a pragmatic approach to analyzing customers

A Pragmatic Approach to Analyzing Customers

Mark Madsen

www.ThirdNature.net

@markmadsen

http://www.thirdnature.net/

What happened in the overall market?

© Third Nature Inc.

The Internet happened and companies are still reacting

The internet is unlike all prior media channels: it allows for two-way communication, enabling entirely new practices.


From the brand as controller of messages in a channel…

© Third Nature Inc. © Third Nature Inc.

Bad news if you want to reach people via advertising

Number of prime-time 60 second TV commercials required to reach 80% of 18-49 year-olds

▪ In 1965: 3

▪ In 2002: 117

People with DVRs watch 12% more TV

90% of them skip the ads


The obvious solution hasn’t worked very well.


One-to-One marketing concepts fared little better.


Changes to Communication Channels

Old model:

▪ One-way

▪ Outbound

▪ Interrupt-driven marketing

▪ Messaging

New model:

▪ Two-way

▪ Inbound, outbound and between peers

▪ Event-driven marketing

▪ Messaging and listening


Social Software Affects Buying Behavior for Business


Everyone is a Direct Marketer Now


The (mass) Marketing Process

This is designed for a mass market where you create a few

to dozens of stable segments, with hundreds of thousands

or millions of members in each.

Analyze and segment the

market and/or customers* Create offer

Execute

campaign

Record

results

Feedback

*or not


Hard because measuring results is hard

?

The assumption is this:

What happens in the

middle is largely a mystery

because of the nature of

mass communication and

transaction channels.

Mass marketing treats people similarly, but with slightly more

refinement owing to segments associated with channels.


Direct Marketing is Not Mass Marketing

Responses differ, so craft offers for segment-specific goals.

More offers, segments, faster feedback, constant resegmenting.

Many segments, and most

often derived from behavior.

Individuals uniquely identified.

Create

customized

offers

Execute

campaign

Record

results

Feedback


Better, but more complex, result measurement

?

The assumption in

direct marketing is this:

There is more visibility

into which campaigns

best lead to actions

and which customers

are best* but still a

visibility gap.

Direct marketers still treat people similarly, but with more

refinement owing to direct response visibility.

?

?


The Obligatory Funnel Diagram

Most of marketing’s efforts today are in the area of customer acquisition.

Marketing tries to generate awareness within an audience, some of which become prospects.

When prospects are interested they become sales opportunities.

When they are interested enough to consider your product they are leads.

If they take action (purchase or donate) they are customers.

Satisfied (happy) customers can become proponents.

Audience

Prospect

Opportunity

Lead

Customer

Proponent


Conversion Rate: Where linking to the DW becomes important

Most marketing to sales metrics (particularly with web analytics) track back to a single core transaction metric: conversion rate.

Rates usually bounce around between 1% and 5%, depending on industry.


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Bounce rate

Take rate

Qualification rate

Abandon rate

Online conversion

Viewing Conversion Rates Properly

Mis-targeted

Opportunites

Leads Losses Customers


The funnel concept is too narrow

It ignores non-transactional marketing. It ignores the customer’s post-acquisition interactions with the organization.

After spending money to acquire a customer, attention must be paid to service aspects of the relationship, and there are multiple feedback loops.

Marketing analysis for brand

campaigns and customer acquisition.

Analysis for customer retention and

customer lifecycle management.

Au

die

nc

e

Pro

sp

ec

t

Op

po

rtu

nit

y

Le

ad

Cu

sto

me

r

Pro

po

ne

nt

And these areas are not normally marketing’s

responsibility so not in their customer repository.


Post-purchase data is needed for experience measurement

You need to get post-purchase data and support tracking of marketing activities for the post-sale period.

▪ 60% of consumers of facial skin care products research more after purchase.

▪ 20% of evaluated CPG purchase decisions are made differently at the point of purchase

You have to look at data from the perspective of customer phases of activity:

Useful beyond marketing: product features, design, sales experience (store, web, phone), service

Consider Evaluate Purchase Post-purchase


It’s a Multi-Channel World

There isn’t one funnel, there are many small funnels that combine to form the big picture.

Each of these new channels has measurement data coming from different sources, often external and out of your control.


High Level View of Common Primary Channels

Communication Channels

Radio

TV

Print

Online

Web site

Email

IVR

Social networks

SEM

Display ads

Mobile

Games

Each of these has individual metrics, some of

which are standardized in the traditional

channels but only for some online.


