OWOX BI research on the state of digital analytics: Laura Patterson interview

Laura Patterson is an expert in proving and improving the value of marketing. She’s a trusted advisor with global expertise in customer engagement within the technology, financial services, life sciences, and manufacturing industries.

Laura is a results-oriented executive with entrepreneurial experience and a passion for helping companies gain insights from data, develop metrics, and design processes to drive growth, create customer value, and improve business and marketing performance.

Mariia Bocheva, the Business Development Executive at OWOX BI, had a great opportunity to get insights from Laura as part of our research on the state of digital analytics. All thoughts are broken down into the following categories:

Laura Patterson: Analytics is the ability to apply calculations or math to data. Essentially analytics is the ability to analyze and perform analysis. In our world, this ingredients of this analysis is data. If you’re going to work in analytics, marketing, or any other part of the organization which is related to analytics, you need to develop strong skills in several areas and I’d like to talk about five of these skills.

So, the first skill must be in data. You must know how to harvest data, how to determine what data is important and relevant, and how to make sure the data is clean. You’ll need to have knowledge of methods to organize and manage your data.

Second, as an analytics professional you need to embrace math. You will need to understand various analytical techniques, how to apply them, and draw meaning from the patterns that emerge.

This takes us to the third skill, modeling. Analytics is the foundation for creating important models, such as segmentation models, attribution and mix models, campaign risk models, customer churn, defection, and loyalty models. As an analyst you want to be proficient in using analytics to create and build your models.

Fourth, a good analyst is skilled at translating the data into meaningful, relevant, and actionable insights that can help the organization make better decisions. While it might be easy for an analyst to see the patterns and insights in the data, good analysts help the people relying on them see this as well. This is why it’s important for analysts to be good storytellers and come with pictures.

Being an analyst requires a good ability for visualization.

Data visualization allows others to see what you see and understand what’s important. The data you choose to visualize should help people understand why you are recommending a particular course of action or why a particular event is occurring.

And the last one I want to mention is maybe the most important point is communication. Communications is a hard and soft skill at the same time. You need to be able to write and articulate what your findings are. If you can’t communicate what your findings are, you will struggle as an analyst.

LP: Three soft skills that immediately come to mind are problem solving, critical thinking, and collaboration. What’s the point of doing analysis if it’s not going to help the organization be more successful. In marketing, we need to help the organization find, keep and grow the value of customers. So we need to understand how to use analytics to help the organization address these opportunities and potentially any problems that may arise in achieving them.

To be an analyst is also to collaborate with other people.

Gathering data, building models, coming up with an action plan is not a solo act. Most likely you will need to work with others in the organization, whether these are people in finance, sales, service, etc. Good analysts must be able to gather people together and collaborate with them to build the models and collaborate on the action plan.

Lastly, one of the most important skills is being able to ask good questions. The point of doing research, the point of doing analytics is to answer questions. Good questions help analysts in making decisions about the market, customers, products, competitors, etc.

LP: I think that if you’re an analyst you must be familiar with and current with analytical tools, processes, and methods. You are probably not going to be proficient or competent in all of them. But you need to know what they are and when to use them. If you don’t have the expertise, then reach out to internal or external experts. I find it helpful to have a network of people who are experts in various fields, methods and tools.

LP: We all make mistakes. Own up to them and fix them. The mistakes can result from bad or incorrect data, incorrect math, or missing a key variable that affects the results. I remember in my early years, I made a particular mistake and my mentor asked me, Did it do real harm to relationships with a customer? Yes or no? Is it really going to take our business down? Yes or no? The real purpose was to make me get clear: If I made a mistake, what did I learn out of it? What would I do in the same situation next time?

“It’s easy to struggle with a mistake; nobody is perfect. Striving for perfection and excellence is great, but you need to remember that sometimes good is good enough.”

We are not flying to the moon, we aren’t building a rocket ship, this is not our typical work. We are trying to help the organization make data-driven evidence-based business decisions. We want to be accurate and provide clear directional guidance.

That means we need to speak in the language of business. This is maybe one mistake many analysts make and that every analyst must work on. We can become enamoured with the data, the math, and the patterns. These are only good if we can communicate the merit, the value of these, to the people in the business. We work in business, we have to be business people, so we must speak the language of business. It doesn’t matter who is your internal or external client: CTO, CEO, sales team, marketing team, developer team – you must speak their language so they’ll understand you. That’s one of the most important things I’ve ever learned.

LP: Most of our challenges are around the data. Challenges such as there’s no data at all, or its quality is poor, or its hard to access, or its in so many places and in so many formats it is not easily or quickly usable. Also, there is a process question. There are no well-defined processes on how to manage data, how to do analytics “space” projects, how we use analytics and present the results. 

If we see that the data is bad or not reliable, we take a step back and ask ourselves, What can we do to get better data? Sometimes this requires us to conduct research to acquire primary data. That can slow things down. Even so, there’s no reason today for any company to feel using data is beyond their reach. Most often, the real challenge is knowing which data to use.

LP: I think this is a continuation of the previous point. Good analytics takes good data and the right data. This circles back to quality data management processes and tools. For example, does your company have a data inventory or library. This tool helps you know what data you have, where it is kept, how old it is, who owns it, and so on. There needs to be a way to classify the data and information about how the data is used. These are fundamentals and yet too often people are more intrigued by the shiny new tools and toys.

It’s like building a house, we can become excited about all the appearance and overlook some important basic mechanisms, such as the plumbing. So you have a beautiful home, but your plumbing doesn’t work you might find yourself unhappy . As the plumbing is part of the infrastructure of your house, the data is part of your infrastructure, and you have to pay attention to little details and keep it in good working order.

