Why Personalize?
Personalization refers to customizing the user experience to the unique needs of each user including selecting appropriate content to recommend to a user (personalized recommendations), showing content that is related to a particular item and user (relevant items), reordering results to provide personalized results (personalized search), and personalized notifications/promotions (only sending relevant promotions/notification to a specific user).
Personalization and recommendations are creating significant impact in terms of the customer engagement and improving the overall lifetime value of your customers. For online shoppers, they are more likely to shop on a website that offers and makes personalized recommendations for them. Amazon has had success by offering a customer experience that generates a recommendation engine which looks at people’s purchasing habits and makes the appropriate pairing decisions.
So why not just do it?
At first glance, matching users to items may sound like a simple problem to solve. However, the task of developing an efficient recommender system is extremely challenging and complex.
Building, optimizing and deploying real-time personalization today requires specialized expertise in analytics, applied machine learning, software engineering, and systems operations. Few organizations have the knowledge, skills, budget and experience to overcome these challenges, and they often end up either abandoning the idea of using recommendation or build under-performing models.
Amazon Personalize to the Rescue!!!
For over 20 years, Amazon has built recommender systems at scale, integrating personalized recommendations across the buying experience – from product discovery to checkout. Amazon has made incredible personalization advances with its artificial intelligence, machine learning and predictive analytics to help all AWS customers do the same. Amazon has recently launched Amazon Personalize which is a fully-managed service that puts personalization and recommendation in the hands of developers with little or no machine learning experience.
Amazon Personalize allows the customers to create private, customized personalization recommendations that is built off of the customer data.
How does Personalize work?
With Amazon Personalize, you provide the unique signals in your activity data (page views, signups, purchases, and so forth) along with optional customer demographic information (age, location, etc.). You then provide the inventory of the items you want to recommend, such as articles, products, videos, or music as an example. Then, entirely under the covers, Amazon Personalize will process and examine the data, identify what is meaningful, select the right algorithms, and train and optimize a personalization model that is customized for your data, and accessible via an API. All data analyzed by Amazon Personalize is kept private and secure and only used for your customized recommendations. The resulting models are yours and yours alone.
With a single API call, you can make recommendations for your users and personalize the customer experience, driving more engagement, higher conversion, and increased performance on marketing campaigns.
We at Capgemini are leveraging Amazon Personalize for our customers to automate and accelerate their machine learning development and drive more effective personalization at scale.
Why Capgemini?
Capgemini has been selected as a launch partner for the Amazon Personalize service on the back of a decade long strong partnership with Amazon and Capgemini’s strong proven capability in the Data and AI space. Capgemini today has one of the largest Data and AI practice with more than 17,000 professionals across 40 countries focused on creating business impact with insights, delivering real business outcomes, covering end-to-end at scale, and building on ethics & trust.
Capgemini has raised “AI & Analytics” as a portfolio priority and has launched a companywide portfolio under the name ‘Perform AI’. The portfolio encompasses all Capgemini global business lines in order to offer one unique and seamless portfolio of solutions and services to deliver trusted AI at scale services for business transformation and innovation.
