Sales teams play a critical role in driving growth at b2b companies. However, throwing more warm bodies to make phone calls is not the best way to get to that hockey stick curve. The sales process has its own wealth of complexities such as identifying the best opportunities to focus on, determining the most helpful content to give to prospects at the right time, and knowing which marketing channels generate the highest quality opportunities. Before you can reliabily and predictably scale revenue, you need to understand and optimize these dynamics.

We use Salesforce here at Segment, and it provides great out-of-the-box reporting. But, like all out-of-the-box tools, it lacks the ability to answer these types of granular questions about our business. Additionally, the data is silo’d in Salesforce, making it hard to tie sales conversions with product usage or page views on our blog.

Now, with Segment Sources, we can send our sales data into a data warehouse, where we can JOIN across product usage (collected by Segment), as well as other Sources like Zendesk and Stripe. Having this data accessible allows us to not only make faster and better decisions, but also to launch measured growth experiemnts with more confidence.

In this post, I’ll outline the major questions and related queries combining various datasets for our sales team at Segment. We used Mode Analytics since they have put together some great resources on Salesforce data, like this Salesforce CRM data eBook.

Kindly note that we replaced the number values in these charts with fake data, but the trends and percentages we saw are real. Many thanks to analysts Willand Perry on our team for helping me with some of the queries!

What opportunities should we focus on?

It’s commonly understood that the most effective salesperson is someone who has maniacal focus on time management—that is knowing which opportunities to work and which to de-prioritize.

Knowing which opportunities to focus on ultimately depends on the context. For example, if your quota is based on the number of logos added (i.e. deals closed), then you’d want to optimize for shortest sales cycle and highest close rate. However, if your quota depends on the size of the deals, then it makes sense to aim for the larger opportunities.

We’ll provide the queries for each of these angles (close rate, average sales length, and deal size by ARR—annual recurring revenue), but for this analysis, we’re going to look at these dimensions: employee count and vertical.

This chart that just looks at ARR reaffirms some existing thinking that we had about the perceived value that we’re offering our enterprise customers. Our largest ARR deals have been companies with more than a thousand employees. This makes sense, as they certainly are capable of paying more.

Looking at the other dimensions like close rate furthers our confidence that the 51-200 employee count companies’ pain align with our sales messaging. Understanding these other two dimensions helps us allocate our sales resources to only nudge along opportunities with the highest likelihood of closing or chasing after the deals that we know are easily repeatable and predictable.

Through this lens, the sweet spot for closing deals is in the 51 to 200 employee range: that’s where we have the highest close rate at 64% and shortest average sales cycle of 41 days. Anecdotes from our sales team confirms that these companies both have the financing available and do not want to build and maintain their own in-house analytics infrastructure.

Similarly, we conducted the same analysis, but this time looking at the industry/vertical of the opportunities. We use Salesforce’s industry property that is tied to the accounts object:

We see that ecommerce and advertising companies both have high average deal size and pretty strong close rates. To help us bring this sort of success to the other verticals or even improve our sales success there, it’s important we understand what their use cases are with Segment. Maybe there’s a particular use case among ecommerce clients that really make Segment valuable to them. If so, we can create content to really grease the wheels with future ecommerce companies. Or we can draw parallels and try to identify the pains with other verticals to better align our messaging towards them.

Which channel sends us the highest quality leads and opportunities?

Once we have learned what kinds of opportunities we want to chase after, we want to know how to source more of them.

There are three main approaches:

  • Salesforce campaigns: These are created with the marketer in mind. We can set up these campaigns in Salesforce to manage everything from lead generation to campaign performance. The only downside is that these have to be setup before for it to be working properly. Therefore, we can’t retroactively analyze the performance of campaigns.

  • Source field on Lead objects: Similar to Salesforce campaigns, this is a simple way to provide a high level attribution to leads. This is typically auto generated when the lead is created, defaulting to “web” (since most leads are web-to-lead). But we set that field whenever we upload a list of leads.

  • Segment UTM params or referrer domain: Since we’re using Segment, we can tie in product and page view data with Salesforce, which allows us to leverage UTM params from our marketing campaigns as a source of attribution for Salesforce qualified leads and opportunities.

If we’re just looking at qualified leads and their sources:

Since the majority of our leads are inbound (our BDRs reach out to our registered users), it makes sense that the lead sources are mostly “web”. However, that doesn’t help us out too much if we want to know where on the web these leads are coming from.

We can try JOIN ing the salesforce.leads table with Segment pages to see what referrer s are most common:

While this is better than the previous analysis, we still are getting Unknown for the majority of the referring domains. Unfortunately, there are many reasons whyUnknown is a referring domain, most notably clicking a link from outside a web browser (or a mobile app) or clicking from a domain that has HTTPS.

