The document discusses the challenges of modern marketing, including issues with lead-based approaches, messaging, privacy, and the predictability of buying patterns. It emphasizes the importance of utilizing predictive marketing techniques through data analysis to improve targeting and effectiveness, highlighting various data metrics and profiles. Tools for predictive analytics and outcome forecasting are also mentioned, including platforms like Salesforce Einstein and Amazon AWS.

1. PREDICTIVE MARKETING
a fine line between useless data and patterns that sell
Richard Fallah

2. PROBLEMS WITH TODAY’S MARKETING

3. PROBLEM WITH MARKETING
1. Lead based is broken
Too much noise, forms and permissions

4. PROBLEM WITH MARKETING
2. Messaging can be invasive
Wrong message delivered to the wrong audience

5. PROBLEM WITH MARKETING
3. Privacy issues
Strict compliance underway

6. PROBLEM WITH MARKETING
4. Buying patterns
aren’t that predictable
People’s habits are shifting

7. PROBLEM WITH MARKETING
5. Too much manual work
is still required while most departments and
channels are operating in silos

8. PROBLEM WITH MARKETING
6. Technologies can be pricey
and AI is still in it’s infancy

9. DOES THE SOLUTION LIE IN THE DATA?

10. IS THE SOLUTION IN THE DATA?
Thousands of metrics are
stored from each
interaction and growing.
Which data is relevant and
which is not?

11. IS THE SOLUTION IN THE DATA?
How to create models from
these data set through
deep learning and
understanding?

12. IS THE SOLUTION IN THE DATA?
How to repurpose past
data into future forecast
using intuitive data
representation?

13. IS THE SOLUTION IN THE DATA?
How to make this actually
work for teams who just
want results driven
campaigns?

14. IS THE SOLUTION IN THE DATA?
How can you predict a
customer’s likelihood of
making purchase decision?

15. IS THE SOLUTION IN THE DATA?
How does the ideal
customer journey look
like and how it can adapt
per customer?

16. PREDICTIVE MARKETING!

17. WHAT IS PREDICTIVE MARKETING?
Analytics Predictive Analytics
PRESENT
Analyze past data and current signals to predict
Future outcomes

18. LET’S LOOK AT THE DATA SET

19. LETS ANALYZE THE DATA SET!
Conversion
Page visits
Mouse and scroll engagement
Real-time attribution
Content engagement
Media Engagement (Video, Hotspots)
Demographic -
Geographic data
Page engagement
Source
ANONYMOUS
Devices Language
Technologies

20. Conversion
Page visits
Mouse and scroll engagement
Real-time attribution
Content engagement
Media Engagement (Video, Hotspots)
Demographic +
Geographic data
Page engagement
Source
DANIELLA
Devices Language
Technologies
User multi-touch patterns Social Media engagement
Audience segmentation
In-depth email engagementCustomer sentiment
Firmographic dataTechnographic data
Search dataCustomer LTV Buying history Product engagement Product engagement
Voice data
Market Signals
Psychographic data
LETS ANALYZE THE DATA SET!

21. MACRO PROFILES
DEMOGRAPHIC:
• Macro Daniellas are 25 -35 year old female CTO
• They work in in the education space
• Based in NYC
FIRMGRAPHIC:
• Using xyz technologies on their website (ex: Shopify, Mixpanel,…)
• Company size ranges from 1 to 20 employees
• Making 1 to 10 M in revenue yearly
• C-level
CONVERSION PATTERNS
• Similar profiles within the Vbout big data cloud respond better to
email on Tuesday before 2pm
• They interact with social media 2-5 times per week
• They convert better after the 8th interactions
• These are 4 sets of journeys that triggered the highest engagement
• They have the tendency to engage 5 times before potentially
churning
PSYCHOGRAPHIC:
• Shoe fashion, wearable
• Tech contributions
• Event networker
MARKET SIGNALS:
• Average 10-20K yearly shopping
• Average 5 online purchases/mo
+100’s of other data points

22. MACRO PROFILES
With thousands of profiles built and analyzed for a business, new prospects can be
matched against MACRO profiles to be served relevant personalized content, at the
right time and frequency. Predictions can also be made according to how far or
closely they match the MACROs.

23. UNDERSTANDING THE
PREDICTIVE JARGON

25. MULTIDIMENSIONAL SEGMENTATION

26. ACCOUNT BASED MARKETING (ABM) CYCLE
http://chiefmartec.com/2016/04/buzz-account-based-marketing-abm-martech/

27. ATTRIBUTION

28. MACHINE LEARNING vs DEEP LEARNING
http://searchbusinessanalytics.techtarget.com/definition/deep-learning

29. DEEP LEARNING
example
https://www.youtube.com/results?search_query=chor-rnn
10 mins after learning 6 hours after learning

30. SAMPLE PREDICTIVE
APPLICATION

31. PREDICTIVE SOCIAL
• Find the best times to post
• Suggest the most engaging
content type
• Pre-sentiment analysis
• Calculate reach estimate and
Post ROI
• Get an accurate Ad
management prediction
• Measure competitor impact

32. PREDICTIVE LEAD INTELLIGENCE
• Contact enrichment
• Analyze cross-channel
engagement
• Predictive lead scoring
• Detect behavioral signals
• Macro profile assessment
• Estimate closing probability
• Adapt best messaging based
on predictions
• Buying propensity

33. PREDICTIVE SALES PIPELINE
• Assess Pipeline movement
• Deal trends
• Detect agent productivity
outcome
• Forecast revenue
• Predict Company LTV
• Build retention patterns
• Repeat purchase frequency

34. PREDICTIVE CONTENT RECOMENDATION
• Serve favorable content
• Adapt the UI to the user
• Display Product based on interest
• Deliver targeted media, images and
videos
• Initiate smarter chat interactions

35. SOME TOOLS

36. SOME TOOLS
6 SENSE
Predictive intelligence platform
SALESFORCE EINSTEIN
Predicts outcome of client lifecyle
VBOUT
Predictive Backbone Marketing
RADIUS
Predictive marketing data
EVERSTRING
Predictive sales dev platform
AMAZON AWS
Machine learning and prediction
infrastructure
GOOGLE CLOUD
Cloud prediction API

37. THANK YOU!
Richard Fallah
Vbout.com @vboutcom