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The use of scanner data on non-food products

This article discusses the use of scanner data for non-food products in price statistics, including the calculation method, data sources, and challenges in combining different sources. It also highlights the implementation of the ENVA classification system for defining non-food products.

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The use of scanner data on non-food products

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  1. The use of scanner data on non-food products Pia.Ronnevik@ssb.no StatisticsNorway

  2. SD use in the Division of price statistics SD from three pharmacy chains in HICP/CPI New calculation method at elementary level for Food First use of SD from grocery chains in PPP work HICP/CPI: First contact with grocery chains “Full” scale use of SD in HICP/CPI for Food 1997 2013 2005 2003 2010 2009 2005 2012 2013 2001 2003 “Full” scale use of SD in HICP/CPI for pharmaceutical products PPP food survey fully based on SD SD from four petrol chains in HICP/CPI SD from all grocery chains in HICP/CPI SD from the first petrol chain in HICP/CPI SD from the first pharmacy chain in HICP/CPI

  3. SD on non-food products • Non-food products are included in these COICOP groups: - 0213: Beer - 0220: Tobacco - 0454: Solid fuels - 0531: Major household appliances whether electric or not - 0540: Glassware, tableware and household utensils - 0552: Small tools and miscellaneous accessories - 0561: Non-durable household goods - 0721: Spare parts and accessories for personal transport equipment - 0722: Fuels and lubricants for personal transport equipment - 0931: Games, toys and hobbies - 0933: Gardens, plants and flowers - 0934: Pets and related products - 0952: Newspapers and periodicals - 0954: Stationery and drawing materials - 1111: Restaurants, cafes - 1112: Canteens - 1213: Other appliances, articles and products for personal care • SD on non-food products is received from: - grocery and kiosk chains - petrol chains

  4. Current production system of SD on non-food products • Statistics Norway have build an application to deal with non-food products: • One GTIN matched to one representative item • If a GTIN is missing, then it’s replaced. • An automatically suggest of a replacement according to product group and turnover. • If the replaced GTIN is of different quality, indirect quality adjustment is made. • Weakness: • If turnover is twisted towards other GTINs, re-coding/re-matching should be done. • Resource demanding to follow changing turnover figures. • If we don’t control the matching over time we may follow “unrepresentative” GTINs. • Statistics Norway want to use more of the SD on non-food products.

  5. ENVA-classification(EAN Norges Varegruppestandard ) • GS1 Norway has developed their own classification system called ENVA (not brick): - Gives information about which product group an GTIN belongs to. - Takes into account whether the product is fresh, canned or frozen. - Is applied only by the grocery chains. • Advantages with ENVA-classification: - Secure a mutual understanding about product groups. - Makes it possible to compare groups from different chains.

  6. ENVA-classification • Statistics Norway doesn’t receive ENVA-classification from all the chains: - The biggest chain since 2001. - One chain started in May 2015. - The last chain will be able during 2016. • Statistics Norway wants to utilize the ENVA-classification to define non-food products at our unofficial COICOP-6 level. - Mostly a direct link between ENVA-classification and COICOP. - Try to reduce the use of text searches.

  7. ENVA-classification

  8. ENVA-classification

  9. ENVA-classification

  10. ENVA-classification

  11. The new production system for SD on non-food products • An automatically routine that connect the GTIN with COICOP-6 groups for non-food products each month. - Mostly directly linking between new GTIN and COICOP through the ENVA-classification. (grocery chains) - Directly inking between new GTIN and COICOP through chains’ own classification. (petrol chains) - Text searches in some COICOP groups. - Manual checks of the automatically linking. • Make a basket of non-food products in December each year. • Calculates unweighted Jevons indices and the percentage changes from basis month, for COICOP-6 groups. • If the replaced GTIN is of different quality, indirect quality adjustment must be made. • If no replacement is done, then impute missing prices.

  12. The challenge with non-food products • In some COICOP groups we have different data sources: - Scanner data - Questionnaires filled out by the stores (- Web scraping) • How to combine different sources? - One index for each data source? • How to weight these indices together? - Turnover? - Other sources?

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