Ecommerce Spring Forecasting

Are you feeling that “spring is coming” bug? Well, rightfully so, because right now is the time to plan for your spring marketing and tidy up a bit from the winter sales.

Most ecommerce stores have some season trending. This is very unique to both the store and the product line. So a store selling green widgets can be expected to trend sales in a similar manner to another shop selling green widgets… But not exactly, as they are many additional metrics that influence one’s sales.

So you should be planning for your spring and early summer products, marketing and trends now… So that you are prepared for this period in your business. Even especially if traditionally spring is a low volume period for sales.

Here is a checklist of sorts to help you get started for planning any marketing period, including this spring.

Trending: Unless you site is brand new, you should have some sales and traffic data that you can use to trend or predict the data for the upcoming period. I tend to concentrate on sales and traffic. I use both as they can be very unrelated for some websites. There are clearly some periods where even if the traffic is there, conversions are down… So to properly trend our potential for this upcoming period we should use both. You may even have other metrics such as, bounce, average order or similar that are specifically a target for your store.

Gathering the data is the easiest part, as you should be using a proper analytical stat program to record your data. We will use Google Anayltics for our example, as it is very popular.

When gathering your data, we are looking for specific trends within the matching period from previous years to predict, affect and produce a proper marketing plan for the upcoming period. So login to Google Analytics and lets get to work.

So we will pull data for all of March, April, May and June to cover our bases and provide some overlap. You will want to pull at least on year, more if you have them. I would not be concerned with using more than 3 years as things in your business and on the web change very quickly and it’s not likely to be very relevant any longer.

The example store I am using had a 2009 average daily visits of 279 uniques a day. There conversion rate for the year was 3.87%. You can see by the graph that holiday traffic is a crucial part of this store’s success and that spring is rather soft by comparison. You can also see that our spring period it right at or just a bit above average for them.

2009 Traffic
2009 Unique Visits

We can clearly see from this data, that traffic could be improved for this period…. Especially because they have a genuine promotable product line for spring sales. Now let’s have a look at conversions in relationship with this traffic… Do they convert well in this period?

2009 Conversions

We can see that last year, while traffic was average, they converted pretty well the end part of spring. looks very much like March should be our focus area.

Next you will determine your trend. You can use data from previous tears to do this… But if you lack that data no worries, this old restaurant manager has the equation to get you close.

Obviously, any data you do have is clearly relevant… But let’s say you have little or none. To determine your current rate of growth in both of these metrics we will poll the last 4 months. This is a weighted process with the greatest weight on the most recent data.

This applies to any metric. Gather the data for these metrics for Nov 2009, Dec 2009, Jan 2010 and Feb 2010.

We will start with March’s data from last year 8,043 unique visits and a conversion rate of 3.13%.

  • Nov 2009 8,789 & 4.85%
  • Dec 2009 14634 & 4.5%
  • Jan 2010 7,604 & 3.67%
  • Feb 2010 6,395 & 3.52%

For this purpose, with holiday data so much higher we will exclude Nov & Dec, unless we have 2008 numbers… Which we do. Nov 2008 7,419 & 3.18%, Dec 2008 8,861 & 4.00%, Jan 2009 6,146 & 2.99% and Feb 2009 5,742 & 3.22%.

Here is the math:

Period Unique Visits Conv %
Nov. 2008 7419 3.18%
Dec. 2008 8861 4.00%
Jan. 2009 6146 2.99%
Feb. 2009 5742 3.22%
Mar. 2009 8043 3.13%
Nov. 2009 8789 4.85%
Dec. 2009 14634 4.50%
Jan. 2010 7604 3.67%
Feb. 2010 6395 3.52%
YOY Growth
Nov. 1370 1.67%
Dec. 5773 0.50%
Jan. 1458 0.68%
Feb. 653 0.30%
Trend Weight
Nov. 18.49% 1.67% 12.50%
Dec. 65.15% 0.50% 12.50%
Jan. 23.72% 0.68% 25.00%
Feb. 11.37% 0.30% 50.00%
Current Trend 22.07% 0.53% Up
Last Year March 8043 3.13%
This Year March 9818 3.66%
Daily Visits 318

The math is easier than it looks….

Step 1: YOY growth, which is simply current year minus previous year.

Step 2: Trend. Like I said we will weight this for the most current monthly data. So 100% being the whole, we will use 12.5% from Nov & Dec, 25% from Jan and the remaining 50% from most current Feb. Something like this:

  • 1 part : Nov. 18.49% plus Dec. 65.15% = 83.64 divided by 2 = 41.82%
  • 1 part: Jan. = 23.72%
  • 1 part: Feb. =11.37%
  • 1 part: Feb. =11.37%

= The whole (88.28) divided by 4 = (22.07%) Current Trend

Last year March (8043) apply trend 22.07% = (1775 growth) This year March forecast (9818) unique visits… Into 318 average daily visits.

Check our math:

Last year average daily visits March = 260

This year forecast = 318

% of predicted growth = 22.3%

**Note that rounding changes these just a hair, but not to worry this should be pretty reliable data.

So this “math” can be applied to the entire period as a whole, or each month individually computed. The point here is to have an idea of what to expect, AND and basis to measure the effectiveness of your marketing this spring. For example if you did nothing last spring, and you know (above) what to expect if you do nothing this spring… Then you have a pretty good benchmark to measure the effectiveness of a marketing campaign this spring.