In the time it will take you to listen to this podcast episode, parts of it might already be out of date. AI is evolving at a breathtaking pace, yet most companies still struggle with adding it to their workflows in ways which will add business value. And this is the uncomfortable truth that few have addressed head-on: if AI can improve your employees’ efficiency and productivity, but you apply it to the wrong tasks, it could be doing more harm than good.

In our new podcast episode, Mammoth Growth CTO Drew Beaupre interviews Ethan Aaron, CEO and Founder of Portable. Ethan and his team at Portable are well on their way to building a platform that can manage integrations with 10,000 other systems. What’s surprising here is not the pace of their growth, but Ethan’s unconventional take on AI. Upon hearing of Portable’s success, most people’s knee-jerk reaction is to suggest AI as a method for automatically building these API calls even more quickly. But Ethan points out something very few other people are talking about: the real test of AI is whether it can add value to the business based upon each of these new integrations. Connecting one app to another just to move data only has limited value, until we can find ways to surface and activate data that can help companies hit their goals.

Ethan’s perspective on AI as it relates to Portable highlights a much larger issue facing every company right now. AI is not the product itself, but simply a tool that can be used to enhance other products by automating tasks and aiding decision-making. If we don’t clarify what types of behavior we need to change to hit our goals, AI won’t help us make better decisions. If we don’t trust the data we’re handing over to AI, or if we’re feeding it inaccurate data, then again, AI won’t help us make better decisions. On its own, AI is not a guarantor of faster, more accurate insights. First, we must understand our business and our data to the best of our ability. Only then can AI deliver on its insane potential.

Right now, AI is an answer in search of a question. But ironically, AI has revealed a universal truth that savvy marketers have known for decades. AI will allow people to build similar products faster than ever before. And that means that if everyone competes on features and price, commoditization is inevitable as we race to the bottom. The only way to avoid being squashed by this trend in the years to come is to focus on your brand. AI can help you build your website faster, produce content more quickly, and shorten development time for your product. But it can’t tell you what your brand should be, or what it could be. As Drew and Aaron point out, that’s up to you. And that’s something that will never be out of date.

Thanks for listening!

Visit us at: MammothGrowth.com
Follow us on LinkedIn

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Since 2015, over 850 companies have trusted Mammoth Growth to surpass their marketing, product, and business intelligence goals. We offer consulting, integration, and strategic planning services in the following areas:

- Data Engineering / Governance
- BI Reporting & Dashboarding
- Customer Data Platform (CDP)
- Product & Behavioral Analytics
- Lifecycle / Email Marketing
- Modern Data Pipelines
- Multi-Touch Attribution
- Machine Learning
- A/B Testing

Mammoth Growth has deep expertise in a wide range of tools, including Amplitude, Braze, Heap, Mixpanel, Optimizely, Rudderstack, and Segment.

We’re your Growth Team as a Service.

It’s the foundation of every technological initiative. But what happens if you build on a shaky foundation? Data governance is frequently missing from conversations about the value of product analytics, and Avo is out to change that. In this episode of the Mammoth Growth Podcast, Avo CEO and Co-Founder Stefania Olafsdottir chats with our EMEA President Stuart Scott about the challenges companies face with poor data governance, and how they can resolve these issues and spend more time building great products.

Product analytics is a discipline just like sales or marketing, and just like these, it will reward a high level of planning. While it’s one thing to understand the impact of a company’s product analytics on their overall decision making (analytics for your analytics), data governance addresses the very core of product analytics:

• How can we possibly make good decisions with bad data?
• How do we restore trust in our data?
• If we have a gatekeeper to confirm data quality before we ship, how do we deal with that bottleneck?
• How do we ship accurate product analytics based on good data, faster?

Stefania has seen this sequence play out over and over again, from really bad data to good data to self-serve data governance for product analytics. 

One of Stefania’s breakthroughs was starting small, by finding the one person on a team who was passionate about data quality. Starting there, it’s much easier to demonstrate success with data governance by focusing on tactical wins - for example, improving adoption of one feature at a time, or increasing the conversion rate for a single cohort of users. How to approach data governance projects is just as important as the technology you implement along the way. Stefania’s insights into successful data governance led her and her team to create Avo, which fixes data quality for product analytics so every company can build on a solid foundation.

Thanks for listening!

Visit us at: MammothGrowth.com
Follow us on LinkedIn

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Since 2015, over 850 companies have trusted Mammoth Growth to surpass their marketing, product, and business intelligence goals. We offer consulting, integration, and strategic planning services in the following areas:

- Data Engineering / Governance
- BI Reporting & Dashboarding
- Customer Data Platform (CDP)
- Product & Behavioral Analytics
- Lifecycle / Email Marketing
- Modern Data Pipelines
- Multi-Touch Attribution
- Machine Learning
- A/B Testing

Mammoth Growth has deep expertise in a wide range of tools, including Amplitude, Braze, Heap, Mixpanel, Optimizely, Rudderstack, and Segment.

