Sentrana ifma board_meeting_final

The Impact of “Big Data” on Foodservice
Transforming Data into Actionable Insights

© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Agenda
1. Who we are
2. Industry Point of View
3. The Importance of Big Data
4. Opportunities for Leveraging Big Data in Foodservice
5. How to Get There
6. Summary / Questions

Introduction to Sentrana
1. Sentrana’s Foodservice experience goes back
to 2006 when we began piloting software to
deliver predictive analytics and optimized prices
with the Sysco the nation’s largest foodservice
distributor
2. Our MarketMover® suite combines predictive
recommendations and analytical tools to help
Sysco:
 Optimize prices for all “street” accounts every day
 Deliver specific cross-sell opportunities to each
Sales Associate for their accounts
 Design and execute corporate promotions, with
targeted products and prices as well as optimized
timing
3. Our expansion into the manufacturer space
was motivated by an opportunity to uncover
insights in manufacturers’ existing data that
can be immediately translated into market-
facing actions
1. Rapid Time to Value – Our domain expertise
and existing software-as-a-service (SaaS)
infrastructure puts tools and insights in users’
hands quickly without deep IT integration
2. Domain Expertise – Our experience in the
foodservice industry lets us go beyond simply
providing a repository of information.
3. Proven Data Management Experience –
Sentrana created hosted data warehouse
solutions for large enterprises comprised of
billions of records sourced from multiple,
disparate systems.
4. Advanced Analytics Capabilities – At its
core, we are a predictive analytics company
with the expertise and ongoing R&D to
maximize insights from data (especially
imperfect or “messy” data)
5. Continuous Improvement – We are
continuously innovating and improving the
products and services to make the insight
execution process more seamless
Deep Foodservice Expertise Unique Solution Values

 Category Management poses
collaboration opportunities, but
not everyone can win
 Efficiency targets may further
compress margins
 Whether you are direct or
broker sales force time in front
of operators is limited
 Need to maximize and prioritize
opportunities
 Distributors’ margin pressure is
partially pushed to suppliers
 Food shows, earned
income/shelter and other
obligations remain in place
 Minimal industry growth makes deep market awareness critical as competition for market
share and distributor obligations both intensify
Implications for Foodservice 2013 & Beyond:
Manufacturers Need to Change the Game …..
Distributor Obligations Limited Sales Force Category Management
Where are we
gaining vs.
losing share?
How do we get
opportunities out
to brokers and
field sales?
Where are our
best sales
opportunities?
How do our
Trade and
Marketing
investments
influence P&L?
Foodservice
Manufacturers
 GPOs are proliferating as
operators look for ways to save
 More street accounts are turning
to Cash & Carry’s, adding to
margin pressures
Operator Changes

In Case You Haven’t Noticed…

Big Data in Brief…
Big data (also spelled Big Data) is a general term used to describe the voluminous
amount of unstructured and semi-structured data a company creates -- data that would
take too much time and cost too much money to load into a relational database for
analysis.
Although Big data doesn't refer to any specific quantity, the term is often used when
speaking about petabytes and exabytes of data.
A primary goal for looking at big data is to discover repeatable business patterns. It’s
generally accepted that unstructured data, most of it located in text files, accounts for at
least 80% of an organization’s data. If left unmanaged, the sheer volume of
unstructured data that’s generated each year within an enterprise can be costly in terms
of storage. Unmanaged data can also pose a liability if information cannot be located in
the event of a compliance audit or lawsuit.
Big data analytics is often associated with cloud computing because the analysis of
large data sets in real-time requires a framework to distribute the work among tens,
hundreds or even thousands of computers.
-Margaret Rouse, Editorial Director, WhatIs.com

Big Data’s Impact on Foodservice Partners
1) Using their transactional data, distributors
can Optimize Prices to for every customer,
every SKU, every day
2) Sales data reveals Cross-sell
Opportunities that can be passed to the
DSR
3) Customer tendencies and preferences
can be inferred through transactional
attributes
4) Distributors can infer the customer’s total
purchase basket from all suppliers to make
more relevant offerings
5) Distributors have the ability of using their
transaction and program data to inform
Category Management initiatives
1) Ability to identify non-contracted
opportunities (both new and existing)
and gain additional revenue at DCs
2) Quickly identify unit compliance issues
and how business is trending at the unit
level
3) Improve vendor relations by providing
information on the latest consumer habits
and trends
4) Improve Unit Performance by identifying
opportunities across different geographies
or concepts and sharing the knowledge
Distributors GPOs and Contract Management Cos.

1. Spot Opportunities - Discover which customers have unmet needs and
determine the size of the volume opportunity at each customer
2. Manage Contract Buyer Relationships
 Identify “white space” in contracted business
 Break down performance trends by geography, product, segment
 Identify and communicate double dips to field sales for resolution
3. Understand the Street - Use loyalty program and food show data to
understand the “street”
4. Arm the Brokers & Field Sales - Provide guidance as to priority and cross-
sell opportunities to sales without HQ effort
5. Know the Distributors’ Value-Add – Negotiate trade spend with knowledge
of where your business is, and how much is contracted in each region
Applications of Big Data for Manufacturers
Harmonizing customer information across your different feeds provides a more
granular view of the customer and makes it easier to execute against new insights

