Segmentation for maximum output

Profilering, segmentering en targeting op je website

21 oktober Jeroen Bouserie

‘Frozen segmentation’ does not work

‘Multi-dimensional, dynamic segmentation’ is much more effective – it may be challenging to create, but it will be less painful in the long run

Segmentation needs to be actionable. In the old days, you segmented customers on the basis of recency, frequency, and monetary value (RFM). Today, you segment customers based on what is meaningful to your business (value!).

What we’ve learned

You need to know something about people before you start trying to personalize

How many segments is too few, just right, or too many… is a matter of judgement

Law of diminishing returns: You’ll get 80% of the benefit from 20 segments, 90% from 200 segments, and 100% from 2,000 segments

What ve’ve learned

Managing segments can be overwhelming

Segment when you have ability to take action. Otherwise it’s just an analytical exercise

What I’ve learned

One size does not fit all

Customers expect you tailor your marketing approach to perfectly match their lifestyle—in addition to their product wants and needs

Boost relevance (make the right offer / show the right content at the right time)

Reach higher conversion rates

Avoid churn

Improve customer experience

Why?

Before you do anything else, ask yourself what you’re hoping to achieve from segmentation:

Identifying customer needs to make propositions more suitable for them?

Improving customer profitability by driving up average pricing?

Identifying new target customers?

Improving customer retention?

Identifying opportunities to grow or gain market share?

Be clear on business objectives and strategies

Segmentation is part of a Value Creation Chain

Segmentation sits at the beginning of the value creation chain.

It is used for probing and assessing a situation.

It is used to generate ideas on what to do.

The other analytical powerhouse, Predictive Models, sit at the other end.

It serves a different purpose from Segmentation

Segmentation vs predictive models

SegmentationIdea

GenerationMarket Launch

Measure Outcome

Predictive Model

Toronto Data Mining Forum Oct, 2011

Segmentation vs predictive models

Toronto Data Mining Forum Oct, 2011

Create persona’s

Set up taxonomy for your website & emails

Learn about a visitors preferences and interests without asking

Link behaviour / interactions / engagement toscores

Act, learn / fail, analyse, adjust (repeat)

Recipe for success

Persona

definition

Online

Value

Propositions

Outside-in

view

Inside-out

view

Functional

requirements

Content

requirements

Customer

journeysOff-site

(acquisition)

On-site

(conversion)

The approach

Persona

definitionOutside-in

view

Inside-out

view

Persona definition

How does our company perceive

its Persona’s

How do the Persona’s perceive our company and

products

Who are we targetting?

Their goalsTheir needs

Their value (CLV)The opportunities

13

Inside-out view

How is our company perceiving its target groups?

Who do we want to target?

Who is benefiting from our products / services?

(Include both internal and external stakeholders)

How are they related?

Who are they influenced by?

How important are they?

What do we want them to do?

How can they bring benefit to our business?

What value can we bring to them?

Persona definition: Inside-out view

Inside-out

view

How?

Workshop with

key persons in the

company.

Creating persona sheets

Persona sheets

Picture

Name

Identity – age occupation

Abilities/skills

Context

Recurrent issues

Goals

Needs

Fears / OpportunitiesInfluencers

Outside-in view

How do target groups perceive us?

Who is looking for us/our products?

What questions are they asking?

Where are they looking for us?

What other companies / products are they also looking at?

What are their needs?

What are their tasks?

What are they telling about us?

Persona definition: outside-in view

Outside-in

view

How?

Workshop with key

persons.

External survey

Social listening (optional)

Wikipedia: “the practice and science of classification.” A taxonomy can help you organize an unstructured collection of information

Taxonomy is used to identify the user’s preferences through the use of specific criteria called ‘tags’. These tags can be linked to sensors in order to trap the user’s click and qualify it. A taxonomy list contains the definition of these different tags.

What is taxonomy?

Taxonomies Put Content in Context

Implicit behavior is what visitors are doing on your site, such as what type of pages/categories are they looking at, or what site path they follow.

Explicit behavior is demonstrated when visitors take action or submit data, like voting in a poll, searching for a specific keyword, or filling out a form.

Types of behavior

Content scoring

Lead scoring

Engagement value scale

All types of scoring

Content Strategy: Taxonomy and Scores

TaxonomyScoring

Male factor(M minus F)

Motorization Innovation Design Power Eco Technology Safety Exterior Design Interior Design

behavior (clicks)

1 click(s) 0 click(s) 1 click(s) 0 click(s) 0 click(s) 0 click(s)

html(tags)

2 1 1 0 1 0 1 0 0 0

database(%)

100% 50% 50% 50% 50%

VALUE MAIN DIMENSION INTERESTS

SelligentTable

Top 1 VALUETop 1DIMENSION

Top 2DIMENSION

Top 3DIMENSION

Top 1INTEREST

Top 2INTEREST

Top 3INTEREST

prospect #1

MALE MOTOR INNOVATION DESIGN POWERTECHNO-LOGY

ECOPROFILE INFOper Prospect

Emakina Belgium - Audi

Personalization is a three step process:

1. You identify the visitor.

2. The visitor is classified according to which target group or persona he or she most closely matches.

3. All or part of a web page on your site dynamically changes to show relevant content, bringing the visitor closer to the action you would like him or her to take.

Profiling visitors

The future (is here)

Identify visitor intent via behavior and target with relevant content and actions

Main concepts

Profiles & Profile Keys-personas, buying phases, etc.

Profile Cards –assign to content

Pattern Cards –create and store behavior patterns for expected browsing behavior

What is predictive personalization?

Automated Personalization automatically identifies meaningful segments based on hundreds of out of-the-box variables, including:

Site behavior (e.g., new/returning visitor, search behavior, browsing behavior)

Temporal (e.g., day of week, time of day)

Referral (e.g., direct/bookmark, link from e-mail, search/display ad)

Environmental (e.g., operating system, device, mobile/pc, browser).

Automated predictive personalisation

Augment profiles with

CRM-data

3rd-party segmentation data

offline sales

social crm data

Automated predictive personalisation

The cornerstone of Automated Personalization is visitor data. That is what allows us to differentiate between visitors and predict their propensities for conversion.

The performance of any machine learning system is only as good as the quality of the available data.

Systems will:

Predict testing (A/B – multivariate) outcome

Propose tests on your page

Evaluate on your skills

Guide marketers with understanding and defining personas and profiles

Automated personalisation

360° view of the customer

Sitecore 7.5 / 8

Adobe Experience Manager

Selligent Target

Who to watch?

ThanksQ&A