Sales Analytics - the IQOO Framework
Apparently, you need to build Sale Analytics - not just Product & Financial Analytics.
IF you’re building a company in India for India - it is quite likely that after your PLG (Product Led Growth) / organic adoption stage - you will be forced to build a sizeable Ops Team (Field Sales, Call Center, Relationship Managers etc) to grow revenue.
In fact, as a founder - even with a terrifyingly good VP Sales - you will need to spend some time with the VP and your EPD team to build out your Sales Analytics stack.
Reason: Margins per unit Sales Rep in India might not permit you to purchase the fancy tools used by B2B SaaS Sales Reps in North America. However, even your FOS (Feet on Street) needs a layer of Sales Analytics to improve their own productivity.
You’re in for a treat - after sacrificing many Sundays (including this one to write the post), I have a framework called IQOO (Inputs - Quality - Output - Outcomes) to build Sales Analytics.
Basic definitions
MQL - Marketing Qualified Lead - lead from marketing w/ more than basic details
Some Sales funnels would replace MQLs with PQLs (Product Qualified Leads) where a user has performed a certain set of in-product actions which warrant a Sales Rep to follow up to see if a deal can be closed
SQL - Sales Qualified Lead - MQL where your Sales Rep has spoken positively w/ customer - Sale potential
Revenue pipeline coverage = {Total revenue from this quarter's open cases} / {Total quota for this quarter}
This metric is a leading indicator of whether the Rep can hit their quarterly quota or not
Our team tracks EVERY metric mentioned in the IQOO framework - but this was an iterative 240+ day process starting with just 4 metrics.
In the interim - the business grew x30 in 8 months - so the framework has expanded to accommodate a larger team & more data.
10 practical tips on how to get started
KISS (Keep It Simple Silly) - start small
Day 0 - 4 Reps & Rahul - only tracked Inputs (Call Count, Talk Time) and Outcome (Quota v/s Actual)
This was getting a pulse on what matters
Day 60 - 15 Reps, VP & Rahul - started tracking funnel metrics (MQL :: SQL % and SQL :: Sale %)
This was breaking down outcome metrics to find the bottlenecks
Day 120 - Hired 1st dedicated Sales Trainer, now started tracking Quality Metrics (such as Certification scores, Call Recording scores)
This was identifying what qualitative factors makes a “good” Rep good - so that we build repeatability
Day 240 - 45 Reps, VP & 3 Team Leaders
Most manual metric computation is eliminated - automation has kicked in for scaling the Org
Definitions MATTER & so does consistency in definition
This might seem banal - but misunderstandings mostly emerge due to words such as “average” (mean v/s median) and “top performing” (100% percentile v/s Q3 i.e. top quartile)
Please document definitions of your metrics like Math’s formulas in school - numerator & denominator explained in simple words
Publish the document to ALL your mid-level Sales Managers (make reading this part of training)
Analytics needs an Ops layer due to changing demands / channel
We’ve all heard of “MIS bhaiya” (a 1 :: 30 ratio of MIS :: Reps suffices for channel exploration)
In new initiatives (e.g. channel testing), MIS guys are SUPER useful to pick up the Ops work of doing Excel calculations for channel specific metrics which are NOT yet automated
Also, a good MIS hire can explain business logic & metrics to core Engineering staff for completing the Sales Analytics automation once a channel is maturing.
Problems are identified at outcome level - solved at input level
We’ve all heard of the psycho CEO who starts shouting “where are the numbers?” Please don’t be this guy
Let’s say your problem is “Rep {5} didn’t hit Sales Quota for Q4” - some dumb birdie tells you “FIRE THE DAMN GUY”
NO, please don’t - this is as much your fault as it is the Rep’s fault
Instead - In M1 of Q4 - IF your VP was actively tracking INPUT & OUTPUT metrics, (s)he might have realized:
Output level: Rep {5} has a MQL :: SQL % of just 15% versus a 25% benchmark
Input level: Their Call Count is on benchmark but Talk Time of ~3 hours per day is 1 hour below benchmark.
