Candidate360: Google Cloud and Deloitte Product Improves Universities' Enrollment and Admission Processes - Build What's Next
Case Study

Candidate360: Google Cloud and Deloitte Product Improves Universities’ Enrollment and Admission Processes

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Candidate360, a product of Google Cloud and Deloitte pave way for universities and higher educational institutions to manage admissions and recruitment remotely. Michigan State University recently deployed a student enrollment strategy with Candidate360 that helped them achieve enrollment increase by over 20% and generate $5M in additional net tuition revenue in a single year. Read on to learn more about the solution's real-time and predictive insights gave the university an understanding of the enrollment pipeline as well as leverage AI and predictive analytics functionalities to optimze the overall process.

Given the May 1 deadline for students to enroll, colleges and universities have been carefully watching the numbers of students who put down deposits and commit to a school. Like nearly every other sector in the U.S., colleges and universities have been hit hard by the pandemic and economic downturn, making enrollment numbers more important than ever to maintain financial health and meet student body goals. Technology and AI-based models are providing a way for institutions to manage their admissions data efficiently and cost-effectively.

For example, an admissions officer may want to offset drops in international or out-of-state enrollment by attracting local talent so they can achieve their target class profile. Additionally, institutions have sought to increase the availability and access of higher ed and are intentionally recruiting more low-income and underrepresented populations. New technologies allow admissions officers to have a stronger grasp of the incoming class’ details. With the press of a button, users can switch between overall, in-state, out-of-state, and international enrollment figures. The “How Do Regions Compare to Last Year” tile lets users view the percentage of change and the total number of candidates by region, comparing 2020 and 2021 side-by-side. With access to data, regional recruiters can focus on individual candidates and develop a plan to change the numbers.

An innovative enrollment strategy at MSU

Michigan State University was looking to improve how they managed the recruiting process. They implemented an enrollment strategy that saw their out-of-state enrollment increase by over 20% and generate $5M in additional net tuition revenue in a single year. “We knew we needed better real-time and predictive insights about our enrollment pipeline, and we understood the value of bringing in external data on prospects, but we couldn’t do that by ourselves. 

The solution really helped us be innovative with our analytics and improve our enrollment outcomes,” said someone close to the project at Michigan State University.

Data-driven insights support informed enrollment decisions

Student admissions and marketing groups are turning to Google Cloud and Deloitte to help support their enrollment decisions with predictive, actionable insights across recruiting and admissions processes. The partnership resulted in Candidate360, a solution that helps institutions process and analyze large amounts of data and develop meaningful insights to support their enrollment goals and mission. Part of Google’s Student Success Services offerings, Candidate360 uses artificial intelligence and predictive analytics to help higher education institutions improve enrollment and matriculation, as well as optimize financial aid decisions.

With nine AI/ML models, Candidate360’s capabilities help university leaders, enrollment teams and recruiters do things like:

  • Identify regions with clusters of candidates or see dips in application numbers
  • Deploy marketing resources and admissions staff to the right geographic areas 
  • Understand applicants’ needs in terms of academics, safety, and social activities
  • Make intelligent financial aid decisions to recruit and retain talented students from underrepresented communities
  • Respond faster to the students who are most likely to be accepted and then go on to enroll, stay enrolled, and graduate
  • Work towards the ideal class composition and increase competitiveness with peer institutions

Enhancing the student experience

Enrollment is the first key touchpoint with incoming students, and tools like Candidate360 can help institutions build a student preferences profile throughout the application process. They can understand each student’s prospective major, areas of interest, dream job, and more, allowing colleges and universities to personalize recommendations and advising for enrolled students.

With integrated toolsets, Candidate360 and Google Cloud’s Student Success Services can help colleges and universities emerge from the pandemic stronger than ever. 

To see a demonstration of Candidate360 in action, contact our sales team. 

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Israeli centenarian newspaper, Haaretz relied on on-prem infrastructure to serve readers digitally. As the need for scalability, security and business intelligence grew alongside their readership, Haaretz was looking for more than just a cloud-based solution to replace their infrastructure. Inon Gershovitz, CTO, Haaretz takes us through the journey of recreating digital news experience, serving growing reader traffic and keeping up the editorial standards with Google Cloud.

Watch the video to hear from Haaretz’ leaders on delivering personalized content to readers with Google Cloud solutions!

