optimization reduce our Salesforce license or tool spend?
Sometimes. An org review often finds inactive users still holding licenses, add-on products nobody configured and third-party apps that duplicate native features. We report those alongside the adoption findings so you can decide what to reclaim or activate. The main value of optimization is usually better use of what you already have, though, so treat license savings as a possible side benefit rather than the reason to start.
Will users have to relearn Salesforce after the changes?
Not from scratch. Most changes remove clutter or reorganize what people already see, so the core of their routine stays familiar. We release changes in batches, explain each one in plain terms and train by role on live records. Users usually notice that pages load with fewer fields, reports match what they expect and fewer steps are needed to log an activity or update a deal.
Can reporting improvements change how reps behave?
Often they do, because visibility shapes habits. A building-products manufacturer we worked with set up an automated weekly scorecard that emailed each rep their own numbers every Friday. It eliminated 1-2 hours of manual weekend reporting and drove healthy competition once reps could see each other's activity. Reporting that reflects individual effort gives people a reason to keep their records current.
Should we fix our data model during optimization?
Only where it blocks the goals of the project. Small model changes, such as replacing a free-text field with a picklist or adding a lookup that reports need, fit well inside optimization. Restructuring core objects, like splitting how accounts represent parent companies and locations, touches integrations and history and usually needs its own plan. A health check beforehand helps decide which kind of change you are facing.