We’re your Growth Team as a Service.

Analytics teams are positioned to guide everyone in a company to make better decisions. But how well are companies measuring the performance of their analytics teams? What types of questions are they dealing with, and how do their actions impact the company’s growth trajectory?

We dive into these topics and much more in the latest episode of our podcast. Our CTO Drew Beaupre chats with Osman Ghandour, Co-Founder of Soal Data. Osman explains that ad hoc analytics refers to unplanned, reactive work that data and analytics teams often have to handle. While this type of work can easily be seen as a distraction, it can also be an opportunity for these teams to ease decision-making throughout a company. As Osman points out, there’s a fine line between resolving a one-time ask vs recognizing the types of questions that come up over and over again. Soal Data’s mission is to shine a light on all types of analytics questions, to show which ad hoc requests should be given more close attention. Drew clarifies their value this way: if analytics and data teams receive repeat ad hoc requests of a similar nature, there’s a much higher likelihood that those requests point to valuable areas for the business to focus on. Soal Data helps companies identify these patterns automatically. Think of their solution as analytics for your analytics. 

Data and analytics teams can be structured in three distinct ways to address ad hoc requests:

No special structure. A company’s existing analytics and data teams must drop whatever they’re doing whenever an ad hoc request comes in. In this scenario, the same people must manage proactive and reactive work at the same time. While this scenario is the most common, it’s also the most likely to increase context switching, burnout, and employee turnover.

1) A hybrid approach rotates the responsibility for addressing ad hoc requests among different members of the analytics and data teams on a weekly basis. This scenario reduces context switching and its associated burnout, and it has the added benefit of giving everyone on the team a broader perspective of what the team is working on.

2) The fully dedicated ad hoc team. Osman often sees this setup in certain industries that receive a non-stop flow of questions, especially in insurance, finance, or anything related to compliance.

Osman highlights the importance of measuring the value of ad hoc work, since today’s ad hoc requests might very easily become part of a company’s product roadmap tomorrow. Soal Data allows companies to identify that tipping point quickly and efficiently.

Thanks for listening!

Visit us at: MammothGrowth.com
Follow us on LinkedIn

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Since 2015, over 850 companies have trusted Mammoth Growth to surpass their marketing, product, and business intelligence goals. We offer consulting, integration, and strategic planning services in the following areas:

- Data Engineering / Governance
- BI Reporting & Dashboarding
- Customer Data Platform (CDP)
- Product & Behavioral Analytics
- Lifecycle / Email Marketing
- Modern Data Pipelines
- Multi-Touch Attribution
- Machine Learning
- A/B Testing

Mammoth Growth has deep expertise in a wide range of tools, including Amplitude, Braze, Heap, Mixpanel, Optimizely, Rudderstack, and Segment.

We’re your Growth Team as a Service.

Welcome to another episode of the Mammoth Growth podcast! In this episode, Data Consultant and former VP Data of FREENOW Dr. Tim Wiegels joins EMEA President Stuart Scott and one of our Consultants, Paddy Doran, to discuss the privacy-related changes coming in iOS 17 and their impact on marketers and data professionals. 

Tim, Stuart, and Paddy discuss two major changes to the privacy landscape courtesy of Apple: link tracking protection and privacy manifests. With link tracking protection, Apple will start cutting UTM link parameters that may identify users when they open links from mail, messages, or Safari private browsing. Paddy notes that users can already opt in to link tracking protection on iOS 17 beta, and that in the coming months this could affect user tracking and targeting. Paddy then points out that you can think about privacy manifests as an extension of an app’s “nutrition label” in the Apple App Store, where apps need to disclose the data they collect and how they use it. Third-party SDKs will also need to create privacy manifests, increasing transparency and accountability. 

The hosts also discuss the challenges and opportunities these changes present, such as the end of fingerprinting / probabilistic attribution and the role of Mobile Measurement Partners in managing privacy and data. Tim then makes a key observation, that as users’ privacy concerns and Apple converge towards heightened privacy protections, “...that we might not need MMPs anymore because if you push something to the Apple App store, they will be your MMP.”

On the topic of bias in marketing attribution, Tim relates a quick story about setting up mobile attribution during his work with FREENOW. Since they thought only a maximum of 30% of their users on iOS had opted into tracking, they considered using these 30% of iOS users and 100% of their Android users to build their mobile marketing attribution heuristics. But they ultimately decided against this approach. They realized that this large sample size contained mostly Android users, but they knew most of their revenue was coming from iOS users. “So really using this large chunk of non-revenue generating users to predict the revenue generating users didn't make sense,” Tim and his team at FREENOW concluded. With this anecdote, Tim reveals the difference between building a marketing attribution model on enough data vs building one with the right data.