Perhaps, there is a “probing” problem here - might need some coaching / let’s listen to their calls
1 month of help from your Sales Trainer MIGHT invert the scenario (I’ve seen 4 /10 cases of PIP - Performance Improvement Plan - end up super positive)
Be aware of Psychology - don’t become a Psycho
Goodhart’s Law - "When a measure becomes a target, it ceases to be a good measure"
Don’t say something like “I want to see a higher {INPUT / OUTPUT / QUALITY} metric”
Because you ask - and you will get (magically)
Instead, keep the focus on OUTCOME (Sales) & then explain how working on problematic driver metrics can help unlock more Sales
Cobra Effect - “Solution to a problem makes the problem worse”
You might push a team to improve their MQL :: SQL % since their MQL :: Sale % is poor
As a result of your push, the no. of SQLs has increased & therefore, Sale rates have fallen because Reps can’t spend enough time with each SQL
So, my genius, you just made the shortfall of Sales even more acute!
Observer Effect - “Disturbance of an observed system by the act of observation”
The moment you (CEO / VP) applies tunnel vision to a given metric - it magically moves (not because of your immense persona - but because others notice & take - mostly- short term corrective action)
Leverage 3rd party tools (where possible) - build v/s buy but don’t die
Most CRMs let you track e-mails sent per Sales Rep
EVERY dialer gives you a built-in APR (Agent Productivity Report) to extract basic metrics such as Talk Time, Call Count & Call Remarks
Metrics / Analytics should be built to be fractal (Engineering consideration)
Good software design factors in anticipated changes (important if you’re growing >= 30% MoM)
If your Analyst adds a metric - say MQL :: SQL % - please make sure this metric extends across ALL teams, ALL channels & future teams / channels / sub-divisions
i.e. if a metric is defined for 2 teams today in a given channel - write code today assuming it will be used in 6 months time by 4 teams in this channel & 2 in another channel
Different GTMs have different analytics requirements
Common sense - Call Count and Talk Time apply to office based Sales staff and NOT field Sales staff
In office Sales - monitoring isn’t as much of a challenge as field Sales - so your monitoring metrics (e.g. location check ins) become super important.
Be prepared for constant change
NGL, we add & remove some metrics each month - you will too…
Desired Outcome of Sales Analytics is to drive {higher/better} Sales - you CANNOT escape the Ops side
No point tracking metrics IF you don’t take follow-up action
As a CEO - you might want to discuss certain metrics {each week / month} with your VP Sales
And, you would expect a POA (Plan Of Action) from the VP (or their direct reports) - each month - on areas for improvement
This means phone calls, Slack messages, Notion documents & in-person meetings (and then the catch-ups)
Sorry, but you can’t delegate this - you need to do this part of the Ops yourself.
FAQs section
I posted the IQOO framework on Twitter for feedback & got some replies:
For outreach: Is unique calls a better metric v/s number of calls/talk time? [1]
Ans: Depends on how you look at it - even with a high number of unique outreaches - low talk time and / or poor conversion itself is a dead giveaway of mindless work. Unique outreaches are still a quantity metric - not really a quality metric like dialer talk time
How does one use IQOO in the real world? [2]
Ans: Example from my experience - we found that MQL :: Sale % is high for a subsegment (business type) which became our ICP (huge ROI on CPL reduction via targeting)
Closing thoughts
Building a Sales Org has several areas of focus - Hiring (incl Retention), Training, Compensation and Analytics. I thought I’d start by covering Analytics since this is a topic which is oft forgotten.
Once you have a good grasp over your Sales Analytics, you will find an immense amount of clarity on your business model (which will help to scale from Series A & beyond and answer in-depth questions from financial investors.)
Happy to hear your thoughts, comments & feedback. Until next time!