What's Hiding in Your Point of Sale Data?
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Retail's BIG Show January 15-17, 2017 What's Hiding in Your Point of Sale Data? Irad Ben-Gal, Stanford University/C-B4 Miki Cisic, C-B4 Analytics Joe Gauthier, Wesco, Inc.
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What's Hiding in Your Point of Sale Data?
- 1. RETAIL’SBIGSHOW2017|#NRF17 What’s Hiding in Your Point of Sale Data? IRAD BEN-GAL, Professor and Chairman, Stanford University/C-B4 JOE GAUTHIER, Director, Operations, Wesco, Inc. MIKI CISIC, Director, Sales, C-B4 Analytics
- 2. RETAIL’SBIGSHOW2017|#NRF17RETAIL’SBIGSHOW2017|#NRF17 The content of this document is C-B4 Confidential & Proprietary. If you are not the intended recipient and have received this document, any use or distribution is prohibited. Please notify support@c-b4.com immediately by e-mail and delete this message from your computer system. What's Hiding in Your Point of Sale Data? Explore the differences between market basket analysis vs in-store consumer behavior
- 3. RETAIL’SBIGSHOW2017|#NRF17 Basket Analysis (a.k.a Affinity Analysis) • Which group of items are likely (or less likely) to be purchased together? For example, beer & potato-chips or shampoo & conditioner…. • Provides a better understanding of the individual purchase behavior of the customer (“impulsive customer purchase”). • Well established & helpful in many applications P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S3 | C C
- 4. RETAIL’SBIGSHOW2017|#NRF17 Market Basket Analysis vs. In-store Purchase Pattern Analysis P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S4 |
- 5. RETAIL’SBIGSHOW2017|#NRF17 BreadButter Milk DiapersBeer {Bread & Butter Milk} Mon #1 Mon #2 Tue #1 Tue #2 Wed #1 Wed #2 Thu #1 Thu #2 • Probability {Milk}: 5/8 ~ 60% • Probability {Milk given Bread & Butter }: 2/2 = 100% Can we learn more at an aggregated daily/store level? {Beer Diapers} • Probability {Diapers}= 6/8 =75% • Probability {Diapers given Beer)= 3/4=75%, Lift = 0% • Lift=40%: (When selling Bread & Butter the probability for selling milk increases by 40%, but it applies to only 2 transactions) P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S Purchase Example (sourced from Wikipedia) • No correlation is found at a transactional level 5 |
- 6. RETAIL’SBIGSHOW2017|#NRF17 P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S Purchase Example (cont.) • Reflects a non-transactional consumption preference pattern {Beer Diapers} are correlated at a store level • Is this pattern significant (statistically)? • How can we use it at the chain level to analyze the stores’ performance? • Let’s check this pattern across all stores 6 | BreadButter DiapersBeer Mon #1 Tue #1 Wed #1 Thu #1 Milk Mon #1 Mon #2 Tue #1 Tue #2 Wed #1 Wed #2 Thu #1 Thu #2
- 7. RETAIL’SBIGSHOW2017|#NRF17 Consumer Purchasing Patterns at a Store level (Beer & Diapers example - out of trillions of combinations) • Automatically analyzing millions of consumption preference patterns • Root cause of anomalies: - Operational failures - Availability issues - Other local effects P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S7 |
- 8. RETAIL’SBIGSHOW2017|#NRF17 Basket Analysis (Individual behavior) • Focused on individual purchase behavior of a customer • Personal Applications: Cross-Selling & Up-Selling, personalized coupons, personalized emails • General Applications: Store Design, Loyalty programs, Promotional plans… P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S8 | Bottom Line: Benefits of both Methods In-Store Purchase Pattern Analysis (store behavior) • Does not require transactional & personalized data (faster & simpler POS data) • Reflects non-transactional patterns (even by different customers) • Lower Error Rate (aggregation reduces false rules by random associations of products) • More effective for store behavior analysis – correcting operational failures, availability issues, localizing assortment, analyzing local effects
- 10. RETAIL’SBIGSHOW2017|#NRF17 Proven to increase same store growth by 0.8 to 3% Uncover local consumer purchasing patterns within simple sales and inventory data in order to: • Fine tune assortment at a store level to better fulfill local preferences • Detect + correct in store operational anomalies that prevent high volume sales 1 0 No Hardware Automated No External Data
- 11. RETAIL’SBIGSHOW2017|#NRF17 • Chain of 52 convenience stores in Muskegon, Michigan • Family owned • Started by Bud Westgate in 1952 with a single store and 3 used gas pumps • Continuous growth, expanded to include: • Distribution center • Central bakery and deli • 6 Subway locations • Bulk fuel and propane business + Wesco Energy division • For 55 years the mission has been Q-PPAS: Quality People, Products, Associates and Service P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S1 4 |
- 12. RETAIL’SBIGSHOW2017|#NRF17 P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S1 5 | Pilot: Your total effort is less than a day 2 hours Wesco 3 hours CB4 1/2 Day Together 5 min CB4 On-going Stores
