John Pattullo, CEVA Logistics on 'How Supply Chain Innovation Can Drive Customer Value'
Understanding the Customer Value Chain...The Customer Value Chain Can apply statistical analysis to...
Transcript of Understanding the Customer Value Chain...The Customer Value Chain Can apply statistical analysis to...
1
Understanding the Customer Value Chain
Building Evidence-Based Models
Michael J BrockmanPricing Seminar 13th June 2008
Improving Bottom Line Performance
ProfitOptimisation
StrategicOptimisation
•Marketing•Relationship Management
•Channel Strategy•Growth Objectives•Reputation Risk•Capital Management
PriceOptimisation
•Each Product/Channel
•New Business/Renewal
TechnicalAnalysis
•Risk Premium Models•Make/Model Analysis
Geographical•Demand Models
The Customer Value ChainCan apply statistical analysis to understand customer behaviour and value at each stage in the customer value chain. Each stage has a cost
Different types of analyses are appropriate at different stages
Up-Sell
>media modelling
>DM response propensity analysis
>lead value models
>campaign response value optimisation
>propensity analysis
>value models
>trigger-based communications
>campaign response value optimisation
>channel utilisation propensity analysis
>contact centre optimisation
>Training
>price optimisation
>Training
>POS discounts
>price optimisation
>communications strategy
>relationship management and service levels
Gaining Customers Retaining Customers
Growing Relationships
Retention Management
Cross-Sell
Lead Conversion
Prospect Identification
Lead Handling
Lead Generation
>“lookalike”analysis
>geographical analysis
>value models
Maximised Customer
Value
The Leads Pipeline
Unaware
Aware
Consider
Enquire
Buy
Mass Media Advertising
Direct Marketing
PRICING gets you across this threshold, along with:quote scriptingcontact centre
response/operator
OBJECTIVE is to get (ultimately profitable) customers across this threshold, cost-effectively
Customer
Lead
Prospect
“I know that half of my advertising is wasted
I just don’t know which half”Lord Leverhulme
An Age Old Challenge
Still looking for answers in the current landscape
An explosion of communication channelsBusinesses seeking robust measures to demonstrate accountabilityFragmentation of strategic advice from numerous specialist agenciesNeed for an evidence based framework, delivered from a neutral perspective
Strong relationship between media strategy and pricing strategy
Invest in media Invest in price discounting?
In some industries media strategy and customer pricing fulfils two very different roles
Generate Leads
Media and Headline
Offers
e.g. save 35%, 15% off for
online
Customer Pricing
Question
PROFITABILITYVOLUME
Convert
Media modelling techniques recently developed now mean we can understand this relationship
Supply side led
Cheapest media deals
What was done last time
What competitors did
Gut Feel Decisioning
First Media Models
Basic Econometric
Analysis
Generalised Linear/Non Linear
Modelling Techniques
Intuitive belief in what worked
Codified this belief into a “model”
High level link between sales success and media strategy using some form of time series analysis
Detailed link found between pricing and media activity and the volume and profitability of new business
What is the Benefit?
Performance efficiencies of 10-30% gained through more efficient decision making
Overall level of investmentGeographic allocationMedia Mix including BTLProduct mix (home/motor)Which brands to supportTiming/phasingMedia costs perAdapting to competitor activities
Primary focus of Media Modelling is exposure across channels
The focus is on the marketing mix rather than channel specific performance
The process is independent of channel specific ‘source code’ analysis
Projects focus on macro analysis across channels
The micro analysis of the deployment of media within channels is sometimes a secondary consideration
National Press
Local Press Journals Outdoor Direct
Mail Online etc
Day in Week
Daypart
TV
Position in Break
Genre
Station
Identifying Cause and Effect Between Spend and Sales
Inputs Outcomes
Exte
rnal
Fac
tors
Inte
rnal
Fac
tors
Market Conditions
Competitor Activity
Brand Strength
Performance Efficiency
&
Brand Growth
EnquiriesProducts /
Offers
Creative
Media Used
Sales
Many Variables Initially Considered (Between 400 and 500)
Predictive Factors
Radio Ads Short Term
Online Search Engine Impressions
Community Press
Radio Ads Long term
Total Media Exposure
TV TARPS (25-54)
TV Exposure
Press Exposure
Magazine Ads
Magazine Exposure
Online Impressions
Radio Exposure
TV Frequency
Stats
Online Exposure
Cinema/Outdoor Exposure
Media
CreativeOffer
Competitor
Market (Related to Industry)
SeasonalityTechnology Index
GDP Growth
CPI Housing Growth
Unemployment Growth
Housing Purchases
Interest Rates
Consumer Confidence
Radio Ads
Product LaunchesPress Ads
TV TARPS
Cinema/Outdoor Spend
Total Media Spend Discounted
Rate
Added Value Offer
Free Service
Trial
Campaign A Campaign
BCampaign C
Rainfall
World Events
Press Most Expensive
TV Reach Stats
Cinema/Outdoor Spend
TV TARPS Overall
The modelling can be carried out at the postcode district level…
Weekly media exposure for each channel is allocated at a postcode level
TVPr
ess
DM
TVPr
ess
