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Lagging, leading and lever KPIs

Live presentation #8

February 28, 2024

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Mirze Rey

Go-to-Market Manager at Microsoft

Currently

Previously

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Today we’ll cover

What are lagging, leading, lever KPIs?

Hands on cases:

Different GTM motions, different KPIs

Best practices:

Driving KPIs effectively

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Introduce yourself!

Come off mute and say hello! ☺

Name
What do you do currently?
Expectations for the session?

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Pulse check

On a scale of 1-5 how much do you know about the different GTM Metrics/ KPIs?

5: I live and breathe KPIs, and use it effectively to drive my GTM motion�

4: I leverage both leading, lagging, lever KPIs, but could do with learning some best practices �

3: I know what they are and have used them, but don’t feel comfortable with KPIs/ metrics�

2: I think I know what they are, but haven’t used them�

1: GTM KPIs.. What are those?

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Getting started

KPIs & the GTM Motion

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Why are KPIs important in the GTM process?

Measures progress vs. destination

Allows you to course correct

Aligns stakeholder priorities and actions

Builds your influence, which drives more impact

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Leading vs. Lagging vs. Lever KPIs

Lagging KPIs

Output, realised results
Measure of past performance
Example: Revenue, growth rate

Lever KPIs

KPIs that drive/ affect leading indicators
Example: Feature usage, referral rate

Leading KPIs:

Inputs, early indicator of performance
Predictive in nature
Examples: Pipeline, click through rate

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Using KPIs effectively

Choosing the right KPIs

Measurable, relevant, actionable

Deriving insights from KPI

Aligning stakeholder priorities & actions

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Hands on examples:

Different motions, different KPIs

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Case 1: Product-led growth

Lava graphic design software:

You are seasoned Product Marketing Manager at Lava, a leading graphic design platform known for its user-friendly interface and diverse range of design tools. Lava has identified a significant growth opportunity within the education sector and has decided to launch a tailored version of its platform called “Lava for Education."

Objective:

100,000 Paid Users in Year 1

Additional Info:

Purchase through the school/ university, or purchase individually

User count as it relates to the objective is per user

MAU- intentional action, using 2 features

Freemium Model

What are some of the KPIs we may want to track?

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Metrics we can consider:

Lagging Metric:

# of paid users

Leading Metrics:

User sign up
Trial Activation Rate
PQL
Freemium conversion rate
Referral rate

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Metrics we can consider:

Lagging Metric:

# of paid users

Leading Metrics:

User sign up: New accounts created
Trial Activation Rate: % of users who signed up for a trial and activated the trial
PQL: Customer lead with strong buying signal based on usage data and behaviour
Freemium conversion rate: % of free users who upgraded to a paid version of the product
Referral: # of new users acquired through referrals

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We may also want to keep an eye on

Customer Acquisition Cost

Cost of acquiring new customer
Total Sales & Marketing Expense/ Total number of customers

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Lava Graphic Design-

	Monthly Sign ups*	Total Accounts Active (end of the month)	Total PQL	Total Paid Users	Sign ups via referrals	Monthly Activation Rate
Month 1	1000	400	200	180	20	40%
Month 2	1500	1500	900	855	500	73%
Month 3	3000	3000	2100	1995	1500	60%

*Assume unique users

https://bit.ly/3uMoWDH

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1. Are we generating enough sign ups? What do we need to start/ stop doing.

�2. Are the sign ups the right profile, do they turn into paid users

�3. What’s paid user conversion rate? How many sign ups do we need to reach target?

4. Are there any features that drive paid user conversion?

�5. How are referrals driving sign ups?

�6. What is the cost of acquiring one customer? How’s CAC trending?

Questions I’d ask:

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Analyzing sign ups

	Sign ups	Growth
Month 1	1000
Month 2	1500	50%
Month 3	3000	100%
Q1	5500

What are some initial insights from the data?

What further data would you want to check?

How can this inform your GTM plan?

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Analyzing sign ups

	Sign ups	Growth
Month 1	1000
Month 2	1500	50%
Month 3	3000	100%
Q1	5500

Sample insights/ analysis:

Upward growth trajectory- how does it compare to expected/ benchmark growth?

E.g. 100% month on month increase in sign ups would result to 1.5M sign ups by year end

2. How many of these activate the trial?

How many go on to become paid users? What’s the churn rate? How much did we spend acquiring those customers? Did CAC grow as sign ups increased?

