Posts Tagged ‘analytics’

Snice OR OSEMN – taxonomy of data

August 1, 2017

What a scientist does in roughly chronological order: Obtain, Scrub, Explore, Model, and iNterpret

  1. Obtain: pointing and clicking does not scale.
  2. Scrub: the world is a messy place
  3. Explore: You can see a lot by looking
  4. Models: always bad, sometimes ugly
  5. iNterpret: “The purpose of computing is insight, not numbers.”

Hilary Mason: A taxonomy of data

 

There are important things we can’t easily or accurately measure.

July 6, 2016

If we could read user’s minds, then we could in theory design the perfect experience for them. Unfortunately, we’re not all Jean Greys, so we make due with what we can measure to try and take educated guesses as to what people care about. In this day and age, what we can measure has its limits, and it’s important to always remember that. Simply looking at what people are doing in your product can’t tell you:

  • the degree to which people love, hate, or are indifferent to your product or any of its specific features
  • whether a change increases or decreases people’s trust in your product over time
  • how simple and easy to use your product is perceived to be
  • how people see your product versus other similar products in the market
  • what things people most want changed, added, or fixed
  • how people will want to use your product as time passes

Julie Zhuo: Metrics Versus Experience

Metric types and kinds of analysis

September 7, 2015

Metric types

  • Absolute metric
  • relative metric vs peer
  • relative vs goals
  • relative vs benchmark
  • Relative vs competitors
  • relative over time
  • causal and non-causal relationships

Kinds of analysis

  • raw scores, rankings, and lists
  • trending over time
  • distributions and histograms
  • matrices
  • unit oriented analysis
  • basic regression and correlations
  • key driver analysis and measures
  • benchmarking

barnes/kelleher 2015 p.248

Sample customer experience metrics

September 7, 2015
  • abondonment Rate/customers lost
  • annual sales per customers
  • average customer size
  • average duration of customer relationships
  • awareness percent
  • brand development index
  • brand penetration
  • brand recognition
  • category development index
  • complaintS
  • complaints resolved on first contact
  • customer acquisition rate
  • Customer lifetime value
  • customer loyalty
  • customer profitability
  • customer satisfaction
  • customer service expense per customer
  • customer visits to the company
  • customers per employee
  • direct price
  • frequency/number of sales actions
  • hours spent with customer
  • market share
  • marketing cost as a percentage of sales
  • numbe of ads placed
  • number of customers
  • number of proposals made
  • number of trade shows attended
  • penetration share
  • percent of revenue from new customers
  • price relative to competition
  • relative market share
  • repeat volume
  • response rate
  • response time per customer request
  • Retention rate /loyalty
  • Return rates
  • sales per channel
  • sales volume
  • share of requirements
  • share of target customer spending
  • total cost to customer
  • unit market share
  • wallet share
  • win rate (sales closed/sales contacts)

barnes/kelleher 2015 pp 244-5