Industry

Digital, software and AI for SaaS and software companies

Buyers who evaluate suppliers professionally, sell to people who do the same, and measure everything — which raises the bar on both sides.

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Software companies are the hardest clients to sell marketing services to and among the most rewarding to work with, because they already measure everything and will check what you claim. That removes a lot of the vagueness that survives in other sectors.

It also means the work is different. A SaaS company rarely needs to be told what a funnel is; it needs the measurement to survive consent loss, the onboarding to activate more of the signups it already gets, and reporting that holds up in a diligence process.

Why this sector is moving now

Acquisition economics have tightened. Paid channels cost more, free trials convert at rates that have not improved, and the payback period on a customer has lengthened enough that growth funded by acquisition alone is harder to defend to a board.

Attribution has degraded at exactly the wrong moment. Consent requirements in Europe and browser restrictions mean a material share of the journey is unobservable, and models built on the remaining data are confidently wrong rather than usefully approximate.

Enterprise sales brings a security review that most smaller vendors are unprepared for. A SOC 2 report is requested routinely before technical evaluation proceeds, which turns a compliance exercise into a revenue gate.

The pressures behind it

Lengthening payback: Acquisition costs rising faster than conversion, which boards notice before marketing does.
Attribution degradation: Consent loss and browser restrictions making a material share of the journey unobservable.
Activation rather than signup: Trials that start and never reach the moment the product becomes useful.
Security review as a gate: SOC 2 requested before enterprise technical evaluation, blocking deals rather than delaying them.
Diligence-grade reporting: Metrics that have to reconcile with billing when someone competent examines them.
Content saturation: Every competitor publishing the same category education, at volume.

Where the work usually starts

The systems and channels a software companies typically needs joined up. Most engagements begin at one spoke and widen only if it earns it.

Usually measurement, because everything else is being judged against numbers that do not currently reconcile. Server-side conversion tracking, consent-mode handling and CRM-side truth fixed first, so later decisions are made on something defensible.

Activation work follows, since improving the conversion of signups already being paid for is cheaper than buying more. Paid acquisition scaling comes last, when the destination and the measurement both hold.

Marketing and brand for software companies

Brand Strategy & Development: Software positioning defaults to the category — the platform for X — which puts you in a comparison you may not win. The stronger position is usually a specific situation or team the product fits unusually well, and it is uncomfortable because it narrows the addressable market on paper.
Brand Management: The product interface is the brand for a SaaS company, more than any marketing surface. Consistency work that stops at the website while the application drifts is solving the smaller half of the problem.
Social Media Strategy: LinkedIn for B2B and, increasingly, founder-led content that outperforms the company account by a wide margin. Community presence where your buyers actually gather is worth more than broadcast, and it is harder to sustain.
Social Media Management: Product-led content — showing the thing working — outperforms thought leadership for software companies. It also requires product access and a release cadence the marketing team is inside of rather than downstream from.
Content Creation & Creative Production: Category education is saturated because every competitor publishes it. What still works is content only you can write: data from your own product, genuine technical depth, and honest comparison including where you lose.
Digital Marketing: The measurable difference in this sector is reallocation discipline against pipeline rather than signups. A channel producing trials that never activate is producing cost, and signup-level reporting conceals that completely.
Paid Advertising: Competitor and category terms are expensive and frequently unwinnable for a smaller vendor. Where paid works is capturing existing intent — integration searches, migration searches, specific problem searches — rather than creating category awareness.
Search Engine Optimisation: Integration pages, comparison pages and specific use-case pages are the durable assets. They capture people already in a buying process, which is the traffic that converts, and most software companies under-build them in favour of blog volume.
Email, SMS & WhatsApp Marketing: Lifecycle is where the return is: onboarding sequences that drive activation, usage-triggered messages, and expansion prompts based on real product signals rather than time elapsed. Product-triggered beats scheduled every time.
Lead Generation & Prospecting: Technographic targeting is genuinely possible here — identifying companies using a complementary or competing tool — which makes outbound more precise than in most sectors. It still needs a lawful basis and a separate sending domain.

