1. Personalized Offers

2. What is a Personalized Offer?
Personalized Offers are a means to leverage a user’s real-time and historical data (user’s preferences, shopping history, viewing history, clicks, etc.) to deliver meaningful and appealing discounts, products and services to the individual. They enable a more efficient engagement between business owners and customers, which greatly enhances user experience and leads to a increase in repeat visitors and an opportunity to cross-sell or up-sell with little to no human labor.
Common techniques for offers often miss their mark and can be completely ineffective due to their lack of focus and appeal to the individual user. The most common ways of displaying offers are:
Arbitrarily
Targeted approach
Marketing plan based on historical data
Marketing strategies demonstrate improved conversion and effectiveness when offers are personalized and in real time.

3. Why Use Personalized Offers?

4. Personalized Offers vs Other Offers
Targeted Offers
Banner Ads
Site
Focused
Demographic
or Firmographic
User
Profile
Real-time
Historical
Analytics
Intelligence
Dynamic
Placement

5. 1700 0.1% a typical person sees banner ads per month of them1
but clicks on just
0.1%
*your chances are better at finding a 4-leaf clover (1 in 10,000)

6. Personalized Offers | Goals
Increase
Revenue
Improve
Experience
Ensure
Quality
Build
Profiles
Increase
Traffic
Shoppers are more likely to respond to an offer that is based on their current interests and needs.
Users feel focused on and that they have a personal connection.to the vendor or brand.
Using real-time and historical data helps to avoid misplaced or irrelevant offers and keeps customers.
Customer preferences are aggregated and analyzed regularly, making them more focused
Customers that feel catered to are more likely to become repeat visitors

7. Percentage of shoppers surveyed that believe personalized shopping experiences provide a valuable service2

8. Building a Personalized Offer

9. Cortana Intelligence Suite unlocks the power of data that is already available

10. Personalized Offers | Data Integration
Actions
Clicks, abandoned pages, views, cart information and purchases help to build an accurate model and make better offers. Depending on the goal of the project, the definition of ‘action’ varies.
Demographic and Firmographic Information
User specific information such as age, gender, etc., which assists prediction, as customers with similar background may have similar preferences
Behaviors
It refers to customers’ past and present visits to the business and contains detailed customers’ information from which the predictive models can learn customers’ preferences.
Product/Offer Information
This information helps to characterize the preferences of customers & determine how well other potential products and offers fit customers’ needs
General information: Other useful data can include: weather, holiday seasons, time of year, etc.

11. Personalized Offers | Architecture – Hot Path
Aggregates user-data into time intervals and sends it to the Table Store.
Azure Stream Analytics
Analyzes raw data and builds profiles based on this data.
HD Insight
The hot path stores current user data and also aggregates it into multiple time periods. Those results are then combined with machine learning scores to create real- time personalized offers.
Stores raw data.
Azure SQL Database
Stores the aggregated data. The site sends a query for the user's data. This data is then passed through the site to Azure Machine Learning.
Azure Table Store
This highly-scalable service ingests and distributes user-action data.
Azure Event Hub
Coordinates data transfer and triggers components to run according to specifications.
Azure Data Factory C ON T
OSO M A R T
Compares aggregated data from the Table Store and the model to make real-time personalized offers.
Azure Machine Learning
Stores analyzed data and profiles. Data is sent to PowerBI to be used for visualization and reporting. Data is also sent to Azure Machine Learning for model retraining.
Azure Blob Storage
Creates reports and visualizations based on the real-time data.
Power BI

12. Personalized Offers | Architecture – Cold Path
Analyzes raw data and builds profiles based on this data.
HD Insight
Sends raw data to a database for storage.
Azure Stream Analytics
Stores raw data.
Azure SQL Database
The cold path analyzes the users history and profile and provides its results to facilitate real-time personalized offers.
Stores the aggregated data. The site sends a query for the user's data. This data is then passed through the site to Azure Machine Learning.
Azure Table Store
This highly-scalable service ingests and distributes user-action data.
Azure Event Hub
Coordinates data transfer and triggers components to run according to specifications.
Azure Data Factory C ON T
OSO M A R T
Retrains the model based on data received from HDInsight to make personalized offers.
Azure Machine Learning
Stores analyzed data and profiles. Data is sent to PowerBI to be used for visualization and reporting. Data is also sent to Azure Machine Learning for model retraining.
Azure Blob Storage
Creates reports and visualizations based on the historical data.
Power BI

