Building Customer Reviews Strategy

I think customer reviews are the single most important factor for product purchases today. Think about the way you buy… When purchasing a new product, what do you trust more: a sales pitch, a product video about features, or a real user review? 85% say they check reviews. Whether you’re buying through an online or offline channel - you will google to compare prices but also to see what others say.  1 customer experience has the potential to influence 100 others. The harsh reality for brands (and I see founders complaining about this all the time) is that humans have a strong negativity bias. Hence, consumers tend to review more on a negative experience than a positive one. The data says: → Only 47% of consumers share +ve experience, but as much as 95% shout from rooftops about a -ve one. → And, 1 -ve review reduces the likelihood of purchase by 42% Clearly, managing this is crucial. So, what should brands do? Getting rid of the review section all together is not an option. Here’s what I’ve seen works: ✅ Engage with the detractors:    Customers feel  +ve after seeing a business owner responding to a review. If we got a very negative review I’d pick up the phone and talk to the customer. Trust me, honesty and an apology go a long way! ✅ Get as many reviews as you can: Yes, customers look at the number of reviews. 92% of customers hesitate to make a purchase when there are no reviews. And no, buying reviews or faking reviews is not the answer. It’s a dangerous activity that can get a brand banned/cancelled or in a place where customers completely lose trust. So, do it the ethical (and long-term) way: ➡ Incentivize reviews: think coupons, discounts, or even freebies. ➡ Spice things up with contests ➡ Trying is believing: send out samples to these folks – Follow up with discounts on condition of reviews ✅ Display reviews everywhere: On your website, product packaging, social media, marketplace listings and anywhere else you can think. What very few understand, More Reviews = Strong UGC = Stronger SEO Reviews naturally contain relevant keywords that enhance the visibility of search results and align with the algorithm preferences of search engines like Google. I’d say reviews are an underrated marketing tool. In the most basic sense, it’s what customers are saying about you. An advantage here can enable D2C brands to compete with incumbents. This is something we experienced firsthand at Dr. Vaidya's ! Reviews and UGC will become even more important over the next 5 years as the marketplace gets more competitive and democratic. Winning in this sphere is a must for any brand to win. Do you agree? How much do you index reviews on your purchase decision? #consumerinsights #d2c #customer #reviews #brands

Do you know what your audience think about your sport sponsorship? (Ever heard of sentiment analysis?) As a brand investing in motorsport, you can track how your audience react to your sponsorship. Not just in numbers, but in sentiment. What is sentiment analysis? It’s a method that uses AI and natural language processing to analyse public perception. Whether the sentiment on a brand/event/sponsorship is positive, neutral, or negative. In sponsorships, this is a game-changer. → It helps brands understand if their investment is truly engaging fans or if adjustments are needed. *** Take the recent ELEMIS x Aston Martin F1 sponsorship. When ELEMIS announced its sponsorship of Aston Martin F1, social media buzz reflected strong positive sentiment. Words like love, amazing, and ELEMIS stood out on IG. And terms like wow, duo, and dream team reinforced excitement around the collaboration. This is a word cloud: a visual tool in sentiment analysis where the size of each word reflects how often it appears. It helps brands quickly see which keywords dominate the conversation and determine whether audience sentiment is positive or negative. Why does this matter for brands? 1. To know exactly how fans feel about your sponsorship. 2. To spot negative trends before they escalate. 3. To create content that truly connects. 4. To measure ROI beyond visibility. *** Data-driven sponsorships + expertise win. Those who know how to strategise, listen, learn, and adapt turn partnerships into real business growth. P.S. Ever heard about sentiment analysis and word cloud?

