In the zero-interest world, SaaS sales teams were frequently built with an aggressive growth mindset. Hire more reps, deploy more quota to the street and worry less about costs.

The last two years have upended that view. Business plans evolved to balance growth against costs to achieve said growth. The new model is to build a solid growth plan but also to use formulas like the Rule of 40 and the SaaS Magic Number to measure that growth’s efficiency.

This paradigm shift impacted sales teams immediately – sales teams either got reduced or leaders ceased replacing sales reps who left, or both. Meanwhile, we saw another output: More sales reps missed quota during this period, even with fewer reps inside the business. This is a double whammy—reps being laid off and the remaining reps still missing quota… not the definition of a winning locker room.

Typically, SaaS companies plan for 60-80% of their sales reps to achieve quota in any given period. But it was common in 2022 and 2023 to see performance dip to around 10% – 35% of reps making quota in a given quarter—a big decrease from previous periods.

If we changed the model to run the business more efficiently, yet are still seeing more reps missing quota, shouldn’t we explore new quota deployment models?

A tale of two sales models

Let’s explore some fresh approaches to building the sales plan with a focus on efficiency, while creating a winning sales floor. Consider this example:

Company A achieved $5.25M in new business, and it cost just shy of $1.9M. This would be a good team to be on – lots of reps selling software and achieving high performance. But most companies in recent years have looked less like Company A and more like Company B below.

Company B produced $2.6M at a cost of $1.5M, and likely didn’t have anyone hitting their quota. Worse yet, they assigned out $7.5M of quota against a $5M plan to achieve $2.6M. No bueno – this is a low morale team.

Real talk: It’s time to right-size your sales team

So, here’s the million (or even billion) dollar question. Your company performance looks like Company B for the last 2 years. If less than 50% of your sales team is attaining quota, why are you carrying more reps (and more expense) than is necessary? You’re acting with the false hope that simply by having quota capacity deployed, you’ll somehow hit quota.

Have you considered reducing the number of reps, therefore reducing cost, while producing the same bookings and producing a “winning” feeling on the sales floor? This cuts to the heart of the issue – it absolutely sucks to lay people off. But if your company is still spending too much to support a bad plan, you need to reckon with the scoreboard.

Ask yourself this: In the last 8 quarters, how many board meetings have you confidently walked into showing a beat for last quarter’s quota and a forecast above next quarter’s plan? It’s time to get your targets set right and your achievements in positive territory. A right-sized plan is not only pragmatic in terms of cost; it also helps the rep you have on the street be successful and thus fosters a winning culture.

If you’re tempted to protest, “But I’m keeping rep capacity for when the economy gets better,” I’d reply that all indicators suggest that any improvement will be gradual. If that prediction holds true, you can always build milestones into your plans (i.e. if 75% of my reps hit quota, then I’ll hire an incremental rep) that let you re-start the hiring machine.

So, let’s look at Company C’s sales plan:

Company C lowered the goal to $3.75M and likewise lowered their costs by nearly $300K versus Company B. But because there is more available surface area per rep, this model showed 85% productivity producing $3.8M, which is $1.2M more than the Company B bookings at a heck of a lot lower cost. Why would Company C’s 6-member team outperform Company B’s 10-person team? Because winning is infectious… great sales leaders will recognize this phenomenon.

Let’s go further. What if this entire team outperformed their quota? What if every rep hit 110%, which would produce $825K of bookings per AE and a total of almost $5M? Assuming you paid all commission dollars at 125%, that would mean you booked $4.9M ($825K x 6) and it cost you $1.57M ($110K x 6) + (6 x $110K x 1.1 x 1.25).

Optimize for AE attainment rate–and watch success beget success

I’d argue it’s time to start exploring how to deploy quota to the street. Consider moving away from the traditional “insurance plan” of 20-30% over-assignment of street quota to company plan, and instead optimize for AE attainment rate. It’s worth repeating but winning is infectious. Most sales leaders have witnessed how success begets success.

One final note: All the examples above are just theoretical models and are therefore imperfect. Company C’s model still has risk–if even one rep quits, it throws the whole model off. It’s also worth noting that all quotas were kept constant in the three examples. Adjusting quota itself might be another area to explore based on the last 24 months of actual performance.

Bottom line, I wrote this post to ask thought-provoking questions. If what we’ve been doing for the last 24 months hasn’t yielded the results we want and has created lower morale in the sales team, then it’s worth revisiting the plan’s framework. Happy modeling!

The post It’s Time to Right-Size Your Sales Model. Here’s How to Get Started. appeared first on Battery Ventures.

