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Leveraging POS & Category Data for Seasonal Success 27 Mar 2025, 3:22 pm

As the retail landscape shifts with the seasons, manufacturers and brands face a common challenge – anticipating demand and ensuring that the right products are in the right places at the right times. 

Retailer merchants have 1,000+ brands and items to MANAGE. In many cases, these merchants do not have the opportunity to DEEP DIVE into their products, categories, and aisles. Merchants are focused on driving sales, profit, and shopper satisfaction, which means they want product availability during peak seasons and to fill gaps with both baseline and trending segments/flavors/brands/items.  

This is where YOU come in. Being an expert in Point-of-Sale (POS), Category & Shopper trends, and Category Management/Analytics becomes invaluable. Whether you’re selling into Mass, Club, Convenience, Grocery, or DIY retailers, leveraging data-driven insights can help maximize sales potential while minimizing out-of-stocks and inefficiencies. 

Why Seasonal Planning Matters:

 
  • Retailers plan their category resets and inventory levels months in advance, being prepared ensures brands secure shelf space. 
  • Demand fluctuates with seasonal spikes (e.g. grilling season for condiments, holiday baking for flour, or back-to-school snacks). 
  • Promotions and pricing strategies need to align with peak buying periods to avoid missing key sales opportunities. 

How POS & Category Data Can Help: 

 

  1. Forecasting Demand by Retailer & Region – Looking at past seasonal sales trends allows manufacturers to anticipate demand patterns and adjust production accordingly. 
  2. Optimizing Assortment & Inventory – Data helps identify which SKUs should be prioritized and where additional distribution is needed, ensuring the right mix for each retailer. 
  3. Executing Promotions More Effectively – Using POS insights, brands can measure promo lift and adjust future strategies based on past performance, maximizing return on investment. Equally brands can avoid unnecessary margin erosion promoting the wrong products or promoting at the wrong time. 
  4. Reducing Stockouts & Improving Shelf Presence – Category data reveals where products are selling through too quickly or where they may be overstocked, helping brands adjust replenishment strategies. 

Retailer-Specific Seasonal Planning: 

 

  • Mass Retailers (e.g., Target, Walmart): Plan inventory resets up to 33 weeks in advance; data-backed recommendations can improve shelf positioning. 
  • Club Stores (e.g. Costco, Sam’s Club): Bulk packaging and limited assortment mean brands must optimize SKUs for everyday inventory or more so for seasonal/promotional surges (e.g. MVMs in Costco 4-6 weeks in advance). 
  • Grocery (e.g. Kroger, Albertsons): Seasonal fluctuations in perishable goods require precise forecasting. 
  • DIY (e.g. Lowe’s, Home Depot): Demand for seasonal products like grills and patio furniture spikes months before actual consumer purchases—a typical DIY category review in September for a Spring Merchandising planogram is standard. 

Real-World Application 

 

In previous work with a big CPG baking company, we used POS data to track seasonal baking trends. By analyzing store-level data, we helped adjust inventory allocations to ensure top-selling SKUs were in stock before peak demand hit. This not only reduced stockouts but also increased retailers’ confidence in the brand’s ability to meet consumer needs. 

Common Mistakes & How to Avoid Them:
 
  • Waiting Too Long to Adjust Assortment: By the time seasonal demand spikes, it’s often too late to react. Brands need data-driven planning 6+ months in advance. 
  • Ignoring Regional Differences: A top seller in the Midwest may not move in the South. Localized data is crucial. 
  • Not Measuring Promo Effectiveness: Promotions aren’t always effective. Use POS data to measure true promo lift. 

How Krunchbox & Category Management Help: 

 

  • Krunchbox’s POS analytics allow manufacturers to see demand shifts in real-time and make data-driven recommendations. 
      • Sales Trend Reports – Identify peak sales periods and plan inventory accordingly. 
      • Promotion Lift Reports – Measure past promotion success and adjust future plans. 
      • Out-of-Stock Alerts – Spot and prevent stockouts in high-demand periods. 
      • Velocity Reports – Understand which SKUs move the fastest and require priority replenishment. 
  • Utilize your Category Management analyst to identify seasonal trends to help develop optimal assortment strategies throughout the year.  I would recommend that you consider Channel, Shopper, and Competitive differences when developing the strategies. 
 
