Introduction

What are Predictions?

Predictions are a codeless AI-powered tool built on the top of Synerise analytics to predict any type of event or action in a customer’s journey.

What can you predict?

Regression and Classification predictions

You can use the Regression and Classification prediction type to predict any type of event or customer’s action that can be calculated in an expression and produce a numerical or 1/0 (true/false) value. Here are a few examples of what you can predict:

• Churn likelihood
• Conversion
• Open rate (OR)
• Click-through rate (CTR)
• Click-to-Open rate (CTOR)
• Who will visit
• Purchases from abandoned basket

Lookalikes predictions

You can use Lookalikes for discovering new segments of customers, extending reach, picking the most promising new comers with regard to their similarity to your best customers. The list contains a few examples of what you can discover:

• Segment of customers likely to convert to your yearly repeating marketing campaign
• The best offline, fresh customers
• Best customers in a specific location
• And many more

Propensity predictions

Propensity predictions let you evaluate how likely customers are to buy products with specific features, such as:

• Brand
• Category
• Color
• And many more

Best Fit predictions

Best Fit predictions let you determine the most suitable item or value of an item attribute for each customer.

Key characteristics

• Codeless - Neither ML expertise nor coding are necessary.
• Based on Synerise Analytics - You can can base your predictions on already created segmentations or expressions.
• Based on already stored events - There is no need of additional data ingestion
• Universality - Predictions are industry-independent - you can use it regardless of industry (telco, banking, retail, ecommerce, and so on) your business belongs to.
• Readiness for multi-level analytics - You can analyze predictions outcomes both on individual (customer) level and aggregated (segments) level.
• Easily adjustable to your needs
  • You can schedule recalculation of predictions.
  • Predictions module can take into account both standard events such as page.visit or transaction.charge and custom events.

Reasons to use predictions

There are many reasons to use Predictions, however, the following seems to be of the highest importance:

• Lowering overall communication costs, keeping within the budget (for example, sending a newsletter only to the customers with the highest propensity to perform a certain type of action)
• Directing communication precisely (avoiding sending communication to already lost clients)
• Unveiling new potential among your existing and new customer base
• Improving UX by personalizing communication (you can adjust the content of your messages to the preferences of the recipients)

Basic requirements

Regression and classification

Minimum requirements:

• transaction.charge events from the last 3 months (10.000 events per month),
• A segmentation that contains 50.000 profiles,
• 500 positive samples (for example, if you want to use this prediction type to predict the likelihood of churn, you will need the historical data of 500 customers who have left)

Recommended requirements:

• All minimum requirements
• Over 1.000.000 page.visits
• Over 10.000 positive samples (for example, if you want to use this prediction type to predict the likelihood of churn, you will need the historical data of 10.000 customers who have left)
• At least 200.000 customers in a segmentation
• Other custom events related to the phenomena to be predicted

Lookalikes

Minimum requirements:

• A segmentation that contains at least 200 “model” profiles (this will be your source segmentation),
• At least one event selected in the Lookalikes settings (part of the Enabling Lookalikes procedure)
• Create an item feed in Settings > AI Engine configuration
• product.buy events from at least one month (more than 10 000 events per month),
• The item IDs in the product.buy events must be consistent with the item IDs in the item feed

Recommended requirements:

• All minimum requirements
• A segmentation that contains at least 1.000 “model” profiles (this will be your source segmentation),
• All events selected in the Lookalikes settings (part of the Enabling Lookalikes procedure)

Propensity and Best Fit

Minimum requirements:

• product.buy and transaction.charge events from 2 months (more than 10.000 events per month),
• Over 100 purchases of items that meet condition of the filters you would like to apply (for example, if you want to narrow down the filters in the settings of the prediction to purses, you will need at least 100 product.buy events for purchasing a purse)
• Create an item feed in Synerise and enable the Propensity module for this feed
• the item IDs in the product.buy events must be consistent with the item IDs in the item feed

Recommended requirements:

• All minimum requirements
• More than 100.000 page.visit events
• Over 500 purchases of items that meet the conditions of the filters you would like to apply (for example, if you want to narrow down the filters in the settings of the prediction to purses, you will need at least 500 product.buy events for purchasing a purse)

The more interactions per customer, the better.

How can I get started?

1. Enable the Prediction module. Decide which of the predictions types you would like to enable:
   • Lookalikes
   • Regression and classification predictions
   • Propensity predictions
   • Best fit predictions
     Further steps depend on whether your workspace contains the minimum required data.
2. Once the set-up is ready, it usually takes a few hours to initialize predictions on your workspace. Once the initialization is done, you can make your first prediction.