DM
TVPr
ess
DM
TVPr
ess
DM
E14 E16
SE10 SE7
This Provides the Necessary Richness of Data
Media exposure in adjacent postcodes in a given week may be quite similarHowever differences between weeks and geographies allow us to gain a true understanding of the impact of media exposure on customer behaviour
By considering multiple years of data across all geographies, we have a large number of ‘experiments’ or data points on which to conduct the modelling
Modelling at the postcode level gives us greater certainty around the impact media has on enquiries/sales
By looking at the data in a more granular way, more understanding can be drawn about the relationship between exposure and response
Identifying the slope and shape of the relationship offers significant benefits
Lead
s G
ener
ated
Exposure
120
115
110
105
100
95
900 40050 100 150 200 250 300 350
Pure Effects of Each Variable Modelled for Influence
The specific effect of each influence is isolated (Media and non media factors)
These ‘pure effects’ can be used to guide each channel’s optimal deploymentLe
ads
Gen
erat
ed
120
115
110
105
100
95
900 40050 100 150 200 250 300 350
No uplift in leads below 50 GRPs Point of diminishing
returns No additional leads after
300 GRPs
Impact of Number of GRPs (Ages 25-54) In Last 7 Days
Number of GRPs (Ages 25-54) In Last 7 Days
Demographics vary by postcode
Populated mainly by younger people
London
0
0.1
0.2
0.3
0.4
0.5
0 to 14 15 to 29 30 to 44 45 to 50 60 to 74 74+
Age Demographic
Prop
ortio
n of
Peo
ple
Honiton
0
0.1
0.2
0.3
0.4
0.5
0 to 14 15 to 29 30 to 44 45 to 50 60 to 74 74+
Age Demographic
Prop
ortio
n of
Peo
ple
Evenly populated
New Milton
0
0.1
0.2
0.3
0.4
0.5
0 to 14 15 to 29 30 to 44 45 to 50 60 to 74 74+
Age Demographic
Prop
ortio
n of
Peo
ple
Populated mainly by older people
Not surprising because demographics vary so significantly by region – example age
Modelling at the postcode level allows for many different types of media
Include above the line and below the line accurately
Because different areas have different intrinsic value
In some areas you are more competitive
Have higher conversion rates
Have higher customer lifetime values
Modelling impact of media requires a specific approach
We employ generalised linear modelling (GLM) techniques, which are best practice within insurance pricing
GLMs permit “error structures” which correctly describe the natural uncertainty in the data
GLM approach solves key challenges within “traditional” methods:
Multicollinearity of factors, appropriateness of fitting method, testing of residuals, heteroscedasticity, interaction searching and testing
GLMs are:
Able to handle large quantities of data and sparse observations
Robust to changes in exposure mix and changes in the predicted response
GLM Modelling
Channel PerformanceEach channel’s effect can be understood in isolation
Note: Illustrations only
RadioCompetitor
TV
Online Press
Number of ads per person last 7 days GRPs in last 2 weeks
Impressions per person last 4 weeksOnline impressions last 7 days
Rel
ativ
e le
ad v
olum
e
Rel
ativ
e le
ad v
olum
e
Rel
ativ
e le
ad v
olum
e
Rel
ativ
e le
ad v
olum
e
We have developed this approach so we are better at predicting future behaviour
Since we now have sufficient data to incorporate all of the factors which influence lead outcomes and the right shape and slope for each factor
0
1
2
3
4
5
6
7
8
9
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
Week
Lead
s G
ener
ated
Typical Econometric
Prediction Range
GLM Prediction Range
Spend by Month
0
500
1,000
1,500
2,000
2,500
3,000M
ar-0
4
Apr-
04
May
-04
Jun-
04
Jul-0
4
Aug-
04
Sep-
04
Oct
-04
Nov
-04
Dec
-04
Jan-
05
Feb-
05
Mar
-05
Apr-
05
May
-05
Jun-
05
Jul-0
5
Aug-
05
Sep-
05
Oct
-05
Month
Spen
d ($
000'
s)
TV Info Pr - OTP CPr - OTP Mag - OTP Pr - Ins CPr - Ins Mag - Ins Doordrops Online
Sale
s (V
olum
e)Sa
les
Volu
me
Spen
d ($
000s
)
The benefits of analysis beyond straight lines
Consider September 2005
a high level of media spend was not met with a peak in sales
Media Spend and Sales Volume
September 2005 was Particularly Heavy in OTP Press and TV Exposure
Almost £1m was spent in the month beyond the point of zero marginal returns as identified in our modelling
Relativity by TV Impressions Per Person Last 2 Weeks
60708090
100110120130140
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5TV Impressions Per Person Last 2 Weeks
Rel
ativ
e Pr
open
sity
to
Purc
hase
00.10.20.30.40.50.60.70.80.91
Expo
sure
Exposure - Excl Sep05 Exposure - Sep05 Prediction
Allowing for Customer ValueAllowing for Propensity AND ValueWish to direct marketing spend towards the most valuable prospects
…or towards those who can be influenced to increase value.
Lead Response Propensity
High Propensity
Low Propensity
High Retention
Low Retention
High Retention
Low Retention
Retention Rate
Most Valuable
Prospects
Medium Value
Prospects
Least Valuable
Prospects
High Profitability
LowProfitability
High Profitability
LowProfitability
High Profitability
LowProfitability
High Profitability
LowProfitability
Profitability
Segment Characterisation –The Customer DNA
Can characterise segments, and identify segment-specific levers of influence, and trigger events – and so frame targeted activities.