 
Final Thoughts

 

Success in seasonal retail is about anticipation, adaptability, and execution. Manufacturers who leverage POS and Category data can proactively optimize their assortment, prevent stockouts, and secure prime shelf space before peak demand hits. The key is planning, tracking trends, and aligning with retailer needs well in advance. 

Brands that embrace data-driven insights will not only win at the shelf but also build stronger partnerships with retailers who rely on accurate forecasting and strategic recommendations. 

Are you ready to ride the next seasonal surge with confidence? 
Try Krunchbox
with your own data
When you sign up for a demo, we load your own retail data into our software so that you can experience the full benefits of Krunchbox for your organization.

The post Leveraging POS & Category Data for Seasonal Success appeared first on .

Selecting the Right Forecast Aggregation Level for the Right Task 3 Mar 2025, 6:35 pm

Selecting a forecast aggregation strategy that aligns with specific organizational objectives is essential—no single level of detail can address all decision-making needs. While high-level forecasts provide stability, lower-level forecasts offer actionable insights but require more detailed data. Point of Sale (POS) data adds vital context to wholesale forecasts by capturing actual consumer demand at the point of purchase, offering a more complete picture of how products perform at the end consumer level.

 

Understanding Forecast Hierarchy 

Building on this foundation, understanding forecasting hierarchies is essential for effective demand planning. Forecasts can be organized at multiple levels: at the top, overall company or category demand helps guide high-level financial decisions; the middle level, which includes product groups, regional demand, or key customer segments, provides a practical sanity check for the more detailed forecasts below; and at the bottom, individual SKUs or specific customer purchases offer granular insights but also increase complexity. Each layer caters to different operational needs: top-level forecasts may guide strategic budgeting for finance teams, middle-level forecasts can inform marketing strategies and resource allocation across regions, and bottom-level forecasts support day-to-day operational decisions such as precise inventory management. While aggregation at higher levels tends to smooth out variability, disaggregation into lower levels allows for more detailed planning, requiring more nuanced management to handle the complexity.

 

Augmenting Wholesale Forecasts with POS Data

 
Continuing from the importance of managing complexity at lower levels of detail, one powerful way to refine bottom-level forecasts is through Point of Sale (POS) data. By capturing actual consumer purchases (sell-through) rather than relying solely on wholesale shipments (sell-in), POS data provides real-time insights into demand shifts and emerging consumer trends. This level of detail enhances bottom-up forecasting accuracy, particularly when adjusting for promotions, seasonality, and potential stockouts. However, leveraging POS data also brings its own challenges—such as ensuring data availability, reconciling figures with internal shipment records, and accounting for the inventory buffers maintained by retailers—which can complicate the forecasting process if not carefully managed. Organizations like Krunchbox help address these complexities by integrating disparate retailer data streams, standardizing formats, and providing analytic capabilities that enable brands to spot and resolve reconciliation issues more efficiently.

Choosing the right level of forecast aggregation is ultimately about matching your forecasting approach to specific business needs. Organizations can use their data more effectively by recognizing how top-level, middle-level, and bottom-level forecasts each inform different decision-making processes. Incorporating POS data into these hierarchies further refines accuracy by capturing consumer demand, providing a timely view of market shifts, and enabling agile responses to promotions or stockouts.

Although challenges like data reconciliation and retailer inventory buffers remain, specialized solutions such as those offered by Krunchbox simplify the task, allowing companies to unify their data streams and focus on strategic growth.

Additionally, while technology solutions are crucial, successful forecasting relies on skilled teams and well-defined processes to interpret and act on the data effectively. When done correctly, a well-structured forecasting hierarchy—amplified with accurate sell-through data—empowers businesses to optimize both short-term tactics and long-term planning.

Ready to Unlock the Power of Your Retailer Data?

  
Whether you’re grappling with data extraction, need help with data interpretation, or are looking to leverage your data for actionable insights, we’re here to help.
 
Get in touch with us today for a free consultation and discover how Krunchbox can empower your data-driven decisions and fuel your growth.

The post Selecting the Right Forecast Aggregation Level for the Right Task appeared first on .

