How can you improve your data analysis skills as a Product Marketing professional?

If there is one advice that you can take for your professional life --> Learn Excel. Excel has been the basis in many aspects of my job. Learn how to play with data and read it to have actionable insights.

A common pitfall for anyone wanting to tool up their analytics prowess is focusing on tooling at the exclusion of learning (or relearning) the basics of statistics. Statistics equips you with the knowledge to become a better and more confident storyteller in the results any tool can spit out. It can also inform whether or not an analysis you are presented is misleading, which otherwise might result in the wrong decision, even though the data may technically be right. Beyond Statistics, I believe SQL is a valuable and approachable language to learn, as it continues to underpin many analysis tools. Finally in terms of tooling, I'd say learning whatever BI tool your org has as its standard is a good spend of time, as they are all similar.

Mastering data literacy involves more than technical skills; a curious mindset is crucial. Cultivating genuine curiosity about your data enhances analytical capabilities, prompting the right questions and revealing valuable insights. Simple tools like a pivot table can tell intricate data stories, making it a gradual "crawl, walk, run" journey. Begin with basics like Excel, pivot tables, SQL, or preferred BI tools. Small steps yield substantial benefits, progressively enhancing analytical skills and insights, fostering a stepwise progression toward a profound understanding of data intricacies.

Data analysis involves both a creative aspect and a practical skill. The creative part helps in setting goals, picking the right KPIs, and making data more understandable through visualization. The skill part comes with experience and can be improved by learning from books, blogs, workshops, and real-world practice. To truly master data analysis, you need to apply what you've learned in practical situations.

Do some research about suitable KPIs to drive your goals, and think how you will like to visualise that and a daily dashboard. Work with data professionals to create those trackers and dashboards. As a product marketer, don't feel daunted if you cannot manipulate raw data. What you need is to understand how to make meaning from processed information.

Democratizing data science has been a focus during my time at Microsoft and now LinkedIn - While I may not be a subject matter expert in data analysis, I enjoy partnering with my data science partners to understand how to run queries. When collaborating with DS, I ask my partners for 1:1 tutorial sessions to understand how to run a script correctly and always ask if I'm able to record the tutorial. Referencing the recordings has allowed me to replicate and grow familiar using tools.

For a PMM, the below tools are essential: Analysis Tools: Excel, Google Sheets, and SQL or R(advanced analysis) Data Visualization: Tableau or Power BI Web Analytics: Google Analytics or Adobe Analytics CRM Tools: Hubspot or Salesforce Marketing Automation: Hubspot or Marketo All the above tools take less than a week to learn with focus. I prefer learning Excel, Power BI, Tableau, Hubspot, and Google Analytics as a PMM

Leveraging Pendo helped me collect, analyze, and synthesize crucial data points. Not only does their dashboard present information in a visual way, but their latest AI enhancements speed up the process of analyzing data significantly. Genius hack - While reading a set of data/graphs, I often ask myself the question - "...which means?" Example: "Usage of product feature X dropped in the last 6 weeks... which means?". This helps me get to my insights or takeaways quicker.

As a product marketing manager, in my opinion you should focus and learn these 3: 1. Excel 2. Power BI 3. Google Analytics Having a visual way of representing your insights will make you a key performer. Eg. One of my first assignments as a PMM was to organize our campaigns data in order to have a sense of cost per lead, origin of leads, ROI of my campaigns, etc (Excel and PBI).

The advice here is too generic. Some of the tools are not conducive for marketers to use unless you come from a technical background. To be helpful, I think you need to breakdown the analysis that you need to do and then match each with the right tool. For more technical tools, provide tips as to where/how you can get help to use them.

Once you have the basics down, start applying data analysis methods to your product marketing work. This could involve: Segmenting your customer base Identifying customer needs and pain points Tracking product usage and engagement Measuring the effectiveness of marketing campaigns Forecasting future sales and demand

I contend that a crucial element often overlooked in the directive to "Apply the methods" within the expansive realm of product marketing is the profound significance of understanding your audience. What I mean by emphasizing the importance of knowing your audience is delving into the fundamentals. At the core, it involves aggregating information about the engaging entities – discerning their roles, determining if they are decision-makers directly involved in the proposed marketing plan, or if they wield significant influence in the decision-making process.

