Consumers don’t always make a purchase within the same day or even the same week that they click on an ad, but that doesn’t mean the ad didn’t drive a sale. Many online marketers see significant latency between a paid click on an online ad and a purchase.
Moreover, your most valuable customers are the ones who make a purchase and keep coming back, compounding the challenge of measuring latent conversions and optimizing bids.
In general, as time passes, marketers often see more and more value attributed back to an initial PPC click, but revising bid calculations after the fact results in chasing a trend rather than predicting one.
How you track latency and follow-on conversions can have a big impact on your bidding decisions and revenue-on-ad-spend (ROAS) calculations. Imagine pausing all your bids, yet conversions continue to come in attributed to Paid Search – it may seem like free traffic, but it’s not.
As an example, analytics packages such as Omniture SiteCatalyst or Google Analytics use “Date of Conversion” attribution, meaning that an order is attributed to the day when that order occurred, as opposed to “Date of Click” attribution.
Conversely, with “Date of Click” attribution, as seen with the Google AdWords Conversion Counter, conversions and revenue will accumulate onto clicks from days or even weeks earlier. You want to be a responsive marketer, but you are working with incomplete data.
Consider the following sequence of events for a single visitor and how they will appear to the marketer:
This makes several challenges apparent:
- For many marketers, the first order is just the beginning of the story. You want to optimize bids, creative, and landing pages based on the expected lifetime value of a new customer.
- Waiting for follow on orders to be recorded over time means that bid optimization would be constantly, but slowly, revising bids upwards – however, in many cases when the keyword in question is no longer relevant to a seasonal or promotional campaign.
- It is unclear how much of your customer loyalty should be credited to paid search. This will be a hot topic with your merchandising and service teams, who have created a compelling customer experience which generates its own loyalty. However, the important marketing question is whether search visitors would have found your site on their own? If not, then the visit has incremental value.
Smart Handling For Latency
Below is some sample conversion latency data from an actual online retailer. The green line indicates first-time orders as a percent of their eventual (one-year) total, charted by the length of time after a paid click; 87% of first-time orders occur on the same day as the click. The blue line is subsequent orders as a percent of eventual total subsequent orders.
As you can see from the chart, while 95% of “first orders” occur within a week of the paid click, only 83% of subsequent orders occur during this time period. It’s this latency with follow on orders that trips up even the most diligent marketer.
Your optimization and bidding should take into account the latent revenues driven by a paid click, but also needs to be responsive to market conditions. Some advertisers deal with latency by ignoring recent performance data and using a longer look-back window to calculate bids, but this ultimately prevents you from being able to react quickly to market changes.
Alternatively, most advertisers simply increase the value attributed to a click when latent orders arrive days or weeks after the fact. While this may be accurate, it still results in a missed opportunity to fully incorporate purchase trends into bid decisions.
Instead of waiting for follow-on conversions to happen, marketers can estimate expected revenue from subsequent sales at the time of the first sale, ensuring maximum responsiveness to changes in conversion rates and buying patterns.
By using historical data to estimate the expected value of a consumer from their first conversion, you can make more timely bidding decisions at the time of the initial sale. The concept is simple – if you knew that a consumer typically places 2.2 orders on average following their initial purchase, wouldn’t you bid more for that initial purchase rather than waiting for the additional orders to arrive?
Analyzing historical conversion data gives you enough information about consumers to estimate the average lifetime value given particular characteristics. (In the example chart above, 50% of the revenue was from follow-on conversions.) The most accurate and responsive attribution and bidding systems should use this estimated lifetime value to calculate bids, making appropriate bid adjustments much closer to the date of click instead of the date of latent conversions.
In a world where seasonal and promotional cycles can be as short as weeks, speed can mean the difference between winning and losing for PPC marketers. Estimating lifetime value allows you to react more quickly to changes in user patterns to maximize total revenue.
Using this type of modeling, you can use a lifetime value estimate to make decisions based on conversion data closer to the date of the paid click. Being able to predict the impact of early consumer indicators and factor it into bidding decisions will allowing you to capitalize on trends before the competition does, increasing revenues and ultimately market share.
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