Tim, Paddy, and Stuart emphasize the importance of adopting incrementality testing and uplift modeling to understand the impact of marketing efforts and make data-driven decisions. Paddy advises companies to be careful about plugging gaps with user-level data, since this will likely provide only short-term relief and can quickly turn into a game of whack-a-mole. Tim concludes the conversation by asserting that companies can weather the coming privacy apocalypse by focusing on their KPIs instead of fixating on minute fluctuations in user activity. 

Thanks for listening!

Visit us at: MammothGrowth.com
Follow us on LinkedIn

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Since 2015, over 850 companies have trusted Mammoth Growth to surpass their marketing, product, and business intelligence goals. We offer consulting, integration, and strategic planning services in the following areas:

- Data Engineering / Governance
- BI Reporting & Dashboarding
- Customer Data Platform (CDP)
- Product & Behavioral Analytics
- Lifecycle / Email Marketing
- Modern Data Pipelines
- Multi-Touch Attribution
- Machine Learning
- A/B Testing

Mammoth Growth has deep expertise in a wide range of tools, including Amplitude, Braze, Heap, Mixpanel, Optimizely, Rudderstack, and Segment.

We’re your Growth Team as a Service.

Welcome to the latest episode of our podcast! In this new interview led by Mammoth Growth CEO Ryan Koonce, Mixpanel SVP Product Neil Rahilly establishes Mixpanel’s value for event-based analytics. Neil makes the important point that Google Analytics (GA) is more focused on website performance and traffic analysis, while Mixpanel allows for more in-depth analysis of user behavior and complex questions. Ryan offers more context to this, sharing that while GA can tell you what happened in different website sessions, it’s not designed to tell you who did what in those sessions. And that means, you can’t ask questions about particular users or cohorts in GA, since it wasn’t built to be a true cross-platform behavioral UX tool. 

When Google sunsetted their popular Universal Analytics (UA)  on July 1st 2023 and forced growth marketers onto Google Analytics 4 (GA4), Mixpanel took the opportunity to define a very specific niche:

• Whereas many people found GA easy to use, oftentimes you couldn’t get the answers you needed for even the most basic questions about user engagement and conversion. GA4 has many of the same limitations with an even steeper learning curve.
• On the other hand, while SQL is both extremely powerful and flexible, and it can give you answers to just about any question related to business intelligence (BI), it is too difficult for most people to use, and much too slow.

Mixpanel has positioned their event-based analytics solution at the sweet spot between GA and BI, offering everyone from growth marketers to product developers the opportunity to quickly get meaningful insights from their customer data. 

Neil concludes the conversation with a taste of Mixpanel’s AI tool, Spark. While Mixpanel has done everything they can to make event-based analytics as accessible as possible, Spark aims to reduce any remaining barrier to entry even further. Ask Spark a question about your customer data, and it will automatically build a chart to answer that question, and you can see exactly how it did it. Neil hints that Mixpanel’s next application for AI could be a tool that looks for spikes, drops, or anomalies that might indicate a broken data pipeline.

Thanks for listening!

Visit us at: MammothGrowth.com
Follow us on LinkedIn

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Since 2015, over 850 companies have trusted Mammoth Growth to surpass their marketing, product, and business intelligence goals. We offer consulting, integration, and strategic planning services in the following areas:

- Data Engineering / Governance
- BI Reporting & Dashboarding
- Customer Data Platform (CDP)
- Product & Behavioral Analytics
- Lifecycle / Email Marketing
- Modern Data Pipelines
- Multi-Touch Attribution
- Machine Learning
- A/B Testing

Mammoth Growth has deep expertise in a wide range of tools, including Amplitude, Braze, Heap, Mixpanel, Optimizely, Rudderstack, and Segment.

We’re your Growth Team as a Service.

In this latest episode of our podcast, Mammoth Growth EMEA President Stuart Scott chats with Yair Dovrat, Founder of Zaraz, a lightweight tool which makes any website 40% faster with a single line of code. 

Yair shares how Zaraz began, explaining how they identified the problem of data accuracy in web analytics and decided to automate the solution. He and his co-founder Yo'av Moshe initially built a QA software for web analytics and third-party tools, but when they joined Y Combinator in the Winter of 2020, they faced challenges in selling it. Through customer feedback and coaching from YC’s then-managing partner Michael Seibel, Yair and Yo’av realized the need for better management and visibility of third-party tools on websites, and that’s when inspiration struck them.