Communication channels are not transaction channels. One drives people to the other.

▪ Measuring communication channels is about the communication process and the expected outcomes in transaction channels.

▪ Measuring transaction channels is about the performance and processes of those channels.

Transaction Communication

Marketing Measurement = Channel Measurement

Online

Print

Radio

TV

Online

In-person

Phone

Campaigns can cross

communication channels.

TX channels overlap and

cannibalize each other.


The Online Channels Have Advantages

The online channel can provide realtime feedback

where old marketing channels sometimes have a

data lag of months.

Messages and offers can be

tested while a campaign is

running, rather than between

campaigns.

This requires that you collect

the data to manage the

process, while the process is

running.


Customer data is usually functionally aligned Each function has specific applications and processes,

usually supported by local reporting and BI.

Sales automation

Audience

Prospect

Opportunity

Lead

Customer

Proponent

Customer master

Marketing automation

Call center automation

Sales BI

Marketing BI

Call center BI

Unifying customer information and contact processes

was something CRM was supposed to do for us.

Web?

Social media?

Mobile?


What Marketers (and Organizations) Say They Need

Individual, cross-channel, lifetime history: 360o


The customer experience is of supreme importance

…and it’s missing from the 360o view.


People do not experience the world in channels. “Online” is just part of everyday life. They see one organization with multiple touchpoints, not fragmented channels. This is how you have to view your organization’s interactions.

Your view of them Their view of you


Customer experience has fuzzy definitions

A measure of quality, the total of all interactions a customer has with your organization’s product or service.

▪ Utility

▪ Quality / reliability

▪ Ease of use / usability

▪ Aesthetics

An attempt to measure how good your product or service is.

The basic thing being measured is (quality of) interactions.


Experience is about integrating individual interaction preferences


Customer

Experience

Experience drives other metrics

Loyalty and satisfaction have a direct impact on financials (revenue, profitability, market share), so the goal should be to improve experience, which means (a) fix problems (b) make things better

Utility

Quality

Usability

Aesthetics

Loyalty

Satisfaction

Likelihood to:

Repurchase

Discontinue

Defect

Recommend


What about Word of Mouth?

People talk about experiences. Online measurement and social software allows for watching something which could only be surveyed for in the past: what people say.

▪ Awareness

▪ Sentiment

▪ Reputation

▪ Buzz

▪ Feedback

But these things are harder, fragmented across multiple external channels and sources.


Measurement? What measurement?

forrester


Kinds of customer analysis questions: acquisition, management, retention, reactivation

What are the characteristics of our customers?

Who are our most profitable customers and how do we retain

them?

How to improve customer satisfaction?

How do we reactivate lost customers?

Attract new customers?

Increase sales?

Make our less profitable customers more profitable?

Manage high -risk customers?

Reduce acquisition or customer management expenses? Dubai

April 28th,

2013


There’s a lot of hype related to big data and analytics


Closed loop process to monitor marketing

Create an offer

(promotion, call to action, thing of great beauty)

Publish it Monitor it React to it

Make adjustments

(push it, tweak it, fix it)

Stop it or move on

Unlike older media models,

online/digital allows for

rapid adjustment.

We’re interested in the monitor/publish part, which is

more complicated than the usual BI work


Why is this more complicated?

The data sources

▪ Often (usually) not a DB

▪ Frequently want to see up-to-date results

▪ Many different sources / elements for one campaign

The data

▪ Need to see data for elements as well as the aggregate (which might mean different/missing data for different elements, e.g. video, twitter)

▪ Reaction / adjustment may change data collected

▪ Implies multiple, possibly changing data models

▪ Adjustments should be annotated, this is text on a timeline

▪ May throw it all away at end of a campaign

Create an offer

(promotion, call to action, thing of great

beauty)

Publish it Monitor it React to it

Make adjustments (push it, tweak it, fix it)

Stop it or move on


3 common customer measures

Only sales taken into account. No valuation of differing

product margins, service costs, lifetime value of

customer. (Average Revenue Per User)

Revenue minus the cost of the product / service

(Average Margin Per User) Average across users, not

an individual metric

Total revenue revenue over the time the person has been

a customer to date (LTV) and forecast to an expected

date when they will stop being a customer (pLTV)

Averages are easy to calculate in BI, but these are usually an average,

e.g. sum(customer sales) / count(customers). They are not often

calculated for each individual in the database and they are missing

important information that would change how they are served.

