Most companies rely on out-of-the-box reports from Salesforce to understand how their marketing efforts are translating to pipeline and revenue. The assumption is this data is accurate, yet there are flaws in how Salesforce tracks marketing impact. In fact, these flaws are big enough to cause as much as 50% of data about marketing sourced/influenced pipeline to be totally inaccurate in reports.
It starts with how marketing impact is tracked on individuals — leads and contacts. If you’re like most, you’re writing to fields like Lead Source to track the first (or last) touch, and using campaigns to track the different programs that a given lead/contact has interacted with. These two data points — lead-level fields and campaign membership — are the basis for nearly all Salesforce marketing reports, and the reason why you can make statements like “we generated 200 leads from search engine marketing last month” or “50 people registered for the webinar”.
It’s reasonable to assume that these same insights should translate well to tracking pipeline impact too, right? The answer, it turns out, depends on how the opportunity was created.
Opportunities created through lead conversion
If a lead flows through your funnel in a perfectly orderly way, the lead gets converted to an account, and an opportunity is generated. This is how Salesforce lead conversion is designed to work. When this happens, the new opportunity will automatically have the lead (now a contact) associated to it. The opportunity will also inherit key lead details, namely lead status, and any campaign history that the lead was a part of prior to being converted.
With all this information attached to the opportunity, you can accurately say where the opportunity was sourced from, and even what influenced it. Since all this association of data happens automatically, you can safely rely on it for reporting purposes. Meanwhile…
Opportunities created manually
We all like to think our sales processes fit into a nice and tidy flowchart, yet the reality in most organizations is that opportunities get created through other ways than just lead conversion. For example, suppose an existing customer expresses interest in a new product. Since that customer wouldn’t be a lead in the system anymore, the sales rep will most likely create an opportunity directly from their existing account.
The problem, though, is that a manually created opportunity won’t have a contact associated with it by default. This also means that marketing data like lead source or campaign history are often blank. It’s why your campaign pipeline reports never seem to add up properly with the total pipeline reports — if no one is added to the opportunity, no campaigns will be added either.
Bottom line, manually-created opportunities, left to their own devices, make measurement of marketing impact completely unreliable. And this isn’t just an edge case. In nearly every Salesforce org I’ve seen, manually-created opportunities — those created outside of the typical lead conversion process — account for 50% or more of the total opportunities in the system. This means that 50% of your marketing pipeline data is just not accurate.
You can quickly confirm how big of an issue this is in your organization by running a simple opportunity report, and checking how many lack a primary contact.
Never fear, this can be fixed, and without buying any specialized software. Work with your Salesforce admin to modify the default way opportunities can be created, and ask them to remove the ability for opportunities to be created anywhere besides a lead conversion, or directly from a contact. This requires a bit of customization to accomplish, but should be a relatively straightforward change for your admin. Just make sure you train your sales team on the new process, as you’ll want to ensure they still know how to create opportunities.
You can also use 3rd party tools to address this issue, though they’ll come in handy later, when you’re trying to get more than just one contact on each opportunity. Just the step above will make a big difference to fill in gaps in your data.