As a leader, you are wired to solve problems, fix what is broken, and improve what is lacking. But here is what we often overlook: positive feedback is not just a “nice to have”; it is a performance multiplier. Here’s why: ✅ People need to know they’re doing a good job Even top performers can feel like they’re falling short without reinforcement. ✅ Recognition fuels progress. Startups and corporate life are tough. Positive feedback helps people see how far they’ve come, which keeps them motivated through challenges. ✅ People will give  discretionary effort When people feel valued, they go above and beyond, not because they have to, but because they want to. The best leaders don’t just correct mistakes; they celebrate wins, big and small. This doesn’t have to be a big, flowery speech. But it should be specific. You could say: 👍“Your report turned out great - thanks for the effort you put in.” 👍“Even though you didn’t get that deal, I saw how much you hustled. Keep that up and you’ll get the next one.” 👍“Your energy with your coworkers is so positive that you help keep everyone up. I appreciate it!” How do you incorporate positive feedback into your leadership? Share in the comments so we can all learn together. 

In an era where digital threats are ever-evolving, Continuous security monitoring, analysis, and proactive response are essential elements in maintaining a strong security posture. This involves continuously observing and analyzing data from various sources within an organization, such as network traffic, system logs, application logs, and security devices like firewalls and intrusion detection systems (IDS). The primary goal of security monitoring is to safeguard an organization's digital assets, data, and systems by identifying and mitigating security vulnerabilities and unauthorized activities in real-time or near real-time. Here are some things to note to ensure the efficiency of monitoring: 1. Clearly Defined Objectives: Know what you're protecting and why. Define your monitoring goals, the data sources, and the threats you're guarding against in line with the business objectives of the organization. 2. Comprehensive Toolset: Resist the urge to over-procure. Invest in the right monitoring tools tailored to your organization's needs. 3. Baseline Understanding: Establish a baseline of normal activity on your network and systems. Anomalies stand out more when you know what "normal" looks like. 4. Data Encryption: Encrypt sensitive data. This adds an extra layer of protection against interception. 5. Log Retention: Define a data retention policy for logs and ensure they're stored securely to maintain their integrity. 6. Continuous Training: This cannot be overemphasized. Continuously assess your security team’s competencies and train them in line with their job descriptions, latest threats, and best practices. 7. Regular Audits: Conduct routine audits to identify areas for improvement in your monitoring systems and processes. What other proactive steps can enhance an organization's security monitoring capabilities and stay ahead of emerging threats?

Your Monitoring System Is Like Bad Fitness Tracking "You've reached 1,000 steps!" "You've climbed 10 stairs!" "You've been active for 5 minutes!" So many notifications, you've started ignoring them. Until one day, it tried to warn you: "Abnormal heart rate detected!" But you dismissed it without looking. Just another meaningless alert, right? This is the monitoring paradox in IT systems. When everything triggers an alert,  nothing feels important. The more you monitor,  the less you actually see. I recently worked with a team drowning in alerts: 200+ daily notifications across systems Engineers playing "alert roulette" - who responds? Critical issues buried among false positives Key business impacts going unnoticed The solution wasn't adding more sensors. It was making the existing ones smarter. With AI-powered observability: ✅ Systems learn what's normal for YOUR environment ✅ Alerts only trigger for meaningful deviations ✅ Implementation takes just 1-2 hours Just like a good fitness coach who only speaks up when it matters. Not for every rep,  but for the ones that could hurt you. Your monitoring should work the same way. Fewer alerts.  More meaning.  Real protection. P.S. How many alerts did your team ignore today? The answer might surprise you.

If you're a UX researcher working with open-ended surveys, interviews, or usability session notes, you probably know the challenge: qualitative data is rich - but messy. Traditional coding is time-consuming, sentiment tools feel shallow, and it's easy to miss the deeper patterns hiding in user feedback. These days, we're seeing new ways to scale thematic analysis without losing nuance. These aren’t just tweaks to old methods - they offer genuinely better ways to understand what users are saying and feeling. Emotion-based sentiment analysis moves past generic “positive” or “negative” tags. It surfaces real emotional signals (like frustration, confusion, delight, or relief) that help explain user behaviors such as feature abandonment or repeated errors. Theme co-occurrence heatmaps go beyond listing top issues and show how problems cluster together, helping you trace root causes and map out entire UX pain chains. Topic modeling, especially using LDA, automatically identifies recurring themes without needing predefined categories - perfect for processing hundreds of open-ended survey responses fast. And MDS (multidimensional scaling) lets you visualize how similar or different users are in how they think or speak, making it easy to spot shared mindsets, outliers, or cohort patterns. These methods are a game-changer. They don’t replace deep research, they make it faster, clearer, and more actionable. I’ve been building these into my own workflow using R, and they’ve made a big difference in how I approach qualitative data. If you're working in UX research or service design and want to level up your analysis, these are worth trying.

