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5min read

1,000 Experiments Club: A Conversation With Jonny Longden of Journey Further

Is experimentation for everyone? A resounding yes, says Jonny Longden. All you need are two ingredients: A strong desire and tenacity to implement it.

There’s a dangerous myth lurking around, and it’s the idea that you have to be a large organization to practice experimentation. But it’s actually the smaller companies and start-ups that need experimentation the most, says Jonny Longden of performance marketing agency Journey Further.

With over a decade of experience in conversion optimization and personalization, Jonny co-founded Journey Further to help clients embed experimentation into the heart of what they do. He currently leads the conversion division of the agency, which also focuses on PPC, SEO, PR — among other marketing specializations.

Any company that wants to unearth any sort of discovery should be using experimentation, especially start-ups who are in the explorative phase of their development. “Experimentation requires no size: It’s all about how you approach it,” Jonny shared with AB Tasty’s VP Marketing Marylin Montoya.

Here are a few of our favorite takeaways from our wide-ranging chat with Jonny.

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The democratization of experimentation

People tend to see more experimentation teams and programs built at large-scale companies, but that doesn’t necessarily mean other companies of different sizes can’t dip their toes in the experimentation pool. Smaller companies and start-ups can equally benefit from this as long as they have the tenacity and capabilities to implement it.

You need to truly believe that without experimentation, your ideas won’t work, says Jonny. There are things that you think are going to work and yet they don’t. Conversely, there are many things that don’t seem like they work but actually end up having a positive impact. The only way to arrive at this conclusion is through experimentation.

Ultimately, the greatest discoveries (for example, space, travel, medicine, etc.) have come from a scientific methodology, which is just observation, hypothesis, testing and refinement. Approach experimentation with this mindset, and it’s anyone’s game.

Building the right roadmaps with product teams

Embedding experimentation into the front of the product development process is important, but yet most people don’t do it, says Jonny. From a pure business perspective, it’s about trying to de-risk development and prove the value of a change or feature before investing any more time, money and bandwidth. 

Luckily, the agile methodology employed by many modern teams is similar to experimentation. Both rely on iterative customer collaboration and a cycle of rigorous research, quantitative and qualitative data collection, validation and iteration. The sweet spot is the collection of both quantitative and qualitative data — a good balance of feedback and volume. 

The success of building a roadmap for an experimentation program comes down to understanding the organizational structure of a company or industry. In SaaS companies, experimentation is embedded into the product teams; for e-commerce businesses, experimentation fits better into the marketing side. Once you’ve determined the owner and objectives of the experimentation, you’ll need to understand whether you can effectively roll out the testing and have the right processes in place to implement results of a test.

Experimentation is, ultimately, innovation

The more you experiment, the more you drive value. Experimentation at scale enables people to learn and build more tests based on these learnings. Don’t use testing to only identify winners because there’s much more knowledge to be gained from the failed tests. For example, you may only have 1 in 10 tests that work. The real value comes in the 9 lessons you’ve acquired, not just the 1 test that showed positive impact. 

When you look at it through these lenses, you’ll realize that the post-test research and subsequent actions are vital: That’s where you’ll start to make more gains toward bigger innovation. 

Jonny calls this the snowball effect of experimentation. Experimentation is innovation — when done right. At the root, it’s about exploring and seeing how your customers respond. And as long as you’re learning from the results of your tests, you’ll be able to innovate faster precisely because you are building upon these lessons. That’s how you drive innovation that actually works.

What else can you learn from our conversation with Jonny Longden?

  • Moving from experimentation to validation
  • How to maintain creativity during experimentation
  • Using CRO to identify the right issues to tackle
  • The required building blocks to successful experimentation
About Jonny Longden

Jonny Longden leads the conversion division of Journey Further, a performance marketing agency specializing in PPC, SEO, PR, etc. Based in the United Kingdom, the part-agency, part-consultancy helps businesses become data-driven and build experimentation into their programs. Prior to that, Jonny dedicated over a decade in conversion optimization, experimentation and personalization, working with Sky, Visa, Nike, O2, Mvideo, Principal Hotels and Nokia.

About 1,000 Experiments Club

The 1,000 Experiments Club is an AB Tasty-produced podcast hosted by Marylin Montoya, VP of Marketing at AB Tasty. Join Marylin and the Marketing team as they sit down with the most knowledgeable experts in the world of experimentation to uncover their insights on what it takes to build and run successful experimentation programs.

