During the past two months we talked about squinch, or square-inch post-mailing analysis, to help merchants and the creative team understand what is selling based on how much space is allocated to items, product categories, and price points. We looked at square-inch analysis by page, item profitability, product category, and price point.
This month, we’ll look at the front end, or merchandise preplanning, using two types of planning strategies that most successful print and Web catalogs rely on to organize their work. A product forecast and buy plan worksheet in inventory planning serves as a tool for planning initial product buys down to the SKU level, while a product category and price-point grid helps buyers determine whether the upcoming catalog offering has balance and depth. Let’s see how these two worksheets support merchandising.
The product forecast and buy plan
Each catalog is a new season and needs to be planned independently. The inventory management team starts with a product forecast and buy plan to estimate how many items will be sold in the catalog. The chart shown below is based on a particular marketing forecast plan (see the box at the top right of the chart).
This product forecast and buy plan is organized by page number and identifies each item or SKU, starting from the left:
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item name (not shown below)
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item number
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description (not shown below)
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cost
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retail or catalog selling price
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initial “on order” quantity
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back up or “hold for confirmation” quantity for secondary orders
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forecast total for the catalog’s life
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repeat carryover (quantity carried over from a previous catalog)
The information in these columns can then be projected into financial numbers (dollar amounts), shown on the right side of the buy plan, as follows:
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on-order cost (cost x units on order)
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forecast cost (cost x total forecast)
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on-order retail (units on order x retail)
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forecast retail (forecast x retail)
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cost of goods expressed as a percent (cost of the product divided by retail selling price) Say an item costs $12, and the retail selling price is $24.95. Cost of goods = $12 ÷ $24.95, or 48.1%.
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average cost
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average retail
Also, products are summarized into their respective categories, as shown at the bottom of the chart. The overall chart helps buyers place their purchase orders. For instance, if you forecast that you will need 600 units of a particular item but know you already have 100 on hand, you can promptly scale back your purchasing orders to 500 to meet your plan.
How does a buyer know how many sales of SKUs to forecast? This is where merchandise selling history, knowledge of vendors, and good common quantitative sense comes in. A merchandise forecaster typically looks at the previous selling history of an item or similar items in a product category. For instance, products of a similar price point will give the forecaster a clue as to expected sales. If this is the first catalog, or if there is no relevant history, then it comes down to the second bit of information — knowledge of the vendor, including such information as:
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vendor’s lead time for the item (time to manufacture if importing it yourself, or time to manufacture and dock it if buying from a distributor)
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shipping time required to deliver the item to your warehouse
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vendor reliability in fulfilling product. You don’t want to count on a certain quantity if a vendor can’t deliver goods.
Additionally, the forecaster needs to consider other historical and physical information about the product, including:
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the space devoted to the product
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the product’s position on the page and in the catalog (for instance, is it in a “hot spot” — an especially prominent position in the book?)
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the number of comparable items that have sold before. Many catalogs keep statistics on units sold per 1,000 catalogs mailed.
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whether the item a “high impulse” type of product
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the seasonality of the item
As we mentioned earlier, the product forecast and buy plan must be coordinated with the sales or marketing plan. As the chart in our example indicates, the marketing forecast plan for a mailing campaign is:
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to mail 1.25 million catalogs
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to have an order response rate of 2.49%
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to receive 31,125 orders
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to expect an average of 1.6 line items per order
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to receive 49,800 unit or item orders
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to expect a $59 average order value (AOV)
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to expect 2% cancellations
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to expect 7% returns
The buy plan and the marketing plan need to be in sync. If marketing is expecting to sell nearly 50,000 units based on its circulation plan, but product development plans its buys and merchandise forecast at 75,000 units, it’s likely that the catalog will have goods left over at the end of the book’s life cycle. Conversely, if marketing expects to sell 50,000 units, and product development plans its buys at 25,000 units, the company will likely run into a merchandise shortage.
Smart merchants will give the buy plan a final review after seeing the creative pencil layouts of the catalog. The product development people should be involved in the creative kickoff, or as it is sometimes called, the turnover meeting.
When the layouts are done, the merchants typically review the pages and the buy plan to give the plan a final tweaking before starting to issue purchase orders. In a perfect world, the product buyers or forecasters will look at the buy plan after the catalog photography, color separations, and printing are complete to make final adjustments.
The product category and price-point grid
The product category and price-point grid is another analytical tool merchandisers use in planning the product line for each catalog. To set up the grid, you would place the product categories on the left vertical column and the price points across the top in a horizontal row. (For a downloadable version of this chart, go to www.CatalogAgemag.com.)
