Cross-Merchandising Techniques

My favorite customer service tool isn't a survey. It's gemba. Gemba means "the actual place." Going "to the gemba" or doing a "gemba walk" means going to the place where customer service happens. You can learn a lot by observing. A university parking team got a lot of complaints from faculty and staff about the process used to issue annual parking passes. They got some insights from an existing survey, but not enough. Going to the gemba was essential. Visiting the parking office during renewal time made it immediately obvious why people were unhappy: 1. Going to the parking office was an inconvenience 2. Ironically, parking was scarce near the office 3. Wait times to get the pass were long All of this made people feel like they were wasting time. The parking team identified an easy fix: bring parking passes to faculty and staff. Stations were set up around campus during renewal periods. This allowed people to quickly get their pass near where they went to work. Give gemba a try. Pick a customer service challenge. Use three principles to guide you: 1. Go see. Observe the operation in motion. 2. Ask why. Talk to customers and employees. Ask why they do what they do. 3. Show respect. Demonstrate respect for employees and customers alike. That last one brings an unexpected benefit. I've found that respect makes employees very honest. They'll readily tell you why they do what they do if they believe you're there to help.

I’m delighted to launch our latest thought leadership research with Transportation, Shipping, & Logistics at Amazon, looking at how delivery can drive loyalty. 🔍 Our pan-European analysis across UK, Spain, France and Italy uncovered some super interesting insights. For one (see graph), the affluence-age relationship isn't just a demographic split – it's aligned to a lifetime value predictor that’s heavily influenced by delivery. Knowing which consumer cohort to target and how, is a critical component of profitability. The data highlights a growing divide in consumer behaviour, emphasising the need for a tailored approach: agile, customer-centric delivery for the younger, affluent segments, and value-driven strategies to attract and convert older, more cautious shoppers. Another way of identifying target cohorts is to look at repeat purchases. Our research reveals a clear trend: affluent GenZ and Millennial shoppers not only buy more frequently, but also exhibit higher loyalty. From these cohorts, fast and convenient delivery options are crucial to capture their repeat business. Conversely, older and less affluent consumers are more price-sensitive and cautious, indicating a different value proposition is needed to engage and retain them. 🎯 The Strategic Imperative: This isn't just about who's buying more – it's about the fundamental reshaping of retail economics: 💥 The Loyalty Multiplier Effect: When high-affluence millennials increase their purchase frequency, they don't just buy more – they create a compound growth effect. Each additional delivery satisfaction point translates to a higher likelihood of repeat purchase. 💥 The Hidden Cost Dynamic: Less affluent customers show more price sensitivity, suggesting a different value proposition is needed to engage and retain them. When retailers align delivery pricing with segment-specific price thresholds, they can potentially reduce the cost to serve by consolidating consignments or extending delivery windows. Smart delivery segmentation can be a profit opportunity when mapped correctly to purchasing power. 💥 The Generation Bridge: The 35-44 affluent segment isn't just buying more – they offer foresight into the behavioural patterns that are likely to cascade down to other segments. Their behaviours today provide a glimpse into tomorrow's consumers in terms of life-stage, omnichannel behaviour and loyalty drivers. Ultimately, delivery options require a tailored strategy depending on the customer. There is no one-size fits all. Our report with Amazon Shipping is packed full of more insights so download for free and take a look! Download our FREE report now 🔗 https://lnkd.in/eJnCu3wW