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Retail engagements have changed drastically over the last few years, and by the virtue of COVID-19 pandemic, retail data and customers’ expectations plummeted. To drive value and transformation across the entire value-chain retailers can make most of Google Cloud’s secure, reliable IaaS by migrating their SAP systems and taking advantage of integrations, insights and innovations. In times of change retail companies can gain maximum visibility of SAP data unlocking Google Cloud’s infrastructure modernization and Big Data and analytics capabilities. Watch the video to understand how Google Cloud and SAP partnership is a golden handshake for retail businesses’ future.

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Google Dataflow Named Leader in The 2021 Forrester Wave™: Streaming Analytics

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Google Dataflow shines with the highest scores in Forrester's 12 important criteria and bags the title of a Leader in the The Forrester Wave™, Streaming Analytics. Read further to understand Dataflow's strength in harnessing real-time data.

We are excited to announce that Google has been named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report. Thank you to our strong community of customers and partners for working with us to deliver a customer focused product. We believe Forrester’s recognition is an acknowledgement of our leadership across an integrated set of capabilities that rely on data to drive transformation. We were also honored to be named a leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021

Forrester gave Dataflow a score of 5 out of 5 across 12 different criteria and according to the report: “Google Cloud Dataflow has strengths in data sequencing, advanced analytics, performance, and high-availability. Google Dataflow’s sweet spot is for enterprises that have a preponderance of real-time data generated on Google Cloud Platform or wish to simplify all data processing by using a single platform that unifies both streaming and batch jobs.” 

Harnessing the power of real-time data

The speed with which businesses are able to respond to change is the difference between those that successfully navigate the future and those that get left behind. In order to accelerate their digital transformation, reimagine their business and leverage the power of real-time data,  today’s data leaders require a streaming analytics platform that provides both depth and breadth.  

Cloud Pub/Sub and Cloud Dataflow, based on more than a decade of experience in internet scale systems for Google’s own needs, provide customers with a reliable, scalable, performant platform. In addition, we’ve designed these products for ease of use to make streaming analytics accessible to more users, which is why customers such as Sky and others from across all industries use Dataflow to run streaming analytics workloads.

5 out of 5 across key streaming analytics criteria

While Forrester gave Dataflow a score of 5 out of 5 in 12 criteria, the product achieved the highest possible scores in areas that are top of mind for our customers.

streaming analytics criteria.jpg

We continue to be focused on solving problems that matter to you. For example, just in the last month we announced Dataflow Prime and  Auto Sharding for BigQuery – two new auto tuning capabilities that bring efficiency and simplicity to your streaming pipelines. 

Dataflow achieves highest score possible in strategy 

With Google, organizations gain an industry leading product and a partner that has the vision and strategy to help you tackle new business challenges and provide delightful experiences to your customers.

highest score in strategy.jpg

In summary, we are honored to be a Leader in The Forrester Wave™, Streaming Analytics, and look forward to continuing to innovate and partner with you on your digital transformation journey. 

Download the full report: The Forrester Wave™: Streaming Analytics, Q2 2021 and check out these smart analytics reference patterns. To learn more about Dataflow, visit our website and get to know the product by taking an interactive tutorial. You can also watch recordings from the Data Cloud Summit event (May 2021), where we provided an in-depth view of new product innovations in Dataflow and other data analytics products.

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Swarovski’s Journey towards Online and Offline Conversion with Predictive Analytics

Luxury brand and leader in crystals and glass production, Swarovski has charmed customers with its exquisite collections for over 125 years. To understand their customers better and map their online behaviors, Swarovski had to overcome prediction hurdles as majority of the purchases are not frequent or habitual. They are mostly impulse buys or have no rational behind the purchase in order for the brand to accurately map customers’ interest and delight them with relevant personalization or website customization strategy.

Swarovski used a machine learning (ML) model to predict the most performing SKUs and list of products based on both online and offline indicators to target buyers. A score was assigned to each product in the list and was personalized at the country level that delivered relevant insights. Swarovski is aiming to expand the product listing page to personalize at customer level. Watch the video to dive deep into Swarovski’s data analytics efforts to answer complex questions, reporting and prediction using both online and offline data.

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CCAI Insights: Answer Customers’ Queries & Understand Them Better with Conversation Data

With CCAI Insights, businesses can drive contact center efficiency, solve customer problems and leverage data from customer interactions to understand them better!

CCAI Insights, a core piece of the Google Cloud’s Contact Center AI product suite is built to help contact center management dive into data to adjust business needs, preempt problems with timely analysis of customer conversations and keep agents prepared. Additionally, businesses can automatically feed data into Insights from other areas of CCAI like Dialogflow CX or another product sources. Watch the video to find out more benefits and capabilities of CCAI Insights in elevating CX.

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