How can you analyze data to better understand customer needs?

Good data and proper analysis can lead you to amazing insights and understanding of the target customers. Of late, we have accumulated millions and billions of data points about consumer behaviour and other related parameters. But, it's essential to have a data roadmap to insights. The data roadmap can help you document the ideas you want to test the validity of. And can help in answering questions related to customer needs that are hidden in the dark data collection. Once documented this roadmap will lead the analysis to fruition instead of randomly jumping from one insight to other and trying to connect them in some meaningful way.

Defining your objectives is a great approach, I don’t think you can successfully analyze data correctly without a goal. But I’d like to add that while you’re defining the objectives, ensure to put five factors in view 1. The user’s need and pain point 2. Market trends or industry needs 3. Competitions 4. Business goal 5. Product vision With these factors in view it will help to create a relevant objective that leads to relevant data and it’s analysis

First of all, you must always sit with your customers and listen to them. Customers may not always be right but they talk from their own perspective. Efficient Product Manager can understand the need and pain then translate their words into meaningful features and functions that are within the context of the product. Data at the end is Qualitative and Quantitative. Quantitative will give you the precise and accurate information, however Qualitative will allow you to read the complete situation

Understanding customer needs is crucial for creating a successful product, and as a product manager, it's our responsibility to ensure that we're always putting our customers first. There are several effective ways to analyze data and gain a better understanding of our customers, such as asking for feedback directly from them, monitoring their behavior on our website or app, staying up-to-date with industry trends, and conducting focus groups. By utilizing these methods, we can gain valuable insights into our customers needs and preferences, make informed decisions about how to improve our product, and ultimately create a product that meets their needs and provides them with an exceptional experience.

To better understand customer needs, I use data to identify trends and patterns in their behavior. This helps me determine how our product is performing and what we need to change. I also look at our competitors' products to see if there are any features or functions that we're missing that our customers would appreciate.

To collect valuable customer insights, it's crucial to embrace a personalised and customer-centric approach. Put yourself in your customers' shoes, engage in conversations through diverse communication channels, and utilise data from customer interactions across various touchpoints. Some of the strategies I frequently find effective include Focus Groups, Customer Feedback Surveys, and Social Media Monitoring.

Once you set direction or your roadmap to insights, collecting the right data becomes quintessential in uncovering customer needs. This involves identifying various sources of data, such as customer surveys, feedback, social media interactions, website analytics, and sales data. Ensuring the data you collect is relevant to your objectives is crucial for drawing meaningful insights. The sample data should be large enough to produce meaningful and reliable insights and should be free from errors and bias otherwise, the insights regarding customer needs will not lead to the fruitful results the analysis intends to achieve.

It's like choosing the right tools for a treasure hunt to collect these data. Each source is like a specialized instrument in your toolkit, serving a specific purpose in your quest for insights. Whatever data you collect is like a puzzle piece until only these pieces fit together seamlessly and they reveal the complete picture. Your data sample should be substantial enough for insights. In data analysis, a substantial sample size ensures your insights aren't just empty crumbs but a hearty meal of understanding.

Considering the Best Bakery example, the relevant data to test whether the blueberry doughnuts have inelastic pricing are historical data for the blueberry doughnuts, including the quantity sold, the price at which it was sold, and the date/time of sale. They will need to have data for a long enough period to identify patterns and trends. They have been open for 1 year, so will use all of the data they have.

Irrelevant data delays or adds noise to the expected results and might change the focus of the PM and Team. So, always remember - Capture objective driven data - Only top 3 relevant data points (remove the fluff) - Tools most effective to collect a particular data Use 1 or 2 methods in parallel to validate the data points if you’re not convinced. Ex: Survey & Interview

Because we collect data from multiple sources to have an all-encompassing data model covering various parameters and factors that influence or lead to the customer needs and define their preferences, it is conspicuous that we cannot perform analysis on these varied data sources simultaneously unless we perform data cleaning and align our data series in a way that would lead to quick analysis. It's worth noting here that remember, data cleaning and preparing are sometimes the hardest and most time-consuming processes in the entire data analysis lifecycle. However, once cleaned and prepared, analysts can manipulate and visualize data in a way that is easy to understand for decision-makers.

It's often said that "garbage in, garbage out" – means, that if your data is messy or inaccurate, your analysis will likely yield flawed or unreliable results. In that case, clean data is like a crystal-clear lens. Though the cleaning process is often time-consuming, it is the foundation upon which meaningful insights are built.

My favourite tool to cleanup data is Excel! Slightly old school but the most effective till date. If the database is very huge, then can take help of scripts with the help of tech team to clean up the data by excluding the irrelevant data parameters.