2025 POS Data Analytics Study 30 Jan 2025, 7:43 pm

2025 POS Data Analytics Report

Discover how over 240 product suppliers are leveraging POS data to optimize their operations and drive strategic growth across the retail landscape. The 2025 POS Analytics Study dives deep into the challenges and best practices that matter most, whether you’re responsible for crunching data, boosting sales, managing supply chain operations, or excelling at in-store execution. 

By understanding the insights revealed in this comprehensive report, you’ll be equipped to make more informed decisions, build stronger partnerships, and stay ahead in an ever-evolving market.

What you’ll uncover?

  • Role-Specific Insights: Explore dedicated sections for Data Analysts, Sales Executives, Supply Chain Managers, and Territory Sales Reps, so you can quickly find what’s most relevant to your day-to-day responsibilities.

  • Real Challenges & Best Practices: Learn how leading product suppliers are conquering issues like data quality, forecasting accuracy, and inventory optimization, and get actionable tips to tackle these challenges head-on.

  • Key Metrics & Trends: Understand the metrics that top performers prioritize (like sell-through rates and weeks of inventory on hand) and discover the tools and technologies that make a difference.

  • Real-world Examples & Takeaways from 240+ Participants: Gain perspective from hundreds of industry professionals who share their experiences, successes, and lessons learned, helping you stay competitive in a fast-paced market.

 

Access Your Free Copy
of The Report

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How Brands Can Successfully Adopt POS Data 15 Oct 2024, 8:12 pm

In today’s fast-paced retail environment, consumer brands often rush to implement new technologies, like point-of-sale (P.O.S) data analytics systems, under the assumption that simply purchasing a solution will yield immediate results. However, without a comprehensive change management strategy, these investments can fall short, leading to wasted resources, low adoption rates, and missed opportunities for optimization. The risks of such an approach include employee resistance, misalignment with business goals, and a failure to achieve the desired impact, ultimately undermining the potential benefits of the technology.

 

To ensure the successful adoption of P.O.S data, brands must go beyond just implementing the technology—they need to embrace change management as a core part of the process. This article will explore four critical areas of change management essential for internalizing P.O.S data: leadership engagement, crafting a compelling change story for stakeholders, role-based training, and establishing proper measurement protocols. Each of these areas plays a pivotal role in driving not only the acceptance but also the effective utilization of P.O.S data within the organization.

 

Leadership Engagement

Leadership involvement is critical during the buy-in process, going far beyond simply approving the budget. Leaders must actively articulate why adopting P.O.S data is crucial, connecting the initiative to broader company goals and demonstrating a commitment to its success. This includes participating in key meetings, offering strategic guidance, and visibly supporting the sales and forecasting teams as they work to implement the new analytics system. By championing the initiative, leadership can foster a culture of collaboration and accountability, ensuring that all stakeholders understand their roles and are motivated to contribute to the successful utilization of POS data.

Additionally, leaders can help mitigate resistance by addressing concerns directly, providing the necessary resources, and recognizing the efforts of teams that drive the project forward. Their ongoing engagement is essential in setting the tone, maintaining momentum, and ultimately ensuring that the adoption of POS data delivers tangible results for the organization.

Crafting a Compelling Change Story for Stakeholders

 
The change management process involves every level of the organization, and understanding how the new data initiative will impact different roles is crucial for gaining buy-in. A compelling case study can be an effective tool to illustrate this. For each department—whether it’s sales, supply chain, or demand planning—identify examples from other industries or competitors that demonstrate successful transformations using POS data. 
 

For the supply chain team, a case study could highlight how P.O.S data enabled more precise inventory management, reducing both stockouts and excess inventory. This demonstrates how adopting POS data can lead to more efficient operations and cost savings, making their job easier and more effective.

For the sales division, the case study might show how P.O.S data led to more accurate sales forecasts and improved store level promotional targeting, resulting in increased revenue. This could alleviate concerns about the value of the data and illustrate the direct benefits to their performance metrics.

For the demand planning team, showing how POS data has been used to enhance demand forecasting in other companies can help illustrate how it leads to better alignment with actual consumer demand, improving forecast accuracy and reducing uncertainty. This could make the team more open to integrating P.O.S data into their planning processes.

