Driving Retail Success with Machine Data Intelligence
-
Upload
sumo-logic -
Category
Technology
-
view
668 -
download
3
description
Transcript of Driving Retail Success with Machine Data Intelligence
Driving Retail Success with Machine Data Intelligence
John Atkinson, Technical Director of Strategic Alliances, Sumo LogicGreg Lord, Senior Product Marketing Manager, Akamai Technologies
Delivering seamless customer experiences The threefold challenge:
– Performance: Preparing for optimal performance and scalability– Security: Maintaining security and demonstrating compliance– Insights: Identifying trends and growth opportunities in real-time
DemoHow to get started
Agenda
Slower pages = higher bounce rates & less engagement
Slower pages = lower conversion ratesSource: Torbit
Web Experiences Directly Impact the Business
Delivering Fast, Consistent Web Experiences
Improve Web & Mobile Experiences With Akamai
Edge closest to end-userEdge closest
to origin
Scale Offload - Maximize origin efficiency
Availability - Always online
Performance Accelerate - Faster responses
Prefetch - Anticipate requests
Reduce Requests - Fewer round trips
Minimize Payload - Fewer bytes
Expedite Rendering - Better perceived performance
Intelligence Insight - Understand end-users
Logic - Empower edge & origin
Akamai Intelligent Platform
Cloud Monitor Provides Real-Time API Data Feed
Edge closest to end-userEdge closest
to origin
Akamai Cloud Monitor • Device, Geo & Network, & Last Mile Data• Request timing, Request Headers, & Business Context• Middle Mile Network & Latency Data• First Mile Latency & Origin Response Data• Object Delivery Timing & Response Headers• Page Rendering Timing• WAF Security Events
Akamai Intelligent Platform
How to derive business value and actionable insights from the Akamai Cloud Monitor data feed?
Optimize Availability, Performance and Customer Experiences
John Atkinson, Technical Director of Strategic Alliances, Sumo Logic
Search & Correlation
Visualization
Proactive Alerts
Applications
Mobile
Internet of Things
Network and Server
Transforming Machine Data Into Meaningful Insights
2014-04-06 23:52:37 10.20.11.105 GET /Tradejames2/StockTrade.aspx action=sell&symbol=s:156&holdingid=9875 80 Jayden 214.115.233.69 Mozilla/5.0+(Macintosh;+Intel+Mac+OS+X+10_7_3)+AppleWebKit/536.5+(KHTML,+like+Gecko)+Chrome/19.0.1084.54+Safari/536.5 200 0 0 333
2014-04-07 00:30:23 10.20.11.101 GET /Tradejames2/StockTrade.aspx action=sell&symbol=s:142&holdingid=9066 80 Lily 219.5.73.118 Mozilla/5.0+(Macintosh;+Intel+Mac+OS+X+10_7_3)+AppleWebKit/536.5+(KHTML,+like+Gecko)+Chrome/19.0.1084.54+Safari/536.5 200 0 0 206
2014-04-06 21:23:56 10.20.11.103 GET /Tradejames2/StockTrade.aspx action=sell&symbol=s:126&holdingid=4867 80 Hayden 233.134.69.149 Mozilla/5.0+(Macintosh;+Intel+Mac+OS+X+10_7_3)+AppleWebKit/536.5+(KHTML,+like+Gecko)+Chrome/19.0.1084.54+Safari/536.5 200 0 0 237
2014-04-06 20:58:23 10.20.11.102 GET /Tradejames2/StockTrade.aspx action=sell&symbol=s:168&holdingid=9932 80 Thomas 150.205.10.108 Mozilla/5.0+(Macintosh;+Intel+Mac+OS+X+10_7_3)+AppleWebKit/536.5+(KHTML,+like+Gecko)+Chrome/19.0.1084.54+Safari/536.5 200 0 0 110
2014-04-06 13:33:20 10.20.11.103 GET /Tradejames2/StockTrade.aspx action=sell&symbol=s:189&holdingid=3802 80 Harper 33.168.5.129 Mozilla/5.0+(Macintosh;+Intel+Mac+OS+X+10_7_3)+AppleWebKit/536.5+(KHTML,+like+Gecko)+Chrome/19.0.1084.54+Safari/536.5 200 0 0 398
2014-04-06 21:30:43 10.20.11.105 GET /Tradejames2/StockTrade.aspx action=sell&symbol=s:175&holdingid=4147 80 Parker 120.22.112.139 Mozilla/5.0+(Macintosh;+Intel+Mac+OS+X+10_7_3)+AppleWebKit/536.5+(KHTML,+like+Gecko)+Chrome/19.0.1084.54+Safari/536.5 200 0 0 398
2014-04-06 08:31:03 10.20.11.101 GET /Tradejames2/StockTrade.aspx action=sell&symbol=s:186&holdingid=4576 80 Maya 11.208.155.200 Mozilla/5.0+(Macintosh;+Intel+Mac+OS+X+10_7_3)+AppleWebKit/536.5+(KHTML,+like+Gecko)+Chrome/19.0.1084.54+Safari/536.5 200 0 0 168
Powerful Analytics & Event Detection/Correlation
CorrelateExpress complex correlation logicEnrich data with contextual infoDetect compound/complex events
Discover PatternsAutomatically extract patterns with LogReduceTM Analyze across multiple sourcesReduce output by 3 orders of magnitude
Detect AnomaliesBuild baselines based on patternsDetect changes across multiple sourcesFind anomalies in all your data in real-time
Find a single event among billionsSearch across all data sourcesScale to terabytes of data per day
Filter/Search
Applications
Network and Servers
Mobile
Next Generation Infrastructure
Elastic and Built to Scale
Stream Engine
Index and Search
Dashboards
Real-TimeAlerts
InteractiveSearch
Improve Site Availability and Reliability – Reduce checkout problems by detecting server errors early– Improve customer satisfaction and conversion rates
Strengthen Site Security and Meet Compliance– Protect brand reputation by understanding attack vectors and
proactively building defenses
Leverage Business Insights – Identify trends and business growth opportunities in real-time– Improve targeting based on geo customer behavior
Key Use Cases
Cloud Monitor and Sumo Logic
Configures in MinutesNo Software to InstallNo Hardware to Buy
“Cloud-to-Cloud” Integration
Real-Time Akamai Data
Origin Performance
Reduce MTTI and MTTR by 50% or more
Reduce conversion errors Maintain steady revenue stream Elastically scale to meet unforeseen
spikes
Improve Security Posture: Attack Spikes
Proactively detect security breaches Monitor web application firewall (WAF) Uncover compliance violations Reduce compliance audit costs by 30%
Identify purchasing trends in real-time Deliver improved service, build loyalty Rapidly uncover customer experience
issues Track customer activity e.g shopping
cart abandonment Monitor Ad statistics for optimal
placement
Demo
How to Get Started
22 © 2014 Trace3, All rights reserved.
© 2014 Trace3All rights reserved.
Trace3 Customers
Q&A
Machine Data
Intelligence
Sign Up for Sumo Logic Free 30 Day Trial www.sumologic.com/signup