An automated program triggered when the price of a product is reduced, which sends an email to all customers who have previously viewed it, but did not purchase.
This demand generation Tactic is enhanced with product recommendations in the same product category, with incentives for leads, defecting and active customers.
It supports the Revenue objective and contributes to the following strategies:
- Convert leads to first-time buyers
- Convert first-time buyers to active customers
- Retain active customers
- Win back defecting customers
- Win back inactive customers
The prerequisites for the Price drop Tactic to work are the following:
- Web Extend
Product view events are sent via Web Extend, and the Product catalog is loaded into Predict. Uploading of product data should happen once per day and the load should be completed before the calculation is scheduled.
Manually triggered or Product Data updates triggered by Integrations like (Shopify, Magento) will not have any effect, those product data files won't propagate.
- Calculation happens at 11:00 UTC.
- Any Data Platform to Relational Data load after the calculation will replace the previous state, so only fresh data will be stored in the Relational Data database.
- We suggest scheduling the Automation Center campaigns outside of the loading hours. The approximate loading hours for Price drop Tactic is 11:00 to 14:00 UTC.
- Currently, we use the last 2 versions of the product catalog to feed our calculations. This means the calculation works best if a customer doesn't load their product catalog more than once a day.
- E.g: the product catalog load is scheduled to run every even hour. In this case, the price drop calculation will compare the product catalogs uploaded at 8:00 and 10:00 and not the changes that happened from one day to the next.
- Product views which are followed by purchase are not filtered out from the calculation.
- Price drop calculation requires at least 2 product catalog uploads in the last 7 days. It uses the price field in the Product catalog to calculate the price drop events.
Localized (language and currency) values cannot be used for the calculation (e.g. price_usd, price_eur, etc.).
It always compares to the previously uploaded Product catalog to the current one. After all products with a price drop are listed, the algorithm checks who has an affinity to the products. A consumer considered having an affinity to a product if they checked the product at least once in the past between 12 hours and 90 days ago. We store price drop events only if their value is at least 10%, therefore the calculation filters out all products which have less than 10% price drop or if they are not available. Product views followed by purchase are not filtered out, therefore all contacts who made a purchase in the last 14 days are excluded.
Automation Center program
The program looks like this.
The program starts with a Recurring filter node that runs every day and uses a Predict segment that returns all customers who have purchased within the last three days.
The first segment filters on contacts who visited a product in the last 90 days which price dropped now. The Predict segment is used to filter out those contacts who already bought something from sending a reminder email about the price drop.
The program then waits three days after the initial email before filtering again to see if a purchase has been made. If TRUE, the contact exits the program. If FALSE, a reminder email is sent.
- The lowest possible setting of the segment is 10%. Even if the setting is lowered, only products with at least 10% will be considered.
- If a higher number is entered into the segment template, it sets a range for the produst to be considered. For example, if the setting is All contacts who have at least one product whose price has dropped by 30%, the Tactic will consider all products that have a price drop between 10% and 30%.