Data at the Heart GS1 Presentation 28 June 2017

Transcription

1 Commercial in Confidence For GS1 organisation only Data at the Heart GS1 Presentation 28 June 2017 Adam Courtier FSNI Merchandise Operations Manager 1

2 2 Food is at the heart of our customer s journeys

3 3 Data is at the heart of providing our Customers a frictionless experience

4 We are Foodies on a mission! We make sure New Zealanders get more out of life. By sustainably delivering shopping experiences so awesome, New Zealanders have more of what matters to them. To do this, our strategy is to be: IN TUNE with our customers, people & communities IN FORM to deliver what they need: sustainably, efficiently & profitably IN FRONT innovating to make sure they have more of what matters most to them. Our team of 22,000 New Zealanders are aligned to be courageous, agile, respectful and excellent. We are New Zealanders, for all New Zealanders getting more out of life.

5 We re turning 100 years old in 2022!! The basics over the years have not changed, but the foundations of this have evolved continually Customers needs and wants supported by innovation in food, technology and data. 5

6 Things were a lot simpler in One TV channel One type of family Long, planned shopping trips Everyone ate the same things Food was pretty functional Bonus points if you can spot something now illegal in NZ (there are 3)

7 No two customers are the same The you experience through relevance & personalisation Being data-driven grocers Building a worldclass loyalty programme 7

8 Customers expect the best for them NOW! Big expectations - instant gratification & entertainment Used to instant information at their finger tips Collect data for beneficial personalised experiences Be where I want you to be, when I want you to be there Universe is centred around me (the blue dot effect) 8

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10 Making it happen Being a data driven grocer requires big cultural shifts Convert as many people along the way into believers Learning patience transformation is a long game that never really ends A massive thirst for data to understand our customers Requires strong collaboration between Suppliers and Retailers (and of course GS1!) 10

11 Challenges we re facing Poor data quality (or incomplete) from our suppliers Some suppliers do not keep all article data electronically Suppliers see it as a burden and supply the bare minimum The bare minimum used to work in the past Our data requirements continue to expand Online re-launch, CRM, PIM, Ticketing, Promotions It s slow to get more data (and manual intensive) Our range is 105,000 articles (approx. 300,000 GTIN) and climbing Operating a GS1 platform and our own (DEX) 11

12 Our vendor supplied data goes everywhere! National PIM (or DEX following the same format) FSSI SAP FSNI SAP FSA AS400 FSW JDE Transport planning shipments to our stores and suppliers (we are one of NZ s largest logistics companies). Warehousing order, receipt, store, pick, pack, dispatch, invoice, settle, pay, inventory manage. Sell mailers, online, shelf tickets, social media campaigns, customer portal, marketing posters, promotional tickets, scale labels, POS, SCO. Analyse space planning, customer insights, sales trends, category strategies, range management, promotional effectiveness and planning, financial reporting, rent. 12

13 When it goes wrong, it goes wrong all over Details not provided on-time or incomplete Wrong UNOM provided Omitted or incorrect allergens Poor descriptions Incorrect net contents Incorrect costs Can t order, receive, sell or pay for it. Emergency logistics for product launches. Delays to logistics, distribution and payment. Correction of orders/invoices. Incorrect consumer decisions with potential severe health implications. Products not ranged in stores as it is not clear what it is, odd tickets for our customers. Potentially misleading pricing per 100g/ml for our customers to compare pricing. Incorrect pricing for our customers. Products not ranged by stores. 13

14 When it goes wrong, it goes wrong all over Feedback The current setup does not allow for feedback on data from the customer or retailer back to the distributor or brand owner in a structured manner for resolution. Data Cascades Online Vendor ProductFlow National PIM Approvals and Workflows POS WMS 14

15 You can tell a lot about a person from what s in their basket

16 What are happy people vs unhappy more likely to buy (lifestyles survey vs. purchases)? Read more here:

17 What are happy people vs unhappy more likely to buy? Happy Unhappy

18 What are happy people vs unhappy more likely to buy? Happy Unhappy - Omega rich - Fresh wholefoods - Tasty protein cuts - Fats Good versions - Lower sugar - Beer & wine - Low in good quality fats - Bland tasting protein - Very high sugar - Smokers - Convenient meals

19 How much of this information could be obtained from our Product Data in a centralised/automated manner? - Omega rich - Fresh wholefoods - Tasty protein cuts - Fats Good versions - Lower sugar - Beer & wine - Low in good quality fats - Bland tasting protein - Very high sugar - Cigarettes - Convenient meals

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