Archive for November, 2010

Busting the Myths of Retail: #4. Clustering and Localization

by Quantum Retail • November 24th, 2010 • 1 Comment »

MYTH #4: I can localize my merchandise by clustering based on store attributes

TRUTH: Grouping stores together seasonally based on store size, geography or sales performance will only deliver average results at best. Retailers can hone the localization of their stores with intelligent clustering at lower levels of merchandise including merchandise attributes and the actual current performance of products.

Clustering stores that performed in similar ways still perpetuates the problems associated to the use of aggregated data. Instead of pretending you have, say, 1,000 stores that all perform alike, you now pretend that in each of maybe 10 different store groups, you now have around 100 homogeneous stores. The reality we know is that every store performs differently. Though it is not realistic to manage the creation of an assortment plan for each of your stores, you can begin to step in the direction of localization by creating merchandise specific clusters that group products together based on their true historical demand (including lost sales).

For example, if your clusters were ranked from A to F by volume of sales per category, one store could be an A level in women’s jeans. However, if you went one level lower to womens fashion jeans or even lower to womens fashion jeans by True Religion, for example,  you could see that store move to an F in fashion jeans but a C in True Religion jeans. By going across the chain and identifying which merchandise performs at which level at each store, you can send a more accurate volume of each product to each store. The more specific you can be by product and attribute cluster, the closer you can get to creating a localized plan for every store in your chain. And the more constantly you update those clusters, by week or month at least, the truer you will stay to actual store demand.

Three major problems to look out for in clustering

With few exceptions, the vast majority of clustering has three major problems associated with the process. These three issues are seriously inhibiting the retailer from truly localizing their stores.

  1. Clusters are almost always based on historical sales performance alone.
  2. Clusters are typically locked in for a season or similar time period. If the recent economic climate has taught us anything, it is that store behavior changes and it changes rapidly.
  3. Clusters that group an entire store are incredibly inaccurate, even clustering by department or category won’t achieve the goal of true localization. Significant value can be gained if stores are clustered at or near the individual product level based on merchandise and location attributes which have been proven to have a direct correlation to product behavior. This must be executed using historical, current and expected product demand.

Plan for future demand, utilize historical demand

Clustering based off history alone is a mistake that almost every retailer makes. But the typical merchandiser does not have much of a choice. History is the only thing that they have at their fingertips on which to cluster. But, this means of clustering misses the quite obvious fact that product performance last year will not equate to product performance this year. It also misses the not so obvious fact that each store likely missed sales opportunities during periods of stock-outs.  That’s why history is not the best base for clustering products together.  A consideration of expected future behavior must be made alongside a historical adjustment for what could have happened in the past, had the inventory been there to meet demand. Clustering on a trend or, better yet, a forecast at the merchandise level is a better way to cluster products.

Stores are dynamic

The second issue mentioned is that clusters are typically created and locked in for a season or more.  An individual store that performed as an A cluster last year during the spring season in women’s tops will be clustered again as an A store for the entire spring season this year. However, we know that store will frequently not repeat the same performance year over year especially with same or similar products across all departments. Stores need to be able to move within a cluster to more closely align their actual performance with unique merchandise.

If stores don’t move with their performance, they aren’t being localized. Stores will underperform and be left with merchandise to markdown or overperform and stock out. If, however, stores actual current product performance defines and modifies the cluster and thereby their merchandise levels and breadth of assortment, these problems are less likely to happen and the store is being localized more effectively. By doing that, we are continuously introducing small amounts of change into the way that products are being assorted into stores, which is more manageable and timely in reacting to the way that customers are really acting in the stores. That’s an incredibly powerful piece of the merchandising puzzle.

The best way for stores to be localized given that it is impractical to expect an individual assortment per store is by having dynamic clusters. The assortment planning process should include a periodic review of each store’s product performance versus the product’s cluster and make a recommendation to move a store to a different cluster or modify it’s depth based on a variety of criteria. This allows merchants to fine tune the assortment that will perform best in a store given the store’s behavior right now, not last year.

