This report addressed the use of business analytics and its effectiveness for the company Quay. Real-time data assess the functionality of Quay and provided leverage to its efficiency. The report highlighted the business effect on Quay due to global and change in the business model through decision making by machine learning and predictive analysis for central change in the working of Quay by leveraging decision making through descriptive and predictive analysis for change in strategy at Quay
Table of Contents
Developing marketing strategy.
Development of AI tool
Challenges associated with implementation.
Resource allocation and analytics at Quay.
Quay is an Australian restaurant led by Executive Chef Peter Gilmore. The restaurant has an organic space setup that matches the vibrancy of the city Sydney harbor. The restaurant serves contemporary Australian cuisine. It has been awarded as world’s best restaurants and named as Restaurant of the year 6 times by Good food guide (Quay 2020). Quay is fine-dining and does not use an online interface. The use of data analytics will ensure Quay's presence in online delivery other than coupon code to leverage sale in the times of pandemic.
The total revenue of the eating out industry in Australia is $45 billion. The fall in sales has reduced the revenue to 53% with minimal occupancy at restaurants as online orders as a preference (BCG 2020). The shift to digital will require a business model change for customer retention with growth in business approval.
The hospitality segment is wide with the restaurant business by Quay working immensely to operate through its dining experience (Quay 2020). The dip in sales and changing global scenarios requires a retail dashboard and customized solution for target customers and retention. To change its operations business tool will help them edge through predictive analytics using historical data. This will ensure analytical software for product and menu change for the specific area and revenue dynamic change. A dynamic way to lead is by cost and billing and using customer insights through the existing structure for analysis
Data analysis project will take a period of 8 months before pilot check as per the approval of management. The company at Quays is using an offline model to engage with its customer with limited engagement through social media. The option selection at work classification at Quays will take several measures through analytics to research optimal suggestions to implement in organization
Predictive analytics nurture and creates data insights for meaningful computation to assess the situation though algorithms (Leventhal 2018). Using the data tool the order accuracy can be established for-profit matrix to analyses whether the same can be used for pin code other than Sydney and break-even cost already determine through descriptive data. The time duration and offer delights can be used to analyses the funding variable used by the venture team for funding the new operation arm of delivery services at Quays. The web portal already functional Quays will be used for order food by customer segments approach towards Quay and customizing a menu for delivery other than dining operations
Machine learning will support decision making. Decision making through computer-based decision-making will help in planning and organizing organization through fast-changing times. The restaurant industry is highly competitive and fast-paced. Decision tree help in calculating conditional probabilities through strategy descriptive weight assignment (Ribeiro 2018). The analysis through decision tree can handle both numerical and descriptive data for processing decision making
Information gain is utilized by featuring every step in building trees for decision making vital for the scenario. The decision tree is a flow structure with classification to set up representation for valuable decision making. It is easy to understand applicable to any outlier due to definitive questions. The structuring change at Quay can be subject to validation through the decision tree
The information extraction through text mining helps in business operation improvement for QUAY. The result shows QUAY presence in Sydney leads to less outreach to other parts of Australia and provide feedback for restaurant culture improvement to make it more accessible
Opening file ‘QUAY_ reviews and quality.txt.’
‘QUAY and peter txt.’
Read the reviews through log and customer connect and feedback. Some of the excerpts provide a quality and cuisines as a specialty but social distancing reduces restaurant closure for the said period
The information extraction through data set combines files with extensive configuration with more than 15 search engine for detailed view using document collection through URL (Carrot search 2020). Consumer interest and further analyses of the restaurant on the keywords give us QUAY s strengths and opportunities that can be used to work upon. The extraction through type |name, ’type’, ‘token’
The revenue model and operations of the client work relate to the functional aspect of customer visit and scaling through online presence. The social media presence of the company helps in user engagement and identifying customer preference and their work. The response system helps in moderation for optimism revenue by unit matrix through sales generation by operating under the online delivery market to sustain functionality in the post- COVID time (EMERAJ 2019). The search pattern through the mining address the same.
Tokenizing creates the sequence of characters in document for the normalized procedure in small breakups. This uses the keywords to monetize through dashboard in the system. This helps in sentiment analysis and summary for action. The word through “macron”, “Sydney harbor” creates part of text tagging for market insights (Carrot search 2020)
Linear Regression in machine learning is a model focusing on the assumption for linear relationship between the inputs variables with single input (Verhoef 2015). This classification calculates the linear combination. Quay s relationship with respect to sales and restaurant engagement shows the variable s impact with respect to sales in the months adjoining to decline witnessed
The sales and restaurant engagement figure are taken in million through figures of past 5 months on approximation. This shows variable change with respect to restaurant engagement. This can be addressed through changing Quay business outlook through social media engagement and food delivery services in Sydney
The human brain processes information through graphs and chart format over spreadsheet data for understanding and layout (Kraus 2018). This will help in cost benefit analysis. The quick overview over experimental and behavior analysis of consumer on trends across the population by predicting sales volume for the operative structure of delivery and factors inflecting demand. The infographic and data visualization creates audience target and understanding of the competition at Quay. The analytic options can be categorized at a high level into three distinct types. These options are in together fruitful, and in fact they co-exist with, and complement, each other. In order for a business to have a holistic view of the market and how a company competes efficiently within that market requires a robust analytic environment which includes the new tool for enhancement in the virtual set for culture assent (Morrison 2015).