Continuing the Best Bakery example, they can collect the historical data into a spreadsheet and check for any duplicates. They should also check for missing or inconsistent entries and make corrections. They can consider using programs to identify and correct inaccuracies. They will also need to ensure consistency in how the data is documented, including units of measurement, currency, and time intervals. One way to check is to cross-verify data from different sources to ensure consistency. Any inconsistencies found should be removed or corrected. Best Bakery can use this information to determine the blueberry doughnut test price.

In the data analysis cycle, now is the time to actually start analyzing and extracting insights to interpret customer needs and behaviour. Data Analysts generally use statistical techniques and data visualization tools to uncover patterns, trends, and correlations within the data. By examining customer behaviour, preferences, and feedback, we can gain valuable insights into their needs and expectations. Effective data interpretation helps you draw actionable conclusions. With the help of high-quality graphs, interactive dashboards which allow us to check scenarios given changes in certain parameters empower us to interact with data in a way that is insightful and drive conversations with stakeholders through data-centric models.

More on Best Bakery, the historical data showed that the demand for blueberry doughnuts have been consistent for 1 year even with a 2 month increase when there was a blueberry shortage. Because of this, Best Bakery decided to increase the price of their blueberry doughnuts by 5% for 2 months. They have put processes in place to have accurate data reporting of the blueberry doughnut sales.

Charts, graphs, and tables are always used to turn numbers into stories. But they are just like the pictures in a book. You must have the right metrics with you to share your data. The real magic lies in choosing the right metrics from data that can influence business decisions. Just as Sherlock needs the right clues to solve a case, you need the right metrics to uncover insights and present them effectively. It's all about picking the right pieces in the data puzzle.

3 best ways to consume data 1. Tables - Self reference and generating graphs 2. Charts & Graphs - For presentations to different stakeholders for an easier way to convey things 3. Dashboard - For a self explanatory data visualisation for teams

Once the data is interpreted, it’s very important to validate the objectives of the hypothesis from the data. Check - if data is validating the assumptions or not - if not, is there something missing? Do you need more data points or another method to validate further? - should the hypothesis requires change and you’ve to pivot to a different user problem?

It can be exhilarating to have great insights which prove your hypotheses correct or match your expectations of success in a particular market or customer segment. However, we should not forget that all data analyses are exposed to bias and errors. To ensure the credibility of your findings, it's important to validate them. We can do this by conducting A/B tests, surveys, or additional research to confirm that the insights you've derived accurately represent true customer needs. Validation helps mitigate biases and uncertainties in our analysis. There have been many instances where skipping data validation has led to total failures of business propositions, so beware!

Testing your findings against other sources is equivalent to seeking a second opinion - it increases your confidence and provides a reality check for your insights. Picture it like solving a puzzle. Even when that last piece perfectly fits into place, feel that sense of validation by confirming, "Yes, I did it!" Validating your conclusions against other evidence or sources is like that final confirmation, ensuring your insights are as solid as that last puzzle piece securely in place to take decision.

When representing results after data validation and tests it is important to: - build cogent narrative with data backed evidence - tailor the communication to the main stakeholders - take into consideration all possible questions about insights and results and match skepticism with evidence - make compelling visuals (charts, user quotations, etc) The main goal of presentation is not just to give main takeaways, but make stakeholders engage with them through narrative. I recommend Situation (context around problem and analysis objectives)-Complication (barriers and data proving them)-Question (what we try to solve)-Answer (solution with data-backed evidence) framework for this. This helps stakeholders to understand why they should care.

With the proliferation of AI/ML tools, it's possible to get lost by running the data through different models & comparing the results without actually understanding it - Going through the data manually (at least a portion from different customer segments) always helped me before applying any data cleansing/analytic models - Sometimes outliers have crucial insights for a niche product idea & simply removing them with standard tools, without looking could cost us dearly - Presenting the data is equally important as analysing it. If the audience are interested in understanding major customer pain-points & which ones to attack first, bombarding them with multiple irrelevant graphs of different combinations will make us lose sight of the price.

I would add another step which is - ?? Turn data into visual stories that convince and compel. With data viz tools you can: ? Spot trends at a glance ?? ? Reveal patterns and relationships ?? ? Back up business decisions confidently ?? ? Present findings clearly to others ?? Take raw analysis and make it vivid. The right visuals are worth 1000 metrics. ??

Agree, and here is where the importance of having the right research modelling becomes crucial. It is imperative for researchers to -understand the context of the data being collected and base the data collection tool/model accordingly. -Since customer behavior data itself tends to be qualitative in nature , capturing the data in a mode where it can be analyzed to get quantifiable or rather actionable outcome often leads to bottlenecks. -Vector-search based models seem promising to combat such situations , however though recent AI /ML developments have shown very promising results but it still has way to go when it comes semantic search and analysis of consumer data.