Realizing the Value of Supermarket POS Data

The Value of Real-Time Supermarket
Point of Sale Data

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Turn Data into Big Actions
Transform Your Data Into a Competitive Advantage!
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Key Platform Features !
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Unify different data sources for a
single operational view !
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Use real-time data to master omni-
channel, personalization and
interactive dashboards!
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Apply machine-generated predictions
to extract deep insights fast !
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Harness powerful big data
infrastructure to store and access
millions of records on-demand!
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Build applications from a robust
developer portal in days or weeks,
not months!
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Embed predictive APIs to make your
applications smarter ! REQUEST A DEMO

Realizing The Value of Supermarket Point of Sale Data....................3!
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Storeplacer Overview
Table of Contents
Granular POS Data is the Catalyst for Competitive Advantage ……………………………………………………………………………………….…..!
Industry Overview …………………………………………………………………………………………………………………………………………..…..!
Key Industry Trends!
Changing Consumer Preferences …………………………………………………………………………………………………………………..…... !
Digital Experiences………………………………………………………………………………………………………………………………..……….!
Mobility………………………………………………………………………………………………………………………………………………………!
eCommerce and Same-Day Delivery……………………………………………………………………………………………………………………!
Big Data and Personalization……………………………………………………………………………………………………………………..………!
Primary Value Areas for Transactional POS Data …………………………………………………………………………………………..…………….!
Storage, Access and Reporting!
Store, Unify and Enable APIs …………………………………………………………………………………………………………..……...…………!
On-Demand Business Intelligence and Reporting……..………………………………………………………………………...………..….……..…!
Predictive Analytics!
Localization………………………………………………………………………………………………………..……….............................................!
Inventory Optimization.………………………………………………………………………………………………………………………………........!
Fact-Based Merchandising and Instant Coupons…..……………………………………………………………..……….......................................!
Labor Utilization..……………………………………………………………………………………..………..............................................................!
Prevent Fraud to Reduce Shrinkage..……………………………………………………………..………...............................................................!
Real-Time Supply Chain Management...………………………………………………………………..…....................................................................!
Supply Chain Collaboration Partners..……...…………………………………………..………………..................................................................!
References and Credits…..…………………………………………………………………………………………………………………………………….!
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10!
11!
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15!
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17!
18!
19!
20!
21!

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Problem!
  Supermarket retailers, regardless of size, are at a significant competitive
disadvantage if they are unable to harness actionable data to address the
modern consumer. Key factors driving this need include:!
  The threat of imminent competition from Amazon.com, who has plans for
AmazonFresh to be in over 20 markets by 2014!
  Changing consumer preferences that favor value and relevance, including
product localization, mobility and personalized offers!
  The drastic decline of storage fees which now allows high volume, high
velocity data, like POS transactions, to be analyzed at a reasonable price!
  The proliferation in machine learning tools that can process large, complex
data to automate decision-making!
51%!
18%!
10%! 8%!
13%!
0%!
10%!
20%!
30%!
40%!
50%!
60%!
POS
Transaction
Data!
Sensors in
Retail
Environment!
Shopper
Feedback!
Automated
Product
Recognition!
Other!
Most Valuable Supply-Side Data Source!
Granular POS Data is the Catalyst for Competitive Advantage
Solution!
  The most valuable data for grocers to extract operational insights and grow cash flow from is point-of-sale (POS) transaction logs1 !
  Historically, POS logs were cost prohibitive to store because even a small retailer could process tens or hundreds of thousands of records daily!
  Real-time POS data also offers a unique opportunity to collaborate with the entire supply chain to boost revenue and operational efficiency!
  POS logs can be organized by metadata (order detail, customer, promotion, location, cashier, etc.), that provide key variables to forecasting:!
  Localization: what are the best products to offer in a given store and at what price!
  Inventory optimization: how can a store minimize inventory on-hand to reduce working capital?!
  Fact-based merchandising: one-to-one, relevant customer engagement and marketing based on known prior behavior!
  Instant coupons: facilitate highly relevant, individualized offers to consumers at checkout, via email and through any digital channel!
  Labor utilization: how many in-store employees to maintain by hour of day each month?!
  Fraud: identify anomalies in cashier discounting and performance!
Source: Brick Meets Click!