3. Top of funnel strategies- is it working or not? Room to optimize? How many of these were driven by referrals?

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Activation rate

	Monthly Sign ups	Total Accounts Active (end of month)
Month 1	1000	400
Month 2	1500	1500
Month 3	3000	3000

	Activation Rate
Month 1	40%
Month 2	100%
Month 3	100%

100% MoM activation rate?

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Activation rate

100% MoM activation rate?

! Data is misleading: Sign ups are monthly, but data is for total accounts active.

E.g. 3k total users, it could all be month 3 sign ups, or only 1500 incremental

What might be a more helpful metric to look at?

	Monthly Sign ups	Total Accounts Active (end of month)
Month 1	1000	400
Month 2	1500	1500
Month 3	3000	3000

	Activation Rate
Month 1	40%
Month 2	100%
Month 3	100%

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Activation rate- Quarterly view

	Monthly Sign ups	Total Accounts Active	Activation Rate
Month 1	1000	400
Month 2	1500	1500
Month 3	3000	3000
Q1	5500	3000	55%

Real Q1 activation rate: 55%

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Activation rate- Quarterly view

	Monthly Sign ups	Total Accounts Active	Activation Rate
Month 1	1000	400
Month 2	1500	1500
Month 3	3000	3000
Q1	5500	3000	55%

Real Q1 activation rate: 55%

Is 55% good or bad?
How is activation trending MoM?

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Activation rate-Monthly view

	Monthly Sign ups	Monthly Activation Rate
Month 1	1000	40%
Month 2	1500	73%
Month 3	3000	60%

	Sign ups	Monthly Activation Rate	Incremental Active users per month	Total Accounts Active
Month 1	1000	40%	400	400
Month 2	1500	73%	1100	1500
Month 3	3000	60%	1800	3000
Q1 Total	5500	55%	3300	3000

What are some initial insights from the data?

What further data would you want to check?

How can this inform your GTM plan?

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Activation rate-Monthly view

	Monthly Sign ups	Monthly Activation Rate
Month 1	1000	40%
Month 2	1500	73%
Month 3	3000	60%

	Sign ups	Monthly Activation Rate	Incremental Active users per month	Total Accounts Active
Month 1	1000	40%	400	400
Month 2	1500	73%	1100	1500
Month 3	3000	60%	1800	3000
Q1 Total	5500	55%	3300	3000

Sample Insights/ Analysis

Month 2 to Month 3? Worth investigating or still within expectations? How could we increase activation rate?

1800 incremental active users in month 3 but only 3000 active in total 🡪 300 customers “lapsed”

3. Profile of customers who continue usage vs. those who became dormant? What insights?

4. At an activation rate of 55%.. We would need 1.8M sign ups, and 100% free to paid conversion to hit the goal

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Product Qualified Lead & Freemium conversion

	Total Accounts Active	Total Product Qualified Lead	% Qualified Lead
Month 1	400	200	50%
Month 2	1500	900	60%
Month 3	3000	2100	70%

	Total Accounts Active	Total Product Qualified Lead	Paid Users	PQL TO Paid Ratio
Month 1	400	200	180	90%
Month 2	1500	900	855	95%
Month 3	3000	2100	1995	95%

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Product Qualified Lead & Freemium conversion

	Total Accounts Active	Total Product Qualified Lead	% Qualified Lead
Month 1	400	200	50%
Month 2	1500	900	60%
Month 3	3000	2100	70%

	Total Accounts Active	Total Product Qualified Lead	Paid Users	PQL TO Paid Ratio
Month 1	400	200	180	90%
Month 2	1500	900	855	95%
Month 3	3000	2100	1995	95%

Sample Insights/ Analysis

70% of Active accounts are qualified leads – what’s the benchmark/ expectation?

Are there trends/ patterns among activated customers who are also qualified leads?

3. Good PQL to paid conversion- 95% of PQL upgraded to paid version

4. Any feature usage that is a strong indicator of freemium conversion?

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Funnel

55%

70%

95%

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Referrals

	Monthly Sign Ups	Sign ups via referrals	% Referral Rate
Month 1	1000	20	2%
Month 2	1500	500	33%
Month 3	3000	1500	50%

What questions are top of mind?

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Referrals

	Monthly Sign Ups	Sign ups via referrals	% Referral Rate
Month 1	1000	20	2%
Month 2	1500	500	33%
Month 3	3000	1500	50%

Sample Insights/ Analysis

What is driving increase in referral rate?
What characteristics are in common between users who refer other users?
Are referrals turning into paid users?

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Sample summary of Insights:

Positive sign up momentum at 100% growth in Month 3 driven by referrals, but only 55% activation rate below the benchmark of X%.

