Drive Collective, Innovative Decision Making within Your Enterprise, a simple approach

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Sheila Pope
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1 TM EzDataMunch A New Way To Discover Your Data Drive Collective, Innovative Decision Making within Your Enterprise, a simple approach Tell me and I will forget. Show me and I may remember. Involve me and I ll understand. - Anonymous 11/06/2014 Page 1

2 CUSTOMER ENGAGEMENT ANALYTICS Social Media, POS, Consumer Reviews, Mobile Data, s, Blogs, Historical Data and More Understand your customer patterns, behavior, spending habits and predict what they will do in the future! 11/06/2014 Page 2

3 Customer and Social Analytics: Key Data Discovery Challenges Facebook Twitter Google Analytics Website analytics campaigns Ad words Customer Service Application CRM Financial Information Blogs, Competitors info RSS Feeds Industry info Product reviews Government data Many more places to discover customer insights Where is my customer? Who is my customer? What are they saying? What are they buying? Where are they buying? How is my brand perceived? What people are saying about my company, products, services, brand, etc.? How do I target them? Which is the most effective medium? Campaign planning what, where & How? Campaign performance KPIs, response rates, ROI, Dreamer to Buyer road-map Revenue forecasting using statistical modeling Facebook / Twitter / Social media presence, top influencers, sentiment analysis, competition analysis, listening post Product reviews, service reviews, financial insight, sales insight, service performance and many more KPIs 11/06/2014 Page 3

4 % within seconds Occupancy rate First contact resolution Quality Metric scores Policies procedures Customer surveys Opinions Productivity Customer experience Up-sell/Cross-sell New customers Incremental revenues Social Media Sales Monitoring Listening Sentiment KPI s Resources Value R&D Marketing Customer growth Retention Insights Competition First call resolution Occupancy Agent Utilization Operations Broken operations Corporate service chain issues 11/06/2014 Page 4

Machine learning Natural Language Processing Knowledge

Transformation Reasoning Events Ontologies Integration

system In a Nutshell Structure/Semi/Unstructured

5 Machine learning Natural Language Processing Knowledge base Semantics Rules Inference Insights Reports Transformation Reasoning Events Ontologies Integration bus Dash boards Actions Predictions Data pool CRM Other system In a Nutshell Structure/Semi/Unstructured Network BI Speech, IVR, Chat,Social. Multi channels 11/06/2014 Page 5

6 EZDATAMUNCH DATA NORMALIZATION ENGINE 11/06/2014 Page 6

7 DATA NORMALIZATION METHODOLOGY DIFFERENTIATION 11/06/2014 Page 7

BETTER PREDICTIVE MODELS We reveal structure and meaning in unstructured, semantic data other platforms cannot use Polarity, appraisal and affective analyses Object and aspect analyses Together,

8 BETTER PREDICTIVE MODELS We reveal structure and meaning in unstructured, semantic data other platforms cannot use Polarity, appraisal and affective analyses Object and aspect analyses Together, these reveal deep structure in natural language data and generate a nuanced, granular understanding of potential customer relationships with products and brands We generate a set of customer profiles or personas that drive sentiment analysis and audience propensity assessment 11/06/2014 Page 8

STATISTICAL MODELS - BETTER STRUCTURED DATA We start our solution with what you have Data are the reflection of real events But they don t always mean what they appear to mean We use our decades of

9 STATISTICAL MODELS - BETTER STRUCTURED DATA We start our solution with what you have Data are the reflection of real events But they don t always mean what they appear to mean We use our decades of experience and expertise in the social and behavioral science to confirm that these are the right data collected and measured in the right ways Basic Statistics & Diagnostic Descriptive & Visualization Significance & Power Sensitivity & relativity Data Reduction Techniques Factor analysis Clustering & Classification Scaling & Indexing Pattern Recognition Correlation & Association Linear, Linearized & Nonlinear regressions Variance component models ANOVA Time series, Fourier analysis, Wavelets, Structural equation models etc. 11/06/2014 Page 9

10 MODELING PROCESS 11/06/2014 Page 10

11 METHODOLOGY & SAMPLE TECHNIQUE 11/06/2014 Page 11

12 METHODOLOGY & SAMPLE TECHNIQUE 11/06/2014 Page 12

13 WHY EZDATAMUNCH? Our Platform is already 80% of your solution Built on robust and tested tools (both open source and commercials) that deliver cutting edge options Python, R, Octave, Weka, RapidMiner, SAS, QlikView, BigML, etc.) Decades of experience and expertise in the science of making sense of data and using it to inform creative, real world solutions We triangulate our solutions, multiple data sources, multiple perspectives, multiple algorithms One approach is biased and limited, our strength is in our diverse approach We focus on selecting, training and tuning the right analytics algorithms from our extensive, tested library Our Software as a Service model insures continual improvement from day of deployment We also have outcome based models for Gain share You don t pay till You see the Gain! 11/06/2014 Page 13

14 Benefits of EzDataMunch TM Service Improve discovery and insight into information and data across the Enterprise Increase capture and sharing of (tacit) knowledge Collectively organize and maintain information and knowledge Build collective intelligence, Anywhere, Anytime Pre-built BI Apps Pre-built Data Models Pre-built integration with ERPs and leading market products Increased efficiency and productivity, innovation, improved decision making, Agility 10/24/2014 Page 14