It’s not that things like this are intentionally overlooked. Who would intentionally overlook their plumbing? Like plumbing though we think it will get taken care of. But that’s just not the case. It’s just more fun to think about the cool stuff - the cool new tool. Technical things are boring. Analyzing information – for example, customer risks – is kind of boring. That takes us back again to why analysts should know how to present their data to make it valuable for business.

LP: I think it’s common for any team. I don’t think it’s necessary to mention it as an issue because miscommunication can appear in any kind of organization. We all need to work hard to make sure that we are listening and clarifying. Analysts need to be good at listening and asking questions. If analysts talk to someone in marketing, they need to remember that the majority of marketers aren’t as experienced with data or analytics or models as they are.

• What kinds of decisions do you want to be able to make?
• Who do you want to share this information with?
• What do you use today? How well does it work or not?
• Why do you think you need a dashboard?

Questions like these help to reduce miscommunication and make sure everyone is aligned. A good analyst has to be able to ask those questions to ensure that what his team brings back is a working product.

LP: There are two points. The first one is the culture. I came from a culture where they said, In God we trust, everyone else brings data. During my career, I’ve worked in different companies, but in all of them I worked for either engineers or finance people - so numbers and data were important. A data-driven culture starts at the top. People have to have an insatiable appetite and passion for data-derived decisions.

Second, we need to get better at managing data and data governance. Today we have so much data in so many formats (structured and unstructured) coming from so many sources stored in so many places. It’s hard to know what true. When I started my career, there weren’t as many applications nor did we have so many sources of data. Think about all the places we must draw data from today - Google Analytics, Social Media, Email platforms, etc. 

Companies need a way to manage the never-ending flow of data. To be able to know “what data we need to answer the question and how we analyze that data in the best way to get the answer.” 

This comes back to what we talked about earlier. Analysts need to be able to analyze the data, recognize meaningful and relevant patterns, and understand and communicate the implications to the business. Analysts need to not only see the data but see if it matters. I think that this thing of knowing where a particular pattern requires attention is missing among analysts nowadays.

LP: If we’re talking about the evolution of the company, every company has its own lifecycle. Whether we’re speaking about a startup, SMB, SME, or enterprise, we are talking about their maturity of the lifecycle. You start at your early stage of growth, then go to the main stage of growth, etc. Your product, the complexity, the number of partners, number of customers, the number of similar products on the market – everything can change. That’s what we mean by the maturity of the business.

You can be a small business in a local market, but you may offer a variety of products or services. It doesn’t matter how big you are, you must think about making the best product or giving the best service.

At the beginning of the early stage, the main questions you’ll ask will be, How do we get traction in the marketplace? How many customers do we go after? Do we build the necessary product? As you get customers, there come other questions: How to keep those customers? How do we grow the values of these customers? Different questions require different capabilities. But I think a good analyst can work all across the lifecycle. It’s more about the questions and data than about analytical skills.

There is no reason why a good analyst who works in a startup can’t work in an enterprise; there is no reason why he or she couldn’t be valuable in a large company. I’m always careful saying, You are an analyst for SMB or You are an analyst for SME.

The maturity of a company only matters in the sense of how big the analyst’s role can be. A startup may need only one analyst, but a really big robust enterprise company might need several analysts, and these analysts will be more specialized. But in a smaller company, analysts are more generalists than specialists. In some way, their skills must be even broader because they are generalists.

And when you go to a very large company, you may become a specific specialist. You’ll be very specialized in the models you’ll build. You might be very focused on things like customer profitability and customer lifetime value. You’ll become really good at looking at such things at the enterprise level. But it’s totally different from things you might be looking at in a startup.

LP: I know that last year was the first year when the spend on tools for marketing analytics was higher than the spend on people who work in marketing analytics. Yes, companies invest more and more in analytics tools. 

Let’s remember that marketing analytics, analytics in general, and data science are not new. Marketing science was in the curriculum decades ago. It was important then and will continue to be a valuable and stable role inside the organization. We need to keep our skills current to remain relevant. 

Being current means being familiar with new techniques and new tools and in some cases being proficient in using them. You may not need the latest most expensive tool. If you have a tool already in the toolkit that can do the job and do it well then maybe you don’t need to spend money on a new shiny toy. The key is to have the right tools that will help you perform your work effectively and efficiently and to know how to use these properly.

LP: I think now is a good time to mention things like machine learning, block chain, artificial intelligence. These things impact what happens to data and the analysis of data. You’ll need to understand the implications to the business and how they affect your work. AI facilitates pattern development. The analyst will need to be the one who determines when something is relevant, when it requires action, what it means to the business, and then choose what he/she needs to do. This type of analyst, I think, will survive and thrive.

LP: I always try to go to meetings related to my profession. I recommend attending something at least once a quarter. Yes, you’ve already got your degree and you’ve worked so hard, but I believe that once a quarter you should put your head up, look around, stop for a moment, and spend time on something that can improve your professional skills.

There are many kinds of activities that can be useful. Take some courses in your city or maybe online, watch some tutorials on YouTube or listen to a podcast – there are so many great opportunities. Find a conference with a topic you’re curious about, go there, meet new people and network with them, build some new professional connections, ask them how they solve their problems. Find out if there are any LinkedIn or local professional communities you can join.

The DAA offers excellent events for analysts. Become a member of the association. Attend their conferences, network with your peers, engage with them. The key thing is to find ways to stay on top of your game.

We really appreciate that Laura found time to answer all our questions and share her thoughts about current issues and the future of marketing analytics. And also we highly recommend to read the recap of Laura’s MASCONF speech on processes management.

Let’s keep in touch! Subscribe to our blog to read the next interviews from Julius Fedorovicius, Brad Geddes, and many more on the state of digital analytics and adapt the experience of top analysts to your business.