Topic: "Adipurush Movie Sentiment Analysis: Harnessing Machine Learning to Understand Twitter Buzz" Link to get code and dataset: https://lnkd.in/d-svqe2M (also in comment section) I am thrilled to share my latest project, "Adipurush Movie Sentiment Analysis: Harnessing Machine Learning to Understand Twitter Buzz," where I explored and analyzed the sentiment of tweets related to the highly anticipated movie, Adipurush. 🎥 In this project, I employed two powerful machine learning algorithms, Multinomial Naive Bayes and XGBoost, to classify the sentiment of tweets into three categories: positive, negative, and neutral. By leveraging the power of Natural Language Processing and supervised learning techniques, I was able to uncover valuable insights from the Twitter data surrounding Adipurush. The key steps of my project include: 🔹 Data Collection: I gathered a comprehensive dataset of tweets related to Adipurush using the Twitter API. 🔹 Data Preprocessing: I performed essential data cleaning steps, such as removing stopwords, handling special characters, and tokenizing the tweets to prepare the data for analysis. 🔹 Feature Extraction: I utilized various techniques, including Bag-of-Words and TF-IDF, to transform the text data into numerical feature vectors that can be processed by machine learning algorithms. 🔹 Model Training and Evaluation: I trained two powerful algorithms, Multinomial Naive Bayes and XGBoost, on the preprocessed data and evaluated their performance using appropriate evaluation metrics. 🔹 Sentiment Analysis: Using the trained models, I predicted the sentiment of the Adipurush tweets and analyzed the distribution of positive, negative, and neutral sentiments. The results of this project provide valuable insights into the overall sentiment of the tweets surrounding Adipurush and enable a deeper understanding of the audience's response to the movie. These insights can be instrumental for marketing and promotional strategies, allowing the production team to tailor their approach based on public sentiment. I am proud of the outcomes achieved through this project and the utilization of machine learning techniques to extract meaningful information from social media data. It highlights the immense potential of data-driven approaches in the entertainment industry. If you are interested in learning more about my project or discussing potential collaborations in sentiment analysis or machine learning applications, please feel free to reach out to me. I would be delighted to share more details and insights from this fascinating project. #AdipurushTweets #SentimentAnalysis #MachineLearning #DataScience #NaturalLanguageProcessing #DataAnalysis #TwitterData #EntertainmentIndustry

"Keep up the good work!" "You're doing a great job!" "I really enjoyed your presentation." "You handled that call really well." Unspecific positive feedback is not helpful. How would any of those statements actually help a person grow? How would any of those statements even help a person duplicate what they did? They won't. If someone is doing a great job, tell them how. What specifically did you like about their presentation? What were 3 things they did during the call that made it a success? Why are those things important? Would something have gone wrong had they not done them? Try something like: "You handled that call really well. I like how you set a prep call with me before-hand so we could get on the same page. You facilitated a good discussion and made everyone feel heard, while ensuring we got through the whole agenda. You then circulated clear action items on your own, without my asking you to do it. Because you did that, there's a great chance we can wrap up this project on time." "Great job" might feel a lot better than "Plz revise thx". But it's just as (un)likely to lead to any growth. #management #growth #feedback #development