Product showing signs of stickiness with 70% of activated users becoming qualified leads

Good PQL signals. PQL strong indication for conversion, 95% of PQL becoming paid within the quarter.

Growth constrained by activation rate, need an additional _____ to hit 100k goal

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Sample summary of Insights:

Positive sign up momentum at 100% growth in Month 3 driven by referrals, but only 55% activation rate below the benchmark of X%.

Product showing signs of stickiness with 70% of activated users becoming qualified leads

PQL strong indication for conversion, 95% of PQL becoming paid within the quarter.

Growth constrained by activation rate, need an additional ~270k to hit 100k goal

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What might be a good KPI to track moving forward?

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Case 2: Sales-led growth

Dynamite Enterprise CRM:

You are a seasoned Go-to-Market Leader at MacroSolutions, the leading provider of enterprise software solutions worldwide. MacroSolutions has developed a cutting-edge, Generative AI powered Customer Relationship Management (CRM) platform, Dynamite, aimed at empowering sales teams to streamline their processes and increase revenue.

Objective:

$20M revenue in year 1

Additional Info:

Revenue is $50 per user per month

What are some of the KPIs we may want to track?

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Metrics we can consider:

Lagging Metric:

Revenue in $

Leading Metrics:

Pipeline
Average Deal size
Win rate
Sales cycle length

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Metrics we can consider:

Lagging Metric:

Revenue in $

Leading Metrics:

Pipeline: Measure of potential sales Representative of where potential customers are in their buying process
Average deal size
Win rate % of total deals that result in a win
Sales cycle length: Average number of days it takes for an opportunity to close

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Dynamite Enterprise CRM

Company operates on a calendar year�
Today is January 1, 2024�
Assume all opportunities were created in January 1, and are qualified opportunities

2024 Pipeline

Year	Number of opportunities	Pipeline
2024	200	17,000,000

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H2 2023 Opps

Opp #	Days Open	Status	Pipeline Value
1	180	Closed Lost	60,000
2	190	Closed Won	45,000
3	195	Closed Lost	15,000
4	197	Closed Lost	200,000
5	220	Closed Lost	100,000
6	250	Closed Won	125,000
7	190	Closed Won	45,000
8	160	Closed Won	45,000
9	140	Closed Won	50,000
10	200	Closed Won	150,000
11	210	Closed Won	60,000
12	220	Closed Won	60,000
13	210	Closed Won	200,000
14	130	Closed Won	45,000
15	70	Closed Won	15,000
16	120	Closed Won	15,000
17	160	Closed Won	60,000
18	200	Closed Won	250,000
19	110	Closed Won	40,000
20	200	Closed Won	150,000

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1. How many of the 17M pipeline will we likely convert?

�2. Are we able to convert in time?

�3. If not, how much more pipeline do we need and by when?

�4. How many qualified leads/ opportunities do we need to hit target?

Questions I’d ask:

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Let’s look at win rate

Opportunity Number	Days Open	Status	Pipeline Value
1	180	Closed Lost	60,000
2	190	Closed Won	45,000
3	195	Closed Lost	15,000
4	197	Closed Lost	200,000
5	220	Closed Lost	100,000
6	250	Closed Won	125,000
7	190	Closed Won	45,000
8	160	Closed Won	45,000
9	140	Closed Won	50,000
10	200	Closed Won	150,000
11	210	Closed Won	60,000
12	220	Closed Won	60,000
13	210	Closed Won	200,000
14	130	Closed Won	45,000
15	70	Closed Won	15,000
16	120	Closed Won	15,000
17	160	Closed Won	60,000
18	200	Closed Won	250,000
19	110	Closed Won	40,000
20	200	Closed Won	150,000

What % of deals closed last year?

How many will we likely close of the 17M?

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Let’s look at win rate

What % of deals closed last year?

Won: 13

Lost: 7

Win rate: 65%

How many will we likely close of the 17M?