By tailoring case studies to each department’s specific concerns and showing tangible benefits, you can help them envision a successful transition and motivate them to embrace the change.

Role-Based Training

 

When introducing a new data-driven approach, it’s crucial to present the entire package to your stakeholders rather than piecemealing the information over time. For example, rather than providing your forecast analyst with sales data first, followed by inventory data a month later, and supply projections two months after that, deliver all relevant data simultaneously. 

By offering a comprehensive view, you enable your team to immediately begin triangulating data to address key challenges and realize the benefits more quickly. This approach accelerates the time to achieve the first significant win, fostering momentum and building confidence in the new process. Additionally, it helps prevent confusion or misalignment that can arise from a fragmented introduction of the data components. A holistic presentation also ensures that everyone understands how the different data sets interrelate, leading to more informed decision-making and more effective problem-solving from the outset.

 
 

Establishing Proper Measurement Protocols

 

Standard metrics around inventory management and forecast accuracy likely already exist within your organization. How will these metrics improve with the adoption of a new strategy? For instance, if a brand is utilizing predictive time series modeling, backtesting new models with additional data features can validate improvements. Engaging a SaaS provider to pull historical projection data enables backward integration and validation in your demand modeling process, ensuring that your new approach is both effective and reliable.

It’s equally crucial for data teams to clearly communicate operational metrics so that business stakeholders understand when data will be delivered each week and if any issues arise. Establishing a triage plan is essential in case data discrepancies occur. SaaS providers like Krunchbox can support this process, offering expertise to investigate issues with retailer API’s or reporting portals, ensuring smooth data flow and timely problem resolution.
 

Conclusion

 

In summary, adopting P.O.S data is not just about implementing new technology—it’s about driving a fundamental shift in how your organization operates. By fully engaging leadership, crafting a compelling narrative, providing comprehensive training, and establishing robust measurement protocols, you can pave the way for a successful transformation. This approach ensures that all stakeholders are aligned, equipped, and motivated to leverage P.O.S data effectively, ultimately leading to enhanced efficiency, better decision-making, and a more resilient supply chain. The real value lies in how well you manage the change, not just in the technology itself.

 

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The Retail Replenishment Process 24 Sep 2024, 8:41 pm

Inventory management systems track sales data, monitor stock levels and make smarter replenishment recommendations. This allows businesses to proactively replenish stores with best-selling items, without overstocking slow moving items. 

 

The formula to calculate how much inventory is needed for replenishing is straightforward and it is: Total amount of inventory required for a specified period of time less amount of inventory available equals how much is needed for replenishment. 


This is the only simple part of the replenishment process as before and after this step occurs, there are decisions to be made such as what parameters to use to run the calculation and how much is available to spend.

Replenishment Parameters

Replenishment parameters are the guardrails used to determine which location needs inventory and how much is required. Used correctly, stores will be appropriately stocked and the retailer will benefit from increased stock turn, sell through and cash flow, as well as an improved GMROI. The parameters to take into account can be categorised as either static, changed infrequently or flexible which can be changed each time a replenishment is run. 

Static Parameters 
 

Lead time: This is the length of time it takes to get from warehouse to store. It is sometimes referred to as transit time. This parameter is static for the simple reason that it takes the same amount of time from where the supplier/warehouse is located to get to stores. Note however that just because a delivery has arrived at the store does not mean that the stock is on hand in the sense that it is on show. 

Replenishment frequency: This refers to the length of time between replenishment runs. The longer the replenishment frequency, the more stock a store will receive. Each store’s replenishment frequency can be different, for instance stores located in areas with low customer traffic may get replenished every two weeks, whereas stores with very high customer traffic may get replenished twice a week, or even daily. 

Minimum and maximum quantities: Setting minimum and/or maximum quantities by location and SKU is another static parameter. These are generally set seasonally rather than each time a replenishment is run. Its purpose is to prevent overstocking stores, while ensuring there is a credible amount of inventory available on display based on rate of sale.

Flexible Parameters  


Rate of Sale: Average number of units sold during a specified period of time. The only restriction to calculating the rate of sale is how much history is available to provide a meaningful average. 

