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VAC: Visual Analytics in Practice
Transcript of VAC: Visual Analytics in Practice
in retail and consumer goods Domain
challenges Data Volumes Every item...
... in every basket...
... at every till...
... in every store...
... on every day = retailers accumulate
of data every week Data Quality Are these all the same product? Despite 40 years of barcodes confusion still runs amok Data Sources As a consumer packaged goods manufacturer... ... you need to manage data from many sources So with huge volumes of data... ... from many
different sources... ... with different frequency and quality... ... attempting
any analysis at all
a major problem! Complex
Problems Price Elasticity "What is the
maximum profit?" Promotions "What is the
effect of promoting
product X on its
category?" "How do consumers
respond to changes
in price, packaging or
pack size?" Brand Loyalty 1. Visual 3. Data Blending 2. Interactive Solution
approach We work to
key principles ... in order to tell a compelling story Colgate a "compelling story" Background Visual Analytics
at work Colgate Palmolive Customers Priorities Sales Analysis Comparing sales and margin over time helps Category Manager to understand impact of promotions
Instant interaction enables comparison of multiple data points - encouraging detailed understanding of cyclical patterns
Visual cues draw eye to significant over/under-performance
Numeric detail presented only once pattern is investigated - allows Category Manager to focus on broader trends Store Stock Availability Anomolies (including data quality issues) are identified rapidly, meaning that store-specific issues can be dealt with 'same day'
Geographic mapping highlights clustering issues e.g. local supply issues from central warehouse
Macro-to-micro interaction encourages detailed understanding of 'actionable issues' Promotional Forecast Accuracy Blending top-down & bottom-up forecasts with actual history provides visual assessment of forecasting accuracy - vital for senior management
Improved understanding of promotional impact on volume has resulted in substantial improvements in Demand Planning accuracy - meaning correct volumes supplied to shelf
Improvements in customer Service Level have resulted in improved commercial terms Internal and market data blended to create more accurate understanding of sales trends
Identifying impact of promoting Product X on Product Y (own product) or Brand Z (competitor)
Addresses both "Cannibalisation" (negative impact) and "Halo effect" (positive impact)
Improving understanding of consumer requirements and enabling strong influence on retailer range Market Analysis Blends data from internal systems, customers (dentists), retailers, wholesalers and market sources
Simple presentation style for field sales personnel - rapid uptake and regular use
Geographic mapping highlights dentist (end-user) and retailer (customer) gaps
Huge number of individual successes - increasing product sales dramatically inside first six months of use Field Sales Insight Return on Investment £millions spent on promotional activity with retail customers - many different techniques, approaches and results
Data blended from many internal systems to create full profitability model
Visualisation highlights return on promotional investment by customer and mechanism e.g. "25% off", "3 for 2" etc.
Enables improved targeting of marketing budget Benefits Speed Willing end-users pick up tools and techniques rapidly
Practical analytical applications built within weeks, sometimes days, often blending multiple data sources
Analysis of large data sets is performed in hours and days, not weeks and months Insight Communication Grow market share
Optimise stock holding
Manage retailer relationships
Maximise promotional return
Meet consumer expectations to improve
profitability ... but get it right... ... and the big picture is revealed,
making sense of the detail! All users report far greater understanding of organisational performance
A greater appreciation for data quality and its impact flows through to changes in operating procedures
Understanding the data, and its limitations, results in great confidence in the stories that emerge Some end-users now present to clients using ONLY the analytical models/applications
The result is that client meetings become an exploration of the facts, rather than a 100+ page PowerPoint lecture
Greater client understanding leads to changes in behaviour for mutual benefit Thank you
www.atheonanalytics.com ... right across
the organisation... Opening up data for exploration, integration, enrichment and manipulation requires new data management practices
Allowing and encouraging self-service may involve a lot of change to IT practices, inter-departmental communication and governance
Change always takes time, effort and... Commitment Change Challenges Suits federal business style where individuals are encouraged to experiment and solve their own problems
Many IT departments fear loss of control, data decentralisation... in short, anarchy!
"Democratic Business Intelligence" is an ideal, not a mission, at present Culture So why doesn't everyone
use this approach? Experimenting is easy, but commiting to new working practices requires resolve, senior involvement and active management
Ongoing education, support and advice helps to build and maintain momentum
It's easy to build advocates and evangelists, but they need a voice