As an EY Partner, I gave feedback to thousands. Master the art of feedback - skyrocket your leadership: Bad feedback creates confusion. Good feedback sparks growth. Use the CSS (Clear, Specific, Supportive) framework to make your feedback land without friction. No more awkward silences or sugarcoating disasters: 1. Give positive feedback that actually feels valuable. ❌ Don’t say: “Great job!” ✅ Instead say: “Hey [Name], I really liked how you [specific action]. It made a real impact on [outcome]. Keep doing this—it’s a game-changer.” Why it matters: → Reinforces what actually works 2 Address underperformance without demotivating. ❌ Don’t say: “You need to improve.” ✅ Instead say: “I appreciate your effort on [project]. One area to refine is [specific issue]. A great way to improve would be [solution or resource]. Let’s check in next [timeframe] to see how it’s going.” Why it works: → Pinpoints the issue without personal criticism 3. Redirect someone without crushing their confidence. ❌ Don’t say: “This isn’t what I wanted.” ✅ Instead say: “I see where you were going with [work]. One way to make it even stronger is [specific suggestion]. What do you think about this approach?” Why it works: → Keeps feedback constructive, not critical 4. Push back on an idea (without sounding like a jerk). ❌ Don’t say: “I don’t think this will work.” ✅ Instead say: “I see the thinking behind [idea]. One challenge I foresee is [issue]. Have you considered [alternative approach]? Let’s explore what works best.” Why it works: → Keeps it a discussion, not a shutdown 5. Handle conflict without escalating it. ❌ Don’t say: “You’re wrong.” ✅ Instead say: “I see it differently—here’s why. Can we walk through both perspectives and find common ground?” Why it works: → Creates space for solutions, not arguments 6. Help someone level up their leadership. ❌ Don’t say: “You need to be more of a leader.” ✅ Instead say: “I see a lot of leadership potential in you. One way to step up is by [specific behavior]. I’d love to support you in growing here—what do you think?” Why it works: → Focuses on potential, not deficits 7. Coach someone who is struggling. ❌ Don’t say: “You need to step up.” ✅ Instead say: “I’ve noticed [specific challenge]. What’s getting in the way? Let’s find a way to make this easier for you.” Why it works: → Focuses on support, not blame 8. Give feedback to a peer without sounding like a boss. ❌ Don’t say: “You should have done it this way.” ✅ Instead say: “I had a thought—what if we tried [alternative]? I think it could help with [goal]. What do you think?” Why it works: → Encourages shared ownership of improvement 9. Close feedback on a high note. ❌ Don’t say: “Just fix it.” ✅ Instead say: “I appreciate the work you put in. With these adjustments, I know it’ll be even better. Looking forward to seeing how it evolves!” Why it works: → Ends on a motivating note — ♻️ Repost it to help others grow.

Fascinating approach to utilising review content for an eCommerce store I stumbled across. This unconventional approach allowed this store to effectively double their organic traffic, with an approach that delivers relevant website visitors. The approach is quite simple that this store is using. They have a sizeable amount of product pages, but each product page has its own review-related extension. For example: www.example(dot)com/products/name www.example(dot)com/products/name/reviews The product page itself features the overall review rating higher on the page, and the bulk of the review content is featured lower down (like normal). The review page, which is an extension of the product URL, features the exact same content, but it is presented differently. The content presents the review content prominently on the page, with it now being the main focus. With the approach they are using, the goal is to capture "[product name] review" type queries, where the product page itself might be less likely to rank prominently for these queries due to the difference in intent. From a structured data perspective, the review page does have the reviews marked up in order to gain the rich result treatment, but it doesn't include information such as availability and price, which is assigned to the primary product page as to avoid confusion from Google's end. Do I recommend that every eCommerce store take this approach? No. But it's certainly an idea worth considering if there is substantial search volume that you're missing out on and the store has a high amount of on-site reviews. When I first came across this approach, my initial reaction was that it didn't make much sense. After looking into it some more, they are clearly on to something. And with SEO, it's often best to let the results do the talking.