If your “conversion from marketing qualified lead to customer” is low, or the “lead volume” isn’t enough to hit your goals, then the number of SDR attempts to connect with the prospect automatically go up.
2016 industry benchmark was 8.2 attempts, do more than that.
Recently, a team of SaaS marketers asked me about how we implemented lead scoring at VWO, and what they should be doing to implement the same for their product. They explained the approaches they were considering, and how they wanted to orchestrate the entire thing.
I asked them if the salesteam was involved, and they said not really. Their Head of Sales was passing by, so I asked him to come in to the meeting room and explain the problems his team was facing with leads, and their prioritization. He went on a slight rant, pointing out multiple issues that spanned process, enrichment, and fake leads from domains like mailinator.com. Interestingly, most of the issues that he mentioned weren’t really being considered by marketing.
I’ve been guilty of this myself… working on lead scoring as a pure intellectual exercise because all the SaaS blog posts out there say marketing should do it, vs. working with sales to understand their problems. The outcome was that we started pushing some lead scoring number into the CRM, but saw that sales didn’t give a damn, and simply ignored it.
There are two aspects that we marketers needs to understand here:
Looking at it from a JTBD framework, the ‘job’ of lead scoring is to:
After implementing lead scoring, sales should be picking up high quality leads quicker, and spending more time on them. Therefore, the way to measure successful lead scoring is:
Marketing should spend more muscle towards acquiring, nurturing and preparing high quality leads.
Since marketing usually builds the lead score, they should approach it by looking to change sales’ behavior by understanding how they currently prioritize leads. If they don’t approach the problem from this key insight, it is likely that the lead scoring exercise will fail to deliver any real results in the long-term.
Also, the entire project requires the correct compensation plan, which incents acquiring, working and closing the right leads.
Anecdote: I once observed a colleague complete a sophisticated data analysis exercise and create a new lead scoring model, which they then dumped on the salesteam in a large meeting. They expected sales to enthusiastically adopt it, because it “came from the data”, but that didn’t happen. Sales was wary, almost hostile to the new model, and on questioning, explained that it didn’t gel with their comp plan. The incident taught me to start with understanding sales’ perspective first, proving to them that am trying to help, gaining some confidence and buy-in, and then proceeding.
I recently read “Aligned to Achieve” by Tracy Eiler and Andrea Austin, respectively the CMO and VP, Enterprise Business at InsideView. It is an incredible book, and I recommend all B2B CEOs, marketers and salespeople read it. The authors dive deep into a problem that everyone knows about, some acknowledge, and very few try to solve so comprehensively.
In the book, one thread that comes up repeatedly is that data and lead scoring are key to great alignment between sales and marketing. Here are a couple excerpts:
What are your thoughts on this? Have you faced any painful issues with either sales or marketing where you felt the other simply didn’t want to work with you, or didn’t trust you?
Who am I writing for?
People who’re responsible for orchestrating Sales or Marketing processes in B2B SaaS startups.
The “sales pipeline” often starts at the opportunity stage, after an SDR has spoken to the lead and confirmed that they are in the market right now, they have the budget to buy, and the size & duration of the subscription plan that they want. Example, two identical prospects looking to buy a helpdesk SaaS which costs $25 per seat, per month. One has a tech support team of 10 reps, and the other has a team of 20 reps. In this case, the pipeline will reflect two potential accounts with a total pipeline value of 30 x $25 x 12 months= $9000.
In a SaaS startup where marketing is responsible for delivering MQLs to sales, it will be the Head of Sales who’ll first feel the need to hire SDRs. As the MQLs increase she’ll realize that her reps are talking to far too many unqualified leads and she’ll want her best reps to focus on the qualified leads. To resolve this, she’ll install an SDR layer to qualify MQLs provided by marketing so that only the serious ones are passed on to closers.
In a startup where marketing is measured on pipeline and not just MQLs, the Head of Marketing will figure out that:
and therefore she’ll push for an SDR layer to be installed in between sales and marketing.
So, in short, whoever manages pipeline will feel the need to install SDRs. If you’re measuring marketing on pipeline contribution, then the SDRs should report into marketing, and if you’re measuring marketing on leads passed to sales, then the SDRs should report into sales.
Obviously, in the end you’ll end up choosing what works best for your organization, but this is a good decision framework to start with.
Useful? Catch me on Twitter: @SiddharthDeswal
When deciding on a lead scoring model for your enterprise B2B SaaS software, base your scoring on the following points:
Most people in SaaS expect (and hope?) that their lead-scoring model will be super scientific and involve lots of data… and they scoff at the notion of marketing just walking up to the sales team and asking them what factors should they give “points to”.
So here’s the deal, if you have a LOT of sanitized and well maintained data, then your data-scientist delivered lead scoring model will be awesome. If you don’t, like every other SaaS in the world, then the data-scientist will come back and say they’ve found no real correlations.
In that case, it’s better to just ask your experienced sales colleagues for the factors they consider important, and run a quick analysis of your Google Analytics + marketing automation data to compare converters vs. non-converters on pages per session, time on site, new vs. returning visitors, number of forms filled, etc.
We did this and the resultant lead score was pretty damn correlated to them being an enterprise customer or not.
Again, lots of people might laugh at you, but those people have no context of the [quality + quantity] of data that’s required to build a real, ‘scientific’ lead scoring model.
P.S. — Did you notice that I missed out on factoring in-app activity in your lead scoring model? Well here’s where ‘context’ comes in. In-app activity is important when your SaaS is mostly self-service, and the majority of your MRR comes from customers adding their credit card details and paying monthly. These are usually apps focused on SMBs.
Enterprise SaaS doesn’t work that way yet. For us, the number of users in the company joining the product demo is a better indicator than their actions inside the app.
