How Digital Marketing Teams Can Use Experimentation to Work Like Data Teams

Data-driven product teams have long used experimentation to perfect user experiences. By constantly running experiments, they can uncover (and verify) critical information about customer behavior and implement changes that directly translate to revenue. They can also uncover blind spots about unexpected customer behaviors, which are extremely valuable in the product roadmap. In short, experiments are a scientific method of observing and analyzing customer behavior in order to generate evidence-based recommendations when making product changes.

It’s time for marketing teams to adopt the same robust experimentation strategy. Traditionally, marketers have been limited to observational analysis techniques like marketing mix modeling (MMM) and simplified attribution models. However, modern analytics platforms like Amplitude have removed the technological barriers to experimentation so marketing teams can operate more like data and product teams. Instead of making subjective decisions with incomplete data, they can run tests to get fast answers.

Amplitude’s integrated A/B testing tool gives marketing teams trial results in minutes rather than days. This new high-speed experimentation ability is already a major competitive advantage. Harvard Business Review research finds that brands improve performance by 30–45% in the first two years by running automated experiments.

What’s different about modern marketing experimentation?

Experimentation in marketing is a structured, hypothesis-driven process used to test and optimize key metrics across digital channels and owned products like websites or apps. It begins with a testable hypothesis and uses controlled, measurable methods—such as A/B testing—to evaluate outcomes and guide decisions with data.

In the past, testing these hypotheses took weeks or months. Today, Amplitude includes automation to facilitate the design and execution of experiments. Setup takes minutes, so organizations can move through rapid loops of testing, learning, and implementing. With AI Agents, teams can move even faster. It’s the scientific method applied to marketing. The result is campaigns that achieve business goals more effectively: more visitors, better conversion, higher satisfaction, etc.

This speed is only possible in Amplitude because our data platform connects all the parts of the experimentation process. By removing unnecessary data connections, Amplitude can collect customer data, analyze behavior, and run experiments without waiting for data to move from system to system. Because the process moves faster, the data is fresher and more reliable. With controlled A/B testing and randomized controlled trials (RCTs), marketers can remove subjective guesswork from their process and have confidence that they are placing the right content on their website to the right audience.

The result of this stack is that marketers can act on data signals that were once only available to product teams. With this breakthrough in RCT efficiency, brands across the globe have already started to build official marketing experimentation practices within their organizations, without having to invest an unreasonable amount of capital or time. Amplitude Experiments combine an intuitive self-service interface with simple automation to isolate causal relationships between marketing efforts and user actions. This produces high-fidelity insights about customers and gives marketers more power to act on that fresh data.

Overcoming the challenges in experimentation

A major barrier to integrating experimentation into marketing strategies is executive sponsorship. Leadership often leans on outdated observational causal inference methods because they’re easier to understand and often do not require new skills or technologies. Senior leaders may have used these methods before, so they trust them. But the technological landscape has changed and executives need to be convinced to update methods as well.

If experimentation technology is separate from the analytics and activation platforms, costs will be a challenge too. If your company requires multiple products to perform at maximum efficiency to run experiments well, it will be quite expensive. When making an internal pitch for experimentation, that disconnected stack simply has too many opportunities to break down. In today’s competitive data environment, stable speed is mandatory. To overcome these obstacles, teams can use a composable architecture that allows the CMO, CDO, and CPO to select their preferred technologies without disrupting the data supply chain required for experimentation. That data supply chain exists in platforms like Amplitude, which combine analytics, experimentation, and activation on a single foundation.

While the opportunities are exciting, many marketers face an uphill battle to create a culture of experimentation and data-driven decisions. The next sections will provide practical recommendations from the Amplitude team to help you run experiments that increase ad performance, maximize acquisition efforts, and improve retargeting techniques.

Best practices for successful marketing experiments

Through years of experience with hundreds of clients, Amplitude has developed a standardized process that we recommend as an experimentation strategy for marketers.

1. The hypothesis: use your analytics and your data lineage

Every experiment begins with a premise about customer behavior, based on the marketer’s goal. For example:

• Can I increase app conversions by changing the form factor of a promotion on our hero banner?
• Do Meta ads featuring user-generated content drive more qualified leads than product-centric creative?
• Will price-sensitive buyers convert better if ads bring them directly to the checkout page?