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Industry Overview
Overview!
  U.S. grocery store industry2!
  $1.1 trillion in sales!
  36,569 stores with >$2mm in sales!
  Typical revenue per store: !
  Chain: $2 million-$50 million!
  Independent: $2 million-$20 million!
  Products sold per location!
  Grocery: 15,000-60,000 items !
  Convenience: 800-3,000 items!
  Financial ratios!
  Net income margin: 1%!
  EBITDA* margin: 3-8%3!
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Key Financial Drivers!
  Inventory turnover: 40-50% of assets2 !
  Labor cost: 8-12% of sales3!
  Product mix and pricing strategy
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Important Trends!
  Changing consumer preferences!
  Digital experiences !
  Mobility!
  eCommerce and same-day delivery!
  Big data and personalization!
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Emerging Threat:!
eCommerce / Same-day Delivery Offering4,5!
  500,000 items available online, including fresh grocery and local products !
  Annual fee: $299 per year!
  Free same-day delivery for orders over $35!
  $7.99 delivery fee for orders below $35!
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History of AmazonFresh!
  August 2007: Begins beta test in Seattle !
  June 2013: Expands to Los Angeles and announces plans to add San
Francisco in 2013 and 20 new locations by 2014!
  August 2013: Acquires 1.5mm sq. feet of space in New Jersey
* Earnings before interest, taxes, depreciation and amortization!

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Consumer Shift to Value Seeker6!
  Shoppers are more focused on value today!
  78% seek discounts often vs. 61% pre-recession!
  Price-driven reasons for not shopping closest to home:!
  Lower prices (61%)!
  Lower prices on specific items (53%)!
  Better grocery product variety and selection (41%)!
Changing Consumer Preferences
61%!
53%!
41%! 39%!
26%! 24%! 23%! 22%!
0%!
10%!
20%!
30%!
40%!
50%!
60%!
70%!
Lower
prices (in
general)!
Lower
prices on
specific
item(s)!
Better
grocery
product
variety
and
selection!
Better
fresh
food
quality
and
variety !
Better
private
brand
variety
and
selection !
Just habit
or more
familiar!
Ability to
shop
more
quickly!
Better
special
item
selection
(ethnic)!
Store Selection Beyond Convenience!
(% Shoppers Whose Primary Store Is Not the Closest to Home)!
61%!
78%!
28%!
11%!
0%!
20%!
40%!
60%!
80%!
100%!
Always exhibited
behavior!
Changed during
recession!
Will revert to
post
recessionary
behavior!
Will exhibit
behavior going
forward!
Consumers Seeking Discounts!
(% of Shoppers)!
Ethnic Differentiation7!
  Grocers will be forced to understand their customers better as
ethnic- and age-based merchandising presents new complexities!
  The ethnic population will increase to 47% of the US consumer
base, a 13% increase in the next ten years !
  Between 2010 and 2025, Hispanics and Asian populations are
forecast to grow the most, 41% and 37%, respectively!
  Age-based shifts!
  The 65+ yr. old segment will increase 7%!
  The under 18 and 25-44 yr. old segments will decrease!
  All other age segments will remain flat
Source: Booz & Company!
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  Shoppers are using technology for discovery pre-trip and in-store!
  52% of consumers use technology while grocery shopping6 !
  32% use online coupons!
  23% check prices!
  31% use mobile devices!
  9% use mobile devices, online coupons and check prices!
Digital Experiences
32%!
23%!
20%! 19%!
16%! 16%!
13%!
9%! 9%! 9%! 8%! 7%! 7%!
5%!
0%!
5%!
10%!
15%!
20%!
25%!
30%!
35%!
Get
coupons
pre-trip!
Check
prices
pre-trip!
Research
products
pre-trip!
Make
shopping
list on
mobile
device!
Read
product
reviews
pre-trip!
Track
shopping
list!
Check
recipes!
Check for
coupons!
Track
spend!
Research
products!
Check
prices!
Check
nutritional
data!
Scan
shelf
lables!
Locate
products!
Value Discovery and Other Activities!
(% Shoppers who use technology for more than 25% of Shopping Trips)!
Other activities!Value discovery!

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  68% of consumers believe mobile will play a signiﬁcant role in the shopping experience7 !
  40% to compare pricing (e.g., GroceryIQ)!
  34% to navigate the store (e.g., Aisle411)!
  34% to use their loyalty app (e.g., Walgreens Balance Rewards)!
  32% to self checkout via a scanning apps (e.g., Walmart Scan & Go)!
  Supermarket consumers actively use mobile technology for building shopping lists,
navigating stores, searching for coupons and at checkout!
!
Mobility
Loyalty! Scan and Go!
Price & Coupons! Navigation!
39%!
34%! 34%! 32%!
24%!
12%!
0%!
10%!
20%!
30%!
40%!
50%!
Shopping
List
Comparison
Pricing App!
In-store
Navigation
Tool!
Loyalty
Application!
Self
Checkout
Scan App!
Mobile
Wallet!
Geo-location
Tool!
Anticipated Mobile Activities!

