Using Predictive Modeling In Seasonal Search Campaigns
Optimal bidding for performance is a complex task for search marketers. When placing bids every day, you must be mindful of the volatility in the marketplace due to changing search traffic, competition, matching algorithms that vary across search engines and the changing needs of your business. For many of you, particularly if you work in […]
Optimal bidding for performance is a complex task for search marketers. When placing bids every day, you must be mindful of the volatility in the marketplace due to changing search traffic, competition, matching algorithms that vary across search engines and the changing needs of your business. For many of you, particularly if you work in the travel or retail sectors, seasonality—the cyclical patterns in the demand for various product offerings—adds an additional layer of difficulty that can make search marketing campaign management seem daunting.
It would be extremely time and cost prohibitive to manually sift through historical data on all keywords used in a large campaign to identify statistically significant seasonal patterns. But this information can be critical to ensuring the success of a campaign. In the retail sector, for example, summer clothing doesn’t sell in winter and vice-versa. Hence, to ensure optimal performance, seasonal keywords must be continuously identified and bid upon to appropriate positions.
The solution to this problem is to build predictive keyword models that correctly estimate expected revenue and spend patterns, while taking seasonality into consideration. Ideally, you should do this within an automated framework that, in addition to analyzing search traffic patterns, also helps to determine seasonal keywords that have a high probability of improving the overall portfolio ROI, and prioritizes the learning of these keywords while keeping the advertising budget in check. This automated identification of revenue-generating seasonal keywords can be particularly valuable to large and medium sized advertisers, as they must often create predictive models on hundreds of thousands of keywords a day.
There are three steps that cover the basics of predictive modeling to enhance campaign success.
1. If you have a seasonal business, try to combine seasonal keywords in a campaign. This will make monitoring easy. For instance, an apparel retailer can have a ‘winter sweaters’ campaign and a ‘summer shorts’ campaign.
2. Ensure keywords reflect the seasonal nature of the product to best optimize for results.
3. If you cannot combine seasonal keywords into a campaign, then make sure they can be tracked. One way to do this is to label the keywords in your database.
Once you have a seasonal campaign established, it’s important to analyze the data and then take action based on your discoveries.
Look at historical data for the season you are interested in and find keywords that have experienced a sharp spike in ROI. Then account for keyword position. In general, the lower a keyword position, the higher its ROI. So if you find an uptick in ROI at a lower position, the spike is not necessarily due to seasonality. The opposite – i.e. keywords with a spike in ROI despite an increase in position – present a strong indicator of seasonality. Remember, seasonality refers to both upward and downward spikes.
If the historical data points to a sharp increase in a keywords’ ROI, promote the keyword by a bid increment. A 20 percent bid increment/decrement is a good starting point. You’ll want to track the performance every two-to-three days and increment/decrement the bids as needed.
In a large campaign that contains many seasonal keywords, manual intervention is likely impossible. In this case, look to an automation tool with logic/optimization built in.
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