Haggai Weiser Of TRACARTS On How To Leverage Data To Take Your Company To The Next Level

Tools on tap. Ingrain good habits without gatekeeping. Engage Early. Engage Often. Don’t forget Qualitative.

The proper use of Data — data about team performance, data about customers, or data about the competition, can be a sort of force multiplier. It has the potential to dramatically help a business to scale. But sadly, many businesses have data but don’t know how to properly leverage it. What exactly is useful data? How can you properly utilize data? How can data help a business grow? To address this, we are talking to business leaders who can share stories from their experience about “How to Effectively Leverage Data To Take Your Company To The Next Level”. As part of this series, we had the pleasure of interviewing Haggai Weise.

Haggai is an accomplished technology executive known for building and scaling successful startups. With a focus on driving innovation and fostering high-performing teams, he excels in translating vision into actionable plans.

He is passionate about empowering teams and driving technological advancement.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

Thanks for having me on this amazing series. Like many kids in the 90s, technology was just something I gravitated to. It came naturally, and I was blessed with parents who despite not being affluent, allowed me to tinker with and break basically every item in the house. I think this desire to tinker, to improve things incrementally, and to a certain extent, being ok with breaking things is what attracted me to startups. As I grew into leadership roles, I discovered that this same passion translates not just into building great products, but also to building incredible teams and organizations, who in turn, build bigger and better products than you ever could on your own.

It has been said that sometimes our mistakes can be our greatest teachers. Can you share a story about a humorous mistake you made when you first started and the lesson you learned from that?

Way before work from home was the topic today, I’ve always wanted to experiment and be at the forefront of work that wasn’t just ‘remote’, but that was fully ‘location agnostic’. We were looking to see just how far we can push remote work such that it really felt like being in the office with coworkers, or at least, just as productive. My bright idea was to trial a day where we work from home using Discord. If you’re unfamiliar, Discord is a lot like Slack but it’s used more often by the gaming and streaming community. Unlike Slack, it had some more immersive features, for example, with a keyboard command, you could talk to an entire group just as if they were at your table. Well, long story short, everyone hated it. It was too invasive. Reaching out without warning, which is generally considered acceptable in a work environment, just didn’t translate at home. The lesson? Well for one thing be careful experimenting on work culture. But also, remote work is not about translating traditional office protocol to the home. It’s a completely different kind of work. And while it can definitely yield as good or better outcomes than in-person work, it requires specific preparations and a realignment of traditional expectations. Unlike Discord, some things we found to actually be effective were more asynchronous communication, clearly defined goals and deadlines, and right sized, right cadence meetings.

Leadership often entails making difficult decisions or hard choices between two apparently good paths. Can you share a story with us about a hard decision or choice you had to make as a leader?

Wow, great question. I think one that is almost universally experienced is something I refer to as “change bombs.” This is where an important stakeholder, or team, demands a significant change in a product, feature or even in organizational strategy, with minimal notice, and often with justifiable cause. While some of these are false alarms, I don’t think I’ve ever had a year professionally where I haven’t run into a “change bomb” at least two or three times. In leadership, I think the position you have to take is just accepting the inevitability of change as “a feature”, a badge of pride showing the adaptability of your organization and the people who work there. At the same time, you want to make sure to treat change as a relatively expensive operation, one that needs to be truly pertinent to sway the course of action. Like many conversations we’ll have today, the key to good decision making here comes down to data.

When a prospective Fortune 100 client demanded that our product integrate with their corporate portal for user authentication (known as SSO), our entire business went into scramble mode. Building a proper solution would knock out half of the organization’s engineering capacity for 3 months, something the product team was adamant it could not afford. The sales team meanwhile could not see how anything was more important than closing the deal at hand. Tempers were flaring and a decision could not come quicker. In a room with all relevant internal stakeholders, a quick reset allowed us to tear down the problem and look at its core elements: the data. What was the net value of this deal and how did we see it expanding? Conversely, what features would have to be pushed off to guarantee we deploy SSO in time, and what data did we have to quantify the value of said features. What about out-of-box ideas? Was it technically feasible to purchase a third party solution and integrate it? Is there any middle ground about the SSO requirement on the client’s side? Weighing these things carefully, and utilizing the best data we could put together, we came to a solution: The sales team was able to get the client to agree to purchase the product as is, with SSO to be delivered within 6 months by the product and engineering teams.