Clustering at the merchandise level

The last issue is clustering by attributes or merchandise rather than by merchandise hierarchy alone.Clustering solely on store attributes, geography and demographics misses a significant opportunity for store localization based on how merchandise attributes collectively perform at an individual store.

An example of this can be found in price point. Let’s look at jeans again. If you cluster jeans based on the price tier (for example: A through F by sales volume), the stores that perform best with higher priced jeans would be assigned the A cluster grade within that product category, whereas a store that has mediocre performance for high priced jeans would be given a store grade F, which would equate to a lower level of inventory sent for that price tier – or perhaps even changing how much of that product the store is eligible to carry. This is even more accurate than grouping the stores based on demographics such as income level. Just because a store is in a nicer neighborhood does not mean that higher priced merchandise will sell better in that store. Honestly, if the retailer creates product-specific clusters with stores that actually perform better in the type of merchandise, the demographic information hardly matters!

In Summary

Today, nobody expects every store to receive its own assortment plan. Every store, however, can receive its own localized, unique assortment even when clusters are being utilized.

Recap:

  1. Cluster on more than just volume and history but include demand, both past and future in the clustering equation.
  2. Constantly update the store cluster assignments based on actual store behavior.
  3. Create localized clusters based on how merchandise attributes collectively perform at an individual store.

By following these guidelines, a merchant can have a positive impact on their chain’s performance and will be able to create localized plans for the individual stores.

Beyond clustering, executing at the SKU level: Quantum’s approach

Dynamic SKU level awareness //

Q serves as a comprehensive demand platform for retailers. It calculates demand and selling behavior for each product, at every store individually, and does not dilute the sku/store demand signals through traditional averaging and aggregating techniques. Q dynamically creates unique SKU/store profiles, for topics including seasonality, day of week, time of day, life-cycle, etc., and manages against supply and demand side events and constraints. This enables Q to make the atomic level adjustments necessary to capture extra full prices sales while greatly reducing inventory investment.

Continuous learning  //

Unlike other solutions, the value and intelligence of Q constantly improves as Q learns from product behaviors. Item profiles in Q are constantly updated by the system whenever new data is available, allowing it to accurately predict how that product will act with the knowledge of how it has acted before, while taking into consideration how it is acting now. This constant learning is unlike anything offered by other vendors.

Real demand visibility //

Q gives you visibility of the real demand in your stores now. With the understanding of lost sales for every product, Q prevents you from missing opportunities so you can capitalize on every potential sale. Q learns from how your product is moving right now, so you do not need a year’s worth of data to predict how a product will perform. By continuously tracking 35 performance metrics, such as: average demand, average sales, seasonal affects to product life-cycle patterns, shelf life, maximum sales per day, average inventory by date, in stock and in transit, this lets retailers calculate potential consumer activity and demand every day.

Download a PDF of this blog series HERE»

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To learn more about clustering with localization, visit: http://quantumretail.com/2010/08/24/the-profit-lab-clustering-with-localization-in-mind/

Debunking the cluster myth in allocation: http://quantumretail.com/2010/06/04/the-profit-lab-the-cluster-myth/

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Busting the Myths of Retail: #3. Business Intelligence (BI)

by Quantum Retail • November 17th, 2010 • Write a comment »

MYTH #3: Business Intelligence (BI) solutions are an effective tool to help execute on real-time demand

TRUTH: In reality, BI solutions alone can help you glean answers from real-time data, but they do not inherently have the ability to execute on those questions, and have a difficult time integrating with your existing systems.

Business Intelligence can be described as the process of enhancing data into information and then into knowledge, from that point, the execution occurs based on what you do with that knowledge. Because of the lack of execution from BI, solutions often fall flat. In fact, “more than half of all BI projects are either never completed or fail to deliver features and benefits that are optimistically agreed on at their outset,” said a spokesman from Atre Group. “There are many reasons for this failure rate: high cost of ownership, lack of ease of use, organizational issues, lack of measurable benefits, benefits restricted to few users, a lack of scalability and so on.” However, there are benefits that can come out of BI, but it must be strategically integrated with retail processes that are driven by merchandising objectives, comprehensively deployed and adopted, and managed in ways that produce meaningful, measurable and credible results.

