Sajal Gupta’s Post

We recently analyzed the top 10% of our client base. Here’s what we found: All our clients have made money within 30 days of working with us… But the top 10% see INCREDIBLE results. For example: Jacob I who made $8K in his first 6 weeks, William N who did $3.5K in his first week, Michal L who generated $10K in 90 days. After our analysis, it was obvious that they all understood 1 thing: Volume. Volume comes first. Yes, you need to test. Yes, you need to analyze. Yes, you need to get creative. But none of that matters if you don’t have reliable data. Prioritize volume. Everything else is secondary.

📈 Quality data powers pricing success. But what exactly should you be looking at during the data collection phase? 1/ Are you talking to the right people? If you ask an audience that hasn’t ever seen or used your product, you may be wasting your time (and money). 2/ Are people who they say they are? It’s perhaps unsurprising that many people lie to get paid for a survey. 3/ Do people understand the questions you’re asking? You can have the smartest, most qualified respondents, but if they don’t understand the questions, they won’t answer correctly.

“With data collection, 'the sooner the better' is always the best answer.” I've been on a data kick 😆 The more, the merrier... With clear data, we're able to make better decisions for our lives and businesses which I know we all secretly want! 👋Say HELLO to Altos - Our new market data tool that provides real-time data about the CURRENT market for any area near you! Receiving stats a month later doesn't work in this market, does it? Want something to present to your sellers showing them they're not alone? Want to help move your listing? It's NOT just another tool... It's how you utilize the tool and the VALUE you bring with it✨ Connect with me and I will happily share a tool that pulls WEEKLY data so you can make the best decisions in today’s market! Knowledge is power, friends! 💪💡

Firstly, I took my time familiarizing myself with the dataset. I checked the shape of the dataset. What column headers I have, what datatypes the columns hold, what values are present in each column. Then I checked number of unique customers, and calculated churn rate, average number of product customer have with bank, average age of these customers, number of male and female customer to provide more context.🔦🍀 After getting summary metrics, I decided to dig deeper by looking at churn rates across age and tenure. I could instantly see the people between age 45 and 60 tend to leave a bank at a much higher rate than other age groups. I could also see that tenure, the years the customers have been using the bank for, doesn't impact the churn rate as they are relatively constant across all tenures.🌎 Then I tried to explore where there is any geographical differences based on the country column. The higher churn rate in Germany stood out immediately basically double out of France and Spain. Active Customers are tend to leave to bank less than in-active members. 💡🍁 Finally, I analyzed churn rate across number of products the customer holds, has any credit card and points earned by customers. Finding here were that the customers with three of four products left more often. Maybe they didn't like the fact that they have been sold so many products. In term of churn rate by points earned by customers, I could see customers with very little points left the bank more often with the rest of them churning at a pretty even rate. I built a story in Power BI to communicate these findings.🎯💡 GitHub: https://lnkd.in/dkZTVDv3 #dataanalytic #customerchurnprediction #dashboard #powerbi #businessanalytic #dataanalyst #visualization #customers #data

If all rebate reconciliation work is funneled to the last few weeks of a season, data inaccuracies snowball. On top of that, agribusinesses are losing valuable opportunities to use in-season data to make more informed decisions. And without those insights, they can’t pull in-season levers, build a long-term strategy, or improve profits. They’re just stuck repeating the same old short-term data mistakes, season after season, without ever figuring out the root cause. Here's how you fix that: https://lnkd.in/gUu_8KN6

Did you know that 62% of statistics are made up on the spot? There, I just did it right now. 😏 Admins, let’s be real for a second, how many times have you seen companies throw out random stats to support their claims? It’s our responsibility to fact-check these stats / data / numbers. Just because someone or a report says something doesn’t mean it’s rooted in solid data. My father used to say, “Consider the source.” It drove me crazy back then, but now I live by it. These days, with companies throwing percentages around like confetti, it’s more important than ever to dig deeper. Where’s the data from? How was it collected? Does it even make sense? So, the next time you see a flashy statistic, do a double-take, and, like my dad would say - consider the source! Have a great day ✨

Even with the promise of comprehensive data, market survey exchanges flop. Users are left to make critical business decisions with outdated, incomplete, and compromising insight gathered from a restrictive lens. See why market survey exchanges should stay in the past! 🤓 https://hubs.li/Q02ySg2z0

Just checked my dashboard for professional insights from April 15 to May 14. My account reached 231,952 accounts, with a growth of +2369%. Interactions from 28,662 accounts increased by +2318.7%! #analytics #growth

Now you have the data. Thousands of responses from a survey with dozens of questions. You worked hard to come up with questions that painted the picture you felt you needed to make the decision with the data you gathered. And now you've got it. Comprehensively. Quantitatively. You're data driven, and now you have the stuff that drives you. You're more sure than ever. But wait a second. Where did that data come from? Your survey, yeah... But where did the questions come from? Best practices? Opinions on what to ask and how to ask it? According to who? Are you sure those words before the question marks were the right ones? What if the questions were asked differently? "How often do you..." vs "How often would you..." vs. "Last week did you?". The responses to those could be wildly different. Did you check your words as closely as you checked your results? If you slide back a few steps, you'll see that it's all a judgement call. How you asked, what you asked, when you asked, which words you paired together, how you started the question... All these things matter. In fact, they are the matter that form the answer. And you know what? There's no right way. Which is the exactly the point I'm trying to make. Your hard data comes from subjectivity. A sense of solidity built from mush. False confidence at the finish line, from a shapeshifting starting line. It's all a judgement call. Even the stuff you can measure. And it's a beautiful thing.

Different Types of Averages Averages help summarize data and provide meaningful insights. Let’s dive into the three main types: 1. Mean: The arithmetic mean is the sum of values divided by their count. It’s widely used in data analytics, finance, and performance metrics, though outliers can skew it. 2. Median: The middle value in a dataset when ordered. It’s useful in income distribution and real estate, especially when dealing with skewed data. 3. Mode: The most frequent value. It finds application in categorical data like consumer preferences. 👉 Which average do you use most often in your work? Share below!