Revenue

(ARPU)

Profitability

(AMPU)

Lifetime

value (LTV)


It’s always better to start simple, e.g. RFM

RFM: Table-based statistics without the stats

>40 year old technique used in direct marketing

Recency: Recent transactions indicate a customer is more likely to transact again

Frequency: More frequent customers are more likely to respond to an offer

Monetary: Customers who spend above average are likely to spend more this time

The model assumes that R > F > M in importance. This may not always be true.

43


RFM: Technique

Based on N quantiles for each attribute, where N is commonly 5 but can vary.

Sort the data by recency, defined as the last time they transacted with you on a voluntary basis (i.e. paying a phone bill isn’t a good example) and assign each record a quantile number

This numeric label is the recency

column in the table and is their recency “score”

44

Record Set

1

2

3

4

5


RFM: Technique

Now do the same sort and assignment for frequency, where frequency is by some meaningful time period for your organization and customers.

Then repeat with monetary, e.g. avg order size, total lifetime spend, etc.

The same row will have three numbers now.

Record Set

1

2

3

4

5

Record Set

1

2

3

4

5

Record Set

1

2

3

4

5

Recency Frequency Monetary


RFM: Technique

You can think of this is constructing a cube and assigning one index value along each axis.

In this model, the cells will having varying numbers of customer records corresponding to their RFM score.

Recency

Moneta

ry

1-1-1

• Test a representative sample.

• Record the response rate by cell.

• Based on this and p(r) * v you can now calculate breakeven by cell and identify the ones to mail to or avoid.


Why is RFM Used?

1. It’s better than random.

2. It’s simple.

3. It’s fast.

4. It’s cheap.

It’s usually not as good as other models – until you factor in the above points.

You can do several campaigns with RFM for 1 run of a complex scoring model.

Why simple might be better: Video Store case

Key Figures:

▪ 200 stores, 200.000 transactions per week

▪ Appr. 1 million active customers, 1.3 million inactive

▪ Average frequency 8-10x per year

Adapted RFM Model: FMR

▪ 'F' determines 'M', 'M' class influences 'R' class

▪ M = Revenue in 52 weeks prior to last visit

▪ Less than 52 weeks of data: weighted total

Assumptions (tested):

▪ No visit in last 52 weeks : inactive customer

▪ First 12 weeks: new customer

Similar model results, 2 minutes vs 2 days, SPSS consulting and licenses and server vs 0 recurring external costs, better overall result


Analyzing the entire customer base another way

Pareto analysis of the share of buyers who make up 80% of sales volume for products.

Data source: CMO council


What makes these customers different? How does this affect a new product launch, or line extensions?

The idea of a mass market is not really true. More a set of small markets within the mass.

This is as true of commercial supplies as it is consumer goods.

Data source: CMO council


Customer metrics today should all be individual

Metrics go through periods of being fashionable. Average customer metrics were fashionable in the 90s. Today it’s all about measuring the individual.

But can you? Probabilities assigned to groups are not an individual predictor.

Both of those rank each individual rather than providing averages.


Survival Analysis, Key to Lifetime Value Calculations

Based on a hazard function (mortality model).

Things survival analysis tells you:

▪ When a customer is likely to leave

▪ When a customer is likely to move to a new segment

▪ When a customer is likely to broaden or narrow the relationship

▪ The factors in the relationship that may increase or decrease tenure

▪ The quantitative effect of various factors on customer tenure

Why it’s worth it: predict churn, risk of defection, predict places to intercede to make things better,


Lifetime Value Calculations

Customer Value (CV) – the actual amount to now

Lifetime Value (LTV) – the predicted value to the date determined from the mortality calculation (or actual value in the case of inactive customers)

▪ Can be calculated based on Revenue or Margin*

▪ Can be absolute or discounted to current dollars

Customer Acquisition Cost (CAC) – the variable and fixed costs to acquire the customer

Comparing any two customers requires knowing their start date and expected tenure.

LTV = (CV – CAC) * Tenure


Satisfaction, Loyalty: CSI and NPS


LTV misuse

There was a flawed article about 5 years ago in a business magazine with the idea that you should “fire your worst customers.” First, find the LTV, then get rid of the ones who are unprofitable.