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eCommerce!
  U.S. ecommerce is projected to account for 8% of total retail sales, though 50%
of in-store purchases are estimated to be inﬂuenced online8 !
  While shopping online for clothes and electronics items is natural, it is still not a
preferred way to shop for groceries!
  46% never or rarely buy food online10!
  Of those who buy online, 12% buy dry groceries and beverages and 4% buy
fresh food and produce!
  However, many grocers are experimenting with ecommerce and in-store pickup!
  22 major chains launched e-commerce in 2012, up from eight in 20119!
  Online CPG sales grew from $5 billion in 2006 to over $12 billion in 20106
accounting for approximately 2% of total CPG sales!
  By 2014, online grocery sales are projected to reach $25 billion10!
  By 2025, 10% of total grocery sales are estimated to occur online7
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Same-Day Delivery!
  While same-day delivery for groceries is new, its future is uncertain!
  Pro case: 57% like the convenience of shopping from home; 47%
like the convenience of home delivery6!
  Con case: The cost to deliver low-margin groceries may outweigh
the incremental contribution margin on those sales!
  60% of consumers were willing to pay between $1.30 and $6.50
for home delivery, but few would pay for in-store pickup11!
  Few people may pay for a product they can get with no effort vs.
driving to a store in the car!
  AmazonFresh may lower fees to “break even” to grow adoption!
  AmazonPrime was estimated to lose $11 per customer but
achieved massive adoption as its members accounted for almost
40% of domestic sales12 !
  InstaCart and Google launched same-day delivery services to help
supermarkets compete with Amazon and Walmart!
eCommerce and Same-Day Delivery
$5 !
$8 !
$12 !
$17 !
$25 !
$0 !
$5 !
$10 !
$15 !
$20 !
$25 !
$30 !
2006! 2008! 2010! 2012! 2014!
U.S. Online CPG Sales ($ Billions)!
Source: Nielsen!

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  43% of retail professionals believe the most important value of big data is faster decisions1!
  CIOs believe high priority big data areas that can transform grocery shopping include
personalization, instant promotions and smart shelves capable of dynamic pricing7!
  Adoption is acceleration – in March 2013, 64% were active in big data initiatives vs. 20% in 2012!
!
Big Data and Personalization
Smart Shelves!
76%!
70%!
43%!
38%!
0%!
10%!
20%!
30%!
40%!
50%!
60%!
70%!
80%!
Strengthening
Shopper
Engagement!
Creating
Personalized
Promotions!
Enabling More
Shopper
Solutions!
Implementing
Store-Speciﬁc
Assortments!
Most Important Ways Big Data Creates Value!
Source: Brick Meets Click! Source: Booz & Company!

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  Even the largest grocery retailers are not fully utilizing detailed POS logs, which hold a breadth of actionable intelligence in three primary areas: !
Primary Value Areas for Transactional POS Data
Storage, Access & Reporting!
  Store granular transaction logs which truly
identify the shopper experience!
  Enable on-demand data APIs!
  Real-time interactive dashboards!
  Reporting!
  Augment data from sensors, offers, loyalty,
SKU catalog, locations and more to gain a
complete operational view!
Predictive Analytics!
High Impact!
  Localization!
  Inventory optimization!
  Fact-based merchandising!
  Instant coupons!
  Labor utilization!
  Fraud prevention!
!
Other Impact!
  Customer segmentation analyses!
  Estimate customer lifetime value!
  Dynamic pricing optimization!
!
Real-Time Supply Chain!
  Faster replenishment means better in-stock rates!
  On-demand inventory eliminates costly logistics,
fuel and hours to count items manually!
  Triggered notiﬁcations!
  Secure, CPG and partner login!

! ! ! ! ! !

  Store and organize detailed POS log data for more granular reporting and relationship analyses!
  Aggregate many disparate systems to gain a single, uniﬁed view of operations!
  Turn your business into a platform with an on-demand application programming interface (APIs) layer!
  Empower IT to do more by shifting focus to front-end development and customer acquisition!
  Test and launch digital experiences in days or weeks, not months!
  Create personalized one-to-one marketing initiatives across any channel!
  Share data faster and easier with distributors, manufacturers and other partners!
Store, Unify Data & Enable APIs

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Business Intelligence!
  Incorporate data into customized predictive models!
  Power interactive dashboards and applications, for example:!
  Category manager: views products, prices, sales, trending items
within each category !
  Store manager: views all data from their store!
  Regional manager: views data from all stores within their region!
  CEO/C-Level: views any data set!
!
!
!
Reporting!
  Storing detailed transactional data allows for more granular reports !
  New technologies, like Hadoop, allow more information to be processed
faster and cheaper than legacy systems!
  Run queries against hundreds of millions of records in seconds!
  Non-developers to analyze business trends via a reporting interface!
  Export results into excel or other statistical packages!
  APIs allow!
  Direct integration into enterprise data warehouse systems!
  Any developer to query datastore securely without direct IT involvement !
  Compete better and faster with APIs that facilitate business intelligence and reporting when needed, wherever needed!

