Though data in Salesforce is mostly structured it can be subject to inconsistencies. These inconsistencies arise due to various reasons – behaviour of users, integrations from other systems, data migration from older CRM’s, organizational changes such as mergers and acquisitions, etc.
With time Salesforce orgs accumulate a ton of data and users rely on reports and dashboards to guide their decisions. However have you wondered what else is in the data that these reports may not be conveying? Is there a story hidden in there that you haven’t uncovered?
SpringML’s algorithms comb through Salesforce data to find hidden patterns and trends. We can answer and highlight things such as:
What are the main differences between opportunities that won vs those that were lost? Do these change by region or product?
How reliable is your open pipeline amount? How many opportunities have been pushed out consistently to the next quarter? How many opportunities have a close date that’s more than a few months out? Can you realistically count such opportunities in your active pipeline?
We know that generally activity picks up during quarter ends but is there any consistent behavior amongst certain reps that may need coaching?
What would you do if you had such information handy? What best practices can you drive so that your existing reports become more valuable?