Too much data

Most retailers have vast amounts of data coming from too many sources. “The theory is that the more you know about your customers and the business problem you’re trying to solve, the better you’re able to solve it,” said Karen Parrish, VP, worldwide sales, BI solutions at IBM. “But by trying to access data from too many sources — data that resides in their own organization, data that resides externally, data that they purchase and bring into the organization, data from the Web and data that sits in e-mail — companies may be shooting themselves in the foot.”

Today one of the biggest challenges retailers face is managing the sheer wealth of data available and selecting what is relevant. “Retailers have always gathered an enormous amount of data, but they don’t always use it very well,” says Jan De Joung, Microsoft’s worldwide retail industry solutions manager. The user must make certain that they are asking the right questions of the data. Instead of looking at how much of a product sold at this time last year, it is more important to look at how much of that product is selling now. When you look at those sales, it is also important to look at not only the maximum sales, but also where you had out of stocks, to understand how much of that product you could have sold. With all of the data available to you, you need to know what information you need to make the best decision, typically this is based on the product’s strategy.

“In the past, BI solutions would tell a retailer some of the facts, such as, ‘you have sold this number of this stock and you made this margin’, but didn’t tell them where they lost margin, in the sense that they didn’t have the right product with the right availability,” says Paul Makin, sales director at K3. “The sophistication of today’s solutions allows people to do far more of that investigation work.”

Real time intelligence

Decisions about short life inventory investment often need to be made months in advance, something that can only be done with access to accurate up-to-the-minute data. “It’s near impossible for any person to get their head around how a decision they’re about to make will effect the entire business,” says Roy Lee of Cognos. “Technology provides an environment where all of the criteria can be entered, the business rules and the business assumptions can be modeled, and those ‘what if’ scenarios can be effectively managed and worked through.” This type of business intelligence, when utilized at the lowest level possible (SKU/store) can immensely increase execution success.

Especially for the fashion sector, accurate and up to date information must be immediately accessible. “The fashion sector is characterised by the frenetic way it has to manage its own business,” says software developer Cesare Dania. “At every trade season, everything starts again and the times are cut drastically. As a consequence, to get information in real time becomes vital.”

Turning intelligence into action

But even with this real time data, if a retailer does not have a strategic and efficient way to act on the data, it’s impact is diminished. However, there are sophisticated new systems that give the user the ability to set up minimum constraints for their product performance that will create alerts when performance falls below those constraints, meaning that they can simplify their time by focusing on the areas of their merchandising process that need attention. The most sophisticated systems available can even use the BI it takes in from SKU/store data, and automatically review the data it needs in order to make decisions and recommendations about what to send, how much to send and where to send it. This is the most advanced way of integrating BI with inventory execution.

Integrating BI into an end to end merchandising solution: Quantum’s approach

BI mixed with strategic merchandising and automation

With Quantum Retail’s system, Q, we combine the business intelligence from product data with automated merchandising processes through a shared pool of knowledge that we call Qi. We follow a strategic approach to merchandising, requiring users to to assign roles and goals to each of their products that will be used to create minimum constraints for every product at every store. Then Qi engine continuously monitors and learns from customer behavior over time, automatically reacting and executing on product objectives to ensure that availability is maintained according to the guidelines set for each product.

Users are not asked to choose the correct data or do the math to meet these objectives, the system does that for you – weighing out the proper execution based on the product strategy, forecast and current demand.

Constant learning

Unlike other solutions, the value and intelligence of Q constantly improves as the Qi engine learns from product behaviors. This means that the value of Q will only increase with time. Item profiles in the Qi engine are constantly updated by the system whenever new data is available, allowing it to accurately predict how that product will act with the intelligence of how it has acted before, while taking into consideration how it is acting now.

Real demand visibility

Q gives you visibility of the real demand in your stores now. With the understanding of lost sales, Q prevents you from missing opportunities so you can capitalize on every potential sale. The Qi engine learns from how your product is moving right now, so you do not need a year’s worth of data to predict how a product will perform. By continuously tracking 35 performance metrics, such as: average demand, average sales, seasonal affects to product life-cycle patterns, shelf life, maximum sales per day, average inventory by date, in stock and in transit, this lets retailers calculate potential consumer activity and demand every day.