Are you working on any new, exciting projects now? How do you think that might help people?

Yes! At Tracarts, our mission is to bring data-driven solutions to physical retail. We’re readying our first flagship product: Trac by Tracarts. Trac brings the existing physical shopping cart infrastructure of retailers to the digital age and in line with customer expectations. It’s crazy to think that in a day and age where data collection is seemingly inescapable, retailers still have zero idea about the location and state of their shopping carts or the customers who interact with them. This leads to millions of missed opportunities every day to enhance shopper experience, and simultaneously, forces retailers to incur millions in cost replacing missing shopping carts and dealing with messy parking lots. By connecting the customer to the shopping cart Trac by Tracrats gives retailers a cost-efficient data-driven solution that virtually eliminates cart loss, dramatically increases cart returns, and pays for itself in year one. But that’s just the beginning. Having the customer and the cart on the data grid allows retailers to engage and incentivize shoppers at a level never before possible. Imagine being able to see not just what a customer bought, but when they entered the store, how long they spent in each aisle, and where they got stuck. Imagine being able to segment this data by demographic, age, gender etc. And again, this works with retailers existing carts, there’s no need to buy new shopping carts or specialized equipment for each cart. In real world environments we’ve seen not just dramatic reduction in carts lost and dramatically increased rate of cart returns, but also a dramatically increased rate of loyalty signups and engagement leading to very happy, very engaged customers who retailers are able to incentivize and delight like never before.

You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

Sure, I’d be delighted to! And by the way, I think this is an excellent exercise. I really had to think through some of the processes that have withstood across different work environments. I hope these will resonate and provide value to others as they have to me.

• Craving the Minimum Viable Process. Build your organizational process the way you build your products. Incrementally, from the ground up, with relevant stakeholders involved. Take only as much process as you need, and set appropriate intervals to revisit them (i.e. not every time there’s a problem). When coming from a large company background, it sometimes takes a while to de-program from endless roadmapping and list-making. I recall one startup CEO of mine outlawing spreadsheets or meetings longer than 20 minutes altogether! While this is extreme, people undoubtedly seem to default to the security of a static itemized list to work off. Make yourself and your leadership team comfortable with plans that are low-fidelity. This allows room for them to grow and evolve with more accurate data and feedback.
• Viewing people as productive by default. People often talk about “motivating” talent to perform, I think this is an incorrect, or incomplete approach. In my experience, talent defaults to being productive. People want to do great work and be valued by their organization. If we take this to be true, then the first question when an individual underperforms is not “how do I motivate them.” Instead look inwards at the org and the situation. Is it clear who is responsible for a specific outcome? Are outcomes clearly defined? Is the project scope correctly sized? What are the variables leading to less than ideal performance, and are you even measuring performances fairly and equitably? I recall one of my early hires at a SaaS startup was not delivering work on time under his new manager. Before his next one-on-one, I had a hunch. I looked at the bug tracker and saw that he took 80% of the workload. Not only that, in a meeting with his manager, he admitted that some customer success managers were messaging him bugs directly to have them fixed instead of going through the system, because “it was faster.” This individual was doing 3x the work of anyone else on the team, and doing so out of a sense of personal responsibility to the team and the business. Having a conversation about load balancing, prioritization and saying “no” is a much different conversation than one designed to motivate.
• Valuing and Protecting Made Decisions. Consensus is fickle. But it’s the most powerful tool for moving forward on a project. Protect consensus strongly. People talk about the need to disagree and commit, but it’s not always obvious when this isn’t happening. One example I’ve seen is the “One More Opinion” strategy. This is when a dissenting stakeholder introduces a third party, usually external opinion about a project or approach because “it never hurts to have another perspective.” In reality, this perspective is hand picked to align against the position of the team. Often, team leaders have a tough time pushing back against this because they might be viewed as “defensive.” But the truth is the opposite. Understand: allowing for extraneous debate after consensus has been reached, or really, at any point after a decision maker of a project has been identified, shows insecurity from the dissenting viewpoint in the organizational process. Constrain this insecurity. Trust your team and embolden their organizational voice. Create barriers that make it harder for them to get derailed. Switching contexts is incredibly damaging to productivity and putting team leaders in a position where they have to randomly defend their positions is at best exhausting, and at worst, toxic. No one knows your company like your team does, and if you really need additional opinions, aim to limit this to the discovery phase, and source these perspectives collaboratively.

Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” For the benefit of our readers, can you help explain what it looks like to use data to make decisions?

Effective data-driven organizations are able to identify critical metrics and data points and convert them into meaningful action (or inaction!). Access to data collection and processing tools and staff are broadly available and expected to be utilized from the jump. It’s not something they have to budget for ad-hoc. Still, there’s rarely time or budget to test every assumption, but strong data driven times identify assumptions early, and prioritize what needs to be validated by value and risk, just like any other feature prioritization exercise. Data driven teams have access to tools and resources that not only give them quick access to relevant user panels, but also help draft quality studies that have appropriate methodology. Similarly, they either have access to staff or tooling allows them to slice data to find hidden patterns and bring interesting insights to the forefront. Finally, these teams habitually share relevant and interesting findings across the org, not just inside a team.

Based on your experience, which companies can most benefit from tools that empower data collaboration?

Most organizations can benefit from data collection and collaboration so as not to continuously shoot from the hip. But particularly organizations and teams where tremendous amounts of pre-work (e.g. POC’s) are needed to get approval from relevant stakeholders. This generally means mid-size orgs and larger, where there are far more stakeholders and pivoting is much more difficult. Validating low-fidelity prototypes early and often, internally and externally is not only good hygiene, it’s essential when the cost of a later-stage pivot would be catastrophic for the project or the business. Of course, it’s also much easier to gain organizational consensus and alignment when an idea has validating data behind it.

Can you share some examples of how data analytics and data collaboration can help to improve operations, processes, and customer experiences? We’d love to hear some stories, if possible.

At Feedback Loop (acquired by Disco, 2021), our core product allowed product management and research teams to validate concepts and ideas with quality user data quickly and confidently. While always useful, when the COVID lockdown hit we saw companies scramble to make rapid adjustments in an era where “shooting from the hip” was no longer a viable option. Organizations that were never all that comfortable with collecting and analyzing data developed an urgency unseen before. How was a leading ride-share company going to make customers feel comfortable that their cars were clean and safe? How was a national pizza chain going to pivot in a time where everyone is cooking at home? They needed answers, and they needed them right away. Of course, we’re all relieved that shutdowns and social distancing are seemingly behind us, but if we can take that urgency to validate assumptions with us, I think many projects and businesses would benefit greatly.

From your vantage point, has the shift toward becoming more data-driven been challenging for some teams or organizations? What are the challenges? How can organizations solve these challenges?

I think the primary challenge organizations face when first trying to transition to data-driven philosophy is something I call “blank page syndrome.” When teams are given the opportunity to ask any question and collect any data point, they seem to blank out. It’s almost too open ended a question. Ideally, organizations should look at these as opportunities to train and grow data driven questions. Break down the questions into smaller problems and rank between them. What data collection tools are available specifically for your problem set and industry? Check out traditional boilerplate templates and even consider utilizing generative AI as inspiration. If a resource you are using offers training, support and best practices, consider engaging with their CS team to help get your org started.

Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”?

