Dave Kellogg on SaaS Metrics, Sales Forecasting and Efficient Growth

Dave Kellogg argues that efficient SaaS growth starts with understanding which customers, sales motions and markets are working. The software executive and adviser joins Harry Stebbings to discuss acquisition metrics, retention, sales forecasting, customer success and product marketing. His benchmarks and forecasts reflect his experience and the market conditions discussed in early 2024.

Kellogg recalls nine years as CMO at Business Objects, during which revenue grew from $30 million to $1 billion and headcount from about 240 to more than 4,500. He later led two companies, one from zero to $80 million and another from $8 million to $50 million before a sale. These are his career recollections, not independently audited figures.

Asked what he wishes he had understood earlier, he chooses corporate power. He once assumed the best argument would always win; experience taught him to read who has influence and what each person cares about. He notes his own background is primarily in marketing, although he has worked extensively with sales teams.

With CFOs scrutinizing budgets, he recommends a calm analysis of customer segments: compare win rates, sales cycles, average selling prices and net retention. The basic question is what works, followed by concentrating resources there. Small companies must use judgment alongside limited data rather than pretending they have the statistical confidence of a large one.

His shorthand is that an ideal customer profile starts as an aspiration and becomes a regression. At first a company hypothesizes a customer, buyer and problem. With more customers and history, it can test which groups close faster, win more frequently and expand. The transition depends on the quality and quantity of evidence.

A very long customer-acquisition payback can indicate inefficient growth, but Kellogg resists a universal cutoff. What a company should spend depends on customer value and lifetime; a ten-year contract can change the calculation. He says venture investors may nevertheless screen out very long payback periods, affecting access to capital.

His preferred operating measure is the CAC ratio: sales and marketing spend divided by new annual recurring revenue. It asks how many dollars the company spends to add one dollar of ARR. Unlike a payback calculation, this particular ratio does not incorporate gross margin; he would examine margin separately.

Simple metrics have fewer opportunities for manipulation, he argues. He says the cost side should include customer success and sales commissions, and favors treating commissions on a cash basis rather than relying on accounting amortization that spreads them over a contract. Consistent definitions matter when comparing companies.

As a rough example, he suggests a CAC ratio of 1.5 or less in enterprise and around 1.0 in small-business sales, assuming larger enterprise customers have longer lives. A ratio of 1 means spending $1 on sales and marketing to add $1 of ARR, not receiving $1 of immediate cash profit. These are illustrative benchmarks, not universal rules.

Stebbings asks about net new ARR, which also reflects losses. Kellogg distinguishes the investor’s desire to see the bucket’s overall change from an operator’s question about the cost of adding new ARR. His first-order ratio uses new ARR; churn and gross margin are separate diagnostics. A strong acquisition ratio alone does not establish a healthy business.

He calls these simpler measures atomic metrics: each answers a particular operational question. Compound metrics can be useful for quickly screening investments, but if a result looks poor, he wants to break it apart to identify whether acquisition efficiency, gross margin or churn is responsible.

Should acquisition costs rise as easy customers are exhausted? Kellogg is skeptical of that explanation when a company remains small relative to its market. He would ask for evidence before accepting it, while acknowledging that competition and market saturation can matter. His expectation that costs can fall is a judgment, not a guarantee.

Net revenue retention compares the current ARR of a customer cohort with that same cohort’s ARR a year earlier, including expansion and contraction. Gross revenue retention excludes expansion and measures what remains after contraction and churn; its theoretical ceiling is 100%. The two measures answer different questions about the existing customer base.

Kellogg recalls that a 120% NRR benchmark had been common in stronger times but estimates that a typical figure was closer to 105%–108% in the downturn under discussion. He attributes much of the change to increased churn, with reduced expansion also relevant. These approximate 2024 observations should not be treated as current universal targets.

CFOs seeking to reduce software spend can reopen vendor decisions at renewal. A customer may compare competitors and demand a lower price rather than automatically accept an increase. Kellogg says vendors must demonstrate continuing value and understand the buyer’s process, not assume renewal is guaranteed.

