Customer research on Amazon contributes to the offline path to purchase
By: German Zenetti, Data Scientist
In a recent opt-in survey of 8,000 randomly selected customers in-market for a laptop or smartphone, we found that customer research in Amazon’s store signaled and accurately predicted purchase intent, regardless of whether the final purchase was made on Amazon or elsewhere.
In addition to the "Add to Cart" button, Amazon also offers a large collection of product information, reviews, prices, and other, similar information. Customers often uses these resources to help inform their purchase decisions, regardless of where they may make a purchase. Advertisers and sellers want to understand Amazon’s role as a source of product information in the customer purchase journey, beyond just driving sales in Amazon's store. To help answer this question, we developed a ratio to compare on-Amazon and off-Amazon purchases in a given category.
Top-performing advertisers have on average 40% higher year-on-year sales growth, 50% higher year-on-year growth among customers viewing their product pages on Amazon, and a 30% higher return on their ad spend (ROAS). These advertisers represented 6% of the 7,000 advertisers included in the study.
Our research emphasizes the opportunity for advertisers to help inform these highly active customers when they are researching on Amazon prior to buying the product on Amazon or elsewhere. Advertisers should not limit their online return on investment (ROI) solely to online conversions. If an advertiser relies solely on on-Amazon return on ad spend (ROAS) as a key performance indicator (KPI) for determining budget allocation, the impact of their marketing dollars on customers’ offline purchase path may be misrepresented. Advertisers should consider marketing their products on Amazon as an opportunity to affect not only on-Amazon purchases but also off-Amazon purchases.
Survey respondent selection and validation: For our analysis, we randomly selected 8,000 laptop or smartphone shoppers and surveyed those who had not purchased in the category in Amazon’s store (between 60 days prior to the survey date and 90 days after). To validate the accuracy of the survey responses, where possible, we compared the survey responses to shopping actions on Amazon. Overall, these tests help us to increase our confidence in the survey instrument and the survey responses.
Modeling approach and calibration: Our model attempts to predict whether the customer purchased a product in the focal category elsewhere (=1) as opposed to their not having bought a product in the category (=0) within a 60-day timeframe. In our predictive model, we excluded customers who made a purchase on Amazon do not require a predictive model. To predict purchases elsewhere, we train a boosted classification tree algorithm to the in-sample training data set. The general intuition is that with boosted classification, the overall model will be able to explain different aspects of variations of the response variable. Due to this reason, the model overall may better predict the response variable compared to, e.g., a model without additional recursive model layers or compared to a bagging approach such as a Random Forest algorithm. Click here to learn more about decision tree models, and here to learn more about boosting in machine learning models.