AI functions as web and online intelligence. The use of Artificial intelligence taps a digital interface for a restaurant to act as aggregators for their business functioning (Kraus 2018). The current interface used Quay is reservations and gift voucher with no other feedback mechanism
The interface use of chatbots helps with user assistance and customer service improvement. The overall digital use by recommended engines will help in updating the menu with design forecasts according to the desired preference by the majority. The use of a neural network will detect spam or abusive content to build better engagement and recommended choice on the web portal.
The conversation and call transcription on contact portal will give insight and breakdown on the same “I need delivery at home, I just came from office”, break down into recommendation. The visual representation through data augmentation creates appeal at cognitive level
Transparency and accountability for data privacy at research and data mining is ensured. The preference and view are kept private and are used in the logarithms only for improving privacy concern. Big data analytics for consumer moderation and wrongful information and recommendation is prohibited (BCG, 2020). Quays ensured the service is maintained well through the same with no transcript and repository for threatening of the customer and any privacy hindrance
Big data for customer experience and technology used for point of sake setup for customer satisfaction. Specific sale and data information leads to loyalty program extension on the web portal for quick virtual setup and information. Big data is useful in consideration due to its velocity for its streaming and collection of data through traditional and modern software sources (Verhoef, 2015). The structured source through POS, accounting creates nexus for large data for use at quays for competitive advantage
Prescriptive analytics use business algorithms and machine learning data for real time data. Quay with the measures taken by multiple data tools shall adopt online interface with the infrastructural support in the given area for specific period to ensure functionality by the data set of its competitor and capital float in operations to prevent loss due to post –COVID impact. The future decision s rebased on organization ability and adaptability through customer preference.
Centralized excellence group
Head team managed by Peter and support office by hiring through the requirement team. The core team will be working with data analytics team for data governance, demand management and resource optimization. The operation unit will be divided into 2 units Dine-in, and online delivery team
Internal consulting and advisors- Centrally group and independent functioning with development and strategy team
Collaborative analytics helps in achieving goals through joint efforts by different staff (Medium, 2019). This will help in actively solving diverse problem for Quay for its functional improvement overall. The business facts are aligned for answers through logic and reasoning on observing current trend and structure of the restaurant industry. The use of common language will assist in common goal and decision making. This shift will assist Quay in application of data through collaborative insights to use delivery model in co-existence for sustainability
From the above discussion and understanding, the analytical shift will be beneficial at Quay. Quay and foreclosure for any plan, which uses statistical models and forecasting techniques to understand the future for sustainability at Quays. The human brain processes information through graphs and chart format over spreadsheet data for understanding and layout. This will help in cost-benefit analysis. The quick overview over experimental and behavior analysis of consumer on trends across the population by predicting sales The web portal already functional Quays will be used for order food by customer segments approach towards Quay and customizing a menu for delivery other than dining operations. The Analytics depicts the possible outcomes and unfolds the new dimension for this hospitality business. This will ensure analytical software for product and menu change for the specific area and revenue dynamic change. A dynamic way to lead is by cost and billing and using customer insights through the existing structure for analysis
BCG .2020. Australian consumer sentiment report. Retrieved from https://www.bcg.com/en-au/capabilities/marketing-sales/australian-consumer-behaviour-economic-recovery-post-covid.aspx
Carrotsearch.2020.Folders. Retrieved from https://search.carrot2.org/#/search/pubmed/QUAY%20Australia/folders
Quay.2020.About us. Retrieved from https://www.quay.com.au/about/
EMERJ.2019.AI Sector overview restaurant industry. Retrieved from https://emerj.com/ai-sector-overviews/ai-in-restaurants-food-services/
Leventhal, B. 2018. Predictive analytics for marketers: Using data mining for business advantage. Kogan page
Ribeiro, V.A. 2018. Deep learning business application for developers: From conversational bots to medical image processing. Apress
Kraus, S., Rosenfeld, A. 2018. Predictive human decision making: From Prediction to action. Morgan and Claypool
Verhoef, C., Kooge, E., Walk, N.2015.Creating value with big data analytics- Making smarter marketing decisions. Routledge
Medium. 2019. Data flows.https://medium.com/data-flows/what-is-collaborative-analytics-2b2d9d64fcb0 Medium .2019
Morrison, R.2015. Data-driven organization design: Sustaining the competitive edge through organizational through organizational analytics. Kogan page
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