- 13. RETAIL’SBIGSHOW2017|#NRF17 1-2 DAYS • Installation • Configuration of data extract • Integration of business constraints into the analysis to increase the relevance of recommendations for store and merchandising managers Deployment in less than a week P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S1 6 | 1. Setup & Installation 2. Training 3. App Deployment Uncovers behavioral patterns and translates them into recommendations *Available on premise as well 5 DAYS • Training - Solution owner/users – 5h - District manager – 3h - Store managers – 0.5h - Merchandising team – 2h • Perform store rides • Perform several dry runs • Schedule automatic deployment of recommendations to stores via email, back office PC, or mobile app 1 HOUR • Installation of back office or mobile app • App is straightforward and does not require dedicated training (comes with manual and video clips that walk the store managers through the process)
- 14. RETAIL’SBIGSHOW2017|#NRF17 Web Console Ability to schedule and deploy recommendations + review dashboards that measure ROI and revenue lift Point of Sale Data CB4 Servers Uncovers behavioral patterns and translates them into actionable recommendations Store managers/supervisors Recommendations to resolve operational opportunities Merchandising Managers Recommendations that help to localize assortments and planograms Feedback from stakeholders is tracked and measured P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S1 7 |
- 15. RETAIL’SBIGSHOW2017|#NRF17 App - Demo P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S1 9 | Review your list of “Open Tasks” Tap on each task to see details Submit findings
- 16. RETAIL’SBIGSHOW2017|#NRF17 P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S2 0 | $1,910,432 Estimated annual revenueLift 2% Operational opportunities detected 582
- 17. RETAIL’SBIGSHOW2017|#NRF17 P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S2 1 | HIT RATE
- 18. RETAIL’SBIGSHOW2017|#NRF17 DYNAMICALLY LOCALIZED ASSORTMENT • 200 new items introduced to assortment • Improved planogram execution IMPROVED CUSTOMER EXPERIENCE • Store associate awareness to product display & operational opportunities. • Weekly feedbacks 0.8 – 3% SAME STORE GROWTH • On track to $2M same store growth by end of fiscal year (2.2% sales increase) IMPROVED AVAILABILITY • Increase availability in stores by 7% • Better Supply chain decisions Value & Benefits P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S2 2 |
- 19. RETAIL’SBIGSHOW2017|#NRF17RETAIL’SBIGSHOW2017|#NRF17 Visit us at booth #4252 for more information
Hinweis der Redaktion
- MY NAME …. Title of the talk
- Let me start with a simple definition of MBA
- If I have to sum main difference between the methods MBA focuses on transactional & personalized purchase behavior of a customer , while In-store purchase pattern analysis is used to find new insights (not necessarily transactional) at a store level, that are mainly relevant for the store operation – CB4 is DOING And I’d like to show it in a small example
- 8 transactions – 2 per day Out of many possible rules
- Even not the same customer Young mothers are buying the diapers while the young father still hangout with friends in weekends and are the ones that buy the beer
- First we see that the pattern is significant over many stores (not necessarily all stores) – when beer is sold high diapers are sold high Now we see two store that deviate from the pattern – although beer is sold high the diapers are sold low. Local Preferences Purpose of slide: Explain how the approach of our analysis is different than most traditional forms of analysis which are based on segmentation - we find similar behavior across segments between subsets of products and stores which is the result of similar external or internal conditions Slide’s core messaging: Retailers typically map their stores into segments. Segments are artificially created by identifying several external and internal conditions that are believed to impact the sales of products in the stores in a significant way (e.g. demographics, climate, size of store, etc.). Although retailers don't necessarily expect stores within a specific segment to exhibit completely homogeneous behavior, they do expect some degree of similarity in the way products are consumed within stores in the same segment. However, what is often overlooked is consumption behavior that is similar across segments and can occur within a specific subset of products. As an example, let’s imagine two stores in seemingly very different environments. One located in Pennsylvania and the other in Texas. These stores are exposed to different internal and external conditions, such as demographics and climate, and will therefore probably be placed in different segments using traditional analysis. However, if these two stores had a school nearby, the proximity of that shared external condition is likely to dictate a similar purchasing behavior within a specific subset of products, in this case maybe products that kids like. And so, despite the numerous different conditions that these two stores are exposed to, we can still expect similar purchasing patterns with regards to that specific subset of products that kids like (although for the rest of the assortment, these stores might exhibit completely different behavior) We can then extend this exercise to all the stores in the same chain that have a school nearby. Using the same logic, we can similarly expect that products that kids like would exhibit similar sales patterns across all these stores. From this example we can see that the various external and internal conditions which stores are exposed to create similar purchasing behavior across stores in different segments for specific subsets of products. Furthermore, as the internal and external conditions change, these common behavioral patterns change as well, creating shifting behavioral clusters. These are what we call fuzzy clusters.
