Prioritisation Surveys: Ranking Questions, Best–Worst Scaling, TURF Reach & Kano Categories

What you'll build

What you will be able to do

Each lab ends with a concrete result that you build yourself and the checker confirms, cell by cell.

• Summarise a ranking question three ways – average rank, first-choice share and points per rank with SUMPRODUCT and COUNTIF – and see why a polarising feature can win first places but lose on average.
• Count best and worst picks per perk from raw best–worst answers with COUNTIF, turn them into scores per appearance and find the top perk and the perks more often rejected than chosen.
• Rescale best–worst scores into shares of preference that add up to 100 % with EXP, compare the top and the bottom perk as a ratio and chart the shares.
• Compare best–worst scores of two member segments with RANK.EQ, find the perks with the largest gaps using ABS, MAX and LARGE, and build a total weighted by segment size.
• Turn interest ratings into reach per class, calculate the unduplicated reach of two- and three-class line-ups with SUMPRODUCT and measure the extra reach one more class adds.
• Complete the reach table of all 20 three-class line-ups, pick the best line-up with INDEX/MATCH, measure how many of its classes the reached members like (frequency) and count the line-ups that meet a reach target.
• Classify Kano answer pairs with the 5 × 5 evaluation table, count the must-be answers with SUMPRODUCT, find each feature's main category and calculate the better and worse coefficients.
• Calculate the gap between importance and today's satisfaction, a roadmap score that rewards important and unmet needs, pick the top feature, count the big gaps and plot the matrix.