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Why data science is crucial for today's marketer: Expert interview

Expert interview: The importance of data science for today’s marketer

Categories: Data science

Data has always been crucial to marketing, but our ability to make the most of it has always been limited to the technology we have available. In 2018, these limitations have almost disappeared thanks to technologies like machine learning and automation, which make big data accessible to businesses of all sizes.

This is why we’ve introduced a new service called Data Science, for businesses and marketing teams that want to get the most out of their data. To help explain why this is worth getting excited about, we’ve asked our head of data science, George Karapalidis, to explain why it plays such a crucial role in today’s marketing.

Q1: Why has Vertical Leap launched this new data science service?

“Data is at the centre of everything we do. We’ve spent years developing and improving our own insights platform, which we call Apollo Insights. The goal was, and still is, to improve our services further by using methods and technologies data scientists use for identifying actionable insights.

Apollo Insights turns big data into actionable insights
Apollo Insights turns big data into actionable insights for businesses of every size.

“There are two things that separate us from other search marketing agencies in the UK: scale and effectiveness. We believe that no one else can do the work that we do, on the same scale, as effectively as we do.

“This is where our data science service comes into play. By understanding more about the markets our clients operate in, their audiences and their strengths and weaknesses, we will be able to help and provide them with all the ammunition they need to grow their businesses.”

Q2: How does data science enhance your other services?

“There are currently five core services that we offer – SEO, PPC, Design and build, Performance UX and Content marketing. Data science is the gear that sits in the middle of all these services and provides them with the additional capabilities they need to deliver better results.” 

Q3: Can you give us some practical examples of this?

“For SEO, we are developing machine learning models for search query classification. We call this the buyer intent model. By classifying the queries for which a site appears in search results based on brand or product awareness, we are gaining access to a lot more data than we ever had before. This way, we can identify strengths and weakness across different areas of our clients’ websites, or compare the performance of the branded terms against their competitors. It allows us to identify queries that we need to target based on traffic or revenue potential. We bring well-hidden opportunities to the surface. 

Buyer intent model
An example of a buyer intent model for PlayStation gamers

“For PPC, our data scientists have developed models that allow them to optimise multiple AdWords campaigns in just a few minutes. It takes approximately 20 seconds per device category, per campaign to adjust bids.

If you optimise for two device categories across 1,500 campaigns, this equates to two days’ work per month. Now, if we multiply this by all the other modifiers that exist e.g. 20 cities, six age brackets and so on, the time required to optimise all these bid adjustments is approximately 500 hours!

“For our PPC specialists to perfectly optimise the bid adjustments is 20 seconds per category (device, location, gender), per account. This equates to 3.3 hours – work which would have taken 500 hours to do manually with the same level of accuracy.

AdWords bid adjustments by hours

“When it comes to content, data-driven stories with interactive elements and eye-catching visuals is what the readers want. We capture and collect data related to the industries our clients operate in and extract insights or interesting information which we then use to produce high quality articles that are informative and visually pleasing.”

Q4: What problems are you solving for customers with data science?

“There are some very common questions that our clients ask. For example, which channels work best and why? Which campaign worked best and why?  Is the data they collect accurate and reliable? Can the data be used for assisting their decision-making process, especially for critical business decisions? And many more.

“We quickly understood that many companies struggle to answer these questions. They struggle to explain why marketing activities fail or succeed. Many of them operate based on past experiences, success stories or best practices, but every business fails or succeeds for different reasons. Something that worked for one company may not work for another and understanding this dynamic is key.

“All of the above requires a lot of data, time and effort to analyse. Being able to collect data accurately, process it and analyse it can be a very time-consuming – and this is what our data science service is there for.”

Q5: How do you turn cold data into creative marketing decisions?

“Every dataset or data point has a story to tell either in isolation or in relation to others. Finding those stories can be challenging. Most of the time, it comes down to how well you know a business or industry but there are times when incredible stories are hidden beneath the surface and data science helps us to dig them up.

“Being in a position to turn cold data into creative marketing decisions requires lots of data, content, expertise and technology. It’s a complex recipe but our methodology does a great job of simplifying it.

Data visualisation is a key part of unearthing hidden stories and opportunities.

“First, we need to know what happened; hindsight. Then understand why it happened; insight. And, finally, predict what will happen; foresight. Not all insights may be relevant to the future of a business. That’s why context is important, but it is also important to be able to extract insights as fast as humanly possible.

“This is why industry expertise and the use of cutting-edge technology are required.”

Q6: What challenges do marketers need to overcome to make the most of data?

“First and foremost, the biggest challenge is collecting accurate data. We have a lot of clients that use dozens of different tools to track campaigns and website performance but these tools are rarely connected and integrated with each other. As a result, the datasets they collect are scattered and it’s extremely difficult to gain insights this way.

“Data reconciliation and consolidation are the two primary challenges that marketing teams need to overcome. Companies that have internal IT teams and support are usually in a better position, simply because they have the technical minds in their business that can guide them to make the right decisions. But, in our experience, many of them only do what is needed to answer the questions being asked by management.

“We’re here to help these companies make more of their data.”

Q7: What mistakes should marketing teams avoid with data and analytics?

“The most common mistake we see is marketing teams only looking at the top level of data they have available. Unfortunately, it’s rarely this simple to extract valuable insights and you need to dig much deeper into your data, cross-referencing different datasets to draw accurate conclusions.

“This is why it’s so important to have the right models for handling data and understand what your key metrics and KPIs really mean. What are the metrics that correlate with traffic or visibility, for example?

“Another common mistake is that businesses fail to validate their results by using data from other sources to check that results are similar. This way you can be sure that the insights you extracted from your dataset are accurate.”

Q8: What roles do AI and machine learning have to play in data and analytics?

“AI and machine learning allow us to rethink the problems we had in the past and come up with solutions that could never be possible before. These technologies may be scary to many marketing teams and business owners but the possibilities they provide are almost limitless.

“Here is a simple example. One of the areas that machine learning excels at is data classification. We can now train a model to classify the data we collect and categorise it based on attributes and features, an example being the Buyer Intent Model mentioned above. This is something that required extensive input from human experts in the past, but can now be completed in a few hours.

“Data classification is a big part of data and analytics.”

Q9: What would you say to business owners and marketing managers who feel overwhelmed by data and analytics?

“Don’t be overwhelmed is the simplest advice I can give. Just because something is difficult to understand or use now, it doesn’t mean you should be afraid of it. Marketers have been turning complex technologies into business solutions for decades and this isn’t going to change.

“Companies like Google or Microsoft will continue to innovate and create technologies that will become more accessible to others. Companies like Vertical Leap need to continue to innovate in the same way. Part of being innovative is finding ways to simplify processes or provide solutions to complex problems.

“Data science is an opportunity to be excited about, not something to be intimidated by.”

Q10: How will you be using data science in five years’ time from now?

“Our data science capabilities will become the foundation of our services. As I said earlier, everything we do in our day to day tasks is backed up with data, and this is what allows us to work at the scale and level of effectiveness that we do.

“We want to introduce complex technologies to our clients that can help them improve their user experience on their websites, or even at their physical locations. We want to inspire them to come up with creative ideas that we can bring to life.

“One of the primary goals we have for this service is to help our clients have control of their data and improve their reporting processes. This way they can spend less time putting the pieces together and focus on the important things, growing the businesses.

“We hope that the tools we create will help them to get the answers they need quickly and more accurately than ever before.

“We are very excited about the future.”

Got a question about data science?

Send us your questions to and we’ll be more than happy to answer them. Or you can tweet George at @gkarapalidis and put your questions to him directly.


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