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Article

5min read

1,000 Experiments Club: A Conversation With Chad Sanderson of Convoy

Chad Sanderson breaks down the most successful types of experimentations based on company size and growth ambitions

For Chad Sanderson, head of product – data platform at Convoy, the role of data and experimentation are inextricably intertwined.

At Convoy, he oversees the end-to-end data platform team — which includes data engineering, machine learning, experimentation, data pipeline — among a multitude of other teams who are all in service of helping thousands of carriers ship freight more efficiently. The role has given him a broad overview of the process, from ideation, construction to execution.

As a result, Chad has had a front-row seat that most practitioners never do: The end-to-end process of experimentation from hypothesis, data definitions, analysis, reporting to year-end financials. Naturally, he had a few thoughts to share with AB Tasty’s VP Marketing Marylin Montoya in their conversation on the experimentation discipline and the complexities of identifying trustworthy metrics.

Introducing experimentation as a discipline

Experimentation, despite all of its accolades, is still relatively new. You’ll be hard pressed to find great collections of literature or an academic approach (although Ronny Kohavi has penned some thoughts on the subject matter). Furthermore, experimentation has not been considered a data science discipline, especially when compared to areas of machine learning or data warehousing.

While there are a few tips here and there available from blogs, you end up missing out on the deep technical knowledge and best practices of setting up a platform, building a metrics library and selecting the right metrics in a systematic way.

Chad attributes experimentation’s accessibility as a double-edged sword. A lot of companies have yet to apply the same rigor that they do to other data science-related fields because it’s easy to start from a marketing standpoint. But as the business grows, so does the maturity and the complexity of experimentation. That’s when the literature on platform creation and scaling is scant, leading to the field being undervalued and hard to recruit the right profiles.

When small-scale experimentation is your best bet

When you’re a massive-scale company — such as Microsoft or Google with different business units, data sources, technologies and operations — rolling out new features or changes is an incredibly risky endeavour, considering that fact that any mistake could impact millions of users. Imagine accidentally introducing a bug for Microsoft Word or PowerPoint: The impact on the bottom line would be detrimental.

The best way for these companies to experiment is with a cautious, small-scale approach. The aim is to focus on immediate action, catching things quickly in real time and rolling them back.

On the other hand, if you’re a startup in a hyper-growth stage, your approach will vastly differ. These smaller businesses typically have to show double-digit gains with every new feature rollout to their investors, meaning their actions are more so focused on proving the feature’s positive impact and the longevity of its success.

Make metrics your trustworthy allies

Every business will have very different metrics depending on what they’re looking for; it’s essential to define what you want before going down the path of experimentation and building your program.

One question you’ll need to ask yourself is: What do my decision-makers care about? What is leadership looking to achieve? This is the key to defining the right set of metrics that actually moves your business in the right direction. Chad recommends doing this by distinguishing your front-end and back-end metrics: the former is readily available, the latter not so much. Client-side metrics, what he refers to as front-end metrics, measure revenue per transaction. All metrics then lead to revenue, which in and of itself is not necessarily a bad thing, but that just means all your decisions are based on revenue growth and less on proving the scalability or winning impact of a feature.

Chad’s advice is to start with the measurement problems that you have, and from there, build out your experimentation culture, build out the system and lastly choose a platform.

What else can you learn from our conversation with Chad Sanderson?

  • Different experimentation needs for engineering and marketing
  • Building a culture of experimentation from top-down
  • The downside of scaling MVPs
  • Why marketers are flagbearers of experimentation
About Chad Sanderson

Chad Sanderson is an expert on digital experimentation and analysis at scale. He is a product manager, writer and public speaker, who has given lectures on topics such as advanced experimentation analysis, the statistics of digital experimentation, small-scale experimentation for small businesses and more. He previously worked as senior program manager for Microsoft’s AI platform. Prior to that, Chad worked for Subway’s experimentation team as a personalization manager.

About 1,000 Experiments Club

The 1,000 Experiments Club is an AB Tasty-produced podcast hosted by Marylin Montoya, VP of Marketing at AB Tasty. Join Marylin and the Marketing team as they sit down with the most knowledgeable experts in the world of experimentation to uncover their insights on what it takes to build and run successful experimentation programs.