GETTING STARTED


Evolution of the Customer Master

Demographic

• Your customer master

• Geography

• Group memberships

• Social network profiles

• Syndicated data

Psychographic

• Clickstream (visits, views, timing)

• Communications

• Content (blogs, tweets, comments, ratings)

• Transactions

• Syndicated data

Socialgraphic?

• Profiles of connections

• Communities of interest

• Connection likes and dislikes

• Syndicated data

The detail and breadth expand as collection improves


Individual profile Metrics (LTV, Satisfaction,

loyalty, influence, etc.)

Product preferences

Brand preferences

Price preferences

Seasonal preferences

Communication

preferences

Service preferences

Transaction preferences

What we need to build: the real customer master, not a customer dimension

A lot more than just demographics. Includes every transaction, interaction, observation about the customer. Not built all at once.

Household Household profile

Transaction history

Service history

Outbound marketing

contact history

Inbound marketing

contact history

Social networks*


The Advanced Companies Are Integrating Social Data into Customer Databases Do you have a process to record and integrate data from social interactions with customers into existing customer databases?

Source: Survey for Social Media Program Managers, conducted by Altimeter Group (Q1-Q2 2011)

143 respondents, all over 1000 employees


Measuring customer interactions

State of practice: ▪ Limited customer master, in a silo. Partial customer contact

histories, outbound only.

State of the art:

▪ A system to gather data about any customer interactions, inbound or outbound, from marketing to service.

Sources are extensive: ▪ web analytics, CRM, sales automation, lead scoring, marketing

automation, email providers, online advertising, call management, IVR, bug reporting, warranty, service management, enterprise feedback mgmt, decision automation, sentiment analysis, conversation management, dynamic case management, VoC, social media, surveys, focus groups, syndicated market data…

Many types and structures, not all relational-friendly, may require new storage and processing platforms.


Different Data and Different Usage Patterns

Be prepared for changed assumptions:

▪ Marketing campaigns and tactics change frequently, particularly online marketing.

▪ This means data sources change frequently, as well as reporting needs.

▪ Much of marketing is like experimental science, and unlike the read-only BI usage model.


Value

1. Web alone, not that much

2. Web plus transactions, big increase

3. Web plus email plus transactions, bigger increase

But the real goal in customer analysis is a full picture, which means an interaction master for everything: online ads, mobile app use, inbound & outbound call center, physical point of presence, direct mail contacts.

i.e. You have to say yes to all new data requests…


The data warehouse has to evolve to support these kinds of uses. This means a more complex data platform

Transient data

Raw data

Infrastructure layer:

Store and manage

Refine and deliver

Application layer:

Analyze and consume

The new model encompasses data at rest and data in motion

Multiple access methods

Standardized

data

Multiple ingest methods

BI, data extracts, analytics, applications

The platform has to do more than serve queries; it has to be read-write.


Define the goal

Decide how to measure it

Baseline

Plan your actions and monitor outcomes

Iterate

Define goal-driven actionable metrics as the starting point, don’t boil the ocean


Getting started

You can’t build everything at once, so don’t try.

1. Build out the convenient: for a few channels that are important, assemble the raw interaction data for each.

2. Store that data in a place where it can be be easily refined from its raw state, and hopefully further linked to DW data on TXs (which will show you outcomes).

3. Add some inexpensive tools to explore the data with.

4. Do some simple modeling to understand a few key customer channel interactions.

5. Keep extending the data and capabilities.


“The future, according to some scientists, will be exactly like the past, only far more expensive.” ~ John Sladek


Creative Commons

Thanks to the people who made their images available via creative commons:

