🌊 Customer Intelligence

The future of post-sales, customer intelligence and net dollar retention.

Dec 04, 2022

Hello! I’m Ivan Landabaso, partner at JME.vc. Join >5K entrepreneurs surfing startup trends. Once or twice per month. From Spain to the 🌍.

Summary

Gm startup riders! This week’s good stuff for your startup brain includes:

🧠 Customer Intelligence: How to setup and leverage post-sales.
🍬 Startup Candy: features vs needs, Goggins’ insights, nike’s creativity.
💵 Deals & Jobs: 15 startup deals in Spain (>€200M).

This month we are going deep on post-sales and customer intelligence - everything that happens after you close a deal - with my friend Javed Maqsood.

Javed and I met working together at Bloomreach in Silicon Valley back in 2014, when he was leading post-sales efforts as Head of Technical Services. Today, after 20 years of work at startups and big software companies - he is focused on providing turnkey consulting services for startups who need to solve post-sales problems.

He holds several EIR (Expert in Residence) types of roles; for VC companies like Peakspan Capital; for incubation services like Imperial Enterprise Labs, part of Imperial College, London - and has a very clear vision on what the customer intelligence tech frontier looks like. Lets get into it:

😟 Problem

Post-sales in SaaS businesses has traditionally been an afterthought.

In today’s SaaS businesses, everyone is focused on a few, much-talked-about key metrics like YoY revenue growth, gross margin, CAC (customer acquisition cost), NDR (net dollar retention) and CLTV (customer lifetime value).

Founders, VCs and industry pundits all hang their hats around these metrics.

During a recent conversation with a VC whose firm has $90B under management I learned - he can easily identify the top 5 companies in his portfolio, with the indicator being NDR. Yet, the best SaaS companies strive to achieve these healthy metrics during their growth phase, but struggle mightily.

What goes wrong? Post-sales orchestration.

🤩 Solution

Customer Intelligence in post-sales has to tightly align with your customer orchestration motions.

Customer intelligence is the data, analytics, signals and sentiments coming from your customer base; structured and organised in a manner that aligns with your success KPIs, aligned with the value that your product brings to your customers.

If done right, customer intelligence can become your number one churn predictor.

⏳ How we got here

Historically speaking, post sales for traditional, on-prem software was all about Services and Support. Account Management was post-sales. AEs would be responsible for upsells and cross sells.

In the early 2000s, the software ecosystem evolved - SaaS was becoming the de facto standard, new terminologies entered the market like ARR. Companies quickly realized that while they were signing up new customers in a SaaS model, customers were also leaving in droves. To address this, the concept of customer success came into play - were pioneered by companies like Salesforce during their tremendous growth phase. The mission was simple - keep customers happy post-sale, make them brand ambassadors.

This new category of customer success also started to bring new software companies to the market - the first cs oriented platform came in 2009. Battery Ventures nurtured and launched Gainsight in 2013 to “help SaaS companies manage their existing customers”. They were the pioneers in defining this market category. In 2020, Gainsight was acquired by Vista Equity.

Through this evolution, the focus of these software platforms have been customer orchestration (i.e. playbooks and call to action) to manage the happiness of customers. But still around 2014-2015, the post-sales community was still struggling with unexpected churn and needed a way to understand how to address this.

Thus came this new category called “Customer Intelligence” that gave these teams insights about customer sentiment. Today, with the advent of technology and wide ranging adoption of AI / ML, the landscape has changed dramatically, very rapidly. A new market category has been born.

As this category matures further, use of AI in Customer Intelligence will continue to be the game changer. Also, PLG (Product-Led-Growth) SaaS companies, their unique growth strategy has revolutionized the CI landscape and will continue to do so.

🗺️ Market Map

There are so many players in this emerging market, but we will focus on the following

There are heavyweights on this list - Gainsight / ChurnZero and Totango. These platforms have been around between 7-13 years collectively and they are more recognized players in the post-sales / customer success ecosystem.

CI is still a young and evolving category that has gathered momentum and life in the last 5 years. So these more veteran companies have had to evolve their platform to make space for CI features. That’s a disadvantage that you should be aware of, if you are in the market to evaluate these companies and market category.

Whereas, a platform like involve.ai was born with the premise of driving customer signals and insights to fight churn. Vitally.io has the message of ‘get your customer insights’ to drive customer adoption and fight churn. Reef.ai talks about cutting the noise in CS and driving customer revenue with the insights that matter.