I met a sales team that tracks 27 different metrics. But none of them matter. They measure: - Calls made - Emails sent - Meetings booked - Demos delivered - Talk-to-listen ratio - Response time - Pipeline coverage But they all miss the most important number: How often prospects share your content with others. This hit me yesterday. We analyzed our last 200 deals: Won deals: Champion shared content with 5+ stakeholders Lost deals: Champion shared with fewer than 2 people It wasn't about our: - Product demos - Discovery questions - Pricing strategy - Negotiation skills It was about whether our champion could effectively sell for us. Think about your current pipeline: Do you know how many people have seen your proposal? Do you know which slides your champion shared internally? Do you know who viewed your pricing? Most sales leaders have no idea. They're optimizing metrics that don't drive decisions. Look at your CRM right now. I bet it tracks: ✅ When YOU last emailed a prospect ❌ When THEY last shared your content ✅ How many calls YOU made ❌ How many stakeholders viewed your materials ✅ When YOU sent a proposal ❌ How much time they spent reviewing it We've built dashboards to measure everything except what actually matters. The real sales metric that predicts closed deals: Internal Sharing Velocity (ISV) How quickly and widely your champion distributes your content to other stakeholders. High ISV = Deals close Low ISV = Deals stall We completely rebuilt our sales process around this insight: - Redesigned all content to be shareable, not just readable - Created spaces where champions could easily distribute information - Built analytics to measure exactly who engaged with what - Trained reps to optimize for sharing, not for responses Result? Win rates up 35%. Sales cycles shortened by 42%. Forecasting accuracy improved by 60%. Stop obsessing over your activity metrics. Start measuring how effectively your champions sell for you. If your CRM can't tell you how often your content is shared internally, you're operating in the dark. And that's why your forecasts are always wrong. Your move.

75% of visitors that land on your PDP bounce. That’s 3 out of 4 potential customers, gone. Why? Because most sites make visitors do the work: • Finding key product details • Figuring out why it’s worth buying • Searching for trust signals before committing But shoppers shouldn’t have to think. They should instantly believe you’re the right solution. In this post I'll be sharing a comprehensive guide of 12 changes you can do to your PDP to highlights benefits and convert shoppers.  1. Show key concerns your product solves. Keep them as a badge and place it above the product title. Gets them interested from the top of the page.       2. Highlight who is this product for. Place this under the product title. Important for skincare, personal care websites.       3. Highlight the quantity they get for the price they pay. This cab be grams, litres, days of supply.       4.  Add a badge like "Best seller", "Most loved". Do this where it's relevant. This builds confidence in their purchase decision.       5. Add the results the product has driven. This can be for other customers or the result of a clinical study you have conducted.       6. Show image thumbnails. The image gallery is the fastest way to tell what's in your product, how to use it, when to use it. Get them to scroll through it.       7. Highlight 3-5 key benefits of the product. Keep this in 1 line and have them in bullets or with icons.       8. Tell WHY is your product effective. In this example, I've added an ingredients section to explain that.       9. Keep add-to-cart as the primary CTA. And not buy now. This is relevant for skincare websites since you can cross-sell other products in this routine.      10. Optimize the area around the add to cart. Highlight shipping time, free shipping, where you ship.      11. Motivate purchase with samples or free gifts on orders. Shopper should spend $X to avail this. Increasing your AOV while delighting the shopper.      12. Add a cross-sell. Like 'Complete this routine', 'Complete this look'. Show which products go well with this one. Make it easy to add to cart from this page.      Other changes I did: • Removed auto slide from the announcement bar • Added breadcrumbs to help navigate to parent category (reduces bounce rate from PDPs) • Underlined reviews and added the review count. What’s one PDP change that made a difference for you? Drop it in the comments. P.S. If your product has not clinically proven to solve a problem, don’t mention it. The goal isn’t just one purchase. It’s about building a brand that lasts. One that's trusted and gets repeat buyers. Not one that dilutes its name for short-term sales.

#CRM: Your Ultimate Sales Wingman Every salesperson needs a secret weapon, and that weapon is a well-utilized Customer Relationship Management (CRM) system. Far from being just a management tool, a CRM is truly a salesperson's best friend that can dramatically transform your sales approach. Why CRM is a Game-Changer CRM helps salespeople "optimize their entire sales process" by providing several critical advantages: 1. Intelligent Lead Management With CRM, you can segment data, identify valuable opportunities, and prioritize leads based on their potential. You’ll have access to communication history, making it easy to know exactly when to reach out to customers. 2. Efficiency Booster: By automating reports, scheduling, and tracking interactions, CRM frees up your time to focus on what matters most—closing deals. Modern CRMs can generate reports in just a few clicks and keep you organized. 3. Deep Customer Insights: A CRM provides a comprehensive view of customer interactions, helping you understand buyer motivations, anticipate needs, and create personalized strategies. Documenting intrinsic buyer motivations and deal specifics becomes seamless. #Customization & #Optimization is the Key The most successful salespeople don’t just use CRM—they customize it to fit their selling methodology. Create fields that track your specific sales process, remind you of follow-ups, and highlight critical deal information. Remember, CRM isn't about management oversight—it's about empowering you to sell smarter, faster, and more effectively. Make it your trusted companion in the sales journey! #Sales #CRM #SalesTips #SalesLeadership #SalesTraining #SalesStrategy #CustomerExperience #BusinessGrowth #DigitalTransformation #LeadManagement