Outdated gumshoe.jpg – http://flickr.com/photos/olivander/372385317/

Card catalog – http://flickr.com/photos/deborahfitchett/2372385317/

Book of hours manuscript2.jpg – http://flickr.com/photos/jeffrey/89461374/

Royal library san lorenzo.jpg – http://flickr.com/photos/cuellar/370663920/

http://flickr.com/photos/olivander/372385317/

http://flickr.com/photos/deborahfitchett/2372385317/

http://flickr.com/photos/jeffrey/89461374/

http://flickr.com/photos/cuellar/370663920/


Image Attributions

Thanks to the people who supplied the images used in this presentation: outdated gumshoe.jpg - http://flickr.com/photos/olivander/372385317/ laptop face.jpg - http://flickr.com/photos/sd/7746599/ anne hathaway.jpg - http://flickr.com/photos/barbaradoduk/177959197/ desert ibex - michael polizia wheat_field.jpg - http://www.flickr.com/photos/ecstaticist/1120119742/ Open air market - http://flickr.com/photos/baboon/309793875/ child workers - http://www.flickr.com/photos/mtowber/4222787490/ whack-a-mole_door.jpg - http://www.flickr.com/photos/mtowber/4222787490/ uniform_umbrellas.jpg - http://www.flickr.com/photos/mortimer/221051561/ train_to_sea.jpg - http://www.flickr.com/photos/innoxiuss/457069767/ riot police line small - http://flickr.com/photos/73594239@N00/25719098/ changing of the guard.jpg - http://flickr.com/photos/mambo1935/160739264/ pyramid_camel_rider.jpg - http://www.flickr.com/photos/khalid-almasoud/1528054134/ chinatown little color gate.jpg - http://www.flickr.com/photos/paullikespics/3248133830/ teapot.jpg - http://flickr.com/photos/joi/411403/ motionless in crowd.jpg - http://flickr.com/photos/cactusmelba/1065738186/ well town hall - http://flickr.com/photos/tuinkabouter/1135560976/ febo amsterdam.jpg - http://flickr.com/photos/jshyun/1573065713/ cadillac ranch line.jpg - http://flickr.com/photos/whatknot/179655095/

Slide 77

http://flickr.com/photos/olivander/372385317/

http://flickr.com/photos/sd/7746599/

http://flickr.com/photos/barbaradoduk/177959197/

http://www.flickr.com/photos/ecstaticist/1120119742/

http://flickr.com/photos/baboon/309793875/

http://www.flickr.com/photos/mtowber/4222787490/

http://www.flickr.com/photos/mtowber/4222787490/

http://www.flickr.com/photos/mortimer/221051561/

http://www.flickr.com/photos/innoxiuss/457069767/

http://flickr.com/photos/73594239@N00/25719098/

http://flickr.com/photos/mambo1935/160739264/

http://www.flickr.com/photos/khalid-almasoud/1528054134/



http://www.flickr.com/photos/paullikespics/3248133830/


CC Image Attributions

Thanks to the people who supplied the creative commons licensed images used in this presentation: Veyron - http://www.flickr.com/photos/guano/414733876/ unrecycle.- http://www.flickr.com/photos/xiaming/94328953 wheat_field - http://www.flickr.com/photos/ecstaticist/1120119742/ Open air market - http://flickr.com/photos/baboon/309793875/ riot police line small - http://flickr.com/photos/73594239@N00/25719098/ Sandblaster - http://flickr.com/photos/stigeredoo/11643424/ subway dc metro - http://flickr.com/photos/musaeum/509899161/ fast kids truck peru - http://flickr.com/photos/zerega/1029076197/ well town hall - http://flickr.com/photos/tuinkabouter/1135560976/ Tokyo forum - http://flickr.com/photos/fukagawa/2004106475/

http://www.flickr.com/photos/guano/414733876/

http://www.flickr.com/photos/xiaming/94328953

http://www.flickr.com/photos/ecstaticist/1120119742/

http://flickr.com/photos/baboon/309793875/

http://flickr.com/photos/73594239@N00/25719098/

http://flickr.com/photos/stigeredoo/11643424/

http://flickr.com/photos/musaeum/509899161/

http://flickr.com/photos/zerega/1029076197/

http://flickr.com/photos/tuinkabouter/1135560976/

http://flickr.com/photos/fukagawa/2004106475/


About Third Nature

Third Nature is a consulting and advisory firm focused on new and

emerging technology and practices in information strategy, analytics,

business intelligence and data management. If your question is related to

data, analytics, information strategy and technology infrastructure then

you‘re at the right place.

Our goal is to help organizations solve problems using data. We offer

education, consulting and research services to support business and IT

organizations as well as technology vendors.

We fill the gap between what the industry analyst firms cover and what IT

needs. We specialize in strategy and architecture, so we look at emerging

technologies and markets, evaluating how technologies are applied to

solve problems rather than evaluating product features and vendor market

positions.