One thing I’ve found helpful is to get input from peers as well. They may come at something from a little different angle and help you uncover important information you might not have found on your own!

A modern product marketing team should take your position as a data analyst, especially in the context with huge load of data from various sources. You need to be first familiar with when and how all levels of data analytics are utilized: descriptive, diagnosis (exploratory/explanatory), predictive, prescriptive. You also should master at analysis tools and software. But the most important thing is that you have to be the team that understands the marketing situation better than any others: how your product portfolio performs? how the market/industry/competitors run and impact to your business? customer journey? and how MKT campaigns metrics work? Data & visualization should not stand alone but go with comprehensive business understanding.

Como comentei anteriormente, pela nossa metodologia Venda+Valor, o diagnóstico é premissa para constru??o de valor. E n?o se iluda pensando que o diagnóstico acontece apenas na concep??o do produto. Ele tem que ser revisitado o tempo todo. Para tanto, sugiro que adote uma metodologia para padroniza??o de todo o ciclo de venda do produto, ou seja, desde a concep??o até a sua descontinuidade, como a Vendas+Valor. Para tanto, o primeiro passo é identificar qual o público-alvo e suas respectivas personas. Quais os benefícios propostos pelo produto, solu??o ou servi?o para cada público-alvo e personas. Por exemplo, imagine que você criou uma solu??o para hospitais (público-alvo) e oferece benefícios que ser?o percebidos pelos médicos (personas).

Data analysis is about consistency in anything we do. We try to be engaging with our content, research, and blogs, etc., to tell very compelling stories following these steps. Step 1: Set targets and strive for those targets. What’s your goal and go from there. Step 2: What do you want to achieve? Are your targets in line with your goals? Step 3: Work the plan. Fail Often, Fail Fast, and Adjust. Step 4: Don’t forget that it’s about constantly monitoring, and adjusting. It’s about identifying what’s working and what’s not. Some days work better than others. There is no magic. There are lots of ways to consume the data, being creative is very important so know your audience and stakeholders.

Great recommendations lead to actions. If you want the brand team to use collected insights to make a difference in the marketplace, make sure you strive to answer the question: Why? Numbers by itself can (and will) always be challenged, quality of inputs, assumptions, timelines… Remember, numbers will always speak the truth, especially under torture! Make sure the conversation leads to finding how to address the purpose of your customers, and not to finding the heroes or the villains.

Not everyone is numbers oriented. Make sure your analysis is clear and digestible. > Start with a point of view: Have a clear idea of what you're looking for. This will help you explain why the data matters. > Use headlines: Summarize findings with short “so what” headlines to focus your audience. > Choose the right format: Use infographics, bullet points, slides, dashboards, spreadsheets or whatever suits the situation/audience best. > Keep it neat and tidy: Make labels clear and ensure data is formatted consistently. > Think about why you're doing this: Make sure you don’t get “lost in the sauce” - ask yourself what you're trying to achieve. Are you making decisions, tracking progress, spotting trends? > Plan for the next update

For optimal communication of your analysis, it's recommended to share your insights through a visual dashboard or presentation during the weekly team meetings with the Product Management, Marketing, Sales, and Customer Success teams. Dedicate a minimum of 5 minutes to keep everyone informed and solicit feedback.

Numbers and charts are important. But the critical question that business stakeholders must understand is "Why does this matter?" While many analysts live by trends and gradients, business stakeholders sit up to listen when the insights become apparent to them. Explaining the implications specific to them with phrases like "what this means for you..." or "this tells us that..." in their language tells them that we are interpreting the data with their context in mind.

