Running effective marketing experiments for multi-location businesses

Every element of marketing seems to change so fast that it can feel hard to keep up with all the new trends and technologies around, especially for multi-location businesses! Structured marketing experimentation gives marketing teams a way to test new ideas and emerging trends while minimizing risks and scaling what works. However, for experimentation to be truly effective, it must be designed with purpose and optimized for impact. Here’s how to build a framework that leads to better insights and better outcomes.

Why marketing experimentation is so important

At its core, marketing experimentation requires a willingness to learn and try new things, knowing it will not always deliver great results. Marketing experiments could be anything from trying a new email platform to testing a new landing page or using a different approach to programmatic advertising, it could even be as simple as testing a new email subject line or media format. With marketing experiments, the hypothesis can be validated in a test environment before being rolled out at scale across multiple locations, saving your teams time and money. And, rather than relying on instinct or historical performance alone, marketing experiments utilize real-time data to guide decisions, leading to data-driven investments and more effective campaigns.

How to excel at marketing experimentation

The first step when starting any experiment is to define the parameters to manage expectations and be able to clearly track results. Some key parameters include:

Region: Where are you experimenting? Are you testing on one location or across several? Know which region you want to target and reach out to the locations in the test region to participate and solicit feedback.
Audience: Who are you targeting? Be specific about demographics, behaviors, or customer segments, and know that this will change based on the region or brand location. This information will also help to guide future experiments, so be sure to keep clear documentation.
Channel: What are you testing? Email, paid social, search, text, or a new tool, are all good options for experimentation, but try and only do one at a time to really measure the impact. Remember, if you change too many things in one experiment, it will be more difficult to measure what actually produced what results.
Timing: When are you going to experiment? While it is important to consider seasonal trends, campaign cycles, and time of day or week, it is also vital to define the timeline for testing as this may directly impact your budget and other campaigns.

Next, come up with a hypothesis. Remember when you first learned how to do science experiments and your teacher emphasized how important the hypothesis was? It’s important here as well. The hypothesis in marketing experiments does not have to be a prediction of what you think is going to happen, instead focus more on the why. Why are you doing this, what do you hope to get out of this, and what would be the best case scenario? This can help to set expectations across the team, create a benchmark for success, and guide adjustments in the next round of experiments.

Creating your marketing experiment

This is one of the best parts of the entire process; designing the experiment. It’s exciting because it requires you to build buy-in, figure out the workings of the platform or channel in which you will be experimenting, and outline the goals and objectives. Start by identifying what you will do and what you will keep the same to determine if the experiment actually gets results. Then, set up A/B testing, analytics, and tracking tools to ensure you collect the right data for your experiment. The whole point of the experiment is to collect data, so without tools or resources to do that, it does not make a lot of sense to invest in experimenting.

Keep in mind that data does not always have to be quantitative, qualitative data speaks volumes as well. For instance, if you are trying to determine if a new email platform is right for your needs, considering interviewing the people that would use the platform the most. You can have them complete a survey at the end of the experiment timeline to get a complete picture of how the experiment went and if the platform should be rolled out to other locations.

Once the experiment concludes, dig into the data. Look beyond vanity metrics and focus on performance indicators as this will tell you if it’s worth the investment. Visual dashboards, executive summaries, and key takeaways make it easy for stakeholders to review and act on results. Be sure to report out to the team as transparent reporting fosters a culture of learning and helps other locations avoid redundant testing.

Scaling best practices and insights

The most important part of experimenting is implementing and scaling what works, while also paying attention to what does not work to prevent redundant testing. If the experiment achieves the best case scenario results, consider expanding the experiment or rolling it out to other locations. If it didn’t go as planned, try making adjustments and doing it again or capture what you have learned and then make space for new experiments. When experimenting, consider having a central library or documentation system that everyone can access to prevent redundancies and ensure the dissemination of best practices and insights.

Not every experiment will work, in fact, quite a few will fall flat or take a couple of adjustments to get right, but that’s just the nature of experimenting. It is vital for organizations to invest in experiments if they want to continue to capture market share and bring new ideas to the table. Stagnant companies rarely make headlines, or revenue goals.