He distinguishes customer-success teams that mainly offer empathy, teams providing technical support, and teams accountable for commercial outcomes. He believes a success manager should be clear that retaining and growing the account is the job, while connecting customers with support or professional services when needed. This is his model, not the only valid organizational design.

For very large enterprise accounts with a dedicated salesperson, Kellogg says a separate customer-success manager may not be necessary. Elsewhere he sees an important role in renewal and identifying expansion. Stebbings raises the conflict between trust and upselling; Kellogg says retaining the customer takes priority over extracting every possible dollar.

He favors crediting both sales and customer success for expansion to reduce internal competition. A small add-on can be handled by the success manager, while a complex cross-sell to a different buyer should involve a specialist salesperson. His warning is not to put an account caretaker against a competitor’s experienced new-business seller.

For incentives, he would give a success manager responsibility for a portfolio and a target such as retaining and growing it to 105% or 110% of its initial value. The manager can draw on a salesperson when an opportunity requires one, with both receiving credit. Actual compensation and targets depend on the business.

Kellogg considers churn forecasting easier than new-sales forecasting because a vendor has usage data and existing relationships. Falling use, data exports or signs of a competing deployment can signal risk. He expects a success team to ask candidly about renewal plans, while recognizing that customers can still mislead a vendor.

For new sales he recommends triangulation: compare the official forecast with independent rep, manager and VP estimates, stage-weighted pipeline, forecast categories and historical conversion rates. No single number is the truth. Independent inputs lose value if managers pressure reps to repeat the manager’s preferred number.

He suggests collecting a rep’s quarterly forecast every week and watching its path toward the actual outcome. A useful forecast is a genuine prediction, ideally approaching the final number gradually; it is not the same as a quota or stretch goal. Both extreme optimism and excessive sandbagging make the business harder to run.

A manager may push a rep who predicts $100,000 to commit to $120,000. Kellogg says that may be a motivational conversation, but the resulting $120,000 is a poor forecast if it is not the rep’s actual expectation. Keep the prediction separate from the discussion of what more might be possible.

He likes hiring three sales reps with the same hypothesized profile, not to stage a competition but to run a more useful experiment. If only one person fails, the company cannot tell whether the profile or the individual was wrong. Three similar outcomes offer more evidence about what to replicate, though they are still a small sample.

In enterprise sales, a six- to twelve-month assessment may be needed when cycles themselves last six or nine months. Earlier indicators include pipeline, activity, notes and, especially, listening to customer calls. He distinguishes an early warning from certainty about eventual performance.

The hardest decision may be retaining an under-quota rep who is doing the right things in an unsupported new market. Kellogg recalls representatives outside established verticals who lacked references and company support; he sometimes rewarded them despite missing quota. Managers need enough direct observation to understand the context behind a number.

He separates three meetings: a forecast call to establish a number, a pipeline scrub to validate value, close date, stage and forecast category, and a collaborative deal review to help a rep win. Mixing storytelling into a forecast call can obscure the prediction; a deal review should not merely frighten the rep.

A founder or CEO should participate personally in a deal that is material to the quarter, he argues. That includes knowing the customer and decision makers, not just hearing a status update. The appropriate involvement depends on deal size relative to the business.

Kellogg takes a demanding view of slipped deals. If a seller cites a deal that moved out of the quarter, he also wants to know what slipped in from the previous one. He distinguishes unforeseeable events from preventable surprises, such as not knowing the signer is away or an approval committee exists.

A close plan is his anti-slip checklist: identify the expected signing date, signer, approval steps, budget authority and process. Add new questions when a preventable failure exposes a gap. An unexpected acquisition and purchasing freeze might be a legitimate surprise; a known approval requirement should have been investigated.

To discover whether a contact is really the buyer, he recommends asking curious, non-confrontational questions: whose budget is involved, who else has a vote, whether the organization has approved a purchase of this size and how the last similar purchase worked. The goal is to understand the actual decision process.

