Loading presentation...

Present Remotely

Send the link below via email or IM


Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.


VAC: Visual Analytics in Practice

Presentation given to the Visual Analytics Consortium conference in Washington DC on 3 May 2011, focusing on real-world use of Visual Analytics tools and techniques in commercial enterprises, with specific focus on retail and CPG/FMCG sectors.

Guy Cuthbert

on 16 September 2014

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of VAC: Visual Analytics in Practice

Visual Analytics in practice tackling the "too tough" problems
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
can present
a major problem! Complex
Problems Price Elasticity "What is the
optimal price
to generate
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

Guy Cuthbert

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
Full transcript