Based on a 65% win rate- $11M to close

c

Opportunity Number	Days Open	Status	Pipeline Value
1	180	Closed Lost	60,000
2	190	Closed Won	45,000
3	195	Closed Lost	15,000
4	197	Closed Lost	200,000
5	220	Closed Lost	100,000
6	250	Closed Won	125,000
7	190	Closed Won	45,000
8	160	Closed Won	45,000
9	140	Closed Won	50,000
10	200	Closed Won	150,000
11	210	Closed Won	60,000
12	220	Closed Won	60,000
13	210	Closed Won	200,000
14	130	Closed Won	45,000
15	70	Closed Won	15,000
16	120	Closed Won	15,000
17	160	Closed Won	60,000
18	200	Closed Won	250,000
19	110	Closed Won	40,000
20	200	Closed Won	150,000

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Looking at average length of sales cycle

Opportunity Number	Days Open	Status	Pipeline Value
1	180	Closed Lost	60,000
2	190	Closed Won	45,000
3	195	Closed Lost	15,000
4	197	Closed Lost	200,000
5	220	Closed Lost	100,000
6	250	Closed Won	125,000
7	190	Closed Won	45,000
8	160	Closed Won	45,000
9	140	Closed Won	50,000
10	200	Closed Won	150,000
11	210	Closed Won	60,000
12	220	Closed Won	60,000
13	210	Closed Won	200,000
14	130	Closed Won	45,000
15	70	Closed Won	15,000
16	120	Closed Won	15,000
17	160	Closed Won	60,000
18	200	Closed Won	250,000
19	110	Closed Won	40,000
20	200	Closed Won	150,000

Will it close within the year

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Looking at average length of sales cycle

Opportunity Number	Days Open	Status	Pipeline Value
1	180	Closed Lost	60,000
2	190	Closed Won	45,000
3	195	Closed Lost	15,000
4	197	Closed Lost	200,000
5	220	Closed Lost	100,000
6	250	Closed Won	125,000
7	190	Closed Won	45,000
8	160	Closed Won	45,000
9	140	Closed Won	50,000
10	200	Closed Won	150,000
11	210	Closed Won	60,000
12	220	Closed Won	60,000
13	210	Closed Won	200,000
14	130	Closed Won	45,000
15	70	Closed Won	15,000
16	120	Closed Won	15,000
17	160	Closed Won	60,000
18	200	Closed Won	250,000
19	110	Closed Won	40,000
20	200	Closed Won	150,000

c

Will it close within the year

Average length of sales cycle:

177 days

Average Deal Size

86k

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Target	20,000,000
Projected Revenue	11,050,000
Gap to budget	8,950,000
Pipeline Needed	13,769,231

How much pipeline do we need and by when to hit the KPI?

2024 Pipeline	17,000,000
Win Rate	65%
Projected Revenue	11,050,000

Year	Number of Opportunities	Pipeline
2024	200	17,000,000

Sense check - average deal size is 85k- close to H2 data!

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By June at the latest!

How much pipeline do we need and by when to hit the KPI?

2024 Pipeline	17,000,000
Win Rate	65%
Projected Revenue	11,050,000

Target	20,000,000
Projected Revenue	11,050,000
Gap to budget	8,950,000
Pipeline Needed	13,769,231

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Summary of Insights so far:

Good conversion rate: Possibly due to low number of opps- how can we maintain?

6 months average sales cycle: Opportunity to shorten while maintaining conversion rate?

Likely 9M gap to budget: how can we increase deal size to bridge the gap?

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What might be a good KPI to track moving forward?

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Another way to analyse based on pipeline velocity

Pipeline Velocity = Number of Opps in pipeline x Average Deal Size x Win rate

Average Sales Length

Pipeline Velocity = (20 * 86.5k *65%) / 177 days

Pipeline Velocity = 6k/ day

Year	Number of Opportunities	Pipeline
2024	200	17,000,000

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Comfort break [10 min]

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Group exercise [20 min]

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Case 1: Product-led growth

Lava graphic design software:

You are seasoned Product Marketing Manager at Lava, a leading graphic design platform known for its user-friendly interface and diverse range of design tools. Lava has identified a significant growth opportunity within the education sector and has decided to launch a tailored version of its platform called “Lava for Education."

Objective:

100,000 Paid Users in Year 1

Additional Info:

Purchase through the school/ university, or purchase individually

User count as it relates to the objective is per user

MAU- intentional action, using 2 features

Freemium Model

What are some of the KPIs we may want to track?

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Breakout Task

You were tasked to present an update on the product launch to the SLT.

How would you describe the state of the launch and progress vs. the goal?
What are some forward actions you will be taking?
What support would you need from other stakeholders?

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Lava Graphic Design

	Monthly Sign ups*	Total Accounts Active (end of the month)	Total PQL	Total Paid Users	Sign ups via referrals	Monthly Activation Rate
Month 1	1500	1050	420	378	1000	70%
Month 2	1950	2610	1175	940	900	80%
Month 3	2535	4638	2089	2570	1200	80%

*Assume unique users

https://bit.ly/3TgNBK6

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Share your analysis!

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Wrap up & final Q&A

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