We use historical purchase data, as well as view history, traffic source, device type, etc.
Traffic source a lot of times is the most impactful. People coming from ads are often more in a browsing mindset, vs. people typing in the url directly have a higher purchase intent.
We don't have abandoned cart rate as a feature in our model, but actually might be something worth looking into adding.
If I understand it correctly, you estimate the probability of purchase given the user characteristics, behaviors, etc. If this probability is below a cutoff, you offer a discount. Did I get it right?
Is the cutoff itself a function of other variables in the data?
Yes, that's mostly right. We also vary the discount value, so it's less a binary discount/no discount and more a range. There is often a cutoff though. Merchants can input a hard cutoff e.g. if they want to ensure everyone gets a discount (great if they also have marketing assets for a sale), or if they want to avoid making their sites feel too 'sales-y'. Otherwise the cutoff is defined by conversion prediction, inventory levels, and a few other inputs.
There's actually a lot more we could do to make this cutoff more intelligent though - e.g. at Uber the cutoff was set to exhaust a certain promotional budget. Or we could target a specific ROI if we eventually have good enough predictions.
Thanks for the reply. Do you use Bayesian models for this? Btw, Pete Fader[1] has done so much work in customer valuation where estimating the probability of purchase is a crucial aspect. Maybe you already use them.
We faced a similar issue with GDPR at Uber. We will definitely need to be careful, but many merchants already have customer opt ins (e.g. the cookie consent pop up) for data processing that we ideally should be able to piggy-back off of.
Does a merchant has to give your tool access to their user data to generate personalized discounts?
Apart from user activity data, what data do you need for maximum effectiveness?
Yes. We're going through Shopify, so merchants agree to terms when they install the app.
There's user activity data, but also contextual data and shop data that we use. 'Contextual' data refers to things like device type, traffic source, time of day, day of week (there have been some interesting trends with corporate vs. non-corporate customers in this one).
Shop data includes things like product profit margin and product conversion rate. Obviously we can go deeper with our discounts on products that are very profitable, and it's typically more efficient to give a discount on products with lower conversion. Merchants also like boosting items that haven't been selling well.
> If we can accurately predict who is unlikely to convert
Do you use historical purchase data to make that assumption? Or someone that frequently abandons carts?
We use historical purchase data, as well as view history, traffic source, device type, etc.
Traffic source a lot of times is the most impactful. People coming from ads are often more in a browsing mindset, vs. people typing in the url directly have a higher purchase intent.
We don't have abandoned cart rate as a feature in our model, but actually might be something worth looking into adding.
If I understand it correctly, you estimate the probability of purchase given the user characteristics, behaviors, etc. If this probability is below a cutoff, you offer a discount. Did I get it right?
Is the cutoff itself a function of other variables in the data?
Yes, that's mostly right. We also vary the discount value, so it's less a binary discount/no discount and more a range. There is often a cutoff though. Merchants can input a hard cutoff e.g. if they want to ensure everyone gets a discount (great if they also have marketing assets for a sale), or if they want to avoid making their sites feel too 'sales-y'. Otherwise the cutoff is defined by conversion prediction, inventory levels, and a few other inputs.
There's actually a lot more we could do to make this cutoff more intelligent though - e.g. at Uber the cutoff was set to exhaust a certain promotional budget. Or we could target a specific ROI if we eventually have good enough predictions.
Thanks for the reply. Do you use Bayesian models for this? Btw, Pete Fader[1] has done so much work in customer valuation where estimating the probability of purchase is a crucial aspect. Maybe you already use them.
[1] https://marketing.wharton.upenn.edu/profile/faderp/#overview
We're using a neural network, not a bayesian model. And we haven't used Pete Fader's work, but thank you for the resource.
> We’ve had to make the model work without rich user data since the majority of website visitors aren’t logged in.
Be aware that this sentence largely disqualifies you from doing business in Europe.
We faced a similar issue with GDPR at Uber. We will definitely need to be careful, but many merchants already have customer opt ins (e.g. the cookie consent pop up) for data processing that we ideally should be able to piggy-back off of.
Does a merchant has to give your tool access to their user data to generate personalized discounts? Apart from user activity data, what data do you need for maximum effectiveness?
Yes. We're going through Shopify, so merchants agree to terms when they install the app.
There's user activity data, but also contextual data and shop data that we use. 'Contextual' data refers to things like device type, traffic source, time of day, day of week (there have been some interesting trends with corporate vs. non-corporate customers in this one).
Shop data includes things like product profit margin and product conversion rate. Obviously we can go deeper with our discounts on products that are very profitable, and it's typically more efficient to give a discount on products with lower conversion. Merchants also like boosting items that haven't been selling well.