13. Personalized Offers | Architecture End-to-End
Aggregates user-data into time intervals and sends it to the Table Store. Raw data is sent for storage.
Azure Stream Analytics
Analyzes raw data and builds profiles based on this data.
HD Insight
Stores raw data.
Azure SQL Database
Stores the aggregated data. The site sends a query for the user's data. This data is then passed through the site to Azure Machine Learning.
Azure Table Store
This highly-scalable service ingests and distributes user-action data.
Azure Event Hub
Coordinates data transfer and triggers components to run according to specifications.
Azure Data Factory
Creates and regularly retrains a model of the user to based on real-time and historical data to make personalized offers in real-time.
Azure Machine Learning
Stores analyzed data and profiles. Data is sent to PowerBI to be used for visualization and reporting. Data is also sent to Azure Machine Learning for model retraining.
Azure Blob Storage
Creates reports and visualizations based on the real-time and historical data.
Power BI

14. of people surveyed enjoy seeing product recommendations on a retailer's site while shopping3
67%

15. O FF E R
Appendix

16. Cortana Intelligence Suite | Overview
Information Management
Event Hubs
Data Catalog
Data Factory
Intelligence
Machine Learning and Analytics
HDInsight
(Hadoop and Spark)
Stream Analytics
Data Lake Analytics
Machine Learning
Big Data Stores
SQL Data
Warehouse
Data Lake Store
Intelligence
Dashboards & Visualizations
Cortana
Bot Framework
Cognitive Services
Power BI
Data
Real-time Data
Historical Data
Action
Dashboards and Visualization
Personalized Offers
Cortana Intelligence Suite delivers an end-to-end platform with integrated and comprehensive set of tools and services to help you build intelligent applications that let you easily take advantage of Advanced Intelligence.
First Cortana Intelligence Suite provides services to bring data in, so that you can analyze it.  It provides information management capabilities like Azure Data Factory so that you can pull data from any source (relational DB like SQL or non-relational ones like your Hadoop cluster) in an automated and scheduled way, while performing the necessary data transforms (like setting certain data columns as dates vs. currency etc.).  Think ETL (Extract, Transform, Load) in the cloud. Event hub does the same for IoT type ingestion of data that streams in from lots of end points.
The data brought in then can be persisted in flexible big data storage services like Data Lake and Azure SQL DW.
You can then use a wide range of analytics services from Azure ML to Azure HDInsight to Azure Stream Analytics to analyze the data that are stored in the big data storage.  This means you can create analytics services and models specific to your business need (say real time demand forecasting).
The resultant analytics services and models created by taking these steps can then be surfaced as interactive dashboards and visualizations via Power BI
These same analytics services and models created can also be integrated into various different UI (web apps or mobile apps or rich client apps) as well as via integrations with Cortana, so end users can naturally interact with them via speech etc., and so that end users can get proactively be notified by Cortana if the analytics model finds a new anomaly (unusual growth in certain product purchases- in the case of real time demand forecasting example given above) or whatever deserves the attention of the business users.

17. increase in adoption of marketing automation in 2015
Personalized Offers | Market Dynamics Overview
85%
85%
78%
of customers will manage their relationships without human efforts in 2020
of the B2B marketers have used marketing automation software for their businesses
of the successful marketers believe that marketing automation is useful for improving their revenue
75%
67%
50%
of the companies using marketing automation can see ROI within 12 months
of the top class companies are likely to use marketing automation software.
increase in adoption of marketing automation in 2015

18. Personalized Offers | Key Aspects
Customer analytics
Customer analytics to capture and effectively leverage customer preference
Proactive Approach
Using a proactive approach by providing solutions to prospects and leads before they ask, rather than passively waiting for interest
Customer Relations
Better customer relations through personalized communication, offers, products, and solutions
Empowering sales
Empowering sales with a better lead list and more accurate data

19. Personalized Offers | References
1http://
2http://
3https://