Sign up to receive updates throughout the series.

To learn more about Q and it’s constantly learning Qi engine, visit: http://quantumretail.com/q-platform/qi-engine

For more information about BI, check out these On Windows articles:
“6 Tips for Getting the Best out of BI”
“BI Strategy in Retail”

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Busting the Myths of Retail: #2. Markdown Optimization

by Quantum Retail • November 9th, 2010 • 1 Comment »

MYTH #2: Optimizing markdowns is an effective way to increase margin

TRUTH: In reality, it is much more effective to prevent markdowns by focusing on the placement of your inventory from the start.

If you’re going to spend money on a markdown optimization solution, you are avoiding the source of your problem. The challenge with markdowns is that it has become a common fall back process for many retailers; in fact, some retailers rely on markdowns to drive consumer demand. If the retailer were to put more effort into product planning, purchasing and allocation throughout their stores, there would be far less inventory left for markdowns. In reality, it is not cost effective to stock more inventory than what is necessary to capture customer demand. There is no need to persuade your shoppers to purchase products at markdown merely because you stocked merchandise they were not looking for. You need to focus your effort in better understanding the whole of the product life cycle, it’s demand and how to support it properly with inventory to reach product and company objectives.

Getting your inventory right the first time

Markdown Optimization has become all the rage of retailers and retail technologists, but what is a markdown and how should we optimize it? A simple definition of a markdown is a reduction in price or the amount by which a price is reduced. To mark down is to alter price in order to raise demand. At one time retailers called this exercise ‘clearance’ and marked down the price of their goods just once a year, if ever. That was in the annual sale, a time when demand was low and the retailer wanted to clear excess stock in order to make way for new products. Today markdowns are a continuous process for the retailer. Clearance sales are seldom annual events. They may be seasonal, and in the fastest moving retailers – fashion in particular – the retailer may choose to mark down items literally every week.

For some retailers the process of marking down products has shifted from being an exercise designed to manage the end of the product’s life to becoming a promotional tool in itself…and customers have noticed. Whether actively promoted or not, if markdowns are near-continuous, regular shoppers become accustomed to the process. This not only increases off-price demand, but also can decrease full-price sales. As some retailers assume ever more aggressive markdown strategies, the net effect is a serious erosion of price and more importantly margin much earlier in the product’s life cycle. Promotions are one of the reasons commonly sited as a key component of Kmart’s struggles. It has been estimated that some retailers actually sell less than ten percent of their products at full price – their customers have been trained well.

The need for markdowns

If the retailer were to achieve perfect product planning, purchasing and allocation, throughout their stores, there would be no need for markdowns. In reality, we know this Holy Grail is unachievable and the need for markdowns will always exist. What more can be done to minimize markdowns before the end of the product life cycle? As product life cycles become ever shorter, getting it right has never been more important in maintaining competitive advantage and profitability.

Most of the technology being deployed today to optimize the productivity of inventory is designed to operate at the end of the product life cycle and is focused on price. Of course the end of the life cycle is the time to execute markdown strategies, but in fact the most effective and profitable strategy is one based on the whole of the product life and also focuses on inventory assortment and placement.

There are three key points in the life cycle of any product where the retailer needs to make the right decisions in order to control demand, price and profitability:

  1. The initial buy
  2. The distribution of the product throughout its life cycle
  3. The pricing of the product, including markdowns

A holistic approach is recommended for managing the complete life cycle of a product. There are several things retailers can do to make the life cycle more manageable:

  • Understand customer demand
  • Learn and build a knowledge base about customer behaviors
  • Track and react to product performance at the store level
  • Plan to replenish products that perform well at specific locations rather than a one-time allocation

The key is to understand customer demand at the micro or store/product level. Maximizing profitability depends upon knowing what customers wanted and when, not just what you sold.

Inventory execution

Some stores are out of stock way too soon in the product life cycle and others are left with far too much stock at the end which has to be marked down. These are fundamental errors in the fulfillment operation that retailers cannot afford to make, but they happen all too often. The key operation between the initial buying decision and the final end of line markdown is in inventory execution – managing the supply of goods to minimize out of stocks, lost sales and overstocking.