Machine Learning Interview Question with Solution for a Walmart Data Scientist Role Question: Walmart collects data from various sources like sales, inventory, and customer behavior. One of the main goals is to predict product demand to optimize inventory levels. Suppose you are provided with historical sales data, including features like Date, Store_ID, Product_ID, Sales_Quantity, and Promotion. How would you build a machine learning model to forecast sales for the next 30 days? Solution: To solve this problem, we can follow these steps:    ⏩ Understand the Problem: The goal is to predict Sales_Quantity for the next 30 days, which makes this a time series forecasting problem.    ⏩ Data Preprocessing:        - Handling Missing Values: If there are any missing values, we need to fill them appropriately (e.g., using median or forward fill for missing sales quantities).        - Feature Engineering: Create additional features such as:             - Lag features (previous sales quantities)             - Rolling averages (7-day, 30-day)             - Holidays (since Walmart sales may spike during holidays)             - Days of the week (sales patterns may differ between weekdays and weekends)    ⏩ Train-Test Split:        Split the data into a train set (e.g., sales before the last month) and a test set (last month of sales).    ⏩ Model Selection: Some of the models we can consider are:         - Random Forest Regressor: Can handle non-linear relationships and provide feature importance.         - XGBoost or LightGBM: These are tree-based gradient boosting models that work well for structured data like sales forecasting.         - ARIMA (AutoRegressive Integrated Moving Average): A classic time-series forecasting model.    ⏩ Training the Model:        - Use historical data to train the model on sales quantity (Sales_Quantity) as the target variable.        - Ensure to include relevant features like promotions, store IDs, and lagged sales as inputs.    ⏩ Evaluation Metrics:        Use Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) to evaluate the model's performance on the test set.    ⏩ Hyperparameter Tuning:        Perform Grid Search or Random Search for hyperparameter tuning to optimize model performance.    ⏩ Deployment:        Once the model is trained and evaluated, deploy it to forecast future sales and adjust inventory levels accordingly. Can you think of any alternate solution? Please share in comments! #dsinterviewpreparation #ml #walmartinterview

If more of your store sales start on TikTok lately, you might wanna read this. 𝘛𝘩𝘦 𝘴𝘢𝘭𝘦 𝘪𝘴 𝘥𝘦𝘤𝘪𝘥𝘦𝘥 𝘣𝘦𝘧𝘰𝘳𝘦 𝘺𝘰𝘶𝘳 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘦𝘷𝘦𝘯 𝘦𝘯𝘵𝘦𝘳𝘴 𝘺𝘰𝘶𝘳 𝘴𝘵𝘰𝘳𝘦. The checkout happens in-store. But the sale happens everywhere else. Here's the reality: This year 60%+, and in 2027, 70% of retail sales will be digitally influenced. I can't emphasize this enough; here's what most brands miss—digital influence isn't just about online sales. It's about shaping every moment before the customer even walks into your store. L'Oréal cracked this code: 100M+ AR try-on sessions driving real conversions. 31 brands orchestrating seamless experiences across 72 countries. No.1 in beauty influencer marketing (29% market share), 20-80% higher conversion rates through enhanced digital experiences. The new customer journey isn't linear—it's layered: - They discover you on social - Research you through reviews and UGC - Try your product virtually through AR - Get retargeted with personalized content - Finally purchase in-store (feeling confident they're making the right choice) Every touchpoint matters, and every interaction influences the final decision. The brands winning today aren't just selling products—they're orchestrating experiences across owned, paid, and earned media that guide customers from curiosity to checkout. Digital discovery is increasingly pay-to-play and shoppers are paying attention. ++ Tactical Recommendations for CPG / FMCG Brands ++ 1. Beyond just having perfect, high SOV product pages, create discovery ecosystems. - Optimize for "zero-moment-of-truth" searches. - Activate shoppable content at scale. - Leverage user-generated content as social proof. Brands that do these see a 35% higher conversion rate from digital touchpoints to in-store purchases. 2. Connect digital engagement directly to retail execution. - Geo-target digital campaigns to drive foot traffic - Create "store-specific" digital content CPG brands using geo-targeted social ads see a 23% higher in-store sales lift in targeted markets. 3. Most important one; stop flying blind—measure digital influence on offline sales. - Implement unique promo codes for each digital touchpoint to track conversion paths. - Use customer surveys at point of purchase. - Partner with retailers on shared data insights Brands with proper attribution see 15-25% improvement in marketing ROI within 12 months. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟲𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁® : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. #CPG #FMCG #AI #ecommerce Procter & Gamble PepsiCo Unilever The Coca-Cola Company Nestlé Mondelēz International Kraft Heinz Ferrero Mars Colgate-Palmolive Henkel Bayer Haleon Kenvue The HEINEKEN Company Carlsberg Group Philips Samsung Electronics Panasonic North America

