Nils Lind of Assertive Yield On How To Use Data To Take Your Company To The Next Level

An Interview With Ben Ari

Data Driven Decisions — Trust in timely reporting and using data as a helpful source for decision-making to improve the outcome you can produce. This applies to budgeting, acquisition, and so many other factors in your company. Does it make sense to acquire another company in this field? Does it make sense to work with that vendor and so on? Data should always play an important role in the decision-making of any company.

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 Nils Lind.

Nils started with a career in the gaming industry and gradually built a network in the publishing industry and made a name of his own among a community of AdTech experts. Having a background in engineering, he built Assertive Yield at a very young age solely by self-learning. His motivation and can-do attitude led him to build a holistic tool to provide data transparency that enables assertive yield optimization.

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?

Hello! Thank you for having me here. It all started very young for me, in my last couple of years of high school. I already had some experience on the development side of websites for small companies. My brother was working with Minecraft and he was playing on a family-run server but he got kicked out because he created stuff that consumed too much of the server’s resources. So, I looked for a new server for him, and when I found one I got inspired to also start playing it and quickly got hooked by the modded Minecraft environment. The person who would actually operate the server eventually gave me access and with time we made it more professional and it became one of the biggest modded Minecraft Networks and is still running today. Basically, in the gaming space, we learned a lot about performance optimizations, and people playing were supporting us through in-game transactions.

I used some of that money to invest into publishing and buying online properties, and domains, which were also focused on communities and user-generated content. At some point, I figured out how to optimize and multiply the amount of revenue for these properties and sites by applying similar principles as we did to the Minecraft system such as improving the loyalty of a user base, getting them to contribute more, and also generating revenue through advertisements. This was the foundation for what we do now as we had to track revenue per user and calculate lifetime value eventually translating into producing a system for publishers to maximize revenue through yield optimization.

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 were first starting and the lesson you learned from that?

From the start, we were very focused on building what our customers and partners would need and want. So we had to sort out and decide what we could actually build. We asked what makes sense to build and not to build? We kept pushing back on some things because we felt certain aspects just did not fit our product model. We would get specific requests and we kept having to say no to our customers. Eventually, the number of people asking for one product became substantial and we ultimately decided if the demand is so high, let’s just build it out. It turned out to be scale-wise, an underestimation of how much work we would need to put into that product. Overall, if I could offer a word of advice, it is to properly scale your core business and expectations for your clients, so that your resources aren’t stretched too thin.

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?

We had the opportunity to sell the company many times, including offers that were far beyond the value of revenue we are generating due to strategic value. So there was a point where we had a hard decision to make do we take the offer, sell, and basically be set for life. However, we had more faith in what we were building, basically, we saw the company as our child and wanted to see it grow. We kept wanting to build it further as we were profitable, with no major issues, and so the decision to sell was ultimately passed on. We understood that this would be the harder path as the company still had much room for growth and if we ever do consider selling, it would be unlikely for us to receive an offer with similar multiple again. . We also saw some similar companies to ours being acquired and dying after some time. At the same time, we heard more than 3 times big publishers investing in an entire team of developers who were trying to build a similar solution — they came to us to hire our platform. We know the market fit for our product and that the technology barrier is not so easy to overcome and maintain.

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

We are really excited about our Yield Manager system. It provides the possibility of complete and total revenue management for publishers, testing scenarios, AB testing with no code, optimal vendor performance, visit, and user lifetime value optimization, and much more. Features like a single source of truth, ease of integration, flexibility, and programmable capabilities are going to give freedom to the AdOps and Product teams to evaluate performance and improve their workflows.

With a lot of the optimizations, one can usually only get from working with companies that take up to 20% of the revenue or that are just very difficult to build in-house without having a large-scale engineering team behind it. We are able to provide this product for both large and mid-scale publishers at a very low cost to help manage more aspects of revenue management, cut out more middlemen in the process, consolidate solutions, and allow them to become more data-driven.

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?

• I think the biggest one is perseverance. Just staying behind the work you do, adjusting and learning to improve. Trying a new approach is always better than giving up.
• Another is being inquisitive, asking questions, and being able to explain what is actually going on within your industry. What are the different reasons? Why is the data like this? How can we improve this? Being naturally inquisitive helps not only your own business but also prepares you for any questions potential customers may have. Being able to elaborate and provide insights into the industry, to help them understand why something is working, and why something is not working, and to help make important decisions themselves.
• The last is being honest and direct when it comes to communication. Transparency and honesty are vital traits to have, especially in our industry. Being able to give a customer a realistic vision of what you and your product can provide for them, in our experience, resonates more than trying to simply sell someone on a single aspect. Many people in our industry have a tendency to not be as direct as they possibly can and at Assertive Yield, we pride ourselves on providing the full picture.