- Without further ado I will invite Miki Sisic our head of Sale to show how this is done in real environment and real data
- Exploration: Is 0.8 to 3% significant for your organization? Are you current running or planning to run a new in-store analytics projects?
- Extended-viewA_Impact
- Extended-viewA
- We deliver fresh products to our 52 locations daily Weekly delivery to all location from our internal Distribution Center
- Proof of concept. Analysis of sample sales data from POS projected same store growth of 2.2% Started partnership Quick On boarding and implementation in stores Include: training to store managers, store supervisors and project leader. Deployment of recommendations once a week through desktop and mobile phone application
- Set-up & Installation Constraints- very helpful to dial in the parameters, including the $ amount and patterns We have excluded certain categories i.e. Lotto tickets and Cig Cartons
- How do you currently communicate with the stores? if a mobile device is not used: Is a handheld device planned to be used at the stores?
- A one time registration instructions will be sent to store directly from CB4 We have turned on Web access for all locations
- A one time registration instructions will be sent to store directly from CB4 We have turned on Web access for all locations
- The revenue is accumulated month by month
- Implementation rate has been 1oo% Hit Rate has been 40% - Treasure Hunt! Revenue increase from 10 store pilot group since August $65,000
- Summarize the benefits Very confident we can produce 1.5-2.3% growth
- Purpose of slide: Show the main value that we derive from extracting these shared patterns. Slide’s core messaging: A pattern, which represent a fuzzy cluster, includes stores and products for which we expect very similar consumption behavior. Within a pattern we can determine with very high probability how much a product should sell. Therefore when a product doesn’t meet the expected sales volume as indicated by the pattern, we can determine that there is an issue. And so the difference between the sales value our software predicts and the actual sales volume of that product at store is the sales lift we provide. Using the same principle, we are also able to determine that if we introduce a product into a store’s assortment, it should sell in high volume (well above the median). We do so by detecting patterns that indicate high sales of a product within a pattern. Stores which are part of the pattern but do not sell the target product, are issued recommendations to add that product to their assortment. Comments: It’s important to note that the introduction of a new product to a store is related to products that are selling already in other stores but not in the store to which the recommendation is provided to. For introducing completely new products we use syndicated data (E.g. Nielsen).
- Difference between distribution to granular analytics at Store/SKU level The level of granularity depends on the question you want answer But not “The more granular the data, the better” Is real-time and granular data always better? No, it’s not. Real-time can be too close to the action. Sometimes, you need to pull back for the long shot to reveal what’s really going on. Big Data is encumbered by a huge amount of white noise. The noise as a proportion of the total signal increases with higher resolution, for example, data by minute rather than by week, or data at a town level rather than state. Do not confuse precision with accuracy. Big Data, in its raw disaggregate form, can be misleading. There needs to be an appropriate level of aggregation to cancel out all the white noise.
- Phrase used in statistics to emphasize that a correlation between two variables does not imply that one causes the other! Give example about Walgreen – (no names) – army of analysts trying to understand what causes customer behaviour Big Data Myth #7: Big Data is a magic 8-ball [Big data can provide good root cause analysis – EXPLAINING why things behave the way they do – STAFF AWARENESS – Instead focus on actionable recommendations even if they are not explainable ] Well, yes, but you need to ask the question in exactly the right way. It’s a bit like when a genie gives you your three wishes. You have to phrase your wishes very carefully. Applying analytics with a lack of precision or detailed hypothesis creation in advance, when dealing with complex data sets such as cell phone or calling network data, can actually lead you astray and give an incorrect answer. You need to ask your questions very carefully of the “Big Data” crystal ball.