5 tips als je nu wilt starten met digital marketing analytics

Brouwen met Data5 tips als je nu wilt starten met digital marketing analytics

Jeroen den Uijl Mateusz SkawinskiDigital Strategist Manager Advanced Analytics+31 6 50 45 97 26 +31 6 15 87 75 [email protected] [email protected]

©2017 Avanade Inc. All Rights Reserved.

3

We can finish what we start

StrategyCustomer

experienceOperations

Platform build

& Integration

Why

Is Digital Marketing

Analytics important


FALSE SENSE OF

COMFORT

Beware of the


“The failure of a single interaction threatens a customers entire

perception of a brand”

Scott Liewehr


“It’s no longer about how brands look but about how they behave”

Jon Bains


The value is clear“The stronger a company’s commitment to analytics, the higher that company’s performance.”

59% 63%2

Outcomes matter

2X

Data matters Algorithms matter Investment matters Talent matters

What are high-performance companies doing more and better than low performance companies?

Source: Winning with Analytics, Accenture and MIT study, 2015

as likely to achieve

business outcomes

from analytics

7or more data

sourcesinvest > ¼ of their

tech spend in

analytics

use partners for

advanced analytics

skills

Leverage Use

or more advanced

analytical

techniques


Data within

organizations that

remains untapped

Marketers who say

they can’t turn

their data into

actionable insight.

Marketers who

think data is

collected too

infrequently or not

real-time enough

Organizations that

don’t use real-time

on-site behavior to

personalize the

website

experience.

95% 39%39% 26%

Altough marketers and organizations have plenty of data, they struggle to mine it, surface their most important customers segments, and respond to theircustomers’ needs in real-time.

Why is it a struggle?

Source: From Big Data to Big Personalization


from 4.6%to almost 22%

of marketing budgets in the next three years,

376%increase

Spending on marketing analytics

Source: The CMO Survey, the American Marketing Association (2016)


a third of available data are used to drive decision making in their companies

3,4% of senior marketers say they have the right talent for the job

Spending on marketing analytics

Source: The CMO Survey, the American Marketing Association (2016)


CHALLENGES

Data to Insights

Speed

Frequency

Capabilities

Budget / ROI

Decision making process

14


Investigate, Automate, Industrialize

Vision Hypothesize,

Test, Refine,

Repeat

ROI/Prove

value

Build

IndustrializeDesign Automate

$

Value Roadmap,

discovery and

outcomes


Vision Hypothesize,

Test, Refine,

Repeat

ROI/Prove

value

Build


$

Value Roadmap,

discovery and

outcomes

An iterative, industrialized approach


GUIDELINES

Think big, start small, prove the

ROI fast

Don’t focus on a single silo

Experiment before industrialize

Fail fast

Be ready to ACT

Guideline #1

Think BIG

Start small

Prove the ROI fast


Business outcomes over business requirementsInstead of asking “What data and tools do we need?” ask “What do we need to accomplish?”

Reduce equipment

outages by 8%Increase sales by 8%Improve customer satisfaction

scores by 10%

Cut fraud loss by detecting

fraudulent activity in progress

Reduce attrition through higher

employee engagement

Increase campaign

ROI by 10%

Hypothetical examples of business outcomes


The Insight Discovery Example

Drive Growth

Drive

Productivity

Digital

Business

Value

Focus on the Customer

Accelerate Decision

Making

Customer Case

Efficiency

Business Objective

Simplify

Customer

Experience

Sales Effectiveness

Relevancy and NPS

Customer Retention

Adoption and Usage Analytics

Trade Promotion Lift and Leakage

Marketing ROI Maximization

Supply Chain Control Tower

Next Best Sales and Marketing ActionPotential up-sell &/or cross-sell opportunities

Optimize sales force investments & productivity

Improve Channel effectiveness

Improve Demand Gen ROI

Improve SC visibility

Predictive Asset Maintenance

Contact center optimization

Reduce maintenance cost

Call Center Process Efficiencies

Increase New Customer Acquisition

Increase Share of Wallet

Drive customer loyalty

Value Driver Analytics Capability Business Benefits

Oppty Win rate 2-4%

Reduce churn

Sales lift

Talent Upskilling and Engagement Human Capital Analytics

Sales lift

Channel spend ROI

Sales lift

Rev/ partner

Engagement scores

Forecast accuracy

Cost of Service calls

Machine uptime

2-4%

10-20%

1.5 – 2.0%

2–4%

1 – 2%

5 – 10%

15 – 20%

5 – 10%

10-15%

500 bp

Build Capability Self Service

Reduce Response

Times

Industry Leading

Retention

Cost Eradication

Digital First Segment of One Personalization Cost of Care 10-15%Asset Utilization and

Uptime


1. Understand the business problem

We really have no way of knowing if our products improve the outcomes of health department evaluations. We know our products are effective, but how effective are they really?

2. How might you solve it?

Being able to draw statistically-significant conclusions from our product sales data (maybe combined with other 3rd party data sets?) would be a good start. No matter how the investigation turns out.

Vision


Global cleaning products vendor increases sales

and customer loyalty with predictive intelligence

Challenge: Maintain sales levels year-over-year.

