Big Data BizViz Restaurant Analytics

Copyright © 2016 BizViz Technologies Pvt. Ltd. www.bdbizviz.com

Elements of Restaurant Analytics in the Big Data World

Where is the Data for Analytics?

What is required to get all these Analytics?

Customer Analytics (Customer Life Cycle Value)

Demand Forecasting, Feedback Analysis, RFM

POS data Ad-Hoc Analysis (Self Service BI)

Agenda


Elements of Restaurant Analytics in Big Data World

Customer Analytics Customer Lifetime Value Customer Sentiment Analytics Feedback Analytics – Polls, Survey

Staff Analytics Productivity

Operational Analytics POS Analytics Inventory Analytics Season based Analytics – Menu Analytics Weather Impact Analytics

Sentiment Analytics Twitter Sentiments (real time feedback) Survey based Sentiments Restaurant Comparison Websites

Branding- Marketing – Loyalty Program Analytics Gift Cards (Avg. Customer Spends 20% more than Gift Card Value) Loyalty Programs (Avg. Customer Spends 46% more to Businesses with

Loyalty Programs)


Where is the Data?

Structured (Inside the Business) – it tells you WHAT? POS (Revenue, Footfall, Customer Details) Suppliers (Inventory, Prices) Operational – Costs, Revenues, Margins Staff – Wages, Salaries, Tips, Productivity

* This data should be available with the Restaurant Chain

Unstructured (Outside the Business) – it tells you WHY? Social Media – Likes, Trends, Tweets, Shares, Comments Customer Profiles and Loyalty Programs – Details, Preferences etc. Weather, Geographical and Traffic Patterns

* BizViz Social Media Browser can be Used to Pull this Data near Real Time


What is required to get all these Analytics

Needed an Platform that is Vertically and Horizontally Scalable. The platform should be able to Integrate with E-Commerce Part of Restaurant Chain. The Platform should be able to Extract Data from POS System, Inventory, Internal Data,

Twitter, Facebook, Weather etc. all together, Seamless, near Real time. Platform has Cloud based access, Analytics is available on mobile devices Platform has ability to do Predictive Analysis on all this data together to find –

Customer Patterns, Outliers, Correlations, Regressions, Classifications Strong Data Scientist Team is required to create Prescriptive Analytics

Social Media Analytics – Sentiment Analytics using Real Time Messaging Service and Sentiment Engine to Trap Emotions of Customers – Real time or from Websites rating different restaurants

Ad Hoc Analysis capabilities – take the POS data and do Slice and Dice – Anyone should be able to do it.

Strong UI and Visualization tools to give Advanced Analytics findings that are easy to understand

Ability to take Polls, Surveys and Feedbacks from Customers on a real time basis Real Time Analytics, Push Analytics (triggers, Alerts), Pull Analytics Distributed deploymentBizViz Platform brings all these capabilities together in a seamless way


Customer Analytics (Customer Life Cycle Value)

Using BizViz Predictive Analysis tool we performed customer segmentation with RFM & K-Means model. This helps in understanding different Customers Life Cycle Value

Analytical Hierarchical Process is used to Segment the Customer in following types - Best, Valuable, Shopper, First-Time, Churn, Frequent, Spenders, Uncertain


Feedback Analysis

Demand ForecastingRFM

Demand Forecasting, Feedback Analysis, RFM


POS data Ad-Hoc Analysis (Self Service BI)

Combine POS data with the Predictive output in BizViz “Business Story” (Self-Service BI) and translate that story into actions. It helps in Engaging the Executive Team by explaining strategies and results more powerfully.

With self-service BI, Business users are able to track the important business parameters without depending on their IT team..

Business Story charts load seamlessly on Mobile app and are 100% responsive Doing Analysis on Business Story takes no time. Business Stories can be copied and shared

with other users


Indicative Timeline to create these Analytics

Short Term – 15-30 Days

1. Cloud Account or Server Installation

2. Push POS data into Business Story Cube to create Ad Hoc Analysis Charts

3. Real Time Sentiment Analytics of Your Business from Twitter and Facebook

4. CXO level Descriptive Analytics on POS data

5. Basic Survey and real time Survey Analytics

Medium Term – 90-180 Days

1. ETL of Different Structured Sources and design of Data Mart

2. Extending Business Story Charts with some Predictive Analytics. Business Story on Mobile

3. CXO level dashboard with data from more than POS databases

4. Extending Sentiment Charts to include sentiments from public websites

Long Term – 240 - 365 Days (This requires clear requirements from business to be defined on time)

1. All types of Analytics mentioned in the main slides2. Many CXO level Dashboards to give end to end KPIs of Business3. All Key Predictive Analytics as described above4. Big Data Analytics – Live streaming Data, IOT, Machine Learning data, Impact due to Weather

etc.5. From What to HOW -> Prescriptive Analytics6. Adding more Analytics Workflows by creating internal Applications7. Mobile Analytics and Mobile Apps implementation8. Clustering and Complex Deployment for Vertical and Horizontal Scaling


A small sample Sentiment Analysis Report (Automated)

Note : The sample data that we took showed lot of Positive Feedback about Red Rooster


Thank You

For further information, please contact:

Avin Jain| Founder-CEOBig Data BizViz LLCPhone: (773)897-0939e-mail: [email protected]: @bdbizviz

Big Data BizViz Restaurant Analytics

Data & Analytics

Transcript of Big Data BizViz Restaurant Analytics