My intention in this article is not to promote any particular platform, but give you the perspective of the history and players, as it relates to CI.

What is your ultimate path, strategy and marching order down the CI road in your post-sales journey? There is not one, single answer. It really depends on where you are in your maturity cycle as a company, platform and your customer base.

🚀 Customer Intelligence: A Business Case

Here is a use case that really could capture everyone’s imagination around CI.

A data driven SaaS company, with a very technically deep product set - product lives in the world of engineering, developers and CI/CD. The platform can run on many different development platforms, across many customer verticals. The company has been around for some time and has built up a solid customer base.

The goal of their post-sales customer analytics team: how do I keep the wheel in motion moving forward in post-sales to:

Keep churn down
Drive incremental product sales
Introduce new Services offerings
Improve customer adoption
Reduce number of support tickets submitted

This is a great setup for driving action through customer insights from the field.

The analytics team’s plan:

Overall goal: Drive NDR up by x%
Hypotheses: Drive some assumption around questions like - What really is an indicator about our customer’s usage of the product? What factors do we think play into customer behaviors?
Need for data: What data would we need to collect to drive answers to these hypotheses?
Gather the data: Bring the right team, technology, platform to gather the data. Do I need product usage data from the product engineering team? How do we gather customer data and sentiment? Do we use an off the shelf platform? Or do we have the capacity to drive this data ourselves?
Assumption: Out of these initial hypotheses, which ones make the final list? Let the data drive those decisions.
Decision: Which assumption will I bring in front of my field team to execute upon?
Impact: What program/process will I introduce to my post-sales team and how will this contribute to my overall goal?

In this real use-case from the field, CI stabilized a declining NDR and started to trend it upwards.

🔍 Customer Intelligence: Signal Vs Noise

Here’s an example to disambiguate between real signal and noise:

When customers log into your portal to build something - this indicates adoption.

But should you count every login as a key KPI itself? Or should you combine this with ‘what the user did’ when he/she logged into the portal?

In this example, most practitioners will indicate that portal login is a good enough of an indicator of adoption - that’s noise.

Be careful not to fall for it - go one step deeper and strive to understand what is the data telling me about the customer journey with my product or solution.

What the user did - is that something increases your stickiness or reduces your overall ROI? Analyzing this and taking these perspectives into account should construct a more complete adoption of features success metric in your post-sales cycle.

Measuring and capturing these important, well thought out metrics are what intelligent CI is about.

As the customer is going through the maturity cycle with your product, remember to:

Determine which metrics matter.
Decide how you are going to obtain these metrics.
Filter out the important ones from the noise.
Measure, learn, implement and iterate.

👌 Hacks

Here are some specific insights on how to think about CI:

Keep your customer journey vision and orchestration simple.
Understand your customers and their ecosystem.
Make sure your data gathering methods (machine driven / human driven) are systematic, accurate and aligned with your customer journey.
CI should not just be limited to your customer success team; what sort of intelligence can you gather from the Support, Services, Sales, Account Management team?
Once you have started gathering the data, start building trend reporting and dashboard that align with your customer health measurement.
Ensure that all metrics and KPIs from a successful customer become your guiding light towards reducing churn and ultimately a healthy NDR
Ensure that you are learning lessons from customer journeys that went wrong.
Bring the intelligence back to other parts of your company - product, engineering, sales, marketing and continue to innovate.
Keep iterating through this; don’t stop learning, tweaking, and improving.

🐇 Follow the White Rabbit 🕳️

Where can you start? Think of three categories:

Data and insights that come to you - mailing lists. Sign up to some mailing list that matters to you. A good one is GGR (Gain Grow Retain)
Insights that you would need to gather as you walk down a specific project, or want to get educated in the topic of CI.
1. Blogs - This is a good place to start - https://city.involve.ai/the-customer-intelligence-movement-44. This is run by involve.ai
2. Open books - Learn from mature post-sales organizations that has an open book policy - https://about.gitlab.com/handbook/customer-success/
3. Podcasts - There are too many out there. But if you really want to learn more about CI, follow a focused one like https://www.smartkarrot.com/resources/podcast/
Forums - Interactive forums and communities where there is live conversation around post-sales, customers, customer intelligence across practitioners. Here is a sample of several that I follow - CS Network