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?

Data can be used to make smart decisions in many ways. According to me, these can be the 3 focus points while using data:

• A lot about data is not directly using it to estimate outcomes and discover what might be working. We can quite often be wrong and miss a substantial amount of inputs that are important to the greater data sets. When we deal with data it is pretty much always about if you have enough reliable data available, and for it to run multiple optimization experiments. Making your data reliable and obtaining it from a single source of truth is the key to success.
• Ultimately the most important question is what works for the KPIs you are looking for. It is not just looking at the data and thinking what solution would be best, it is about exploring and testing different scenarios that result in the best outcomes. Getting holistic data and analyzing it from different sources, angles, metrics and dimensions help to get optimal solutions for any business. The disadvantage of this technique is that it requires a massive amount of data, and involves risks and actions that don’t make sense to see what the real impact is.
• We should not be making assumptions only based on data, it’s way better to always run an experiment and then use the results of that experiment to actually decide what to optimize and where to go from there. The focus should be to carry out continuous optimization with advanced analysis powered by machine learning techniques, to get accurate insights and recognize patterns and trends that can be implemented.

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

Companies that generate a lot of data and have either massive scale or low margins, so that even small incremental changes have a big impact on them. Typically, when something is already optimized to a good degree, it is no longer possible to make any kind of changes that would generate 10% uplift on whatever KPI you are looking for. However it is always possible to find 10 different things that all generate 2%, and combining these actions, you can create that >10%. If you have low margins, you can increase much easier and faster, even with small improvements. If you have a big scale, the cost and amount of time needed to implement and clean up that data will increase. While setting up the experiments, making sure that the outcome is clean, and then also applying the kind of optimization in the field is actually essential and applicable to any company.

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.

The possibility to have systems that let us know if something in the data is changing can be a good indicator of predicting market changes. We can analyze it deeply and identify an opportunity to drive more revenue. In some way, it could be that something broke and we have to fix it. It could also be something like a market change, which doesn’t really have a long-term impact.. So in regards to processes and customer experience, whenever data is available in a clean way, where we can trust with accuracy can help to improve the ecosystem.

We can go back to the exploration where we can test different theories, we can even test randomly to figure out what works. When we look at user experience in regards to a website, people come to their website with different intentions, some are looking for when a famous person was born; so they are just here for a quick answer. Someone can directly search for a brand name as they already have knowledge about their work. We can see a person visiting once every day, another one, once every week, while another user just comes for the comments on the page, and another person has a subscription. Many different variations with different intentions change depending on the time of day or the day of the week.

There are a lot of possibilities and there’s a lot of value that can be gained by starting to use the data available to learn what kind of layouts or processes we need, or what kind of configurations work best. The aim is to give them a better experience and at the same time, improve our bottom line in terms of generating revenue. Depending on the user intention, we can aim towards improving their user lifetime value. We can also aim towards converting people from a short term intention to a long term intention.

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?

It is challenging for a lot of companies in a few ways. One being that to become data driven, your company has to have clean data available, or be able to collect clean data. The other thing needed is a way to understand the data. If we look at a KPI, we are testing out different variations, because we need to know if it would actually work or have a delayed effect. Let’s for example, assume we are optimizing certain parts on our website and sometimes an advertisement is visible, and another time, it is not visible. The advertiser only cares about what is seen because otherwise it has no value. Now, we can make changes and we can increase the amount of revenue being generated, but it could have a negative impact on the amount of ads that are seen by users and near-term the advertisers adjust and spend less with us..

Now, while it might look positive in the short term, the long term effect is actually negative because we don’t know about how much of it is actually viewable.. Nowadays we have different people involved in the business and many advertisers are not going to adjust to it in real time, they’re going to need some historic data in order to be able to adjust to it. You can make the change, now it looks positive, but two weeks later it turns negative. It really is about having that clean data available and understanding how other parties within the ecosystem behave depending on what kind of changes are made. While also considering what other KPIs are necessary to include in order to ensure that whatever we are doing is positive and not negative long-term.

