Periodic Table of Analytics Tools

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Summarise data science tools available under different categories in Predictive Modelling/BI/ML.

IV
InsideVi
ew
MI
Mintigo
SW
SWRVE
MA
Marketo
RI
Radius
Intellige
nce
TE
Teralytic
s
FT
Fliptop
FI
Five9
GD
GoodDa
ta
AI
Argus
Insights
TW
24/7
PY
Python
TS
Tableau
Software
ZI
ZoomInf
o
S
6sense
LE
Lattice
Engines
EX
Exacast
er
KT
Knowle
dge
Tree
BS
Buzz
Sumo
ES
EverStri
ng
LS
Leadsp
ace
IN
Inter
SP
SalesPr
edict
TO
TopOO
PPS
RD
RingDN
A
IS
Insidesa
les.com
SW
SalesWi
ngs
CL
Clari
AF
Affinnov
a
EC
Engage
Click
T
Terminu
s
MR
MobileR
OI
SF
SalesFu
sion
ML
Madison
Logic
EL
Eloqua
AD
Adobe
AN
Angoss
AL
Alteryx
DO
Domo
RM
Rapid
Miner
I
IBM
OS
Opera
Solutions
SS
SAS
HS
Hearsay
Social
SA
SAP
VH
ViralHe
at
MS
Microsoft
ZE
Zementi
s
GS
GainSig
ht
BA
Bluenose
Analytics
PR
Preact
TO
Totango
EN
Enfusen
NA
Nanigan
s
DX
DataXu
MS
Markets
hare
KS
Kensho
o
Sales &Marketing
Predictive Scoring
Sales Acceleration
Targeting
Business Analytics
Social Media
CustomerEngagement
CustomerSuccess
MarketingOptimization
Periodic Table of Predictive Analytics

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As marketing becomes a technology-powered discipline, marketing teams are adding more technical talent to their ranks. Hybrid "marketing technologists" -- people who combine technical depth with a passion for marketing -- make marketing a more sophisticated user of software for automation, interactive customer experiences, and data-driven decision-making. Here we'll examine the role of a "chief marketing technologist" as the CMO's right-hand for marketing technology strategy and management.

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Sales & marketing misalignment: it's a battle as old as time itself. But with the rise of account-based marketing, it's more important than ever to get your teams on the same page. The good thing about account-based marketing is that it forces marketing and sales to be in sync by virtue of the targeted approach it requires. In this deck, you'll learn how account-based marketing solves for many variables that commonly cause misalignment between sales and marketing teams.

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Analyzing online traffic and making sense of all the data is something digital marketers do every day. After all we need to understand what’s going on in order to grow the business. Sometimes our assumptions are based on past experiences, sometimes we can support them with data, but quite often they are based on pure logic. Are they really? Over the years we’ve worked with numerous clients and learned that some assumptions are proven wrong more often than not.

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