Built for Cincinnati teams whose Salesforce org looks fine in a demo yet frustrates the reps, agents and managers expected to live in it every day.
Optimizing Salesforce for a Cincinnati company usually means making a large field team want to use a system it already has. Insurers with agent networks, distributors with inside and outside reps, and manufacturers selling through dealers often carry years of added fields, overlapping flows and dashboards nobody quotes in meetings. We study usage data, interview each role, cut what is not used, consolidate automation into maintained Flows and rebuild reports around agreed definitions, then retrain people on the changes.
Distributors produce at least 9.1% of the metro's 2024 GDP, and their reps judge Salesforce by one test: does it tell them whom to call next? Optimization for wholesalers typically trims account pages to the fields reps use, surfaces order history and lapsed buyers from ERP data, and replaces a dozen legacy reports with a short set leaders agree on. Reps engage more once the system starts suggesting work instead of only demanding data entry.
Carriers and banks in the region often have agents or bankers who log activity only because they must. We look at field-completion and login data to find where the system asks too much, simplify the screens for those roles, and move repetitive steps such as renewal reminders or referral assignment into Flow. Compliance steps stay intact; the goal is fewer clicks around them, not fewer controls. Managers then get a pipeline view they can coach from.
Manufacturers commonly accumulate automation from several admins, with workflow rules, Process Builder and Flow all firing on the same opportunity. That produces slow saves, duplicate tasks and errors nobody can trace. We map every automation on the key objects, merge them into a small number of documented Flows, and test the result against real quoting and order scenarios in a sandbox before switching over, so plant and sales teams get a faster, more predictable system.
Usage logs tell you more than opinions do. Pull login history, blank-field counts and report views for a few weeks up front so the redesign targets real friction rather than the loudest complaint, and so you can show leadership the improvement afterward using the same measures.
Dashboards lose credibility when two departments define a qualified lead or a won deal differently. Settle those definitions in writing with sales, finance and marketing leaders; rebuilding reports without that agreement just produces new charts that people still argue about in the Monday meeting.
Large Cincinnati orgs often have hundreds of custom fields, some feeding integrations or Marketing Cloud segments nobody remembers. Hide candidates from layouts first, watch for complaints or broken syncs over a release cycle, then archive and delete. It is slower than a single purge but avoids unpleasant surprises.
How our salesforce optimization works →
Client names are anonymized, and these projects are not specific to Cincinnati.
With observation. We sit with a handful of users from each role, remotely or on site in Cincinnati, and watch a normal day of work. Combined with usage data, that shows which screens, required fields and approval steps cost the most time. Quick fixes such as layout cleanup ship within the first weeks, while structural changes like automation consolidation are scheduled so they can be tested properly.
Often, yes. Campaign segments depend on clean, consistent CRM fields, and many orgs feed marketing from values that sales stopped maintaining years ago. We identify which fields segmentation actually relies on, fix their definitions and validation, remove duplicate contacts that inflate sends, and align lead status values between marketing and sales, so campaign results and pipeline reports finally tell the same story.
Look at the data model. If accounts, contacts and opportunities reflect how you actually sell, the problems are usually layouts, automation and reporting, all fixable without starting over. A rebuild is worth discussing only when the core structure is wrong, for example when one customer buying in Ohio, Kentucky and Indiana was set up as three unrelated accounts. We can settle that with a brief review of the object model rather than a guess.
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