However, actual enterprise implementation of AI is proving to be more complicated, something that could adversely impact the fortunes of software companies selling into the enterprise, at least in the near term.

Specifically, many organizations considering AI implementations are concerned about the potential loss of corporate data, a risk that weighs heavily on the minds of buyers and their legal counterparts, for example. On the vendor side, many software startups are scrambling to build gen AI products and workflows to enable new user experiences. But software companies cannot take a ‘build it and they will come’ approach but indeed need to satisfy AI-hesitant buyers – or worse, they may damage their long-term growth.

From our conversations with technology and risk-management executives, we have identified many of the primary concerns that enterprises are addressing in negotiations with new software sellers:

• Employee training: As workflows change to incorporate AI, many executives understand that this will require educating their employees on the associated risks, which can create a significant administrative burden.
• Risk assessment surveys and accreditation: Since gen AI is still an emerging technology, there are no industry-standard criteria for its implementation. Until this stabilizes, sellers will get a broad range of new questions in the vendor assessment process which will require attention from sales, product and other teams.
• Non-standard contractual agreements, such as indemnification: Since many AI products are built on third-party tools, sellers are seeing new contract clauses that can expose them to new risks.
• Zero-data retention: For security and data privacy reasons, some enterprises are requiring that their data not be retained for training or other uses.
• Tokenization of data: Often associated with retrieval-augmented generation (RAG), as well as data masking and redaction, tokenizing or obfuscating data into a model can limit its ability to perform iterative reasoning.
• Blocking services: Many enterprises’ knee-jerk reaction is to block large-language model (LLM) services such as ChatGPT, though many approved applications have embedded these LLMs into their product.
• Abstraction of underlying services: Potentially the most interesting (and disruptive) is the concept of abstracting underlying applications via an enterprise-built bot-of-bots.

We saw many of these challenges and anxieties from buyers with the emergence of SaaS years ago. Thankfully, over time, as tech, legal and compliance organizations gained more of an understanding of true risk associated with SaaS and cloud products, those concerns lessened, and the sales process became easier. That process took years, as will likely be the case for AI implementation.

Impacts on startups selling into the enterprise

A patchwork approach to AI implementation, through which cautious enterprise buyers exercise strict oversight over different tools to meet the individual needs of different parts of the organization, can hinder productivity, efficiency and strategy in myriad ways:

• Uncertainty and delays in sales cycles: Inconsistent risk assessments can introduce uncertainty and delays in a startup’s sales cycles. Potential customers may request multiple assessments, leading to prolonged negotiations and increased scrutiny of the product. This uncertainty can deter customers from adopting new products and slow the sales process.
• Increased costs: Working with multiple risk-assessment services providers can be expensive. Sellers need to pay for individual assessments, manage vendor relationships and allocate internal resources to coordinate the process. These costs can strain budgets and limit the resources available for product innovation.
• Splitting the roadmap: In prior technological shifts, such as the transition to the cloud, legacy software providers were forced by many of their largest customers to have a dual-product strategy. Since the product and engineering team has finite resources to maintain the infrastructure and code base, many software companies will make the decision to fire customers that will hold them back.

The fragmented risk landscape and resulting patchwork approach to AI implementation could, we fear, foster an inefficient enterprise environment in which both enterprises and software vendors manage through risk calculations. An industry-standard approach – alongside product and service standards – would simplify decision-making, improve risk-mitigation strategies, accelerate time-to-value cycles and ultimately, drive greater innovation.

Rethinking use of software

There is also an emerging shift greater than legal and product changes. The most successful B2B software companies have built products that engage users in the product and charge based on increased productivity. Many of these revenue models are based on the number of employees with a login. This calculus could change when the product is disintermediated by a higher-level bot.

An example from one major enterprise is a home-built internal gen AI bot that orchestrates across multiple apps and services for employees. For example, to request vacation time off, an employee will simply make the request in natural language to the internal bot. The bot will tee up the bots across a web of apps such as Workday, Office 365, ADP and others, to schedule time off and grant approval, creating efficiencies that track exactly to internal rules and guidelines.

We have seen industry specific companies, such as online travel aggregators (OTAs), abstract the customer away from the company providing the service such as hotels. The travel industry spent years re-building consumer loyalty.

In this bot-over-bot world, both enterprises and software startups will need to navigate what it means to build differentiated product experiences and business models that command the premium required to fund future innovation.

The post What Enterprise Anxieties over Generative AI Implementation Mean for Sellers appeared first on Battery Ventures.

