The document discusses the challenges of traditional CRM systems and introduces a concept of a CRM that operates invisibly in the background. It emphasizes the use of email communication and machine learning to classify leads, automate processes, and provide insights without requiring user engagement. The main features include coaching analytics, a knowledge graph for entity discovery, and automated lead stage estimation, ultimately aiming for a more efficient sales process.

1. 1
NoCRM
Piotr Karwatka
CTO at Divante

2. Agenda
2
How to built CRM that users aren’t aware of.
What's wrong with CRMs?
The concept of CRM that doesn't exist
Architecture
Algorithms
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2
3
4
What’s next?
Q/A
3
4

3. What’s wrong with CRMs?
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1. We sell B2B software services:
- We have 10+ sales team; 50+ new projects / year; contracts for 2+ years
2. We use “Predictable revenue” (see book by Aaron Ross: http://predictablerevenue.com)
3. At this point a CRM is a must – we tried Zoho, Pipedrive, Base ..
4. Most transactions in B2B are made by e-mail – CRM is yet another system and additional work
5. Sales reps. aren’t used to knowledge management systems
6. Main challenges
- leads leaking from CRM,
- no common place for offers/estimations/contacts – a learning company approach,
- unintended cross-communication with customers; insufficient knowledge about customers,
- hard to coach new sales reps.; hard to find what “sells” / suggest improvements,
- The need for the process automation: tracking / alerting leads, analyzing sales signals
- Predicting sales based on sales signals and the whole company history
Key issue with CRM at Divante? Adoption.

4. 4
CRM that users aren’t aware of. The concept.
customer A.
Your company
NoCRM
daily communica4on
- business as usual
Lead discovery &
classification
new value - pa9erns,
Predic4ons, struct. data
Automatic Entity Discovery
Leads, Contacts, Deals, Offers
+ Knowledge base
Sales pipeline &
patterns discovery
No User engagement –
language processing +
machine learning
customer B.
CUSTOMERS E-MAIL STREAM SALES REPS.

5. 5
CRM that users aren’t aware of. The concept.
1. Each email is classified – depending on
whether or not it’s a Lead (labeling /
black listing can be used to filter out
private messages) – messages are
threaded for lead history,
2. At PoC we use domain name as
Company identiy; Sender is used as
Employee identity; communication
paths = graph edges,
3. Attachments – offers/estimations – PDF/
Word/Excel are stored (next steps: to be
full-text-searchable) – knowledge base
building,
4. Next Step – discovery via Google Search
Api / Linkedin employee details; give
hints about whom from your team is
responsible for communication in given
topic (via e-mail summary + graph
connections) to avoid cross-pathing
Contact
Lead name
En-ty Extrac-on + summary (key words marked)
A9achments stored for KB
Sale rep.

6. 1. Imagine CRM that works 100% in background
- A manager adds sales team e-mails in panel, they receive invitations,
- Users authorize Gmail/Outlook/IMAP accounts,
- NoCRM monitors all sent and received e-mails,
- Due to the natural language processing and machine learning we discover patterns,
predict sales results, and estimate lead stages
- UI – No classic CRM UI; 70% Chrome Plugin – augmented e-mails; 10% a shared panel
for search/knowledge graph/statistics; 20% - smart e-mail notifications
2. Key features:
- Coaching: success patterns/prediction; KPIs; alerts & stats for management
- Knowledge graph: discovering entities from e-mails: companies/contacts
communication paths; gathering all the offers/inquiries in one place
- Pipeline and hints: automatic lead stage estimation, action signals, sentiments
CRM that users aren’t aware of. The concept.
6
Next slides: tech highlights how we started to work on PoC & what’s next.

7. CRM that users aren’t aware of. Chrome plugin.
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8. CRM that users aren’t aware of. Knowledge base & stats.
8
NoCRM Piotr Karwatka
Home > Leads > Search results
Leads 42
Type to search…
Team Offers archive 5
Magento B2B
Thesaurus.com by Chris P.
– offered, waiting for approval
JAVA Portal
Alegretto Inc. by Mike O.
– fresh lead, 2 days
Tile with
Microstandard by Piotr K.
– offered, waiting for approval
ORO Commerce
Minority Inc by Piotr K.
– not responding 3 weeks
UX Design
Technostyle.gr by Anna L.
– fresh lead, 1 week
Data mining
Langusta.com by Piotr K.
– offered, waiting for approval
PHP Outsource
Jugo.eu by Ernest T.
– offered, sentyment alert
SEO Optimization
News.co Ltd. by Anna L.
– fail, no response
Team statistics
15 min
10 min
8 min
- Searchable knowledge base – all leads, knowledge diagram, attachments
- Statistics panel

9. CRM that users aren’t aware of. Daily e-mail notifications.
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Daily hints; When no Chrome plugin used – e-mail is the main UI for sales reps. (with knowledge base panel)