Certain categories are more emotive than others. When you blend consumer and shopper needs, we’re very particular about getting some purchases just right. Because the alternative is to disappoint ourselves (the shoppers) and those we’re purchasing for…. sometimes us again or others we’re making the purchase for. The emotional investment is higher. I walked into Dunnes Stores in the Beacon in Dublin yesterday morning. It’s a store which I always love visiting and literally as I walked in, the first encounter was with an ‘emotive’ category solution. Who doesn’t love a donut and the sensory display laid out to tantalise the shopper has a positive side effect. I didn’t buy donuts on this occasion (it was 9 in the morning) but it prompted me to pick up an array of confectionery items for my daughters birthday party while I was there. I was in a Pick n Pay store recently and saw a great explainer for ‘avocados’, aimed to help the shopper make better purchase decisions around their avocados and this attention to detail on one item in the fruit and veg section automatically makes me, the shopper, feel more comfortable about wider purchases in this category. The ‘Fruit and Vegetables’ category is another emotive one. While visiting Woolworths in Canal Walk recently, I stopped at the barista station which is situated within the store on entrance to the section. Again coffee is both an emotive category and one which can be highly sensory. This store had the most beautiful smell of coffee coming from the barista station and that type of sensory experience triggers shopper behaviour. Either to get a coffee while shopping (simple but genius) or to pick up coffee as you’ve been reminded to go down the coffee aisle by the sensory prompt of freshly ground coffee. Prompts like ‘local favourites’ also strike emotional chords with shoppers. Why? Because it tells the shopper that these items are popular to those close to them. That creates both interest, curiosity and fomo. Wine is another emotive category. If you get your wine choice wrong, not only is it costly but you may be experiencing that disappointment on a special occasion, in front of friends and family or just when you are indulging in a treat. You don’t want to get your shopper choice wrong for the consumer audience and so the emotional investment in getting it right is higher than in other categories. This is why the shopper experience in Checkers was an elevated one. Between staff and digital display support, the shopper was supported towards better decision making in the store. Take a look at my recent digital display post on this recent store experience. I’ve included a certain confectionery character in this post. He kept popping up in stores that I visited on my trip to Cape Town recently. Again taps into fun emotion. So within your retail stores, which categories are the more emotive ones for your shoppers and are you tapping into that emotion to support improved shopper behaviour?

