How to Analyze Your Holiday Response

Now that the holiday season is over, it’s time to begin your postmailing analysis. If holiday is your busiest season, it’s worth spending extra effort reviewing the successes and shortcomings of your biggest mailing. It will naturally lead to better, more comprehensive strategic planning for your entire company: marketing, merchandising, and operations.

Pulling it all together
The first step in analyzing data is assembling them into a usable format. This may seem to be a fairly simple starting point, but it is fundamental to your success and too often overlooked. If you spend time looking up each keycode list by list on a mainframe computer, you are not only taking too much time to run your basic analysis but also losing the big picture of how your mailings performed.

Pull your response data as a download, and import them into one spreadsheet. Microsoft Excel or a similar spreadsheet program is the best tool for creating your analysis reports, typically called contribution sheets.

When you assemble your response data, include the English word description of each file segment. Having an actual list description makes your analysis easier. In addition to the keycode and description, you will want offer, quantity mailed, promotion cost, number of orders, gross sales, and cost of goods.

Based on this raw data, you will need to calculate a few metrics:

* Percent response–divide your number of orders by your mail quantity per segment.

* Average order value–divide your gross sales by your number of orders per segment.

* Contribution per segment–start with your gross sales; subtract your cost of goods, promo costs, and any offer costs.

* Contribution per order–divide the contribution per segment by its number of orders.

* Dollars per book–divide your gross sales by the number of pieces mailed for each segment.

Managing untracked orders
If you’re like most other multichannel merchants, you will have a significant percentage– perhaps as high as 50%–of untracked phone and Internet orders. Make an effort to run a matchback, an order reallocation or some combination of the two to track or credit these orders.

You undoubtedly have some Internet orders that were placed through organic searches. More and more catalogers, however, are discovering that the majority of their Internet orders are being driven by their print catalogs: When they compare their untracked Internet order spikes with their catalog drops, the spikes parallel their tracked orders for all channels. If this is the case for your business, your print catalog is likely driving many of those orders, even if they are coming through the Web.

If you don’t make an effort to determine the source of your untracked orders, you will likely think that several lists and segments have failed when, in fact, they were successful. If you are not running a matchback on your orders, this is an excellent time to run a test.

Now that you have all of your data pulled together, here’s how to start analyzing it.

Where to start
For your house file, first study the file segments that produced a positive contribution. When planning your next holiday season, plan to mail those segments. For the holiday 2006 season, assuming your page count and general merchandise mix stay the same, you can use your response rates and average order values (AOVs) as a basis for your projections.

During this process, identify the house file segments that did not make money or did not perform to an acceptable ROI. We’ll get back to them later.

Next move to your prospect list analysis. Once again, separate them into the clear winners, meaning they performed at a profit or an acceptable acquisition cost, and into the lists that did not perform as needed.

For the successful lists, what is their rollout potential? These list continuations may significantly fill your budget needs for your 2006 holiday mailings. If you see potential that you did not budget for in your plans, now is the time to see if you can either find additional budget or shuffle resources to take advantage of those opportunities.

Offer test analysis
Now compare your offer tests. For offer tests, you will want to determine two metrics. First, what kind of lift did you receive from the test? It’s helpful to express the response in a percentage of lift: positive or negative. For example, if your control segment received 300 responses and your offer test received 375 responses on the same segment size, you received a 25% lift in response.

Second, you want to determine the percentage difference in your AOV for your offer test segments and your control segments. It’s not uncommon for an offer to increase or decrease the AOV. Knowing this difference in percentage form is helpful in planning step-by-step rollouts for your weaker-performing segments.

With your offer response data in hand, go back to the analysis of your unprofitable house and prospect segments. Based on your offer tests, which of these unprofitable segments would perform at an acceptable rate if they received the percentage lift that your offer tests gave? Apply both the lift (or decline) in response and AOV. Also, be sure to include the cost of the offer in your contribution formula.

Using these two metrics–the percent response and the AOV–is important. For example, you may get a significant increase in response with a corresponding decline in AOV. This relationship is not uncommon for special, introductory-priced offers. When you roll out your offer, if you apply that segment’s historic AOV to your projections, you will likely be disappointed in the results.

Take the time to run these what-if scenarios on the unprofitable segments. Based on your projections, which of them could be in the black? Pull those segments and offers into the preliminary mail plan for next year. Of course, it is often not this cut-and-dry. You will likely see some marginal segments that could perform to acceptable results. For now, plug those segments in as test quantities.

Order-curve basics
Remailing to your top house file segments is one of the most effective, yet underused, techniques in catalog mailing. (For this article, a remail is defined as mailing the same catalog, usually with a cover change, more than once to the same house file segment during one mailing season.) Your order curve will help you determine holes in your mailing schedule to plug with potential remails.

Your order curve is defined as the number of orders with their dollar value for regular increments of time for the life of your mailing. You can run this by channel–phone, Web, mail–or as a roll-up of all of the channels combined.

Many catalogers have flash reports where they run a daily tally of their number of orders and gross sales. When you place all those flash reports sequentially in a spreadsheet, including the date for each set of numbers, you have your order curve. (Some companies run their reports on a weekly basis, but the same principle applies.)

On your order curve, highlight your mail drops, including the mail quantity separated into house file and prospects. When you compare your mail drops with your order curve, you will likely see significant spikes coincide with your mailings. If you can isolate these orders by channel, the channel curves will often follow the same pattern with some smaller spikes for e-mail blasts or outbound phone efforts.

Studying your order curve let’s you see if your untracked orders are driven by your print catalog. It also gives you a snapshot of when a mailing has produced the majority of its sales. For example, if a mailing has produced 80% of its sales after five weeks, but you still have three weeks before your Christmas cut-off, you could likely fit another profitable mailing into next year’s plan.

If you’re new to remailing your catalog, here’s a conservative rule of thumb for planning a remail. For a top house list segment–say 0-3 months, 2x+ buyers, $100-plus order–if you could receive half of the response and still be profitable, that segment is ripe to receive a remail test. After you run a few tests, you will get more sophisticated in knowing the percentage differences in response and AOV for more-accurate projections.

Once you determine your potential drop dates for next holiday season, plug them into a spreadsheet organized as a simple calendar. You now have an outline of your holiday contact strategy to start filling in quantities, offers, house file segments, continuations, and list tests.

When you follow this process to analyze your holiday season, you perform two vital functions at the same time: You analyze your previous holiday mailing, and you plan your next holiday mailing in one step. And you have a plan that you can share with merchandising and operations for comprehensive strategic planning for your entire company.

George Hague is senior marketing strategist for Mission, KS-based catalog consultancy J. Schmid & Assoc.

Partner Content

Hincapie Sportswear Finds Omnichannel Success in the Cloud - Netsuite
For more and more companies, a cloud-based unified data solution is the way to make this happen. Custom cycling apparel maker Hincapie Sportswear has leveraged this capability to gain greater visibility into revenue streams, turning opportunities into sales more quickly while gaining overall operating efficiency. Download this ecommerce special report from Multichannel Merchant to more.
The Gift of Wow: Preparing your store for the holiday season - Netsuite
Being prepared for the holiday rush used to mean stocking shelves and making sure your associates were ready for the long hours. But the digital revolution has changed everything, most importantly, customer expectations. Retailers with a physical store presence should be asking themselves—what am I doing to wow the customer?
3 Critical Components to Achieving the Perfect Order - NetSuite
Explore the 3 critical components to delivering the perfect order.
Streamlining Unified Commerce Complexity - NetSuite
Explore how consolidating multiple systems through a cloud-based commerce platform provides a seamless experience for both you, and your customer.