After my pivot into the software world, I’ve experienced firsthand how challenging it can be to explain a complex technology in accessible language or reach a niche buyer audience. Good software marketing is hard, but excellent marketing is extremely hard—to explain, to implement and to scale.

I’ve been thinking about this a lot lately, especially after my younger brother founded his own startup, a journey that has inspired some interesting conversations between us about brand and perception in the technology industry. In one such conversation, he raved about PostHog, an open-source software company that creates product and data tools.

PostHog helps customers (like my brother’s company) understand who is using their product and how, providing important insight into user behavior and preference. Understanding your audience is clearly central to the PostHog product roadmap, but it’s also core to the company’s brand. And their brand is truly unlike anything I’ve observed in the software industry to date.

Let me explain, because brand is one of those words that can be easy to sneer at, like “synergy” or “disruptive.” Brand too often becomes the junk drawer of a company’s identity—its reputation, its products, its market positioning, how it appeals to customers, how it appeals to prospective employees and beyond. But a real brand, one with staying power and resonance, is thoughtful and intentional and real. It takes effort. It makes people care.

For many SaaS companies, where marketing is increasingly managed by dashboard KPIs that are levels removed from an actual customer, brands have become depersonalized and mass-produced: logos splayed across billboards on the highway to the SFO airport, on water bottles or pens at a conference or in thousands (even millions) of dollars’ worth of digital ads. These brands explain too much to too many, and therefore, they mean nothing at all.

And as AI tools become more ubiquitous, with copy written by algorithm and cold email dumps lightly personalized and sprayed out to the masses, I fear these brands will be stretched to the limits of what customers (and spam filters) will tolerate.

But PostHog is different. PostHog marketing is so excellent, I took pages of notes scrolling through their website and social media feeds.

The PostHog brand distills exactly what matters to their customer audience and strips everything else away. And it’s working. Most viable SaaS startups hope to recover the cost of acquiring a customer — known as a customer acquisition cost (CAC) payback — in a matter of months to years. PostHog does so in just five days, a true testament to their efficiency and how well-informed and satisfied their customers are.

And so, without further ado, here’s what I love (and what you should know) about PostHog marketing:

They’re transparent.

From the company’s original 2020 launch post on Hacker News to now, the PostHog team has sought external feedback from their ideal customer profile (ICP): involving them closely in the growth and development of the company.

PostHog’s website is a masterclass in customer engagement and transparency. They’ve clearly invested significant resources (time, thought and money) into making their website a welcoming and informative hub for their customers. Today, any visitor to posthog.com can read the company’s handbook: an interactive and continually updated guide to the company’s history, how the products work, why they’ve discontinued products, how the company makes money and more, written with refreshing candor.

In the handbook, PostHog allows visitors to ask questions on each page about anything—its products, the company’s growth plans, pricing etc. This level of transparency and accessibility is unusual, coming at a time when many companies have outsourced customer engagement to chatbots or impersonal FAQ pages. But it’s also emblematic of the company’s values, which they spell out in the Marketing section of the handbook.

The last two lines in the “No sneaky shit” section of the handbook’s marketing overview provide an excellent summarization of what I love about the PostHog approach to brand.

“Don’t pretend our customers are different from us–i.e. more gullible, more susceptible to marketing. We are an engineering-led team building products for other engineers. If you wouldn’t like it, assume our customers wouldn’t either.”

They’re consistent.

Consistency, meaning an alignment of the company’s values, beliefs and behaviors, prevails in PostHog’s effort to cultivate brand. By clearly defining the audience for marketing content – founders, product engineers, existing users and B2B SaaS companies, and by working carefully to determine which kinds of content will most resonate with those people, PostHog is consistent in its message.

But we can see consistency in other, more unusual places. Something I was surprised to see is that 100% of PostHog employees write code. The entire team, marketing included, has developer experience and knows what it’s like to build software, illustrative of their team’s conviction to understand their customers.

True to their roots as an open-source company, too, PostHog builds the marketing function in public. The team discusses plans to create hub pages, experimentation on social media platforms and paid advertising strategy and more openly on GitHub.

The consistently top-down nature of PostHog marketing is also striking. The CEO, James Hawkins, not only is deeply involved in the company’s social channels, but he himself is an open book. On the PostHog website, for example, he responds to user questions – personal and professional – and shares a list of his personal quirks, values and responsibilities.

James regularly posts on Twitter/X updates on the company, memes, even offers to take PostHog users out for noodles in exchange for live feedback on their experience using the platform. His brand, inextricably linked to PostHog’s, is deeply and consistently authentic.

They’re a little weird.

