Ascend Analytics’ Post

"Ever wondered about Amazon's remarkable ability to suggest the perfect product just as you're considering a purchase? By analyzing vast amounts of customer behavior data, Amazon personalizes recommendations, optimizes inventory, and tailors marketing. Amazon's effective use of analytics has enabled them to reduce cart abandonment, upsell and cross-sell products, and drive repeat purchases" This week, we're doing a deep dive on data analytics solutions that make sure your retail business is getting the best bang for its buck 💸 Read on to learn how: https://lnkd.in/dX8Py3sr

How close is your company to being "the data driven enterprise of 2025"? 👀 Whether you've already made data the cornerstone of decision-making at your organisation or you're just getting started, this McKinsey study establishes the 7 conditions likely to define a truly data driven enterprise by next year. Find a link to it in the comments 👇

Retail sales data is more than just numbers; tapping into sales data allows you to discover hidden trends, optimise strategies, and elevate your business's performance 🚀 Today, we're sharing 5 steps necessary to extract maximum value from your retail data, helping you define clear analysis objectives, drive personalised marketing efforts and improve inventory management 💪 Dive into our latest blog to see how: https://lnkd.in/d8SYxCmj

From reducing traffic congestion by 25% to lowering energy consumption, cities worldwide are beginning to use data to improve sustainability, mobility, and quality of life 😌 We're exploring how the likes of Barcelona and Singapore are changing urban living with the power of analytics- swipe ➡️ to learn more 

Identifying Production Blockers and Removing Bottlenecks Using Data 🪄 You don’t have to wait till a machine goes down or you have products you can’t  ship before addressing production challenges. Allow us to explain how to go about it a better way (link to our blogpost on Manufacturing Analytics in the comments): 1. Install IoT Sensors for Real-Time Data Collection: Place IoT sensors on machinery and production lines to track performance metrics like temperature, speed, and vibration. Use real-time monitoring of equipment to quickly identify irregularities that could cause product flaws. 2. Implement Predictive Maintenance Models: Use historical machine performance data to predict breakdowns and schedule maintenance before they occur. Reduce unexpected downtimes, keeping production running smoothly. 🤖 3. Log and Analyse Machine Performance Data: Track and log every instance of downtime, breakdowns, and performance dips to build a comprehensive history. Analyse this data to identify recurring patterns or problematic machines that consistently underperform. 📉 4. Utilise Analytics-Based Reporting Tools: Implement reporting tools like Power BI or Tableau to visualise production data. Use them to identify where bottlenecks are occurring and understand the impact of specific machines or processes on efficiency. 5. Conduct Root Cause Analysis: Use data gathered from sensors and reports to conduct a root cause analysis of bottlenecks or blockers. Identify whether the issue is due to machine inefficiency, process misalignment, or resource allocation. 🤔 6. Optimise Production Workflows: Adjust workflows, resource allocation, or production schedules to alleviate challenges. Reallocate tasks or increase machine utilisation where necessary to streamline operations.

"Hey, I wonder if I could do that with AI..." 🤔 Since Large Language Models like ChatGPT burst onto the scene, AI has captured the public imagination on a scale rarely seen before. Whether you’re asking random questions to Microsoft’s Copilot or using Google’s Gemini to help with work, AI technologies require vast amounts of data to train them to give accurate responses to these prompts. We’re exploring Google’s deal that gives them access to Reddit’s data for this purpose below 👇

"By analysing data from various stages of production, companies can pinpoint bottlenecks and implement changes that streamline operations. For instance, a factory might analyse cycle times across different machines to identify which ones are slowing down the overall process and take corrective actions to optimise workflow." From identifying production blockers to reducing waste, this week, we're looking into how manufacturers can transform their operations with the right data analytics strategy 👏: https://lnkd.in/dUFYP-qC

📢 Next time you're about to make a strategic decision for your retail business, make sure you do the following: 1. Start with the data you already have – Gather information from customer analytics, product performance, and inventory trends. 2. Segment your customers – Break them down into key groups based on behaviour, preferences, and purchasing patterns. 3. Analyse product performance – Identify which products are driving profitability and which need optimisation or discontinuation. 4. Forecast demand – Use historical data to predict future trends and ensure your inventory is aligned with customer needs. 5. Assess marketing efforts – Look at your campaign performance data and see what’s working and what isn’t before making any big moves. Strategic decisions should always be backed by clear data insights. Learn all about using data to inform your decision making with our blog on retail analytics (link in the comments ✨)

Every December, Instagram stories across the world are bombarded with Spotify Wrapped summaries, but have you considered how much data is being processed to present this yearly experience to users? 😳 No longer do you have to scour the Billboard charts to discover new music to listen to, or rely on labels to bring you new artists, thanks to streaming platforms knowing exactly what you listen to and using it to bring you more of what you like 😄 Swipe to learn how artists and platforms alike are strategically using data ➡️

20% of your customers are likely to generate 80% of your future revenue 😲 Acquiring new customers is expensive for any business. When you spend heavily on sales and marketing to meet revenue targets, profit margins shrink, diverting funds that could be better used elsewhere if you were better at getting repeat business from existing customers 😬 Fortunately, with retail analytics, you can identify exactly how much you're spending to acquire each customer, which of them are at risk of churning, and how much revenue you can expect from each individual over their lifetime with your brand. Our comprehensive guide on retail analytics covers this and more: https://lnkd.in/dJ9SwfYh