Inventory management systems have helped retailers to improve in this area of inventory imbalance, but the growing use of markdowns suggests that things are not getting any better for retailers.

In fact, there are two separate areas where better decision making is required:

  • The initial purchase stage – deciding how much product the retailer needs in total and what inventory in which stores
  • Distribution – how and when to allocate that quantity across stores and channels

Markdowns are a fix for things that did not go to plan earlier in the product life cycle, so improvements in product planning and inventory execution to reduce excess inventory will have a marked impact in reducing the need for markdowns and maximizing profit. Many of the mistakes being made at the product planning and inventory execution stages are as the result of simplification – aggregation of data and assumptions across multiple stores – which rides roughshod over the variability of customer profiles and demand from one store to another.

A fashionable downtown store in a major city may need a stock richer in traffic generators and high value image items, whereas an out-of-town store in a low income area may need its mix of products to be higher in value items. Fashion retailers have the added complexity of garment size, which means that they need to have a different mix of sizes too, depending on the stores location.

Clustering

One recent response to the problem of aggregating and averaging has been the practice of clustering. This groups together stores which perform in similar ways, helping to undo the errors that inevitably encroach when operating a system on the basis that all stores behave similarly. While a step in the right direction, clustering still perpetuates the problems of aggregated data. Instead of pretending you have, say, 1,000 stores that all perform alike, you now pretend that in each of maybe 10 different store groups you now have around 100 homogeneous stores. The reality we know is that every store performs differently.

While this approach may have had its initial advantages, now it is working against the retailer to produce imperfect planning predictions and poor inventory execution. If a store’s allocation is based on the predicted performance of an average store, then inevitably some will have overstocks while others have stock-outs.

The problem is also linked to store size. Larger stores tend to have too much stock (and greater markdowns) while smaller stores suffer more stock-outs (and lost sales). Often this is because larger stores are routinely prioritized in the event of scarcity. This prioritization is seldom based on a detailed analysis of individual store performance and frequently exacerbates problems of stock misallocation.

Understanding the variability of store performance is critical to achieving effective assortment planning and inventory execution. Getting this right will deliver a dramatic reduction in markdowns. Another common strategic mistake is to push most or all of the inventory straight to stores without plans for a second allocation. This sort of process inhibits retailers from making replenishment decisions based on local inventory performance during the life cycle of the product. It is much more profitable to push a small amount of inventory to stores and place the rest of the inventory according to where it will sell best by looking at what your customer are asking for at each store.

It is not just the case that each store performs differently – each product performs differently in every store. Intuitively retailers know this and yet the assumption has been that in practice both operational and technological constraints make it impossible to manage the complexity of data processing and analysis. Overcoming and embracing that complexity, rather than attempting to gloss over it, provides the means to strategic control of markdowns.

A retailer’s strategy

A retailer’s markdown strategy needs to be an integral part of the entire life cycle of the product and not just a Band-Aid response. It is no good just covering up the real problem without addressing the root causes of inefficiencies in assortment planning and inventory execution. Proactively determining the role of the product within the overall assortment and having the power to determine at an individual store level the inventory allocation process necessary to meet that role are key components to minimizing markdowns. A new holistic approach to retailing integrates merchandising and fulfillment processes while managing and reporting on inventory from the store-level up, in real-time. It provides merchandising plans, goals and strategies that directly drive product fulfillment. This allows the fulfillment process to be driven by a bottom-up view of item behavior, fused with plans, goals and strategies. Real-time performance analysis enables a rapid response if a product or location is failing to achieve its goals or has the ability to exceed them.

This concept derives trends from relatively short and recent learning to make accurate predictions of future behavior and drive decisions that maximize inventory productivity. It is unlike traditional ’number-crunching‘ approaches that rely on interpreting trends and forecasts based on huge pools of historical data. As a result this method of analysis has the flexibility to respond in real time and at a much finer level of detail SKU/store level than would conventionally be possible.

