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Adopting a New Category of Analytics: In discussion with Shawn Hansen, CMO at Heap

This post was originally published on MarTech Advisor by Ishani Banerjee.

Shawn Hansen, CMO at Heap shares how the advent of cloud has allowed marketers to ask and answer deeper questions instantly and understand their customer’s end-to-end journey – a path typically hindered by missing data or technical resources. He shares useful insights on omnichannel marketing says that hiring the right marketing talent who can ask the right strategic questions to drive business growth is crucial. 

What is the core marketing technology capability that Heap brings to a marketer?

All marketers want to understand their customer’s end-to-end journey but are usually blocked due to missing data or technical resources. Heap makes it possible to understand everything about a customer with no code.

This new category of analytics, called Insight Automation, automatically collects every customer interaction across your website and mobile app and combines it with email, sales, and customer success touchpoints. 

Every click, tap, and swipe is captured automatically without coding. Normally, data scientists are required to combine these data sources, but the advent of virtually free cloud storage and computing finally makes it possible to capture everything. Anyone at the company, whether it’s a marketer or product manager or CEO, can iterate on data-related business questions in real-time. This enables marketers to answer detailed questions about conversion rate optimization, funnels, attribution, etc., and get a richer, full 360-view.

One of the biggest gap areas in marketing today is the need for more data-backed marketing and the lack of adequate skills of marketers to actually turn data into insights. How is Heap addressing this gap?

Heap was founded on the philosophy that everything needs to be codeless, so anyone can use it. Our founder was a product manager at Facebook and realized even given one of the most sophisticated engineering teams on the planet, he was still stuck waiting behind engineers to instrument the questions he asked. So he created Heap with the hope that he could ask questions much faster by bypassing technical obstacles – and allow marketers to ask and answer deeper questions instantly. This problem has only become solvable recently because of the advent of the cloud. Other legacy analytics software founded 20 years ago were not built for the modern marketer.

Heap captures every customer touchpoint, allowing marketers to become totally self-sufficient and ask smarter questions faster.

What is the role of machine learning and AI in analytics? Do you think this is the answer to the analytics skills gap? Will human beings not be needed to make sense of the data to drive decision-making in the future?

Machine learning and AI assumes the underlying data are clean and accurate. The most sophisticated large companies in the world have the advantage of very large data sets, such that even unsophisticated algorithms can yield amazing insights. Every company needs the ability to capture this kind of data. Heap captures an order of magnitude more than legacy analytic vendors. Because we capture every customer interaction in a pristine, trustable data set, we unlock great potential in machine learning and AI to ask new types of questions.

In today’s analytics world, the best and brightest minds are still locked up, spending most of their time organizing and cleansing data.  Much like when the industrial revolution unlocked worker productivity

when the manual work of analytics is automated, our best minds will be freed to do what they do best– ask and answer hard questions.

Humans will always be needed to innovate and think of the next frontier.

This requires current analytics pros to think about the future and how they want to up-level themselves. I have a friend who is one of the top Adobe Analytics Systems Integrator.  He told me recently that he thinks his current job will be fully automated in three years (and Heap is one of the companies that will do it)–and he’s focused on how to become relevant in that new world.

How is Heap different from all the other analytics tools out there? What is your take on predictive analytics?

Heap emerged on the analytics stage at an exciting time when

cloud computing has essentially enabled the ability to capture everything and tie together customer touchpoints that used to be isolated. 

I worked for several years building petabyte-scale data warehouse products, which were only available to the top enterprises that could really invest in this kind of tech. Data warehouses were the only way to answer sophisticated customer journey questions that striped across organizational silos, to try to answer questions like: why does a high-value customer stay with you.

Heap represents a new breed of analytics tools. It makes this kind of compute power available to the normal lay marketer. Now a marketer can rapidly answer so-called “impossible questions” in a natural language-like interface because the underlying data has been captured, joined and cleaned automatically. 

Once the data is in place, the next exciting step is predictive analytics. 

We’re just at the beginning.

Once the marketer has direct access to ask questions without dependencies on slow-moving engineering teams, the sky is the limit.

What are your B2B customers and prospects telling you? What are the biggest challenges with analytics for them? What are your tips to address those top 2 or 3 challenges you hear about most often?

Last year, I remember sitting across the table from the CTO of one of the most sophisticated companies in the world. He leaned towards me and said something like this: “Our biggest problem isn’t with pretty UI’s. We don’t trust the data. We spend most of our time organizing and dealing with the constantly shifting landscape: integrating data, changing schemas, getting the pieces to fit. Can you help us?”

I’ve seen this challenge over and over again: A company has a mission-critical question to answer. They assemble a team, buy the tools, and then spend six months in data excavation: cleaning, organizing, validating, trying to get to a place where they can trust the data. It’s hard, thankless work. Usually, the project takes too long and the team simply gives up. I saw this problem on an analytics team at Microsoft; I saw it as CMO at Mixpanel.

I joined Heap because I felt this problem was a fundamental reason why the failure rate is so high with analytics software projects. The good news is that there is a new wave of cloud-first companies striving to solve this and automate it.

Could you share 1-2 common mistakes B2B enterprises make with leveraging analytics? What could they be doing differently with their analytics efforts?

