Big data - illusion or opportunity (Miha Vogelnik, Valicon)
Upcoming SlideShare
Loading in...5
×
 

Big data - illusion or opportunity (Miha Vogelnik, Valicon)

on

  • 240 views

Learn how to operate with your Big data and get to know your consumer even better.

Learn how to operate with your Big data and get to know your consumer even better.

Statistics

Views

Total Views
240
Views on SlideShare
240
Embed Views
0

Actions

Likes
0
Downloads
13
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • Let‘s start with a littlestory…
  • Let‘s start with a littlestory…
  • So, to summarizethreemainelementsDataAnalyticsActionAreasofapplications are wide, forourcurrentpurposewewillfocus on marketing.
  • Firstofall – no social media is not the same of Big Data. It canbeonly one sourceofdata. Andthenastier. Whybecause it includesunstructureddatawhich is muchharder to analyze. So ifyoudontmasterstructureddataneatlywritter in yourdatabases – do go directly to social media.
  • Objective is faster and more efficient identification of customers needs and behaviour. Consequently more resources for 1to1 and more value for every € spent.

Big data - illusion or opportunity (Miha Vogelnik, Valicon) Big data - illusion or opportunity (Miha Vogelnik, Valicon) Presentation Transcript

  • Big data – illusion or opportunity Miha Vogelnik, Valicon
  • Key components of big data Data SMARTER DECISIONS Analytics Action TECHNOLOGY
  • Source: Nakupna pot 2012/2013, Valicon/IPROM WEB WOM 26% EVENT TV OUT STIMULATION PRINT CATALOG ONLINE 21% RADIO OFFLINE 53% WOM WOM WOM 21% WEB SEARCH OFFLINE 16% WEBSITE RESEARCH SOCNET CONTACT ONLINE 63% WEB PRICE MOBILE WEB RESEARCH WEBSITE ONLINE 15% CONTACT PURCHASE MOBILE STORE STORE PROMO & PUSH OFFLINE 85% STORE MOB WEB RESEARCH SURVEY WOM WEBSITE POST PURCHASE SOCNET 0% 20% 40% 60% 80% 100%
  • Potential sources of customer data POS Transactions Facebook VOLUME VELOCITY VARIETY Twitter Company web portals OTHER SOURCES SOCIAL MEDIA Blogs Call-centre logs INTERNAL DATA VERACITY Surveys Winning games
  • Marketing optimization – less investment in „traditional“ marketing with higher utilization TRADITIONAL MARKETING TRADITIONAL MARKETING General image advertising Segmented communication IDENTIFICATION IDENTIFICATION profiling direct 8 1to1 MARKETING 1to1 MARKETING
  • Advanced analytics helps find meaning in vast quantity of data Classification Grouping customer based on their common characteristics demographic, behavioral Associations Understanding relationship between customers in social network or product in shopping basket Predictions Finding what differentiate buyer from not buyer, churner from not churner and make a prediction
  • Cust_1 Cust_2 Cust_3 WEB EVENT TV STIMULATION OUT AQUISITION PRINT CATALOG RADIO WOM WOM WEB SEARCH WEBSITE RESEARCH SOCNET CONTACT AQUISITION WEB PRICE MOBILE WEB RESEARCH WEBSITE CONTACT MOBILE PURCHASE STORE STORE PROMO &… STORE MOB WEB RESEARCH PERSONALISED SELLING CROSS-SELLING UP-SELLING SURVEY WOM POST PURCHASE WEBSITE SOCNET RETENTION
  • Cust_1 Cust_2 Cust_3 WEB EVENT TV STIMULATION OUT PRINT • Testing which ad/video /tweet will have better impact CATALOG RADIO WOM WOM WEB SEARCH WEBSITE RESEARCH SOCNET CONTACT WEB PRICE MOBILE WEB RESEARCH • Click-stream analysis to present most suitable ad-content to display • Lead scoring and managing in order to achieve best conversion WEBSITE CONTACT MOBILE PURCHASE STORE STORE PROMO &… STORE MOB WEB RESEARCH SURVEY WOM POST PURCHASE WEBSITE SOCNET • Most probable next product recommendation • Combination of most suitable products for cross-sell • Retaining customers by predicting most probable churners • Campaign evaluation
  • Sales Traditional campaigns Days/Weeks 13
  • Sales Traditional campaigns Days/Weeks 14
  • „Smart“ optimized campaigns Sales Smaller more targeted campaigns Days/weeks 15
  • „Smart“ optimized campaigns Sales Smaller more targeted campaigns SMART follow-up campaigns Days/weeks 16
  • „Smart“ optimized campaigns Smaller more targeted campaigns Sales Increase of Sales SMART follow-up campaigns Days/weeks 17
  • How to start with BigData? 1. 2. 3. 4. 5. 6. 7. 8. Clearly identify simple problem Identify suitable data Use existing data Analyze data Decide on action Action! Measure the outcome Improve and do it again!
  • Start with baby steps - NOW!