Solution: Using machine learning algorithms, Avanade discovered that when customers

purchased and installed less of certain products, their risk of failing a health inspection

went up significantly. Given that such incidents have the potential to cost their customers

tens of thousands of dollars per location, this intelligence was of significant value to both

parties.

Results:

• The model was able to predict inspection failures with 90% accuracy.

• By communicating to customers the measurable importance of maintaining order

levels, the vendor arrested falling year-over-year product sales.

• Taking proactive steps to help customers pass health inspections could significantly

increase loyalty and retention by saving the customers large sums of money each

year.

Guideline #2

Don’t focus on a

single silo


Service

Sales

Divided Analytics

Know your customer


The integrated “exhaust” from every

customer and process interaction

is a cash-equivalent asset

But you don’t need to integrate it all

before you see real value


Treasury Wine Estates drives growth and

profitability with seamless, self-service analytics

platformChallenge: Core ERP and analytics tools were fragmented, making it challenging

to obtain actionable insights across a large, global portfolio of brands.

Solution: Refresh core ERP and analytics systems using AMAP to deliver a

seamless global view of inventory, sales, and depletions. The platform

delivers Analytics-as-a-Service with a consistent, up-to-date insights,

tailored to users ranging from the C-suite to local operations and sales

teams.

Results:

• Effective market segmentation supports growth of premium brands

in line with company strategy

• Users are empowered with self-service analysis and targeted insights

delivered across devices

• Scalable foundation supports next stage of analytics innovation

including end-to-end value chain analysis

Guideline #3

Experiment before

industrialize


Don’t build anOlympic Stadium

29© 2016 Avanade Inc. All Rights Reserved.

Experimentation

before industrializationHypothesize, experiment and

discover how data can

deliver insights that drive

outcomes.

Once it’s proven to be valuable,

then you can build the systems to

make it repeatable.


Increased value and

ROI through

increased accuracy

UK

Customer

satisfaction

Leading the market in smart heating solutions

Create a robust intake volume forecast and

insights by region that allows for more

efficient engineer scheduling, reduces

response time and ultimately drives customer

satisfaction

Challenge

• Data enrichment

• weather data

• Output Area Classification Data

• Blended forecasting models: 450 modles

for 14 UK regions

• Additional insights

Solution

• Overall accuracy of 98% vs. a 87%

accuracy

• Increased value and ROI through

increased accuracy

• Actionable insights for the business

• The new forecast minimizes error for all

days

• Significant saving opportunity

Results

Guideline #4

Fail fast


Speed over controls

Traditional mechanisms of control are not built for a digital world. A new, more flexible and open approach is necessary to achieve speed to insight.

By applying the right controls in the right context, you can maximize access while still managing risk effectively.

Take a balanced approach to risk

and reap the rewards


33


34


Advanced Analytics

Business

Intelligence

and

Visualization, the “what”

Operational

Challenges

Present oriented view

Sophistication of Intelligence

Alerts “What actions are needed?”

Query/drill down “What exactly is the problem?”

Ad hoc reports “How many, how often, where?”

Standard Reports “What happened?”

Optimization “What’s the best that can

happen?”

Predictive Modeling “What will happen next?”

Forecasting /

extrapolation

“What if these trends continue?”

Statistical analysis “Why is this happening?”

Advanced

Analytics, the “so

what”

Strategic Challenges

Future oriented view

Source: Competing on Analytics:

The New Science of Winning (Davenport / Harris)

Co

mp

eti

tive

Ad

va

nta

ge


37

Leading global beverage company

Guideline #5

Be ready to ACT


Reading dashboards & scorecards without the ability to take immediate action is likehaving a dashboard without a steering wheel


DIGITAL

TRANSFORMATIONROLE OF AVANADE

Key Objective

To digitally transform how Williams

Martini Racing works on and off the

track, and showcase how Avanade

helps its clients get ahead of the

competition.

Data / Third party data

Analytics

Technology integration: Project integration across all

Williams divisions for example: AERODYNAMICS, DESIGN

AND MANUFACTURING RACING

Cloud: Azure

Sitecore CMS

WILLIAMS

RESULTS

300 windtunnel hours saved

5 weeks to deliver new website on Sitecore in

Azure

28 digital projects in the 3 year pipeline


Vision Hypothesize,

Test, Refine,

Repeat

ROI/Prove

value

Build


$

Value Roadmap,

discovery and

outcomes

An iterative, industrialized approach

How can we help youTo connect the dots?


Getting actionable insight

from data is a

challenging job


We've simplified

it by industrializing these

four steps


Unify Enrich Unlock Enable


Library of Reusable

Analytics Assets

Vision Hypothesize,

Test, Refine,

Repeat

ROI/Prove

value

Build


$

Value Roadmap,

discovery and

outcomes

Get Started


Our in-person Insight Discovery

Workshop proves the value and builds

the foundation for innovation and

transformation through analytics

WE WILL PROVE IT


Small

investment, high

return, rapid

results

WE WILL PROVE IT

Questions

Jeroen den UijlM: 06 50 45 97 26

E: [email protected]

Contact

Mateusz SkawinskiM: 06 15 87 75 36

E: [email protected]