The data-driven goal is for anyone to use data to inform decisions.

It should be easy for people to be right, instead of making decisions based on gut or being the loudest at the table. 

Most companies make the mistake of focusing on making analysis accessible by selecting the simplest UI or as many visualizations as their engineers can crank out.  This is a little misguided: people don’t have an issue understanding graphs or need more analysis features.

The core issue is that people don’t understand the underlying data or don’t have the right data, or the data isn’t trustworthy. 

Without a complete data set that users can understand, data analytics is not as useful. Companies need to invest in infrastructure that solves 80% of the pain: the underlying data itself.

What are the questions B2B marketers should be asking potential analytics partners? What should be the key considerations when investing in an analytics platform?

B2B marketers should be asking potential analytics partners questions about time-to-insight, the speed of iteration, ability to deal with a rapidly changing organization, and data completeness:

●        How complete are my data? Are the data accurate? Useful? How can I be sure?

●        What kind of dependencies do the business teams have on technical resources?

●        How fast can I iterate on new questions? Walk me through what happens to the infrastructure as my organization scales and learns more, and asks questions I did not anticipate.

●        What happens to the underlying data infrastructure when I think of a new question that I did not initially anticipate? Do I need to restart data collection? How do I retroactively ask questions?

●        How can I ask behavioral questions across cohorts of my customers?

●        What happens when I discover a problem or defect? How difficult is it to reimplement when I make changes or reskin an application?

●        How do I get the data out of the system? Can I export for power users to get access to standards-based data warehouses?

●        What kinds of multi-touch marketing questions can you answer? How easy is it to answer these questions?

●        How do you integrate other sources of customer interactions? How fast is my time to insight from start to finish to integrate these?

●        What kinds of customers do you have like me? Are you focused on marketing or are you more general purpose? In other words, how much work do I have to do to get out-of-the-box insights for more than superficial questions?

Omnichannel marketing is the name of the game today. What are some of the proven tactics for pulling in data and insights from non-web analytics systems such as Digital Out of Home, call center analytics, and events and conferences into a single view of the customer?

The two keys to omnichannel marketing are (1) Understanding the new questions you want to ask (i.e. what data sources need to be tied together); and (2) Your capacity to quickly integrate sources from multiple data silos.  This takes the form of either non-technical users asking questions through a point-and-click interface (i.e. marketers and business professionals); or more sophisticated power users who need direct access to the dataset. As you select an analytics toolset, it should be able to do both.

Let’s wrap with a look forward.

Heap this year had a $27 million Series B funding – what is going to be the main focus of that? What is the one area of investment you’d like to make in the immediate future from a marketing technology perspective for Heap as a marketer?

Heap has grown rapidly, with over 6,000 new customers in the last four years across every key vertical, including e-commerce, SaaS, fintech, retail, and media. Now that we’ve hit product-market fit, we are scaling the team and go-to-market capacity. My first investment as a marketer has been to build the instrumentation to see every aspect of the funnel clearly and then tie that to customer value.  This kind of tooling unlocks the ability to iterate quickly to better understand our customer.  Ultimately, with this tooling in place, I can then move to the most important step: hiring the right marketing talent who can ask the right strategic question and really drive business growth. 

How do you see web and mobile analytics evolving in the marketing context? What are the top 3 trends you foresee for web and mobile analytics in the B2B marketing space, come 2018?

For the last decade, marketers have talked about understanding the end-to-end customer journey. This is finally becoming a reality as analytics is built around a customer or user, and not anonymous high-level aggregates like page views. I see the legacy web and mobile analytics vendors who were never built around user behavior struggle to adapt. The disruption of Adobe and Google Analytics is real. Desktop-first vendors struggle to become mobile-first. Analytics tools built to understand page views or anonymous transactions aren’t build to understand the authenticated customer. Siloed tools that can’t tie to other data sources will be stove-piped as customers grow dissatisfied with superficial answers evolve to more sophisticated questions.

In 2018, the three top trends will be:

–          Legacy web and analytics companies like Adobe and Google Analytics will be disrupted as marketers demand deeper insights across the customer journey. 

–          Marketers will evolve to become end-to-end business owners, as new analytics empower them to be uniquely positioned to understand the customer from the first click to final churn.

–          Marketing insights will become much more self-service and high velocity as dependencies on technical resources are automated away.

What about at Heap- what new innovations are cooking for the near future?

Heap recently launched Sources, the ability to auto-track every customer touchpoint. Building on this foundation of Insight Automation, expect Heap to deliver more automatic marketing insights around the customer journey.

 

Ishani Banerjee is a Communication Specialist at MarTech Advisor. Her infectious millennial curiosity to deep-dive into all things tech has led her to track the innovators and disruptors in the martech and HR tech world, and bring their stories to life in through the MarTalk and HRTalk Executive Interview Series.

MarTechAdvisor.com is the flagship media property of Revenu8, a full funnel ABM Marketing solutions brand. MarTechAdvisor.com has evolved to become one of the fastest growing media brands providing unbiased news, industry research, software recommendations, and aggregated job opportunities for Marketing, HR-tech and IT professionals.

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