When Yair and Yo’av were considering what they wanted to do next with Zaraz, they saw an emerging trifecta that they could address:

1. In late 2020, Google announced their plans to rank websites based on their core web vitals like page load speed.
2. At the same time, Google floated the idea of penalizing slow websites by, for example, displaying a warning banner when you loaded the page. Though this latter action never came to pass, both of these points made marketers, web developers, and backend engineers eager for ways to quickly improve their core web metrics.
3. Meanwhile Google Tag Manager and tools like Segment were making it easier and easier to add more tools to your tech stack with less coding, there was no solution available to offload this growing proliferation of third-party pixels.

Yair and Yo’av realized they position Zaraz as an alternative to Google Tag Manager, one that would be faster, more secure, and privacy-safe. By pivoting the value of Zaraz in this way, they positioned the company to be acquired by Cloudflare in December of 2021. 

Zaraz works by loading a website’s third-party tools in the backend instead of in the browser. Since users can customize Zaraz to only pass the data they absolutely need from Google Ads and other third-party pixels, sites can easily reduce security risks by minimizing their surface area to outside attacks. 

Towards the end of their conversation, Yair shares with Stuart the importance of standardizing tracking implementation, and the adoption of common APIs to ensure data consistency and trust. Yair concludes by sharing some of his future plans for Zaraz, including privacy-related features, data loss prevention, and expanding the capabilities of managed components in the years to come.

Thanks for listening!

Visit us at: MammothGrowth.com
Follow us on LinkedIn

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Since 2015, over 850 companies have trusted Mammoth Growth to surpass their marketing, product, and business intelligence goals. We offer consulting, integration, and strategic planning services in the following areas:

- Data Engineering / Governance
- BI Reporting & Dashboarding
- Customer Data Platform (CDP)
- Product & Behavioral Analytics
- Lifecycle / Email Marketing
- Modern Data Pipelines
- Multi-Touch Attribution
- Machine Learning
- A/B Testing

Mammoth Growth has deep expertise in a wide range of tools, including Amplitude, Braze, Heap, Mixpanel, Optimizely, Rudderstack, and Segment.

We’re your Growth Team as a Service.

Join Mammoth Growth CEO, Ryan Koonce and AttributionApp.com CEO, David Hiltner for a conversation about the importance of a strong multi-touch attribution strategy.

As consumers demand more control over how their data is used, we're seeing a huge impact on the privacy landscape across the tech ecosystem. And that means growth marketers face even more challenges in accurately measuring attribution for their teams.

- How can you determine your return on ad spend (ROAS) in this environment?
- How can you be sure you're sharing your value with the right people in the right places, and not burning through cash (or leaving money on the table)?

One of the keys to a successful multi-touch attribution strategy is realizing this fact: wherever you advertise online, each platform wants to claim full responsibility every time you acquire a new customer. But since they don't talk to each other, you'll usually miss out on the full customer journey.  

When you see how to run a successful multi-touch attribution strategy, you'll understand the big picture. 

Mammoth Growth Podcast | Insights From The Trenches

Thanks for listening!

Visit us at: MammothGrowth.com
Follow us on LinkedIn

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Since 2015, over 850 companies have trusted Mammoth Growth to surpass their marketing, product, and business intelligence goals. We offer consulting, integration, and strategic planning services in the following areas:

- Data Engineering / Governance
- BI Reporting & Dashboarding
- Customer Data Platform (CDP)
- Product & Behavioral Analytics
- Lifecycle / Email Marketing
- Modern Data Pipelines
- Multi-Touch Attribution
- Machine Learning
- A/B Testing

Mammoth Growth has deep expertise in a wide range of tools, including Amplitude, Braze, Heap, Mixpanel, Optimizely, Rudderstack, and Segment.

We’re your Growth Team as a Service.

Join Mammoth Growth CTO, Drew Beaupre, as he discusses how our team has helped clients manage the transition from legacy platforms to new technology. 

- What platforms are they retiring, and what new tools are they adopting?
- How can you prepare for unforeseen hiccups that plague any migration?
- What red flags tell a company that it's time to upgrade?

Guests: 
Dylan Cruise, Data Architect & Engineer
Ben Wilson, Sr. Analytics Engineer
John Morrison, Analytics Consultant

Mammoth Growth Podcast | Insights From The Trenches

Thanks for listening!

Visit us at: MammothGrowth.com
Follow us on LinkedIn

-------
Since 2015, over 850 companies have trusted Mammoth Growth to surpass their marketing, product, and business intelligence goals. We offer consulting, integration, and strategic planning services in the following areas:

- Data Engineering / Governance
- BI Reporting & Dashboarding
- Customer Data Platform (CDP)
- Product & Behavioral Analytics
- Lifecycle / Email Marketing
- Modern Data Pipelines
- Multi-Touch Attribution
- Machine Learning
- A/B Testing

Mammoth Growth has deep expertise in a wide range of tools, including Amplitude, Braze, Heap, Mixpanel, Optimizely, Rudderstack, and Segment.

We’re your Growth Team as a Service.