IT, software and AI for software companies

Website Design & Development: Speed and clarity beat design ambition, and the pricing page is the most read and least considered page on most SaaS sites. Documentation quality is a sales asset in developer-facing products, not a support cost.
CRM & Sales Systems: The integration between product usage, billing and CRM is what makes anything else possible. Without it, sales works blind on which accounts are actually engaged and marketing reports on signups that mean nothing.
Business Process Automation: Trial provisioning, dunning, usage-based invoicing, renewal notices and churn signals. Billing operations in particular are frequently manual at a scale that is quietly expensive and error-prone in a way customers notice.
AI Automation Systems: Two directions: automating your own operations, and building AI features into the product. For the latter we bring the evaluation discipline most feature teams skip — a test set, measured accuracy, and a regression check before release.
AI Knowledge Bases & RAG: Support deflection grounded in your documentation with citations, and internal assistants over specifications and past decisions. The failure to design against is confident invention about product capability, which creates support load rather than removing it.
AI Voice & Customer Communication: Uncommon in SaaS and useful in higher-touch or enterprise onboarding. For self-serve products the interaction people want is in the application rather than on a call.
Custom Software & Platforms: Usually engineering capacity alongside an internal team rather than a whole build — a specific integration, a data pipeline, an admin tool nobody has had time for. The constraint is generally hiring speed rather than capability.
Data Engineering & BI: Metrics that reconcile with billing when a diligence process examines them. Recurring revenue, churn, expansion and cohort retention all need definitions agreed and lineage documented, because a number that cannot be traced is a finding.
Cloud, DevOps & Infrastructure: SOC 2 readiness is frequently the driver: logging, access control, change management and monitoring built to satisfy an audit. It is a revenue gate rather than a compliance chore, which changes how it should be prioritised.
Systems Integration: Product to billing to CRM to support, with usage data flowing where it is needed. This is the plumbing that makes product-led growth measurable, and its absence is why most reporting in early-stage software is unreliable.
Digital Transformation Consulting: Less common here, since software companies generally know what to build. Where it helps is prioritisation between growth investments and an honest assessment of whether the constraint is acquisition, activation or retention.
Maintenance & Ongoing Support: For AI features, scheduled re-evaluation is essential because model providers update models and behaviour drifts with no change on your side. A feature that was accurate at launch and has not been re-measured is unmeasured, not accurate.

What is specific to this sector

Selling software into the European Union brings the customer's GDPR obligations onto the vendor as a processor: data processing agreements, sub-processor disclosure, transfer mechanisms and breach notification timelines. Enterprise buyers will ask for all of it, and being ready shortens sales cycles measurably.

SOC 2 Type II is not a legal requirement anywhere and functions as one commercially in North America. It gates enterprise evaluation, and the engineering work behind it — logging, access control, change management — is genuine rather than paperwork.

Where a product makes or supports automated decisions affecting individuals, GDPR Article 22 and the EU AI Act both become relevant, and the classification depends on what the decision does rather than on how the feature is marketed. That is worth establishing before a feature ships rather than during a customer's security review.

Product analytics and marketing analytics are frequently instrumented separately by different teams, producing two versions of the funnel that never reconcile. Agreeing a single event taxonomy across product and marketing is unglamorous groundwork and it is the reason most SaaS reporting disputes exist.

Not legal or regulatory advice. Sector rules described here are scoping context, current to our latest review. Confirm what applies to your business with a qualified adviser.

Questions

Our attribution is broken — can it be fixed?

Improved rather than fixed. Server-side conversion tracking and CRM-side truth recover a lot, and consent loss in Europe means a share of the journey stays unobservable. We report a range with the assumption stated rather than a confident number built on a biased sample.

Should we invest in acquisition or activation?

Almost always activation first. Improving the conversion of signups you already pay for is cheaper than buying more, and scaling acquisition into a funnel that does not activate multiplies the waste.

Do we need SOC 2?

If you sell to North American enterprises, effectively yes — it is requested before technical evaluation. It is a revenue gate rather than a compliance exercise, and the underlying engineering work is worth doing regardless.

Is content marketing still worth it for SaaS?

Category education is saturated. Content only you can write — your own product data, real technical depth, honest comparisons including where you lose — still works, and most companies avoid the last one.

Can you build AI features into our product?

Yes, with the evaluation discipline most feature teams skip: a real test set, measured accuracy, and a regression check before release. Shipping an AI feature without knowing how often it is wrong is the common pattern and the one that produces support load.

Will you work alongside our engineering team?

Usually, yes. The common arrangement is that we take a defined workstream — a data pipeline, an integration, SOC 2 readiness — that the internal team has not had capacity for, rather than duplicating what they do well.

What does it cost?

Quoted per phase after a discovery call, with growth work as a monthly arrangement separate from media spend. We do not charge a percentage of ad spend.

Our product and marketing numbers disagree — why?

Almost always because they were instrumented separately with different event definitions. The fix is a single agreed taxonomy across both, which is groundwork rather than a dashboard.

Is founder-led content worth the time?

In early-stage B2B software, usually more than the company account by a wide margin. The constraint is a sustainable cadence, which is why an interview-led production model works better than asking a founder to write.