Case: Domino’s Pizza Machine Data Analysis

Case: Domino’s Pizza Machine Data Analysis

splunk dominosIntegration Case: Domino’s Pizza

Primary Analytics tool: Splunk Enterprise

Integrated Data: PoS, Web/Mobile, email, display media, IT systems (performance)

Macro Conversion: Purchase

Micro Conversions: Engage/Deepen (profile): social engagement, content engagement

 

BUSINESS CASE/BENEFITS OF INTEGRATED ANALYTICS

Dominos is using Splunk Enterprise to make real-time business decisions based on actual customer ordering actions and behaviors.

Resolved IT issues like: collecting, indexing, monitoring and visualizing machine data (esp. Middleware application logs) and reports

Made data accessible, usable and valuable across the organization—regardless of where the data is sourced or stored.

OBJECTIVES                                           

1. Reduce Downtime Cost (of upto: $100,000/min during High Traffic)

  • Monitor your end-to-end infrastructure to avoid service degradation or outages

2. Data Management

  • Break down Data Silos, Data Parsing
  • Collecting, Aggregating, Indexing, monitoring & visualizing machine data (esp. Middleware application logs) and reports

 3. Track Effectiveness of Marketing Activities

Input:

  • Orders per minute, numbers of transactions per store, what types of pizza and other food items customers order and what coupons they used

Output:

  • Analyze the success of promotional campaigns in real time helps compare the effectiveness of various percent-off coupons. If one is more effective than the other, Domino’s can quickly make the proper online adjustments.
  • Mobile Push Tactics: Use of SPLUNK real time data to track the activity (conversion rate) from various Mobil devices (iPhone, android, Samsung etc). Domino’s can better determine where and when it may be more lucrative to run promotional campaigns.
  • Visualize business sales trends across geographical locations and make high level business decisions based on the metrics.

4. Better Customer experience Online

Input:

  • Past search, Transaction Details, Frequency of Transactions, Website visits Output: Personalization, Speeding the ordering process, Designing Better offers, Improve Sales Input: Integration of data from in-store/mobile devices/ Website/ Browser( e.g. Purchase   History, Credit Card Transaction, Transaction time)

Output:

  • Cross platform data synchronization, offline and online data
  • Uniformity in User Experience. Eg. Once the user has signed in the mobile and started placing the order and then ends up signing on Laptop, he should be able to see the placed order

5. Customer Experience (In-Store), Improve Employee & Store Performance

Input:

  • Credit card transactions, third party data, survey results, mobile app data

Output:

  • Equip managers/ employees to come up with local promotions, understand their customers better.

6. Marketing Effectiveness

  • Opportunity to connect customers to page views, allowing offers to include items specifically, believed to have been viewed by them (on website/other devices/social media sites).
  • If Logged in and a Repeat Visitor, then pull historical data about the average order value/ propensity to purchase. Can help in pushing messages/ promotions through emails, display ads etc.
  • Track Attribution of different platforms/devices/touch points and their contribution towards the Macro conversion

Email Campaigns

  • Run Email campaigns targeting specific audience with targeted offers and messages based on special events and their preferences (superbowl, Valentines Day etc)
  • Based on their ordering behavior/frequency, create email campaigns (e.g. coupon valid only on friday). SPLUNK can be used to track its performance for future reference

QUESTIONS

  1. What are some of the major behavioral patterns being tracked? And How is it used to improve customer experience?
  2. How do they track the effectiveness of the promotion?  Are there any Split tests?
  3. How are they synchronizing repeat visit from SM sites?
  4. How are they doing Sentiment Analysis?
  5. If a user is logged in, are they capturing his past preferences (veg, non veg, allergies etc).  If yes, how are they incorporating the data to enhance the user experience?
  6. How are they utilizing data from SPLUNK to compete with other players in the Pizza Business (esp. Online Delivery)
  7. How is it leveraging upon anticipated events (e.g. super bowl)?
  8. Are they streamlining the whole mobile ordering system, and use it to push notifications/ promotions etc.

 

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