Replenishment period: Number of weeks replenishment calculation is to cover expected sales. This is also known as weeks of cover and is a lever often used to rebalance inventory levels across stores and to keep how much is spent in check. 

Products and locations: Not all products or locations need to be replenished each time. Selecting a subset of products and locations to replenish may be required for a variety of reasons, for example if there is insufficient inventory or funds to replenish all locations. 

How Much to Spend?

Top Down
 

Determining how much to spend on replenishment can be at a top line level, where there is a pre-determined figure the total replenishment must not exceed. This figure is generally known before the replenishment calculation starts. 

 

One advantage of top down replenishment is that costs can be more easily kept in line with budgets and current performance, thereby reducing the risk of overspending. A disadvantage is that the items and locations being replenished may not be ones that will increase sell through or stock turn. 

 

Bottom Up
 

A bottom-up replenishment method looks at what is needed to satisfy demand in all locations for each SKU. The amount to spend isn’t known until after the replenishment has been run and added up across all SKU/ locations combinations. 

 

An advantage for bottom-up replenishment is that stock is replenished everywhere it is needed based on current performance, thereby reducing out of stock situations. A disadvantage is that when added up, the total value from items and locations being replenished may exceed the amount available to spend, leading to a budget/forecast overspend. This is more likely to happen if overall sales performance is below expectations. 

 

Which is Better?
 

With a goal of having the right inventory in stores for customers, the best way to achieve this is without a doubt the bottom-up approach. However, given that this method can sometimes lead to spending more money than is available, it is usually best to use a combination of both approaches. 

A bottom-up calculation can be done to see what the overall spend would be and then compare it with how much is available to spend. If the replenishment needs to be trimmed, it can be done by setting replenishment parameters in a way that doesn’t put stock turn and sell through at risk. 

Final Thoughts

 

The ultimate goal of replenishment is to send stock to locations that stand the highest chance of selling each item as quickly as possible. This is because cycling through inventory as quickly as possible and not having excess slow moving inventory frees up capital and improves cash flow. 

 

Other than this, having the right stock available in stores when needed, contributes to customers having a positive shopping experience (as opposed to alienating customers if there is insufficient stock in stores). This is easier to achieve by having a replenishment system that has flexibility in setting parameters, allowing replenishment specialists to optimise inventory levels at the SKU/location level. 

 
Try Krunchbox
with your own data
When you sign up for a demo, we load your own retail data into our software so that you can experience the full benefits of Krunchbox for your organization.

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Mastering Retail Demand Forecasts 6 Aug 2024, 7:07 pm

Leveraging Data and Insights to Prevent Inventory Dilemmas

Systems are inextricably linked to business operations, but do we understand how they work? As consumer brands strive to stay ahead in a competitive market, a deep understanding of the retailer’s demand forecasting data becomes paramount. Imagine a forecast analyst attempting to predict future demand without fully grasping the nuances of the retailer’s replenishment system that generates purchase orders based on this data. Without this critical insight, how can they accurately identify anomalies and overrides that could impact the bottom line? The ability to decode these complex forecasting systems empowers analysts to make informed decisions and uncovers hidden opportunities for optimization and growth. In an era where data-driven strategies reign supreme, mastering the intricacies of these operational systems is no longer a luxury but a necessity.

 

This article will dives into the inner workings of retailer demand forecasting systems and demonstrate how brands can actively monitor the outputs to prevent inventory issues and become better retail partners. 

Version History of Future Periods

 

Given that each forecast is regenerated weekly, a brand gains multiple insights into how future periods might unfold. This frequent update cycle allows for continuous refinement of predictions, providing a dynamic view of demand. If an item is gaining momentum, these weekly forecasts can reveal directional trends, highlighting shifts in consumer interest and buying behavior. By closely monitoring these evolving trends, brands can respond more swiftly to market changes, adjust their strategies, and ensure they are well-prepared to meet increasing demand. This proactive approach not only helps in capitalizing on emerging opportunities but also in mitigating potential risks associated with rapid changes in the market.

