By Patrick Glenisson, Retailored

1. Lessons&from&Driving&
Analytics&Projects
told&using&a&business&use&case
Patrick(Glenisson

4. DECISION(SUPPORT SMART(PRODUCTS
Decision(Makers(in(an(
organisation
Consumers(using(an(
application
>>"Supports"the"
Business"Model
>>"Is"the"Business"
Model
DATA$

6. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES

7. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES
What(an(exciting(
opportunity(for(
the(company(&(
the(team
Chief$Data$
Officer

8. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES
Cool(big(data(
science,(let’s(
build(a(reco
engine(ourselves
Our(newspaper(will(
not(be(directed(by(
robots.(This(will(
damage(our(brand.(Let(
users(indicate(
themselves(what(they(
find(relevant
More(pageMviews(
yields(more(
inventory(&(
advertising(sales
What(about(filter(
bubbles(&(
clickbaits,(and(
editorial(
freedom(?
Why(invest(in(
this(project(?(It(
seems(like(a(‘nice(
to(have’
Yet(another(
external(supplier(
to(integrate(with(
our(systems
Where(lies(the(
ownership(of(
data(&(insights(?(
Will(the(supplier(
reMsell(it(?
What(an(exciting(
opportunity(for(
the(company(&(
the(team
We(don’t(have(
the(expertise(inM
house,(let’s(buy(
the(solution
Data$Scientist
Chief$Data$
Officer
CMO
Chief$Editor
VP$Advertising$
Sales
CFOChief$Digital$
Officer
CEO
CIO

9. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES
Our(newspaper(will(
not(be(directed(by(
robots.(This(will(
damage(our(brand.(Let(
users(indicate(
themselves(what(they(
find(relevant
CMO

11. Opinie
Interviews
Podium
Homepage
Wetenschap
Opinie
ePolitiek
Buitenland
Wetenschap
Andere

12. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES
What(about(filter(
bubbles(&(
clickbaits,(and(
editorial(
freedom(?
Chief$Editor

14. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES
More(pageMviews(
yields(more(
inventory(&(
advertising(sales
VP$Advertising$
SalesWhy(invest(in(
this(project(?(It(
seems(like(a(‘nice(
to(have’
CFO

16. Optimize(for(what(?
• CTR
• Time(on(page
• Scroll(depth
• Recirculation(rate
• Recurring(visits
• Conversion(to(subscription
• =>(business(case

17. Machine(Learning(((CTR:(6,8%
Editorial CTR:(3,0%

18. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES
We(don’t(have(
the(expertise(inM
house,(let’s(buy(
the(solution
Chief$Digital$
Officer
Cool(big(data(
science,(let’s(
build(a(reco
engine(ourselves
Data$Scientist

20. • SPEED
• EXPERIENCE
• INSPIRATION
• ENABLEMENT
• BETTER(REQ(FIT
• MOTIVATION
• YOU(BUILD(IT(/(
YOU(RUN(IT
• IP(OWNERSHIP

22. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES
Where(lies(the(
ownership(of(
data(&(insights(?(
Will(the(supplier(
reMsell(it(?
CEO

24. CONTENT$RECOMMENDATION
ENGINE$FOR$NEWSARTICLES
Yet(another(
external(supplier(
to(integrate(with(
our(systems
CIO

25. Work&alongside&with&IT

26. Take&aways
1. Invest(in(understanding(the(business(you(are(working(for((to(convince(CXO)
2. Work(endMtoMend((to(drive(the(right(metrics,(or(‘sell’(your(insight)
3. Communicate(SIMPLE((open(the(black(box)
4. Data(quality(is(your(concern(as(well((to(keep(the(metrics(up)
5. Collaborate(closely(&(crossMfunctional((and(break(silo’s)
6. Look(for(a(Productowner,(or(be(the(Productowner (to(drive(new(features)
7. Seek(winMwin(partnerships(with(external(supplier((and(accelerate)
8. Embrace(IT((to(deal(appropriately(with(technical(depth)

28. Patrick(Glenisson @p_glenisson
Thank(you(