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Map (DECOMPOSITIONS (i.e. SPLITS) (Brands (10 (11 (“Critical”), “I want to…

- - - - “It would be a standard report with filter options. total retailer group, but could filter down to subbrands of retailer, or region. also filter by category, brand etc.”
    - - “I want to add a brand filter and be able to see below sub-category.”
      - 11
        
        “Critical”
    - - 09
        
        “First, we would like to see a report that compared all the brands against each other at a total level
      - 09
        
        “Would expect to see an additional chart below that gives you the brand level comparison view. maybe only show top sub brands or categories - 80% of performance maybe”
  - - - “Critical”
    - - “love the drill down - want brand next - SKU level eventually, but not first.”
  - - - Contributions to Sales from:
      - Products you’ve just added - Products you’ve given increased distribution to - Products for which you’ve reduced distribution - Products you’ve taken out - New products
  - - - “Critical”
  - - - Eg Tesco 5 families
  - - - “Want to see regional filter as well as store type. Our customers don't have one head office, each region has different management and makes different decisions.”
    - - NOT11
        
        “Not useful”
  - - - “Need to add banner split”
- - - - “I want to add a brand filter and be able to see below sub-category.”
  - - - “Critical”
  - - - “We need to look at customer types, different affluences, but then if Roshni is down, we want to find out what's driving that. (i.e. filter by Roshni for all KPI tiles)”
    - - We try to understand the shopper, to know whether have the right product in the right place at the right time? To understand the shopper, we look at lifestyle factors, e.g. premium or price sensitive shoppers. This means we can tailor the pricing/promotion strategy for a product to what type of shopper buys it. We look at how different categories of shopper index for a category of product. This is not a regular conversation, but to launch a new thing.
    - - 10
        
        “Expect when tapping 'Filter by customer type': Choose their commonly used segmentations, eg: - Prince sensitivity - Lifestage/style (tbc) - Loyalty”
  - - - “Not useful”
    - - “Want to see regional filter as well as store type. Our customers don't have one head office, each region has different management and makes different decisions.”
- - - - “Loyalty data is especially imprecise for Product Categories for which customers tend to not scan their club cards (e.g. when buying small quantities, eg instant gratification chocolate bars, crisps, or anything at the front of the shop).”
  - - - “I always need to see total EPOS data 1st, then club card data”
        
        “Clubcard buyer data and total EPOS data are different, so the trends are different”
        
        Eg “Clubcard data might show a brand going up 5%, but EPOS data shows a 15% increase”
        
        Eg “Sometimes the Clucbard data is completely inaccurate, eg -2% while total EPOS data shows +3%
- - - - Comparing vs Same Period Last Year year vs Previous Period
        
        Both are important, but comparison vs same period last year is more common
        
        03
        
        “Lots of the work = Comparing vs last year”.
        
        “Or comparing since last range review”
        
        10
        
        “We always look vs. last year, not prior weeks”
        
        13
        
        “We always compare season this year to season last year”
      - Seasonal view
        
        12
        
        “Year to date or previous x week doesn't give the full picture so easily - want season trends. Seasonal view to understand if the segments are strong in different parts of the year.”
    - - ‘Year-to-date vs same period last year’
        
        11
        
        12
      - ‘Past 12 months vs previous 12 months’
        
        11
        
        “I have to convince my Buyer that although my product hasn’t done perfectly well during the last promotion, we’ve been performing very well over the last six months”
        
        12
        
        “Helpful to see 8 week trend - i find it useful to see 1 year to understand the category if i don't know the category to see how it's developing in the year.”
      - Pre-post promotion comparison
        
        10
        
        “I need to do pre or post promotional analysis, so flexible to pick time periods. We need to stay on top of weekly promotions”
      - 1w
        
        05
        
        “Monday morning meetings go through numbers from last week (data loaded on Sunday)”
        
        NOT 01
        
        “More immediate data would be valuable but not most important”
      - 4w
        
        01
        
        Every time that a buyer has a meeting, they want to know how they’re performing up to that point in time
        
        04
      - 8w
        
        01
      - 12w
        
        01
        
        04
        
        05
      - 24w
        
        04
      - 26w
        
        05
      - 52w
        
        04
    - - Needs to be weekly
        
        01
        
        10
        
        “We change promotions weekly, so we need to see data weekly.”