I think a big challenge is really about having the people within the company that understand the product or having the people that are interested enough to want to dig into the issue. Also, getting clean data in a way we can trust it, not from a hundred different sources all producing different variations of data. Ask yourself, how long does it take us to set up a test? How long does it take us to make a decision if this test was positive or negative? And we need to understand our relationship to that specific KPI, like is it dependent on the user? Is it dependent on the visit? Is it dependent on the page view? Is it dependent on just a single ad impression and what is the impact?

So in short the challenges to overcome are access to the data, effective and quick use of the data, elimination of wasted time in setting up different tests, and then actually understanding the KPIs which are important to your business… Consider, what point do we have significance and a high probability that the comparison of the data you’re looking at is not random.

Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.

So, there are different fields where data can be applied.

• Data Driven Decisions — Trust in timely reporting and using data as a helpful source for decision-making to improve the outcome you can produce. This applies to budgeting, acquisition, and so many other factors in your company. Does it make sense to acquire another company in this field? Does it make sense to work with that vendor and so on? Data should always play an important role in the decision-making of any company.
• Using Data to Personalize the Experience -This means we’re going to have a different experience depending on the intention of the user or depending on the device. Depending on the time of the day, what can give a user a different experience and can generate higher revenue for us?
• Understanding and Measuring the Lifetime Value- having the ability to do so gives us a better understanding when comparing last year’s data to this year’s data. Did we actually improve or did we lose? We might have improved in short intervals, but what was the long-term effect of it? Was it positive or was it negative? Other things are identifying and differentiating between internal changes and external factors. We did change internally which had an impact on market changes. So we might have had a bad year, but it may have been a bad year for everyone. We would then look into how we compared to the market average and in some cases, we did better on average. You have to know what kind of market changes are happening and how to align with them.
• Machine Learning — We can use the data to feed it into machine learning systems, artificial intelligence, and so on to let machines take care of the optimization so that we don’t have to worry about it anymore. And they have the ability to adapt faster, basically close to real-time to market changes. So they will be able to adjust when things in the market are changing and at the same time, they will be able to control sudden variations and other things on a granularity level that is impossible for any human to do. They will understand that at 5:00 am a partner’s performance is lower in comparison to that of midnight. So now we have to treat this partner differently than all the others just because our frequency reset is happening at a different point in time which most likely humans would never even notice.
• Continuous Optimization And Continuous Exploration, Experimenting, and Testing Different Strategies — When the market changes, the data might change, the user’s intention might change, or the customer’s intention might change. So then if we keep exploring to a certain degree, we have the ability to notice and react to market changes close to real-time.

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

What has been difficult or is always going to be difficult is having clean data available. It is essential that we can trust and use that data coming from a single source of truth. Discrepancies commonly exist within the data by combining it from different sources and that has been a challenge that has perpetuated itself in this industry and will continue to do so.

I think overall, in this industry, advertisers and bigger companies within the industry, do a lot of machine learning and automation. Some of them do it really well, but on the publisher side, there’s not much happening in that regard. There are some solutions, but they tend to be siloed. It’s primarily focused in regards to one kind of KPI, which doesn’t provide a holistic solution. For example when a company only looks at the banner revenue, but they fully forget about video revenue, subscriptions, microtransactions, affiliate revenue, and so on. I think that yield analytics evolution is a lot about making the best performance and customer experience from different kinds of revenue sources — since everyone is aiming towards diversification of revenue for stability — and, enabling it to work well together.

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?

I think it would be about readjusting incentives. There are a lot of ways to basically “game the industry” which is beneficial for a few small players but a marginal benefit and comes at a big cost to a lot of other people. This creates a lot of inefficiencies that cost everyone money, especially in ad-spend or advertising dollars being taken out of the industry.

What we do is granular data sharing, which sets standards on how the data has been collected, shared, and is accessible, so that it is simplified for everyone. Everyone can have access to a good data set, providing them with the same kind of data, and uses for that data without having to stitch it together and figure out how to make it work. In any industry, the notion is that If I’m big or successful, I don’t want to share any data. I want to keep it all hidden because I want to be superior. This specifically prevents the smaller players and the mid-level players, from being able to get access to most of this vital data and be able to progress with it. That is what we aim to change and make data more accessible and personalized for all players in the industry.

How can our readers further follow your work?

You can follow everything that’s happening in our company on our website https://www.assertiveyield.com/

Also on our company page on LinkedIN- https://www.linkedin.com/company/assertive-yield/

Publishers, SSPs and Ad Networks and those interested in the industry can follow my work on Reddit AdOps community and Slack AdOps channel.

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