Full disclosure, I’m also a little weird. Given what I know about folks building software, my brother included, it would stand to reason that many PostHog customers are, too. So, I think the company is right on point by sharing their collective weirdness with the world. We are starved for authenticity!

‘Marketing-speak’ has a negative connotation for good reasons. All too often, marketing teams and company leadership are hesitant to deviate away from neutral, broadly accessible branding for fear of alienating prospective buyers or distracting from the product itself. But it may be holding you back, particularly among younger generations.

The company’s distinctive hedgehog branding, prominently displayed on its social graphics, its website and its explainer videos, is one such example of welcome weirdness. You recognize PostHog content when you see it in your feed. And from what I can tell from the company’s engagement — likes, comments and reach on Twitter/X—people enjoy seeing it, too.

Personal and creative touches abound in PostHog marketing. On the dedicated landing page for the company’s infrastructure team, for example, visitors are greeted by a button to register for updates on projects the team is building, in addition to a list of Slack emojis created by the team and their individual perspective on whether pineapple belongs on pizza.

As one of the most prolific users of Slack on the global Battery team, I appreciate the enthusiasm for custom emojis. But most of all, I appreciate the dedication to their philosophy about being a little weird in public, again explained in the handbook:

“It’s ok to have a sense of humor. We are more likely to die because we are forgettable, not because we made a lame joke once. We have a very distinctive and weird company culture, and we should share that with customers instead of putting on a fake corporate persona when we talk to them.” Amen to that.

In conclusion…

Building a quality brand is difficult and a little scary, especially when you’re swimming against the tide of what everyone else is doing. What PostHog has done—the hard work of distilling their marketing, what they put out into the world, into exactly who they are and what their customers need—is something to admire.

I see PostHog marketing as the gold standard for a modern brand: authentic, consistent, weird and wonderful. I look forward to continuing to learn from them.

The post The Genius of PostHog Marketing appeared first on Battery Ventures.

In this blog post, we will examine both models and offer real-world use case examples. We’ll also analyze key factors that can guide founders in deciding between free-trial or freemium pricing, ensuring that the decision aligns with a company’s goals and market dynamics.

The Case for a Freemium Pricing Model
You may be familiar with Atlassian, the maker of developer-collaboration software like Jira, Confluence and Trello. Seven years ago, Atlassian launched Stride, a team chat product, to compete with Slack. At that time, Slack was a four-year-old product, growing quickly but nowhere close to being a category leader. Atlassian already had previous products with an existing customer base of over 100,000 – a substantial distribution advantage. So, they saw the opportunity to take on Slack with Stride, pricing it attractively at $2 per user/month. The price point, already considered fairly inexpensive for an enterprise-software product, also came with a 30-day free trial, allowing customers to start for free and either pay or leave after the trial period.

Slack, on the other hand, was 4x as expensive as Stride, with pricing plans that started at $8 per user/month and went up to $15 per user/month. Slack also had a “forever” freemium plan ($0 per user/month), but it came with a limited feature set. The basic plan was sticky, allowing Slack to capture customers for life and, eventually, get them to upgrade to a paid plan.

After a tough two-year market battle, Atlassian’s free-trial approach lost out to Slack’s freemium model and eventually sold the Stride business to Slack. Scott Farquhar, CEO of Atlassian, recently shared on a podcast that Stride had a better product than Slack and was priced much cheaper but ultimately lost the battle. Why? Slack’s freemium plan costs nothing and supports unlimited users, even though it maintains gated features such as search, message history, and others.

Atlassian learned a lesson here: given a choice, customers often opt for freemium over free trial, even if they will eventually have to pay a price for more functionality. Many customers don’t require the extra, paid features and can function just fine on the free, basic option. The company then eventually shifted their GTM strategy from free trial to freemium, launching free tiers for all their cloud products in 2019.

The Case for a Free-Trial Pricing Model
Moving entirely from free trial to freemium is not a slam-dunk decision, however. And it’s not the perfect answer for all companies. For example, Autodesk, a highly successful SaaS provider, thrived under a free-trial strategy, although it took a much longer time for the process to grow the company.

Autodesk, designed for industrial designers, architects and graphic designers, has successfully built a $5 billion business over a span of 40 years, primarily through seat-based licensing. Distinct from competitors like Canva or Adobe, Autodesk opted against a freemium model, choosing instead to offer a 15-day free trial, followed by monthly or annual subscriptions. By sustaining a substantial price point ($250 per user per month) and catering to a relatively limited market (a few million users), Autodesk has effectively captured significant market share within its segment.