The power to manage

This highly detailed, flexible and adaptive approach gives the retailer the power to actively manage the whole of the product life cycle. It can also be used to optimize markdowns down to store level in a way that fully meets both the retailer’s general and product specific goals. If that means building promotional markdowns, then this can be planned, but this enhanced, detailed management of the whole process will minimize the markdowns that result from the inability of the retailer to identify and react to store level behavior.

The markdown is here to stay, but today’s retailers now have the means to halt its seemingly inexorable rise and the accompanying erosion of margin, if they so desire. The objective is clear: get the product in the right place to start with – then fulfill based on how products are really performing at each store – giving the product the best chance to sell at full price and identify when and where markdowns are truly necessary. Knowing the objective is one thing, but today retailers now have the means to reach it – Q.

Sign up to receive updates throughout the series.

Learn more about Quantum’s approach to strategic merchandising: http://quantumretail.com/solutions/q

To hear Linda Whitaker, Chief Scientist of Quantum Retail, discuss “Managing Markdowns: Why Prevention is Better than the Optimization Cure,” visit: http://quantumretail.com/retail-insights/podcasts/#Managing_Markdowns

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Busting the Myths of Retail: #1. Forecasting

by Quantum Retail • November 3rd, 2010 • Write a comment »

This series will cover 5 assumptions that retailers have on the following topics in merchandising: Forecasting, markdown optimization, BI, localization, and time to value. Follow this blog or sign up to receive updates throughout the series.

MYTH #1: If we have a more accurate forecast, we’ll be able to place our inventory better.

TRUTH: In reality, a more accurate forecast alone is only a small part of the puzzle to drive better inventory execution.

The forecast for most product / location combinations for the majority of product is less than 1 unit per week per store. If that value is 0.3 units and a 20% increase in forecast accuracy changes it to 0.36 units how will that change your placement of inventory to support it? In traditional inventory management scenarios it won’t.

Let’s walk through an example: A suburban Walgreens store sells a 3.2 ounce bottle of Degree stick deodorant in Cool Breeze; it has a forecast of .5 units per week. So, the forecast would look like, 1, 0, 1, 0, 1, 0, 1 in the next  7 weeks. While that item may sell hundreds a day across the chain, it sells only every other week at this Walgreens. To execute on this product you either need to guess, or you need to have an objective in place that will help you determine a strategic decision. Ultimately, you need to make a decision whether or not to send a case of Degree Cool Breeze. If they come in packs of 6 and your forecast is .5 units per week, is it profitable to send a whole case? Well, that depends on your strategy.

Creating a product strategy

Product Strategy

Product Role + Objective

Your product strategy is the combination of your product role and objective. The product strategy will drive your inventory decisions and make sense of how you respond to your forecasted sales. When you have a forecast of .6 per week, you are faced with a tough decision, do you send inventory or not? Without a strategy in place for your products, you are just guessing. When you have a strategy for each product, you look at the role of the product, where you are in that product’s life cycle, what the demand forecast is, how much inventory you have in stock and decide if it is necessary to send inventory in order to meet the objective of the product.

Each product within your assortment should have a role:

  • Traffic driver
  • Money maker
  • Image item
  • Core
  • Fringe assortment
  • Loss leader

The role you choose for your products is the basis for your inventory strategy and will be the deciding factor for inventory execution. Every retailer is different, so the roles of your items may not fit these categories perfectly, this is just to give you an idea.

These roles correspond to a specific objective, the purpose of the assigned role:

  • Traffic driver: Maximize sales subject to some acceptable profitability (can be negative profitability in the case of Loss Leaders e.g. milk)
  • Money maker: Maximize profit (High Margin items – Cables at electronics stores – private label basics like tees/tanks at apparel)
  • Image item: Maintain a presentation quantity (specialty appeal: expensive designer dress / 60” leading edge technology flat screen), these items may not create a high amount of profit, but the customer expects to see them when they come into the store
  • Core: Maximize profit subject to maintaining a minimum service objective (everything else – usually a few variations of the objective within this set)
  • Fringe assortment: Capture sales while minimizing markdown exposure (Fringe colors, patterns)
  • Loss leader: A product promoted at a low price to stimulate other more profitable sales

The role of the product will correspond to a specific objective. For example, the objective of core merchandise is to maximize profit by always being in stock, thus you will likely set a minimum service level for this merchandise. If you set 95% as your service level, this merchandise would need to be in stock 95% of the time, even if it would be more profitable to meet a 90% service level, because you have ensured your customers should have 95% availability. If over time it makes more sense to keep up your margin with a 90% service level, you will sacrifice service in this case, but maximize profit.