On discounting, he starts with the value the customer expects and the cost of delaying that value. He also documents prior concessions and what the buyer promised in return. A competitor’s lower price may still require escalation, but immediately conceding can encourage another request. These are negotiation suggestions, not guarantees.

Customer references matter, but Kellogg dislikes using one as the main quid pro quo for a discount. A genuinely satisfied customer should be willing to share its experience; a coerced reference may be weak. He prefers building an ongoing reference program and maintaining a good relationship.

He is skeptical of treating outbound prospecting as a universal rescue for weak inbound demand. High-volume generic outreach competes for saturated attention. Targeted outbound can work well for large deals when the company already understands a use case and has reason to believe a short list of accounts has the same problem.

For example, a vendor that has succeeded with two insurers can coordinate sales, marketing and retargeting toward similar insurers. For a generic $5,000 annual product sold broadly, the cost of intensive outreach may be harder to justify. The choice should follow deal economics and customer evidence.

Kellogg summarizes a buyer’s central question as whether people like them use the solution. The buyer defines what “like me” means: industry, size, problem or use case. References, relevant logos, customer conversations and demonstrated understanding can build credibility; superficial similarity is not enough.

Horizontal products can fall back on a shared use case when customers are in different industries. He recalls comparing JetBlue’s need for aircraft-specific documentation with a publisher’s need for customized textbooks. The analogy helped explain a roughly $1 million deal, he says, but the customer had to recognize the similarity too.

He recalls an object-database company finding traction by focusing on telecommunications network management after broad horizontal selling struggled. The focused business grew, but he says pressure to broaden the story before an IPO later diluted that focus. His account illustrates the danger of expanding a message faster than market evidence supports.

Kellogg imagines an industry-by-use-case matrix: each move into a new cell is difficult, particularly the first customer. A company should treat a new industry or use case as an experiment rather than assume adjacent opportunities will fall like dominoes. Stebbings adds that valuation pressure can encourage premature expansion.

He enjoys helping technical founders understand sales and marketing, and argues that the burden is on the instructor to explain the work credibly. In his experience technically trained founders learn quickly when the explanation is clear. He sees product-oriented founders as particularly fashionable in the industry at the time.

Kellogg expects the pendulum on replacing founders as CEOs to move toward a middle ground. He says investors once acted too quickly, then became too reluctant to make a change. Replacing a CEO need not remove the founder from the company: the founder may continue to lead vision or product while a different executive runs operations.

On AI sales tools, he advises customers to experiment rather than wait for a definitive winner. Vendors should move quickly to establish themselves in a market he expects to consolidate, possibly through acquisitions or features folded into larger products. His timing and market-structure claims are forecasts made in 2024.

He cites a Battery analysis envisioning a sales organization shrinking from 110 to 75 people supporting 30 quota-carrying sellers, and says such a productivity change is plausible. This is a scenario from an outside analysis, not observed proof or a guaranteed reduction. He expects automation to remove routine work more than high-quality work.

AI-assisted outbound may provide a temporary advantage, but Kellogg expects buyers to ignore a flood of personalized messages once everyone has similar tools. He recommends using useful tools while they work without making that transient advantage the entire strategy. The saturation is his prediction.

In quick fire, he warns against adopting a pricing model simply because investors say everyone is doing it. He sees trade-offs in subscription pricing and says changes consume leadership attention. He also warns that enterprise customers have requirements that a product designed for small companies may not meet.

When hiring sales leaders, he cautions against assuming a strong résumé proves individual skill: a successful account can reflect timing or inherited circumstances. Ask how decisions were made and look beyond headline numbers. He believes the caliber of sales talent is sound and that better systems can help it perform.

His central 2024 challenge for SaaS is efficient growth after years of capital-intensive expansion. He believes excessive funding can encourage waste, while acknowledging that some founders preserve capital for later use. He closes with a preference for disciplined analysis over following fashionable advice.