On-Demand Business Intelligence and Reporting

! ! ! ! ! !

  Category management, the widely practiced product strategy philosophy, is the idea that related products are grouped into categories (e.g.
produce, pharmacy) that are treated as unique business units !
  Each manager has separate profitability targets and strategies to determine pricing, inventory and placement that are heavily influenced by
local competition and customer demographics!
  Effective category management localizes the product mix and pricing that brings shoppers into the store and keeps them loyal !
  POS data helps to identify customer demographic impact and market basket trends across stores within a multi-location supermarket system!
  A store system that can optimize each store for their target customer and demographic market vs. an average for an overall multi-location
system is well positioned to increase profitability of each store!
  Within each of those categories, certain brands may perform differently across stores due to the CPG’s local marketing initiatives, which are
unknown to the store manager!
  Product relationships are quite complex to analyze across 15,000-100,000 SKUs with human capabilities but bode well for machine learning!
Machine-generated, adaptive intelligence can derive insights into !
  How can I provide more relevant products to grow revenue and create a better shopping experience? !
  What are the relationships between products (e.g., if a consumer buys X, what else will they likely purchase)?!
  What is the ideal price for a given product by day of week and time of day?!
  How many customer segments do I have and what are they?!
  Does my store product mix match the local demographic?!
Localization

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  75% of retail professionals believe inventory management has the greatest impact to managing the supply-side1!
  Inventory costs are the single most important driver of cash ﬂow and accounts for 40-50% of total assets2!
  Unfortunately, due to its complexity, most independent supermarkets!
  Manage by intuition through experience and knowledge of local customer demographic!
  Do not know day-to-day inventory on hand, rather perform physical stock counts monthly to calculate inventory and shrinkage !
  Successful retailers minimize inventory by anticipating consumer demand!
  Actionable metrics, that combined with inventory lead times and marketing initiatives, can help retailers maximize in-stock rates and plan promotions
to maximize sales and burn off slow-moving or obsolete inventory!
Machine learning algorithms applied to POS logs can unearth answers to key questions, such as:!
  What and how much inventory should be held to maximize cash ﬂow?!
  How many transactions and items will be sold next day and/or next week?!
  How can I project sales by day of week and day of month?!
  How should I handle seasonality or promotional items?!
  When do customers buy various goods?!
Inventory Optimization

! ! ! ! ! !

  Grocers are poor at delivering relevant offers to consumers, even while a high level of personalization has become a mainstay in ecommerce!
  Recommendation models can be built from a variety of digital sources; POS data is widely believed as the top data source1!
  Transaction logs that can identify product similarities can drive recommendations for new customers with no history!
Benefits of Digital Coupons14!
  Higher redemption: digital coupons are reported to achieve an almost 20x higher redemption rate than traditional mediums13!
  Individualized: digital mediums can deliver specific, unique coupons for each individual!
  Tracking: easy to analyze redemption rates for digital coupons in real-time vs. weeks to gather this information for printed coupons!
  Lower costs: retailers no longer have to spend money to print circulars!
  Timing: digital coupons can be launched in 72 hours compared to up to eight weeks for print promotions!
  Virality: digital coupons can be shared much easily to maximize reach!
Fact-Based Merchandising and Instant Coupons
Amazon – Product Recommendation Engine!
  Amazon is a leader in applying machine learning to boost the user
experience through product recommendations!
  Recommendations provide targeted offers based on shopping and rating
history, browsing behavior and other data they collect !
  A cooking enthusiast might receive on-site or email suggestions for
cookbooks and ingredients based on past behavior!
52%!
44%!
39%!
32%!
24%!
0%!
10%!
20%!
30%!
40%!
50%!
60%!
Shopper
Identified
Transactions!
Mobile
Devices!
In-store
Tracking!
Shopper
Feedback!
Social Media!
Top Sources of Digital Breadcrumbs!
Source: Brick Meets Click!

! ! ! ! ! !