☠️ Retail is DEAD….. 𝐫𝐢𝐠𝐡𝐭? ❌ Wrong. 🏢 I have been navigating the Retail CRE Landscape for 6 years now, here’s what I’ve learned: 1️⃣ In the dynamic world of Commercial Real Estate (CRE), retail shopping centers stand as a misunderstood product type, especially for investors new to this space. ▶️ Despite challenges like rising costs and changing consumer spending habits, the U.S. retail sector has shown remarkable resilience and growth in 2023. 📈Retail space occupancy has surged to historical highs. The third quarter of 2023 alone saw an increase in demand for retail space by nearly 15 million square feet, marking the 11th consecutive quarter of positive occupancy growth. 📉 Simultaneously, the amount of vacant retail space plummeted to the lowest since the pre-Great Recession era. 2️⃣ Key Drivers of Demand: A diverse range of sectors is fueling this demand. Food and beverage, fitness, experiential retail, discount stores, health and beauty, and medical services are all expanding rapidly. They benefit from a consumer pivot towards value, wellness, and experiences. ❗️However, more than a decade of limited construction, coupled with consistent demand growth, has led to a scarcity of high-quality retail spaces in prime and even secondary corridors in the U.S. 3️⃣ Trends in Demand and Property Types: Despite a slight deceleration, retail demand has increased by over 69 million square feet in the last four quarters. 👉🏽Retailers are showing a preference for efficient spaces in proximity to consumers, resulting in significant demand for freestanding or neighborhood retail properties. ‼️ These property types accounted for a staggering 95% of all retail demand growth over the past year. 4️⃣ The Divergence in the Mall Segment - The mall segment presents a more nuanced picture. Demand for space in lower-rated malls has declined significantly, with a 4 million square foot drop over the past year. There’s a clear division in the mall sector – lower-rated malls are losing tenants, while luxury and top-rated malls are gaining them. Since 2017, demand for space in lower-rated malls has decreased by over 45 million square feet and is expected to decline further in 2024. 💥For new investors in CRE, especially in the retail shopping center segment, these trends present unique opportunitie. 📝Investing in retail CRE requires a nuanced approach, considering both the macroeconomic landscape and localized market dynamics. It’s not just about the space but about predicting where and how consumers want to interact with physical retail. ❓ 𝐀𝐧𝐲 𝐦𝐲𝐭𝐡𝐬 𝐲𝐨𝐮’𝐝 𝐥𝐢𝐤𝐞 𝐦𝐞 𝐭𝐨 (𝐚𝐭𝐭𝐞𝐦𝐩𝐭) 𝐭𝐨 𝐛𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐫𝐞𝐭𝐚𝐢𝐥 𝐂𝐑𝐄? 𝐃𝐫𝐨𝐩 𝐭𝐡𝐞𝐦 𝐛𝐞𝐥𝐨𝐰 👇🏾 Tagging some of my retail friends! Kevin Fickle Benjamin Kogut Spencer Strong Dan Lewkowicz Barry Wolfe #retail #NNN #commercialrealestate #realestateinvesting

Machine learning beats traditional forecasting methods in multi series forecasting. In one of the latest M forecasting competitions, the aim was to advance what we know about time series forecasting methods and strategies. Competitors had to forecast 40k+ time series representing sales for the largest retail company in the world by revenue: Walmart. These are the main findings: ▶️ Performance of ML Methods: Machine learning (ML) models demonstrate superior accuracy compared to simple statistical methods. Hybrid approaches that combine ML techniques with statistical functionalities often yield effective results. Advanced ML methods, such as LightGBM and deep learning techniques, have shown significant forecasting potential. ▶️ Value of Combining Forecasts: Combining forecasts from various methods enhances accuracy. Even simple, equal-weighted combinations of models can outperform more complex approaches, reaffirming the effectiveness of ensemble strategies. ▶️ Cross-Learning Benefits: Utilizing cross-learning from correlated, hierarchical data improves forecasting accuracy. In short, one model to forecast thousands of time series. This approach allows for more efficient training and reduces computational costs, making it a valuable strategy. ▶️ Differences in Performance: Winning methods often outperform traditional benchmarks significantly. However, many teams may not surpass the performance of simpler methods, indicating that straightforward approaches can still be effective. Impact of External Adjustments: Incorporating external adjustments (ie, data based insight) can enhance forecast accuracy. ▶️ Importance of Cross-Validation Strategies: Effective cross-validation (CV) strategies are crucial for accurately assessing forecasting methods. Many teams fail to select the best forecasts due to inadequate CV methods. Utilizing extensive validation techniques can ensure robustness. ▶️ Role of Exogenous Variables: Including exogenous/explanatory variables significantly improves forecasting accuracy. Additional data such as promotions and price changes can lead to substantial improvements over models that rely solely on historical data. Overall, these findings emphasize the effectiveness of ML methods, the value of combining forecasts, and the importance of incorporating external factors and robust validation strategies in forecasting. If you haven’t already, try using machine learning models to forecast your future challenge 🙂 Read the article 👉 https://buff.ly/3O95gQp