IDM/TFM

Data Analytics –

What all marketers need to knowThe benefits of customer analytics, how to find the story in your data and

an introduction to the fast developing area of predictive analytics

Presented by: Mark Patron

Agenda

• Benefits of customer analytics

• Data mining process

• Data analytics pitfalls

• Predictive analytics

• Economics of data analytics

Everyday we see the power of data analytics

Amazon Netflix

Google Personalized Search Tesco Clubcard

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1980’s 1990’s 2000’s 2010’s

Evolution of data driven marketing

Database Marketing

Target best customers

RFM data

Direct mail

Mainframes

CRM

Customer retention

Transactional data

Telephone

Minicomputers

Campaign Management

Multichannel

Digital data

SearchEmail

Internet

Marketing Automation

Event triggeredPersonalisation

Behavioural data

MobileSocial

Cloud

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How customer analytics helps

marketing business decisions

• Customer analytics uses data from customer behaviour to help make

key business decisions with market segmentation and predictive

analytics

• Decisions include how best to identify, attract, convert and retain the

most profitable customers

Challenges of customer analytics

• Data and organisational silos hinder a meaningful 360-degree single customer view

• A shortage of analytical skills is often a constraint on gaining customer insight

• Many decisions are still based largely on opinions. Even mature marketing organisations struggle to understand their customer’s journey and what steps influence it in what ways

• Increasing volume and variety of data make it difficult to analyse and derive simple conclusions

Agenda

• Benefits of customer analytics

• Data mining process

• Data analytics pitfalls

• Predictive analytics

• Economics of data analytics

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Data mining process

Search CRISP-DM for more detailed process

First be very clear about what

you want to achieve

• Clear goals are critical, you can’t hit a moving target

• “Difficult to complete your mission if your objective is not clear”

• Make sure your goals are “SMART”:

Data preparation and data quality is key

• Data quality is important - rubbish in, rubbish out

• 70% of the work in data mining projects is typically data preparation, cleaning and exploration

There are many data exploration

and analysis techniquesSpreadsheet Venn diagram Predictive analytics

Graph Cluster analysis

Testing needs to be statistically robust

95% confidence means on averageresult will be the same 19 out of 20 times

• Run extra campaigns

• Test new landing pages

• Segment email list

• Review PPC

Sales have dropped… ..for 3 main reasons... ..so we will take the following actions:

Find the story in the data for

real customer insight

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Agenda

• Benefits of customer analytics

• Data mining process

• Data analytics pitfalls

• Predictive analytics

• Economics of data analytics

Correlation does not imply causation

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Interactions in data

(%) Age <50 Age >50

Male 50%

Female 50%

50% 50%

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Top level analysis indicates age and gender are not important

Interactions in data

(%) Age <50 Age >50

Male 25% 75% 50%

Female 75% 25% 50%

50% 50%

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Multivariate analysis shows age and gender to be very predictive

If you only have a hammer

everything looks like a nail

• A 70% homepage bounce rate means you know little about 70% of your traffic

• Top line analysis tells you nothing about interactions in the data• You can only analyse data you have access to, often

most predictive data, say, attitudinal, is not available18

Agenda

• Benefits of customer analytics

• Data mining process

• Data analytics pitfalls

• Predictive analytics

• Economics of data analytics

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Credit scorecard using regression

CreditWorthiness

Age18 90

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CHAID tree segmentation

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How neural networks work

Neural networks learn by trial and error using feedback similar to howchildren learn by being told what they're doing is right or wrong.

The network output is compared to what it was meant to produce thenusing the difference between them to modify weights of connectionsbetween the units in the network.

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How neural networks work (cont.)

More complex tasks such as face or voice recognition require analysis to be divided into multiple chunks or layers

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Agenda

• Benefits of customer analytics

• Data mining process

• Data analytics pitfalls

• Predictive analytics

• Economics of data analytics

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Predictive analytics must be driven by

economic incremental gain

The cost of predictive analytics (including all data prep and

deployment) must be less than the extra profit generated by the

predictive analytics model.

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Economics of data analytics

1. Decision based on single variable –e.g. simple selection based on level of engagement

2. Multivariate analysis – more complex decision based on engagement, number of products purchased and length of contract

3. Predictive model – using regression or CHAID

4. AI – using a neural network or machine learning

• Increasing performance

• Increasing cost

• Decreasing transparency and learning

Analytics value is created by

people not technology

• Avinash Kaushik, Google’s Digital Marketing Evangelist, proposes a simple rule of thumb for web analytics, where for every $100 spent on web analytics spend 10% on the analytics tools and 90% on the people

• Need people who love numbers, have good analysis skills and whounderstand your business

Source: Occam’s Razor27

Thank You

Any questions?

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