  Labor is one of the highest cost items and estimated to average 8% to 12% of total revenue3!
  Store managers are generally responsible for scheduling employees via a schedule for nights/days and weekdays/weekends!
  Sophisticated grocers schedule labor based on transaction history, traffic counts from sensors and other data !
  Scheduling challenges occur for retailers that are constrained by union regulations or that have heavy volume fluctuations throughout the month!
  For example, retailers that services low-income consumers that use food stamps likely have considerably more traffic in the first few days of
the month than the last few days. How should schedules be planned for this volatility?!
  Granular POS data holds the key indicators to forecast optimal staff to service expected site traffic!
POS logs hold key inputs to predicting:!
  How many transactions will occur by hour by day?!
  What is the estimated foot traffic by hour by day?!
  Are certain cashiers more productive than others?!
  Can I estimate the average wait time per terminal?!
  What is the optimal mix of floor staff to achieve the desired customer experience?!
Labor Utilization

! ! ! ! ! !

  Fraud at checkout is estimated to cost $828 million dollars13 annually largely via under-ringing, up charging, sales cancellations, refunds, price
adjustments and other nefarious actions!
  The most typical employee theft is “sweethearting” whereby a cashier provides enhanced discounts or refunds to friends and/or family!
  Today, managers review weekly or monthly reports to identify fraudulent signals, but these reports take signiﬁcant time to analyze!
  Solving problems faster can save thousands of dollars in shrinkage!
Machine learning can automate fraud identiﬁcation; APIs can trigger alerts for detecting:!
  Anomalies in transaction history, such as excess voids !
  Inventory discrepancies!
  Other fraudulent signals!
Prevent Fraud to Reduce Shrinkage

! ! ! ! ! !

  The most untapped area of value for POS transaction log data is to create a real-time supply chain for distributors, manufacturers and other partners to
collaborate on-demand !
  Supply portals may substantially increase revenue and loyalty especially through direct store distribution (DSD) channels!
  Target in-stock rates are generally around 95%!
  Faster replenishment increases revenue with better in-stock performance!
  The primary challenge for making this data actionable is volume, velocity and cost!
  Larger retailers record millions of records of data each hour !
  As a result, most grocers only record summary level snapshots by day or hour severely limiting the intelligence that can be extracted!
  Historically, detailed POS logs were too costly to store though storage fees have dropped considerably (as illustrated below)!
Real-Time Supply Chain Management

! ! ! ! ! !

Direct Store Distributors (DSD)!
  DSD items make up less than 25% of unit sales in grocery, but
more than half of the proﬁt15 !
  DSDs deliver products directly from manufacturers to the store
to help retailers save money by eliminating a distribution layer
and additional costs for delivery, logistics, and fuel !
  DSD helps grocers get money faster and reduce carrying costs!
  The average DSD supplier replenishes within two days, 5x
faster than traditional grocery retail processes!
  In most cases, the DSDs own the inventory enabling
products to hit the shelf faster with less working capital!
!
!
!
CPG Manufacturers!
  Execute targeted hyper-local promotions optimized by hour/day!
  Analyze the immediate affect of local marketing spend !
  Improve manufacturing schedules to meet expected consumer demand!
  Understand product launch trends immediately !
Supply Chain Collaboration Partners
Leading CPG Suppliers!

! ! ! ! ! !

!
1  Brick Meets Click, Moving Forward with Big Data: The Future of Retail Analytics, April 2013!
2  Standard & Poor's, Industry Survey, Supermarkets & Drugstores, January 2013!
3  Duff & Phelps, Food Retail Industry Insights!
4  Techcrunch: Amazon Bets On Web Groceries, Expands AmazonFresh To L.A., June 2013!
5  Fast Company: AmazonFresh is Jeff Bezos Last Mile Quest For Total Retail Domination, August 2013!
6  U.S. Grocery Shopper Trends 2012 (Food Marketing Institute), Booz and Company analysis !
7  Booz & Co, “Tomorrow’s Trends Delivered Today, Food Retailing 2013”, FMI FutureConnect 2013!
8  Forrester Research, Web-Inﬂuenced Retail Sales Forecast, December 2009!
9  Dynamite Data !
10  Nielsen, Five Things to Know About Online Grocery Shopping, May 2012!
11  AT Kearney, A Fresh Look at Online Grocery, 2012!
12  Time, Amazon Prime Loses $11 Annually Per Member … And It’s a Huge Success, November 2011!
13  Cardhub, Credit Card & Debit Card Fraud Statistics!
14  360i, Couponing in the Digital Age, June 2011!
15  Grocery Manufacturers Association, Powering Growth Through Direct Store Delivery, September 2008!
Credits
!
  Authored by Jason Lobel, Bill Bishop and Vanessa Youshaei!
  Designed by Jasmine Yu!
References

Realizing the Value of Supermarket POS Data

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Realizing the Value of Supermarket POS Data