Monitoring Sudden Demand Drops on the SKU Level

 

In addition to uncovering general trends, brands can leverage demand forecasts to identify when items anomalistically drop out of the dataset. This should be treated as a “code red” scenario, demanding immediate attention and swift action. Such anomalies might indicate that an item has temporarily gone out of stock, causing the demand models to lose their signal. However, it could signal deeper, systematic issues requiring urgent communication with your retailer counterpart. Promptly triaging these anomalies allows brands to address potential stockouts, adjust their inventory strategies, and ensure that no sales opportunities are lost. By staying vigilant and responsive to these critical signals, brands can maintain a robust supply chain and strengthen their market position.

Flagging Items with Major OOS Periods from Last Year

 

When an item goes out of stock in year one, it creates a critical gap in the sales data that demand planning tools rely on to forecast future demand. This missing data distorts the accuracy of predictions for year two, as the tools cannot account for the unmet demand due to the stockout. Consequently, planners might underestimate the demand, resulting in potential understocking issues in the subsequent year.

Final Thoughts

 

In the fast-paced world of consumer goods, a profound understanding of the systems that drive business operations is crucial for staying competitive. Retailer demand forecasting systems are pivotal in shaping inventory strategies and ensuring that the right products are available when needed. By leveraging these systems’ detailed data, brands can gain insights into future demand, monitor for sudden SKU drops, and flag items with significant out-of-stock periods from the previous year.


Krunchbox can help brands navigate these complexities with ease. By offering advanced data analytics and business intelligence solutions, Krunchbox empowers brands to actively engage with demand planning data, quickly identify anomalies, and adjust real-time strategies. Their tools facilitate effective communication with retail partners, ensuring a proactive approach to preventing inventory issues and unlocking hidden opportunities for growth and optimization.

As data-driven strategies continue to dominate, mastering these intricate systems with the support of SaaS data providers is essential for making informed decisions and maintaining a robust supply chain. Brands that invest in understanding and utilizing retail sales and inventory data and that employ analytical tools such as Krunchbox will be better positioned to thrive in a dynamic market landscape.

 
Try Krunchbox
with your own data
When you sign up for a demo, we load your own retail data into our software so that you can experience the full benefits of Krunchbox for your organization.

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Allegion Case Study 8 Jul 2024, 8:29 pm

Case Study

Allegion

With more than 30 brands sold in almost 130 countries across the globe, Allegion specialise in security around the doorway and beyond: everything from residential and commercial locks, door closer and exit devices, steel doors and frames to access control and workforce productivity systems. 

Download PDF Version

The Challenge


To effectively manage the transition in stores of a high value product with a new replacement product, and to minimise the return of old product given there is no market value on its’ return.   

The Solution


Using Krunchbox, we were able to quickly rank the stores in terms of where the existing stock of the old item was located.  We reviewed each store’s sales potential based on historic performance and then identified where it made most sense to effectively transfer stock to stores with high stock turns.   Once the analysis was complete from a purely theoretical point of view, stores were then clustered into regions to ensure our logistic and freight costs were kept to a minimum. 

In addition, using the Krunchbox Dead Stock Report, we further investigated stores which had stock but had not generated any sales in 12 weeks to validate whether the stock was physically there. The report extracts are not only intuitive but also quick and easy to send to our merchandising team for on the ground investigation.    

This process effectively saved our organisation thousands of dollars, by optimising the sell through of the remaining stock through the stores with the greatest propensity to sell it. 

Try Krunchbox
with your own data
When you sign up for a demo, we load your own retail data into our software so that you can experience the full benefits of Krunchbox for your organization.
“Without krunchbox we would have been sitting on considerable dead stock and a significant liability from future markdowns. Krunchbox is a game changer!”
Paul Broomfield National Account Manager, Allegion

The post Allegion Case Study appeared first on .

Preparing for a Product Line Review at The Home Depot 7 Jul 2024, 8:08 pm

How Manufacturers Should Prepare for a Product Line Review at The Home Depot

This is the essential guide for manufacturers aiming to carve out a niche at The Home Depot, one of the largest home improvement retailers. Our in-depth eBook offers a detailed roadmap on preparing for a successful Product Line Review (PLR), starting from a deep understanding of The Home Depot’s operational strategies to securing prime shelf space for your products. Learn the key tactics to make a compelling pitch to The Home Depot buyers and ensure your products stand out in a competitive market.