10. 10
NoCRM Architecture
- E-mail agent on steroids,
- Standard big-data architecture,
- MLlib based alg. _ ext. APIs
for data drilling (eg. Entity Discovery)
e-mail
providers
e-mail sourcing
authoriza4on
workers & push
N-phase processing
via Spark & Spark
Streaming + MLlib
Analy-cal DB
+ storage: mongoDB
and HDFS (a9.)
Frontend – nodeJS
+ react
…

11. 11
NoCRM flow. Text processing.
You
Customer Inc.
Lead inc.
1. GO(lang) workers receive e-mails or push notifications (Gmail Api)
and pushes e-mail messages to RabbitMQ queue
2. Async N-phase e-mail processing; RabbitMQ channels - Spark +
MLlib + APIs;
- Ph1: Text Summary – TF-IDF / word2vec with stemming / thesaurus,
- Ph2: Text classifica4on: lead or not; pipeline setup – via MLlib/Naïve Bayes,
- Ph3: Diagram building based on the context - company/contacts/leads
- Ph4: Diagram drilling: En4ty Extrac4on via TextRazor API
- Ph5: Sta4s4cs & hints: counts/groups – history processing
1. Attachments are stored on HDFS (or S3)
2. Frontend works only on Analytical DB - mongoDB
3. Full e-mails can be stored in mongo for search/further processing;
but only TF-IDF and word2vec vectors and meta-information (dates/
counts/paths) are needed for basic operations

12. 12
NoCRM flow. Pipeline.
Leads are discovered from e-mails
Pipeline is built via text processing
(hints from UI can be made)
Pipeline is constantly measured (time, responses,
length) to predict current stage / next steps
Leads
Prospects
Customers

13. Phase 1: Text summary / feature extraction
Text processing:
- parse e-mails (body + subject)
- tokenize and stem the documents (various Lucence
stemmers can be used)
- create a dictionary out of all the words in the
collection of documents and compute IDF (Inverse
Document Frequency for each term)
TF(t) = (Number of times term t appears in a document) / (Total number
of terms in the document).
IDF(t) = log_e(Total number of documents /Number of documents with
term t in it).
- To check: word2vec algorithm for synonyms
https://www.quora.com/How-does-word2vec-work
- Implemented in Spark with MLlib with stemming and
thesaurus – keywords discovery, further classification
source,
Example?
https://en.wikipedia.org/wiki/Rainbow
Terms count:
the: 16
and: 6
rainbow: 5
droplets: 3
Terms count in 5 other articles:
the: 6
and: 6
rainbow: 1
droplets: 1
TF-IDF:
rainbow: 5 * log(6/1) 3.89
droplets: 3 * log(6/1) 2.33
the: 16 * log(6/6) 0.0
and: 6 * log(6/6) 0.0
looks like
keywords!
Example from: http://shiffman.net/teaching/a2z/analysis/#tfidf

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Phase 2: Text classification
1. Very similar to SPAM detectors – also using
Naïve Bayes (via MLlib)
2. Details of implementation:
https://chimpler.wordpress.com/2014/06/11/classifiying-
documents-using-naive-bayes-on-apache-spark-mllib/
3. Use of TF-IDF vectors computed in the
previous phase,
4. To score leads and set proper stages we
prepared reference dataset: e-mails
marked as “win”, “lose”, “prospecting”. At
first place we can create keywords
database like:
- offer, estimation -> prospecting
- agreement, sign up … -> win
- ...
5. Next – we can extend reference via real e-
mails by using Chrome plugin to score or
labeling feature (when not using Web-mail)
6. Same method – sentiment analysis marked as: prospect
marked as: lose
which group I’m similar to?

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Phase 4: Diagram drilling
- Automatic Name Entity Recognition and Entity Enrichment,
- Useful when extending knowledge graph,
- Planned: to use TextRazor.com API (English, Polish + other languages)

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Phase 5: Statistics
Based on lead stages stats:
1. Performance of every sales rep. – stats:
closed deals, time to close, opened leads, e-
mails/day/week
2. Lead statistics - abandoned leads, last
contact, time to first answer + SLA alerts
3. Mail statistics - opened links, read/unread by
recipient - list of events connected to mail
4. Daily “Coaching report” for every sales rep.
- A performance review against the team’s
performance,
- The top sellers’ methods (Eg.: What they
write about and what keywords they
use.),
- A lead loss hazard alert
5. NoCRM will monitor you
- Sales Manager X is already talking with
them

17. 17
What’s next?
- Smarter text analysis – use of Entity Recognition + gathering context data from Google
Search, Linkedin …
- Website / e-mail tracking (tracking links / pixels in e-mails)
- UI enhancements – panel & plugin development,
- Tests, tests, tests, tests.

18. 18
Q/A
Extended version of this presentaJon with text descripJon?
pkarwatka@divante.pl

19. THANK YOU
19
Piotr Karwatka, pkarwatka@divante.pl