In the past, marketers would answer these questions by running one campaign on one channel for a broad subset of customers. The marketing team would analyze the results of that campaign and incrementally modify other campaigns on other channels, hoping that the same insight would hold up when expanded. Ultimately, all the channels could be compared to see which ones were most impacted by the proposed changes.

Amplitude’s integrated experimentation platform has changed the process by connecting to an analytics platform that collects customer behavior and attributes. Instead of guessing about how marketing efforts can impact the business, they can use data lineage to generate a stronger, more objective hypothesis based on up-to-the-minute data. MMM models can provide the data lineage like this, but it can’t be analyzed at any depth without the analytics capabilities of a platform like Amplitude.

Once an experiment is deployed in Amplitude, marketers can continue refining through iterative testing. For example, they can dive into data about the audience population that is not behaving in the desired way. By clicking into experiment results about the nonconverting population, your team can uncover unexpected blockers or new questions to ask in iterative experiments.

Data evolves in real time, and hypotheses need to keep pace. Your marketing team needs experimentation tools that automate testing so you can keep up with your users and their behavior signals.

2. Translate test results into quantifiable company goals

One of the fastest ways to fund a campaign is to present the potential revenue that it can generate. Testing validates ROI projections with metrics, which raises executive confidence in proposed marketing programs. Here’s a simple formula you can use to estimate revenue lift and make a stronger case for running a campaign based on experiment results:

Experiment conversion rate: conversions per experiment / experiment population

Conversion rate increase: experiment conversion rate - baseline conversion rate

Calculated net lift of campaign: (audience size * conversion rate increase) * product price

This basic approach allows executives to make better forecasts. It also shows marketing alignment with the organization’s growth goals. A possible next step might be to include variables like risk factors, seasonality, and more.

All experiments need measurable goals that can ladder up to quantifiable company objectives. For example, the primary KPI in a marketing acquisition experiment may be total qualified leads. Upsell campaigns might use click-through rates. However, leads and clicks are not outcomes. They are performance measurement metrics that track steps on the way to an outcome. Performance measurement is a backward-facing discipline: The results are measured in the present, but the KPIs were decided during the initial planning.

It’s also recommended to track secondary KPIs—data points that aren’t directly related to the precise goal of the initial experiment but can inform future campaigns.

With experimentation, the team can form forward-looking hypotheses. When running an experiment, they are striving to directly achieve a specific business outcome—increase revenue, reduce risks/costs, or improve customer satisfaction. Maybe they achieve all of those goals.

Teams need both performance measurement and experiments to achieve the top-line business goals. By combining both on a single platform, the throughput and productivity of the teams will improve significantly.

3. Create a data-driven culture

Experimentation results will have minimal impact if testing operates as a one-off activity. To maximize their effect, experimentation must be rigorously embedded in organizational processes. That workflow typically operates in these four stages:

1. Design: design experiments to test use cases
2. Qualify: Amplitude provides signals for experiment entry/exit and new data from guides and surveys
3. Run: Amplitude runs the tests and provides results
4. Activate & Measure: Amplitude activates newly discovered audiences directly and also through Snowflake for specific use cases

Outside of this strategic framework, marketers need to constantly collaborate through tactical notebooks and documentation within Amplitude. They can even complement that by running Session Replay to collect supplemental qualitative data.

Marketers have to proactively create a culture of innovation by rapidly cycling through loops of data collection, insights, testing, and activation. As they go, there should be regular standup meetings and open discussions to drive alignment and testing momentum. The team should be empowered to make autonomous, data-driven decisions. Everyone should be encouraged to challenge assumptions and prove new ideas with data.

Start experimenting today

Experimentation isn’t a buzzword; it’s a game changer for marketing. By tying marketing experiments to your company’s larger customer data strategy, you have an opportunity to establish a major competitive edge.

Controlled experiments reveal valuable truths about a brand’s customer base. Rigorous testing will help you drive efficiency, reduce costs, and maximize performance—all while gaining a new depth of customer insights. Most importantly, this information can be amplified across internal collaboration with IT, product, and even legal departments. By presenting validated experimental data to these teams, marketing can gain approval for new campaigns, confirm compliance with data privacy regulations, and become a leader in innovation.