There is little doubt that Business Analytics will become a core differentiator in consumer industries, but even though Retail and Consumer Goods companies view analytics as extremely strategic, they struggle to effectively leverage it across the enterprise. EKN has studied the adoption and impact analytics in these industries for the last 5 years, and this counterpoint presentation will summarize key trends in analytics and shares fresh 2016 data on the state of analytics. Presented by Joe Skorupa (Editorial Director, RIS News) & Gaurav Pant (Senior VP Research & Principal Analyst, EKN Research) at the 2016 SPI Conference.

1. State of Analytics:
Retail & Consumer
Goods
GAURAV PANT
EKN

2. The age of what-i-want-when–i-want it consumers
requires building frictionless customer experiences.
Let’s step back to address the question of our time
How can I continue to be relevant to my customers?
…anymore

3. The bar for a good customer experience is being
continuously reset by players that might not even
be in your industry
(Amazon, Disney, Uber, WeChat, Apple, AirBnB, Tinder)
A great experience today is table stakes tomorrow.
Core = Technology + Insights

5. ROI
94%
of retailers don’t know/ measure
the ROI in analytics
Other
Priorities
Why?

6. How Much
14.6%
CAGR (2016-20) for
BI/Analytics IT budget growth
11%
CAGR (2016-20) for
BI/Analytics IT budget growth

7. Source: EKN Analytics in Retail Industry Survey, 2012 EKN Big Data in CG Study, 2012
Predictive
14%
Basic
Reporting and
Analysis
68%
51%
Basic Reporting
and Analysis
33%
InvestigativePredictive
16%
Investigative
18%
2012 Analytics Maturity:
CG companies led retailers

8. Source: EKN Analytics in Retail Industry Survey, 2015
Predictive
16%
Basic
Reporting and
Analysis
41%
45%
Basic Reporting
and Analysis
Investigative
Predictive
15%
Investigative
18%43%
40%
2015 Analytics Maturity:
Retailers are closing the gap fast

9. Who Else
47%
of retail business users bought
analytics software independent
of their IT Teams
23%
Of CG business users bought
analytics software independent
of their IT Teams

10. Where
RETAIL CONSUMER GOODS
FIRST TIME
Mobile BI
Big Data
UPGRADE
Data
Visualization/Dashboards
FIRST TIME
Data
Visualization/Dashboards
Personalization
UPGRADE
Enterprise BI & Reporting
CHANGE SUPPLIER
SaaS BI Platform
CHANGE SUPPLIER
Master Data Management

11. Still bringing a knife to a gunfight?

12. Amazon continues to be the gold standard for analytics
across all consumer industries.
The gap between it and the industry is widening
8
Source: EKN Analytics in Consumer Goods Survey, 2016
Source: EKN Analytics in Retail Industry Survey, 2016
RETAIL VS AMAZON CG VS AMAZON
AHEAD OF AMAZON
2%
AT PAR WITH AMAZON
18%
AHEAD OF AMAZON
0%
AT PAR WITH AMAZON
10%
BEHIND AMAZON
90%
AT PAR WITH AMAZON
80%

13. Source (2013:: Google patent records-EKN Research (2013). Some media reports have cited number of patents that Amazon has
received to be 1263.
• Amazon is innovating faster than other retailers.
Since 1994, Amazon has received ~1000+ patents to Wal-Mart’s
~*50
• Each business/service area has at least one patent
(Trade-In, Subscription Service, Digital Media etc.)
• There has been a gradual shift in the focus of
patents but there is continued focus on improving
personalization
Personalization/Recommendations -> Search -> Fulfillment - >
Technology
• It has a large number of patents that cover the use of
analytics and customer data to improve the business
(recommendations, usability content, promotions, payment,
sentiment analysis of reviews etc.)
A break-up of Amazon’s patents
Entity
~ No. Of
Patents
Amazon Technologies 560
Amazon.com 180
A9.com 100
Kiva 100
Liquavista 60
IMDB 16
Alexa 13
Clickmarks 12
True knowledge 8
Junglee 3
Total 1052
Amazon uses its proprietary algorithms and patents to differentiate itself
from the competition and create barriers to entry (1-Click checkout patent).
Culture of Analytics + Technology + Arsenal of IP

14. The 4 Pillars of Insights:
Need to move along the insight continuum
(Source: EKN Frameworks )
1
2
3
4
5
6
10
11
12
7
8
9