1. Tools on tap. Teams need to have tools, resources and training available to them from the jump. If a team has to identify tools, allocate budgets and gain organizational approvals inside the time constraints of a project, this often leads to a fail case. It simply just takes too long. When a large digital payments firm sought to use our tools to validate some critical decisions, it neglected to realize just how long approvals would take on the side of the organization’s technical diligence team. By the time IT departments traversed through the 600+ hair pulling questions provided by the organization’s compliance team, the project had already wound down and the team disbanded.
2. Ingrain good habits without gatekeeping. Surveys and feedback collection are only as useful as the thought and expertise put into their construction, methodology and purpose. To be blunt, if you put garbage in, you’ll get garbage out. As the field matures, what we see is dedicated research teams that act as a hub to support the quality of the data driven initiatives of multiple teams. Unfortunately, these teams are chronically overwhelmed. At a leading travel and hospitality site, queues for the research team were growing at a rate 3–4x faster than they were being cleared. The team had a moment of reckoning when a project from the C-suite took over 3 weeks to review. After a few iterations and tweaks to the process, a certification program was created where team members could launch and review data projects without support of the research team, with clear guidelines about when and where escalation was necessary. Now, team members went from turnaround times of weeks and months to days and hours in most cases, and the research team became the superhero group empowering organizational transformation.
3. Engage Early. Although this seems obvious, for whatever reason, many teams only seem to use data to validate assumptions as a last minute sanity check after the work is more or less complete. Higher performing data centric teams understand the opposite approach is correct. And if they don’t understand, they often find out very quickly. When a leading life insurance company approached us, it was excited and revved up about its idea: Selling life insurance gift cards for grandparents to buy their newborn grandchildren. But it was clear after just one panel that real world customers did not share the same enthusiasm for the idea. In fact, of the thousands of surveys we’d launched that year, this idea was one of the most universally disliked concepts we’ve ever tested.
4. Engage Often. When a leading media company wanted to get into streaming, their pilot survey showed customers are interested in a few key features offered by a competitor. Being an experienced research team, the media company chose a product that repeated these tests weekly, to compare data points and changes in attitudes. When a competitor introduced less expensive ad-supported tiers, certain customer segments responded very strongly initially. Quickly though, those sentiments evolved and generally soured across many, but not all consumer profiles. Collecting data consistently gave this media company early market perspective, and maturing context on ad-supported teams, when they work, and when they absolutely do not work.
5. Don’t forget Qualitative. As data and charts start flooding conversation channels and tools become accessible, it’s easy to get lost in the numbers and the direction they imply. But don’t forget to collect and cross check against qualitative data. Verbal, open ended responses, video interviews etc. I remember when an up and coming financial services product celebrated a data point where 75% of users were clicking through and engaging a new feature on their app, only to find out that users were actually looking for a way to disable it when they read through the qualitative comments and user interviews.

Based on your experience, how do you think the need for data might evolve and change over the next five years?

Perhaps more than any other industry, AI is poised to have a dramatic impact and data. As we’ve seen, AI is becoming more affordable and commoditized. The differentiating factor will be the data AI will train on. Organizations with more abundant and more meaningful sources of data will have a tremendous advantage over those who do not. AI will soon be the first in line to answer organizational questions. Picture, for example, an AI trained on multifaceted data and behaviors from specific consumer types. Survey answers, purchasing patterns, even location potential can be used to generate a specialized AI that can be asked to generate instant answers to questions it has never seen before. Imagine being able to guess consumer reaction to a new product concept just by questioning the AI, as if it was a human. Or imagine being able to ask qualitative questions about real human actions, “Why did you purchase this item instead of that one,” and the AI generating hypotheses from the collective knowledge it has about the consumer group. Organizations need to prepare for next generation AI driven data insights by unlocking the sources of the data in their organization. We’ve talked about shopping carts being off the data grid, and that’s a great example. What unique data hides in your organization? And how can you unlock it to be a competitive advantage.

Thank you for your great insights, We are nearly done. You are a person of significant influence. If you could inspire a movement that would bring the most amount of good to the most amount of people, what would that be?

Despite my humorous misadventures with early remote work experiments mentioned above, I still truly and wholeheartedly believe in remote first culture. When done correctly, it can maximize organizational output while yielding best in class culture. I’ve seen it and lived it. But for your employees, it can be truly transformational. It can empower perpetual renters to relocate away from overpriced cities and become first time home buyers. It can allow parents to be more available and involved in their children’s life. I believe in this so strongly that I think organizations should be incentivized to offer WFH via tax incentives. But as mentioned above, good remote organizations really need to rethink what they are actually looking for from their employees and focus on measurable output and outcomes. People ask me all the time: “But how do you know they are working? How do you know if people are not just slacking off?” If you’re asking that question, what you’re actually saying to me is “I don’t really know how to gauge my employees based on their output.” Setting right-sized goals and checking in is not always easy, but it’s also not that hard. Having employees sitting in a chair is not a metric of success, and if it’s the best you got, you’re in trouble. Think of remote as a litmus test for how well your org is able to define expectations, and a reflection on those who set them.

How can our readers further follow your work?

Follow me on LinkedIn for semi-serious posts about data, engineering and work culture. Be sure to say hello!

Thank you so much for sharing these important insights. We wish you continued success and good health!