 

THD ebook cover

What you’ll uncover? 

  • Operational Insights of The Home Depot: Understand the inner workings of The Home Depot, focusing on its retail strategies, buyer expectations, and the critical PLR process that could determine your product’s presence and success.

  • Market Dynamics and Competitive Analysis: Learn to effectively analyze your product’s market fit, decipher The Home Depot’s unique requirements, and navigate through competitive challenges to make your product a preferred choice.

  • Product Placement and Optimization Strategies: Discover strategic insights into selecting the right product mix, optimizing product placement, and employing innovative merchandising techniques to captivate both the buyers and consumers.

  • Effective Marketing and Sales Tactics: Master the art of engaging The Home Depot’s buyers with persuasive marketing and sales strategies that highlight the uniqueness and value proposition of your products. 

  • Actionable Steps for Success: Conclude with practical and actionable strategies tailored to meet The Home Depot’s standards, ensuring your product not only lands on the shelves but also achieves sales success.

 

Download Your Guide

"This guide is an invaluable resource for any manufacturer looking to succeed in a Home Depot line review. It provides a comprehensive roadmap, from market analysis to product innovation, that can help businesses align their strategies with Home Depot's expectations."
Director of Channel Marketing

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Sika Case Study 25 Jun 2024, 7:38 pm

Case Study

Sika

Sika is a global specialty chemicals company specializing in the development and production of systems and products for bonding, sealing, damping, reinforcing and protecting. Servicing both the DIY and commercial trades, Sika is a trusted partner to those working in Building, Concreting, Waterproofing, Flooring, Roofing, Sealing and Bonding.

Download PDF Version

The Challenge


The Retail Account team were concerned that sales opportunities were being missed in the Paints Department due to insufficient stock, and in particular for a bulky 20kg dry bag product. What they needed was clear data to present to the Retail Buyer and Planner that quantified the opportunity to increase sales.

The Solution


Using the Krunchbox Allocator module, they ran a gap analysis, to consider the impact of tweaking the min/max for key products. Conscious of the need to work within agreed stock-weight parameters, they were able to pressure test the system and ensure they had an appropriate stock holding at the store level within the Department. They were able to present fact based analysis together with a recommendation to increase the static minimum, which represented a neat half pallet display in store.

The Retail Buyer and Planner accepted the recommendation and rolled out the minimum display standard nationally, resulting in a sizeable stock order, which gave the Sika team an instant payback on their investment in Krunchbox.

Try Krunchbox
with your own data
When you sign up for a demo, we load your own retail data into our software so that you can experience the full benefits of Krunchbox for your organization.
“What I really like about the Krunchbox Allocator module is that it is targeted by SKU by store within the parameters I set, which optimizes the allocation and does not simply load up stores with unnecessary stock. That gives us authority when presenting recommendations to our retail buyers.”
Christopher Wende
Key Accounts Manager, Sika

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Venmar Case Study 25 Jun 2024, 7:37 pm

Case Study

Venmar

Venmar is a market leader designing and selling innovative products that improve air quality and home comfort, including range hoods, ventilation and filtration systems, and central vacuums.

Download PDF Version

The Challenge


Like so many product manufacturers, supply chain issues resulted in some key lines being out of stock for a number of months. Understandably, the Lowe’s Canada replenishment team took these items off the replenishment manifest. However, when the items came back into supply, they were not flagged as available so three weeks later there was still no stock on order and no stock in transit!

MicrosoftTeams-image (1)

 
The Solution


Using some seasonally relevant sales history at the store / SKU level, the Venmar team were able to use the Krunchbox Allocator to generate a recommended initial order to re-supply stores with an appropriate model stock to kick start sales and the replenishment system. The Lowe’s replenishment team were grateful to be advised of the opportunity and accepted the recommended order. 

Try Krunchbox
with your own data
When you sign up for a demo, we load your own retail data into our software so that you can experience the full benefits of Krunchbox for your organization.
“It can be tricky keeping tabs on the hundreds of products we have listed at Lowe’s and other retailers. However, krunchbox makes it easy for us to track product performance and trends, as well as supply chain data, including our own warehouse position. That keeps us on the front foot when working closely with our retail buyers and planners.”
National Account Manager
Venmar

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