The Impact of “Big Data” on Foodservice
Transforming Data into Actionable Insights
Slide 2© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Agenda
1. Who we are
2. Industry Point of Vi...
Slide 3© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Introduction to Sentrana
1. Sentrana’s Foods...
Slide 4© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
 Category Management poses
collaboration op...
Slide 5© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
In Case You Haven’t Noticed…
Slide 6© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Big Data in Brief…
Big data (also spelled Bi...
Slide 7© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Big Data’s Impact on Foodservice Partners
1)...
Slide 8© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
1. Spot Opportunities - Discover which custo...
Slide 9© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
 Manufacturers receive information about wh...
Slide 10© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Benefits of a Big Data Strategy:
Using know...
Slide 11© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
 Cross-sell
recommendations pushed
out dir...
Slide 12© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Data Warehouses Must Integrate Data rather ...
Slide 13© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Bringing Data Together is Not Enough
Differ...
Slide 14© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
Big Integrated Data and Analytics Capabilit...
Slide 15© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
1. Diverse industry shifts are simultaneous...
Slide 16© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.
“Big data as a technological opportunity an...
1725 Eye St. NW, Suite 900
Washington DC, 20006
OFFICE 202.507.4480
FAX 866.597.3285
WEB sentrana.com
Jim Klass
jim.klass@...
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Sentrana ifma board_meeting_final

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Sentrana presentation to Board - July 2013

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Sentrana ifma board_meeting_final

  1. 1. The Impact of “Big Data” on Foodservice Transforming Data into Actionable Insights
  2. 2. Slide 2© 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
  3. 3. Slide 3© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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
  4. 4. Slide 4© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.  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
  5. 5. Slide 5© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. In Case You Haven’t Noticed…
  6. 6. Slide 6© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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
  7. 7. Slide 7© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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.
  8. 8. Slide 8© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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
  9. 9. Slide 9© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.  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)
  10. 10. Slide 10© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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
  11. 11. Slide 11© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute.  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
  12. 12. Slide 12© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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
  13. 13. Slide 13© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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
  14. 14. Slide 14© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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
  15. 15. Slide 15© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. 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
  16. 16. Slide 16© 2013 Sentrana Inc. All rights reserved. Do not copy or redistribute. “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
  17. 17. 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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