Let’s say we are creating a grid for the gifts category. From left to right, the groups would be listed as follows: category, $0-$25 price point, $26-$50 price point, $51-$75 price point, and so on, followed by the total number of items in each product category, and the percentage represented by each product category. Vertically, the chart lists individual subcategories, such as decor, entertainment, and home comfort, followed by the total number of items in each price-point group, and the percentage represented by each price-point group. When compared to the ideal, or plans forecasted by merchandise square-inch analysis and other forecasting tactics, this analysis indicates “holes” in the planned product mix in either product category or price point.
Say you have four SKUs in the decor category, all priced for less than $100. If you have learned through squinch that you have great success with price points below $100, this would be on target. But if squinch has revealed that your greatest success is in price points of more than $100, and you wanted to increase your offering of items over $100, you will see that you have missed your goal.
You can also use the product category and price-point grid to estimate the catalog’s average order value, based on the average price point offered. To do this, you would multiply the weighted average price point by the average number of units per order (based on average number of units you have historically sold in that particular category).
This is also a useful tool for preplanning and reviewing a new catalog’s product mix. Since a new cataloger wouldn’t have any experience to fall back on, he could substitute widely available benchmark data as a measure of the average number of units per order to expect in that particular category.
For instance, furniture, which is costly and heavy to ship, typically has a lower unit per order than apparel. These units per order by category are fairly consistent from catalog to catalog. Again, this estimate of the average order size is a good check against your goals. Merchants often make product changes at this stage to bring their AOV closer to plan.
The product category and price-point grid also enable you to analyze competitive catalogs to determine their product and price coverage. If you have a direct competitor, you should gather intelligence about its catalog operations. You may want to prepare a product category and price-point analysis on each seasonal competitive catalog offering. Also identify the mix of new items vs. repeat items by season and what the key competitor offers in the hot spots of the catalog. When studying competitive price-point grids, you can ascertain the average unit selling point as well as the average order value, based on some multiple of units or line items per order (again based on benchmark data).
At the very least, you should look for the following factors in the product category and price-point grid:
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balance among product categories
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depth of merchandise
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emphasis on the stronger, more productive product categories and price points (based on the squinch done on previous catalogs)
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balance among price points to produce the AOV upon which you’ve based your financial model. For example, if your break-even analysis is based on a $100 AOV, but the final price point grid is heavily weighted in the lower price point segments — which would make it hard to obtain a high average order — this will be evident in the analysis.
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product categories and price points that differentiate your catalog from the competition and reinforce your brand identity
In addition to merchandise preplanning, you’ve also got to effectively manage your inventory (see “Safety Measures,” left). Every catalog business has its own inventory challenges. Apparel catalogs can expect high returns and cancellations (often 25%-30% of sales) and typically have very few items repeated from season to season. Business catalogs conversely often have a high number of repeated items from catalog to catalog, with only 20%-25% new products, and lower return rates. Since inventory is such a major asset of a catalog, it needs careful attention and management.
Next month: forecasting
The product forecast and buy plan and the product category and price-point grid help start the product selection, sourcing, and inventory planning process. These are essential steps in a successful and profitable merchandising competency.
Next month we’ll consider one more analytical tool — forecasting — to help you with rebuying.
Safety Measures
In an ideal world, a cataloger expects the vendor to back up initial orders with one or more secondary orders called hold-for-confirmation orders. The vendor holds these orders at his warehouse and at his expense. This helps the cataloger to reduce the risk of stocking too few items or too many. Let’s say you expect to sell 600 units of an item. You want the initial 300 pieces in the warehouse two weeks before your Sept. 1 mailing. A second hold-for-confirmation order is dated Oct. 1 for 150 units, and a third hold-for-confirmation order for 150 units is dated Nov. 1.
In the real world, though, not all vendors are totally cooperative when it comes to hold-for-confirmation safety plans — especially if they are accustomed to working with retailers, who don’t use this system.
Indeed, the willingness and ability of vendors to back up catalog orders varies greatly. With higher-ticket items or products that are cumbersome to ship, you may have the vendor drop-ship the product directly from its warehouse to the customer, rather than send it to you for reshipment to customers. Although it sounds simple, drop-shipping must be controlled and monitored to ensure timely shipment and good customer service. After all, you are turning over product fulfillment (including product presentation, packaging, and shipping) to a third party, but your company is still responsible for customer satisfaction.
Inventory management presents even more of a challenge when it comes to direct import programs (in which the cataloger is the importer of record). These give catalogers the opportunity to improve their margins, but with an additional inventory risk. Some catalogs plan import use for two or three catalogs. But if the item is a dog, you could get stuck with cases of product that you can’t sell.
Jack Schmid is president of J. Schmid & Associates, a Shawnee Mission, KS-based catalog consulting firm.
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