Autodesk’s ability to thrive with a free-trial model can be attributed to several key factors. First, the specialized and high-value nature of its software justifies a higher price point, making a freemium model less practical. The complexity and advanced feature set of Autodesk’s products often requires a significant investment in time to learn and integrate into existing workflows, which aligns well with a free-trial approach that allows users to fully experience the software’s capabilities before committing to a purchase. Additionally, Autodesk’s target market of professional designers and architects is accustomed to investing in premium tools that deliver substantial value, making them more likely to transition from a trial to a paid subscription. This targeted approach ensures that Autodesk can maintain high revenue per user while effectively serving its niche market.

Freemium vs. Free-Trial: How to Decide

Both pricing models have their merits, depending on a business’s industry and goals. Here are several of the factors to consider in deciding your own pricing model:

Total Addressable Market (TAM): Finite vs. Large
For a freemium model to succeed, a company needs a large TAM. When a company gives away a product for free, many users will find workarounds to remain on a free plan and not upgrade to a paid subscription. You’ll need a substantial user base to secure enough paying users. By contrast, if you have a smaller, but more dedicated, market, you may see more success with a free-trial model, as did Autodesk.

Business Goal: User Base vs. Revenue
A freemium approach is a better fit for companies with the primary goal of attracting as many users as possible, since it is likely many will opt to remain on the free plan. The potential for these users to upgrade to a paid plan depends on the attractiveness of premium features and the price point – and it’s not guaranteed. This approach is effective for building a substantial user base and generating revenue from a smaller segment of that base. On the other hand, a free-trial approach targets more serious potential customers by offering a time-limited opportunity (15-30 days, typically) to try the product before making a purchase decision. By focusing on users who are more likely to pay, a free-trial approach deprioritizes more casual users to facilitate a quicker transition from trial to paid customer.

Price Point: High vs. Low
Pricing for subscription software varies widely, anywhere between $10 to $1,000 per user per month, with variability stemming from a product’s value proposition and users’ willingness to pay. A freemium strategy typically is most effective at a lower price point – between $10-50 per month – at which individual users can easily manage expenses and seek employer reimbursement. At this price point, users are more likely to seek out and purchase the software themselves without requiring a hands-on sales process. Conversely, selling higher-priced software will often require human interaction to build trust with buyers, understand prospective users’ pain points, effectively communicate a product’s value proposition and aid in implementation. This hands-on approach is crucial to justify the higher cost and ensure successful adoption and integration of the software.

Cost of Goods Sold (COGS): High vs. Low
COGS, in the context of software/SaaS products, refers to the direct costs associated with delivering the software to customers. This includes expenses like hosting fees, third-party software licensing, customer support and maintenance costs. COGS is a significant, often overlooked, factor in software pricing decisions. Historically, best-in-class software companies have kept their COGS low, achieving gross margins of 80% or higher, which enables them to offer portions of their product for free, leveraging the profits from paying customers to sustain their freemium model. Conversely, if you’re running a SaaS business with high COGS (40% or more), the freemium pricing strategy may not be feasible. This explains why data and compute-intensive software and the recent wave of generative AI applications often favor a free-trial strategy over freemium.

Product: Simple vs. Complex
The complexity of a software product is a crucial factor to consider in determining the appropriate pricing strategy. User-friendly tools like Calendly or Dropbox benefit from both a large TAM and simplicity of use, which allow users to quickly realize value. Products with a more complex setup and implementation process, like customer-support tool Zendesk, are better matched with a free-trial strategy. By limiting the trial period (e.g., to 30 days), Zendesk targets serious potential customers and creates a sense of urgency to complete the onboarding process within that time frame. This approach can be enhanced by providing additional sales and support resources to assist customers during the trial, thereby improving the conversion rate from free to paid users.

Competitive Landscape: Blue Ocean vs Red Ocean
In a blue-ocean market, where competition is minimal and your product has the potential to become a category leader, a free-trial strategy might be more sensible. But if you’re playing in a red-ocean market with multiple competitors, a freemium strategy can be a powerful tool to quickly capture market share. In the latter scenario, customers are generally well-informed about software solutions available to solve their problem – offering a self-serve experience with a free-tier pricing plan can set you apart from the competition and fuel growth.

In Conclusion
For any PLG company, careful evaluation of free-trial and freemium pricing strategies is crucial. Neglecting freemium could leave a market gap for competitors or new entrants to emerge and exploit the opportunity. Conversely, an excessive focus on freemium might lead to giving away too much for free, failing to monetize your product’s true value-add. This delicate balance requires thorough consideration of all the discussed factors to make an informed decision.

The post Different Shades of PLG: Free-Trial or Freemium? appeared first on Battery Ventures.