15. Privacy
Mo Data. Mo Problems.
Mobile
Social
(Source: EKN 3rd Annual Analytics in Retail Study, 2014 and 2015)
Top Data Management
Challenges
#1
Data
Organization
#2 Data Integration

16. CIO REPELLENT
Hype Resistant
Twitter: @gpantekn

17. The no spin zone: The reality of Big Data is that few like
the term, and it’s currently the domain of a few first
movers
Top 3 business
challenges
Lack of Budget
Unclear ROI
Lack of
resources
Source: EKN 4th Annual Big Data in Retail Survey, 2015; EKN Big Data in Consumer Goods Industry Survey, 2015;
Consumer Goods 2014 Tech Trends Study; EKN Future of Retail IT Survey, 2015
5%
Avg IT Budget spent on
Big Data Analytics
Value: Accuracy (n=all), Fast, Cheaper
4%
Avg IT Budget spent on
Big Data Analytics

18. Don’t get caught up in the term.
• There is no absolute specification of what Big Data is; each
enterprise must define what it is for them.
• Ways to measure (data):
• Volume – How much
• Variety - How different
• Velocity – How fast
• Traditional Definition
• The strategy, business processes, tools and technologies that pertain
to datasets whose size and complexity is beyond the ability of typical
data- base software tools to capture, store, manage, and analyze.
(Source: EKN 3rd Annual Analytics in Retail Study, 2014)

19. • Big Data is a relative concept.
• Think of it as a generational leap from your current analytics
capability that give you the ability to do a whole lot more from
less than with a whole lot less.
(Source: EKN 3rd Annual Analytics in Retail Study, 2014)
Don’t get caught up in the term.

20. Source: EKN Analytics in Consumer Goods Industry Survey, 2015
1 Limited resources who can interpret the output of analytics tools
Lack of single owner for analytics
2 Absence of a clearly articulated analytics strategy
3
Retail & CG companies agree
on the top challenges

21. Source: EKN Analytics in Consumer Goods Survey, 2016
Source: EKN Analytics in Retail Industry Survey, 2016
RETAIL VS AMAZON
DATA QUALITY
9x behind
DATA MANAGEMENT
4x behind
TOOLS
4x behind
SKILLS
4x behind
The gap with Amazon across dimensions is YUGE!

22. Annoying Orange
2010
A viral sensation that no one
saw coming
Offensive Orange
2016
A viral sensation that no one
saw winning
Well that was unexpected

23. Jun 16, 2015: Why Donald Trump Isn’t A Real Candidate
Jul 16, 2015: Two Good Reasons Not To Take
The Donald Trump ‘Surge’ Seriously
Jul 20, 2015: Donald Trump Is The World’s
Greatest Troll
Aug 6, 2015: Donald Trump’s Six Stages of Doom
Aug 11, 2015: Donald Trump Is Winning The Polls,
And Losing The Nomination
Nov 23, 2015: Dear Media, Stop Freaking Out About Donald Trump’s Polls
Feb 2, 2016 Donald Trump Comes Out Of Iowa Looking Like Pat Buchanan
Data-Fails & biases happen to the best of us

24. Source: EKN Analytics in Consumer Goods Survey, 2016
Source: EKN Analytics in Retail Industry Survey, 2016
RETAIL CONSUMER GOODS
TEAM SIZE (MAX)
312
TEAM SIZE (MEDIAN)
15
TEAM SIZE (MAX)
125
TEAM SIZE (MEDIAN)
3
DATA SCIENTIST
~30%
DATA SCIENTIST
~20%
Size of internal analytics teams

25. Build the skill pyramid.
Don’t use data scientists for pulling data
Source: EKN Frameworks

26. Source: EKN Analytics in Consumer Goods Industry Survey, 2015
Pricing Improvements
Promotion Effectiveness
Customer Insights Needs To Be A Torrent Across The Enterprise
Demand Forecasting
1 Optimize Inventory Levels
2
Out of Stocks
Marketing/Campaign Spend ROI3
Immediate focus areas

27. The coming of Retail’s Watson moment !
Merchandiser
A
Merchandiser
B
Watson

28. Data is for machines. Insights are for humans.
Design for humans and not scientists.
Let machines have their data and automate tasks.

29. Contact Me
•Gaurav Pant
Email: gpant@edgellmail.com
LinkedIn: linkedin.com/in/gpantnyc
Twitter: @gpantekn

30. THANK YOU