From the information we do know, we can continue to optimize for the channels that work for us: Search, Twitter, and our own content (which get onto Hacker News/Twitter and get indexed on Google).

What percent of opportunities dropped off at each stage?

Ah, the age old question about our sales funnel. There are always areas for improvement, so we like to be critical about our own performance as a team. Though we typically track pipeline with Salesforce’s out-of-the-box reporting, we measure overall funnel performance in SQL since Salesforce only tracks last stage changed instead of the time each stage was changed, making it really hard to get the complete picture of all deals.

We have six opportunity stages: Qualified Primary, Qualified Secondary, POC, Business Alignment, Legal, and Closed Won / Closed Lost / Nurture. Each stage occurs after any previous stage, except that at any point, the opportunity can be labeled as “Closed Lost” or “Nurture” when the deal falls through.

To help us holistically understand what stage sees the most problems, we look at which conversion rate by stage. We also group by company size to see if that bears any influence on conversions.

Here is a chart generated by a Mode query that shows the “Conversion Rate” based on opportunity stage and grouped by employee size. We grouped by 1–70 (“small”), 71–500 (“mid-sized”), and greater than 500 (“large”) for simplicity.

Related query can be found here.

Looks like Qualified Primary (the first stage) has the largest drop off of any stage. Also, for large companies, Qualified Secondary also has a significantly larger drop off than the other two company size categories.

Another notable insight is that the Legal to Closed Won is lower for the small and mid-sized companies. This is strange since Legal stage should really be just dotting and crossing the i’s and t’s, as the prospect should already be bought into the deal.

To figure out how to fix these leaky areas of our sales funnel, we’ll have to look at each lost deal and figure out why they were lost.

What content helps move deals along?

Buyers these days are more sophisticated than ever, opting to self-educate with content and their own research rather than speak to sales. As such, aligning content marketing strategies with the common objections and questions from sales is critical to sales efficacy.

But how does marketing know what content to produce to shorten the sales cycle? Which pieces of content today are helping deals progress through the sales cycle?

There are two approaches to answering this question.

The best approach requires some initial setup and ongoing maintenance in your Salesforce, but the downstream analysis is straightforward. You would need to create a custom Salesforce object called collateral. For each collateral that your sales team likes to send prospects, create that in Salesforce. Then, whenever your sales rep sends out a piece of content, he or she can attach that collateralobject to the opportunity. (Note there are services out there such as Clearslidethat solve this problem.)

Unfortunately, this approach doesn’t consider two issues: 1. prospects self-educating—that is, going to our home page, looking at our blog posts, or looking at our case studies, and 2. sales reps needing to manually updating opportunities with appropriate colalteral. The first problem is especially prevalent at Segment, since our audience is extremely tech savvy and like to check us out extensively before talking to us. But since we can JOIN Salesforce data with Segment page views, we can create a table that gives us an idea of what content converts best at which stage.

Related query can be found here.

We can see that case studies have a large impact on demonstrating the value proposition of our enterprise offerings (Redshift), which help facilitate deals early on. However, the technical documentation helps convert later, probably a side effect of the prospect’s developers implementing Segment. From a sales perspective, it makes sense for a prospect who has added Segment to have a higher chance of understanding its value, thereby having a higher chance of ultimately converting.

While this analysis may reaffirm existing intuition held by our sales and marketing teams, it helps to revisit this occasionally to see how different content pieces are performing, as well as know how to coach the sales team about what types of content to send to prospects.

However, this analysis has one major limitation: we can only view the prospect based on userId or anonymousId. We can improve this analysis by using IP ranges. Not only will we be able to get page views of un logged in prospects, but we’ll also capture page views of other employees at the prospects’ offices. We won’t dig into this here, but this can be achieved in SQL since we do collect IP addressesHere is a cool resource that can guide you down this path.

Everyone is in sales

Sales today starts far before the lead object is created in Salesforce. Prospects see a few display ads or read a few blog posts before even looking at your pricing page or talking to your sales team.

In order to stay ahead of the curve and be proactive about pursuing the right opportunities and optimizing the sales funnel, having data and analytics is critical. While a great deal of analysis can be done right from Salesforce’s out-of-the-box reporting, tying sales data to product usage or other customer touch points (email, SMS, etc.) requires significantly more work.

This post has been our exploration into using Salesforce Source data to help improve our sales team. Similarly, Trustpilot used our Salesforce Source for the speed and ease of analysisMesosphere also leveraged Salesforce and Zendesk data to assign a dollar cost to support actions, such as responding to a ticket, to help understand the value their support team provides.

Unite your data today!