 Manufacturers receive information about who their customers are and what they are
buying from numerous sources:
 Mapping units and cases across these feeds is difficult and time-consuming
― The same unit will be recorded differently in every data feed you receive
― A single case might show up in four or more data feeds!
 Harmonizing these disparate data silos drastically reduces the human cost of performing
analysis
 This isn’t data mining it’s Data Fracking!
Manufacturers Don’t Have a Data Problem - They Have a
Data Aggregation Problem!
Foodservice Rewards
Loyalty Programs
Contracted
Rebates
CHD Expert
LTO Coupons
Velocity Data
Distributor Deviated
(Contracted and Foodshow)

Benefits of a Big Data Strategy:
Using knowledge to drive actionable insights
1. Prioritize Opportunities: BSRs or Field
Sales see which units have the most “un-met
case volume” and compliance voids
2. Market Visibility: Spot shifts and emerging
trends in your contracted business to
“protect the base” and double-down on
growth areas
3. Greater Accountability: Give Sales
leadership clarity around Field Sales and
Broker performance
4. “True View” of the Customer: Stop double-
counting cases across different claims; see
which units actually bought which products
and how they are trending
5. Information is relayed directly to the right
audience without HQ interaction
1. Correlate Trade Investment with Sales
Performance: Quantify the link between trade
spend and case volume across distributors
and customer segments
2. Identify Your Distributor Leverage:
Determine the distributor branches in which
your contracted/street business mix points to
adjusting trade funds
3. Spot and Resolve Double Dips: Identify
units that are double-dipping on redemptions
and push this information to Field Sales to
resolve
4. Integrate Trade with Other Spend: Analyze
trade within the context of all spending
designed to move cases (marketing, loyalty
incentives, etc.)
Sales Advantages Trade Advantages

 Cross-sell
recommendations pushed
out directly to Territory
Sales through a
Dashboards
 While Campaign Marketing
analysts use Big Data for
Demand Creation, Territory
Sales can also monetize
the customer-level insights
in parallel
Making Data Actionable in the Field Should Not Create
Additional Work at Corporate
Cross-Sell Opportunities by Customer/Territory

Data Warehouses Must Integrate Data rather than
Simply Collapse It
1. Putting the data into one system
provides some convenience
2. We still cannot look across all sources
to understand the business as a whole
3. It is not clear when there is
overlapping volume for a single unit
4. At best, we only have a partial view of
each customer and redundancy across
sources, and no ability to correlate
anything
1. Using intelligent matching techniques,
we are able to match customer records
across information sources allowing us
to create a better market and product(s)
picture
2. We can then collapse transaction data
across buying groups and other
information sources to provide a richer
and customizable view of customer
behavior
Velocity
Data
Dist.
Deviated
Operator
Rebates
Foodservice
Rewards
Manufacturer
ERP
Velocity Data
Dist. Deviated
Operator Rebates
Foodservice Rewards
Manufacturer ERP
Integrated
Transaction View
Integrated
View of
Customer
Collapse Data Deliver an Integrated View

Bringing Data Together is Not Enough
Different Audiences Need Information Tailored to Their Needs
Make it easy for analysts to
identify patterns and
investigate outliers in the
data
Field Sales needs easy-to-
interpret sales plans and
target lists

Big Integrated Data and Analytics Capabilities Anchor
Continuous Improvement (not “One and Done”)
1 Consolidate & Integrate Data
Integrate all transactions and unify customer
IDs in MarketMover’s data warehouse
2 Refresh Analytics and Predictions
Update summary statistics and opportunity
predictions with latest sales and customer
data
3 Push Updates to Analytics Tools
Update summary statistics and opportunity
predictions with latest data
4 Measure Program Effectiveness
Gauge effectiveness of ongoing programs
and key decisions
6 Protect Base & Capture Opportunities
Field Sales and Brokers follow-up on targeted
leads identified by Management and software
Lost 120 cases
with three
Sodexo units
Compliance
voids with 6
Aramark units
5 Customize Sales & Marketing Plans
Tailor marketing plans for Field Sales &
Brokers; change underperforming
programs
450 incremental monthly
case opportunity in K-12
Logo
Logo

1. Diverse industry shifts are simultaneously forcing
manufacturers to get smarter about their
customers (operators & distributors)
2. You already possess significant data assets, but
the valuable information is scattered across
different files, formats, and systems
3. A system-based solution to harmonize these
disparate data sources makes it easier to access
information about your customers and
performance as well as incorporate new
information over time
4. Different types of users need tools that let them
interact with data in ways that fit with their skills
and responsibilities
5. Successful deployment requires managerial
engagement and a vision for building a
foundational business capability
Summary

“Big data as a technological opportunity and big data as a management theory
are two separate things. However much big data can yield, information will
never be perfect.
As efficient as these data models become, managers will still have to make
decisions with limited certainty about the outcomes.
Data helps and has since the scouts of ancient armies returned with reliable
numbers. Eisenhower at D-Day had more data than Hannibal at Cannae, but
waging war remained a beast of a task.
The challenge for managers has always been the human mind and heart,
which seems punier than ever in the shadow of the terabyte.”
- Philip Broughton
author of "The Art of the Sale
Conclusion

1725 Eye St. NW, Suite 900
Washington DC, 20006
OFFICE 202.507.4480
FAX 866.597.3285
WEB sentrana.com
Jim Klass
jim.klass@sentrana.com 704.562.9794

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