Dismantle the silos. Unite essential stakeholders and ensure they possess a thorough comprehension of the value attained and its relevance to both them and their teams. Despite varying disciplines, all team members should collaboratively strive toward shared objectives! With key stakeholders present these sessions should involve such phrases as: "This is excellent, now how can we do better?" or "It appears we are missing our mark, what can we do to assist"?

The single most important skill you can learn is Jobs-to-be-done theory and practice. JTBD defines a market by the customer profile and the “Job”, or in other words, the problem task they are trying to get done. By studying the problem this way, product management and marketing teams can surface the steps needed to solve a problem, the areas their solution is used in the process, desired outcomes customers have of each step, and the metrics associated with satisfying those outcomes. These metrics are critical to your analytics and invaluable to translating the data into a story that all go-to-market teams can use. Data is only helpful if it provides insight. Jobs-to-be-done is the most effective approach to framing data to achieve this.

For me, the journey of developing solid data analysis skills required a great deal of perseverance, time, and willingness to hear constructive criticism. Putting data skills into practice such as using vlookups and creating macros to solve business problems often required the aid of colleagues to help me succeed. The better I was able to hear where I was falling short, the more help that I would be offered. The same need for aid has stayed constant as I’ve taken data analysis and modeling courses in grad school up until today as I’m working on learning python for machine learning. Developing relationships, being willing to ask for help, and changing based on feedback are critical enablers for growth in data analysis.

Don't assume that the analysis was perfect the first time. Each iteration of the activity will yield better results. After sharing the results, be sure to ask for feedback about the results. Oftentimes the audience will have a perspective that may not have been considered initially. Ask for feedback from a variety of people - customers, coworkers, supervisors, staff. Each will be able to provide a more holistic view on the results. Continue to perform the analysis with the new inputs to increase the accuracy of the results.

I have learned over the years by looking at our own content information that good data and data analysis is always about the journey, not the destination. That is why I recommend always seeking continuous improvement. Understand the correlation between simply looking at the data or the perceived performance. Drill deeper and make assessments based on instincts tied to analysis. We have become a society of immediacy which sometimes distorts the discipline of seeking continuous improvement. But the best results, come from looking back before just looking forward. Always ask questions and ask them again and again. That will give some of the best metrics.

Data is excellent! Q: how many times has data COMPLETELY undermined an otherwise brilliant idea/innovation. Or “great PPT Delores, but why again is competitor A, B and C dominating us and getting ahead of us at every turn!?” A: uhhh More important than knowing how and when to use it…know when to use the supercomputer you were equipped with at birth. GetUVP.com

Um olhar técnico voltado às ferramentas de análise de dados é muito importante. Mas, n?o podemos esquecer que mesmo ao olhar para os números, quando pensamos em Marketing de Produto, sempre devemos considerar as pessoas e histórias que est?o por trás de um enorme volume de dados. Esse olhar humanizado é fundamental para ter o melhor aproveitamento das informa??es.

1. Use gong/chorus to test for adoption of your messaging and strategic narrative by your sales team. Divide sellers into cohorts (adopters and non adopters). 2. Then compare sales outcomes (specifically stage over stage velocity, and close/win rates) by cohort. 3. Uncover correlations between sales outcomes and messaging adoption. 4. Now you have a jumping off point to dig deeper into what is and isn't working.

Be careful not to use data to validate your viewpoint, and to use data as objectively as possible. Data can tell a story, but it might not be the story you want to tell! Infuse qualitative data such as customer sentiments into your data to complete the picture.

Stay up-to-date on the latest trends and developments in data analysis and product marketing. Network with other product marketers and data analysts. This is a great way to learn new things and get feedback from others in your field. Data on the importance of data analysis skills for product marketing professionals A recent survey by the Product Marketing Alliance found that 82% of product marketers believe that data analysis is a critical skill for their role. Another survey by LinkedIn found that data analysis skills are one of the top 10 most in-demand skills for marketing professionals in 2023. A study by McKinsey found that companies that are data-driven are more likely to outperform their competitors.