“Right Company, Right Person, WRONG TIME” : The solution = PQL (Product Qualified Lead)

Attn: SaaS Revenue Leaders — A tactical guide Level 2

Continuing from the original “MQLs are DEAD! Enter the PQL “Product Qualified Lead” = 10x+ revenue impact. Let’s progress to Level 2.

How many times have you reached out to a prospect only to find out the timing is off. What if you could prioritize only those prospects where the timing is right?

Assumptions:

• You have a SaaS time based Trial: if you’re open source or have a freemium tier I’ll post on that later
• You have sales people
• You don’t have data/BI/analyst resources (even though you buy them donuts)

Objective: Reverse Engineer the deals you’ve closed/won (ie “converted”) to understand the top 3 “Key Actions” (in product usage) that correlate to $; therefore, enabling sales/marketing to prioritize those prospects first.

In addition to monitoring for these 3 Key Events, you can also proactively push trialing users to these Events/Actions. An extra layer of sophistication is to Segment those users that match demographic + firmographic profile of your Ideal Customer Profile. For example, a student in the Philippines has a different set of 3 Key Actions to keep them engaged for a different end goal (ie. you want to be in their “toolkit” so when they change jobs they then bring you in. Then the timing might be right to warrant a Sales Rep’s time. (sidenote: one of the all time great toolkit campaigns was “bring google apps to work”)

Definition of PQL: Product Qualified Lead

Individual Users (or Groups of Users) of your product who have reached pre-defined triggers indicating high probability they are ready to pay at the next level

What (indicative behavior) + Who (is involved) = PQL score of X

How do you find the 3 Key Actions inside your product that correlate to $/conversion?

We are going to use Salesforce data and Product Analytics data from inside your product. (note: this is not website data but after the user logs into your product). We are also going to leave out demographic/firmographic attributes to simplify things initially (future post).

Data people (Whalr data science team included!) reading this are going to say this approach is incorrect and “lacking proper statistical rigor”. “Technically” they are right. At this Level 2 though, we are looking to be directionally correct (ie good enough) without needing data resources or statistics. Keep it simple, yet effective at this level.

Now let’s get tactical:

Step 1: Go to SFDC and export all of your Closed Won and Closed Lost Opportunities.

Or export all of your Trial conversions and Trial non-conversions

• Export all of the emails associated with both groups. Add a “converted” column (1 or 0) in the .csv to denote the emails that are part of a “converted” users + accounts

Step 2: Give your product manager a .csv with 4 columns: email, converted, event_name, count_event. Ask them to export all of the Analytics data associated to those emails.

• Format requested: email, event_name, count_event
• Pick 10 unique events. Use your intuition for this first round. It’s likely some of the event names won’t make sense and are cryptic so don’t worry about those. Don’t over think this stage and just pick 10 with your ‘gut’ feel. Note: try to refrain from very obvious events like “Add_credit_card” which are required actions, not user behavior.
• Here’s a template google spreadsheet you can download for reference

Step 3: Review Tab 1 of template

• This shows you the single events that correlate to conversion for this dataset. Find the top 3 that have the highest “Conversion Rate”. These are your Top 3 Key Action.
• In simple terms, these are the events that the users who converted used the most. Note: this does not take into account the combinations (or order) of the events. (future post)

Step 4: “Now you know and knowing is half the battle” — Duke

• Now that you have identified 3 Key Actions, then what? How do you know when a Trialing prospect actually triggers these Key Actions? (Unfortunately there is no simple solution)
• Option A: requires engineering work to add analytics event hooks to send an email alert anytime a prospect triggers these 3 Key Actions regardless of order
• Option B: Setup an “any order” funnel supported by some Analytics tools. Note: You can’t use a standard Linear Funnel approach as this is sequence dependent. A →B →C is the only path to trigger. You’ll miss: B →A →C and all other combos which are equally important.
• Option C: Use this discovery to adjust how you sell + market to the trialing prospect. Suggest they do event A, B and C. Less impactful as you don’t have a system monitoring whether users complete these actions, but beneficial in that engineering effort is not required.
• Whalr’s Approach: our philosophy is that every team has a different process and the technology must morph seamlessly into that workflow. We continuously Discover the PQL behavior, monitor for it and finally inject the Intel into a team’s existing workflow so action can be taken immediately.

As a PQL practitioner I’m personally very interested in hearing your stories + feedback as to whether this helps you prioritize sales opportunities. If you have specific questions just email me at mitch@whalr.com or we’ll host you in our San Francisco office.

When you want to do this at scale know that there’s a platform + team specifically built to teach, implement and measure the impacts of the PQL methodology: Whalr