Yesterday we covered how to take seasonality into account with your predictions, but it relied heavily on having a few years worth of data. What if you just launched a new product or service and don’t have nearly that much history to fall back on?
You can still account for seasonality, but with significantly less accuracy. The key is to find market data that gives you some insight into the magnitude of customer behavior changes that you can use to replace the deviation factor we were able to calculate with historic data. Here are some examples of market data sources you might use:
- Google Trends. Google Trends allows you to see how the use of search terms varies over time historically. If you sell shirts online, you can see how customers searches for “shirts” varies by time of year as a proxy for how interested they are in buying. This works very well if most of your traffic comes through SEO or SEM.
- Government Data. Many governments publish monthly data on the consumption and price of various goods. The US Bureau of Labor and Statistics publishes the Consumer Price Index which includes data on consumption and pricing of many different kinds of goods. It can be easy to see seasonal changes in good consumption depending on your category.
- Customer Fiscal Cycles. If you sell to businesses, you can ask your customers about their fiscal planning cycles which should tell you a lot about their buying patterns. Many organizations will buy at the beginning of the fiscal year (if they have allocated budget) or at the end of the year (if they have budget remaining) so you can build a calendar of customer behavior over the course of the year.
Your goal should be to determine how much your metrics will deviate from the common trend during specific days / weeks / months during the year. While it will not be as accurate as the predictions from actual data, it should allow you to prepare for changes. The good news is that after the first year you will have at least one year of historical data to use!
Tomorrow we’ll cover what you should do if you can’t trust your data completely.