When you have a forecast of .5 of this item sold per week, and you are running low on stock, you would typically send this item no matter what, if it is essential in your core assortment. Whereas, if you had a role of a fringe item, and you sold .5 of that item per week, you would want to assess where you are in the lifecycle of that product, as fringe colors and patterns may be seasonal. If you are at the beginning of the lifecycle, you might consider sending the product in order to meet the sales through the life of that product. However, if you are in the middle of the lifecycle, it may not be profitable to restock the fringe item because you want to minimize the chance of markdowns.

10 Questions to answer in order to meet your product objectives

  1. What is the role of the product at each store (traffic driver, money maker, image item, core, fringe assortment, etc.)?
  2. What is the goal of the product at each store (maximizing profit, maximizing sales, minimizing markdowns, achieving a minimum presentation quantity, minimum service level, etc.)?
  3. Will your objective change for the product at different stores/clusters?
  4. What is the demand for the product at each store on a daily/weekly basis?
  5. When will each unit sell and in which store?
  6. How will any promotions affect the demand of the product at each store?
  7. Where are you in the life cycle of the product? When will the product be replaced with another line or a newer version?
  8. How much of the product do you have in the DC? How much is on order? How much of the product do you have at each store? How much inventory do you need to put to aside to fulfill the demand for the web channel?
  9. How much of the product do you need to order from suppliers? What is the lead time for the product?
  10. Does the product come in packs? Will you still achieve your objective by ordering an entire pack? Or will you be causing a loss in margin from markdowns?

When you understand what role your products play in your assortment, you will increase the effectiveness of your forecast. Merchandising strategies will assist you from both a financial and a merchandising perspective and ensure that every inventory decision that is made is aligned with achieving your product objectives.

You should continually monitor your products over time to make sure that they are acting like the role you set. Sometimes your traffic drivers become fringe items, money makers may turn into core products. As you’ve seen throughout the recession, the way customers buy products changes over time and you need to react to those changes, ensuring that the roles of your products can meet your objectives.

Take a look at this list of difficult-to-forecast merchandise,

Difficult items to forecast:

  • Big ticket, slow movers
  • Sized merchandise
  • Highly volatile selling items
  • Seasonal product
  • Short life product
  • Perishable product
  • Vendor pack constrained merchandise
  • Heavily promoted items
  • Vendor allocated / scarce merchandise
  • Long lead-time items

These are the items that require the most attention. It will pay off to monitor and manage the strategies you set for them.

New technology for strategic merchandise management

At Quantum Retail, we’ve found that the most successful approach to merchandising is through product strategies. Our system requires you to assign roles and objectives for each of your products. A product is in your assortment for a reason, so we help you determine the most profitable strategies for every product at every store and put them into our platform, Q. Q continuously monitors and learns from customer behavior over time, and automatically reacts and executes to product objectives to ensure that availability is maintained according to the guidelines and constraints set for your objectives.

Users are not asked to choose the correct algorithm or do the math to meet these objectives, the system does that for you – weighing out the proper execution based on the product strategy, forecast and current demand.

Q looks not just at sales, but lost sales as well as other factors like days between sales and when an item sells, does it sell one or more than one and so on. Our forecast has proven to be up to 50% more accurate for our customers than when they used traditional solutions, but when prospective retailers come to us expecting that an accurate forecast will solve all their inventory and stock decisions, we insist they look at product strategies and the importance of accurate allocation and replenishment.

When we say ‘what’s your goal?’ It’s surprising to find that most retailers have no strategy for their merchandise. It’s important to be strategic!

We guarantee that the most accurate and profitable way to place your inventory is not by forecasting alone, but by executing a strategy for each of your products.

Learn more about Quantum’s approach to strategic merchandising: http://quantumretail.com/solutions/q

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