PRICE OPTIMISATION: HOW DO WE RESEARCH VALUE AND TEST DIFFERENT - - PowerPoint PPT Presentation

PRICE OPTIMISATION:

HOW DO WE RESEARCH VALUE AND TEST DIFFERENT OFFERS TO DRIVE GROWTH

Established 130 years ago The biggest daily newspaper in Finland Published in Finnish, spoken by 6M people globally 370 000 paying subscribers, paying an average of 330€/year 2017 renewed online business model + content strategy : nr of subscriptions started growing y.o.y after 25 y of decline 70% of subscribers pay for digital content, more than 100 000 are digital only Growth comes from digital subscriptions and from subscribers that are under 40 y.o.

2019: how did HS maximize both revenues and subscriptions?

Ambitious and conflicting targets: Revenue maximization + Subscription growth Accurate pricing and productization decisions across product portfolio are key

• Subscription revenue

beat all time records

• Number of digitally

active subscriptions higher than ever

• Total number of

subscription grew 2%

• Consumer

preferences

• Pricing research

through conjoint studies Consumer research

• Price elasticity
• Cross price

elasticity

• Value analysis

Price analytics

• Price optimization
• Portfolio
• ptimization

Decisions

HS has a framework for maximizing both revenue and subscription growth

CASE STUDY: ACQUISITION SALES OFFERS

Step 1: collect insights through consumer research and develop hypothesis

• 2. How the offer price of
• ne product affects

demand of other products?

0% 5% 10% 15% 20% 25% Current offer Current offer +5% Current offer +10% Full price

% of customers chosing product at price X

Mini Digi Weekend hybrid Everyday hybrid

CASE STUDY: ACQUISITION SALES OFFERS

• 1. Respondents’ demand

curve relatively flat. Can offer price increases be profitable?

• 3. Can we personalize
• ffers?

Step 2: validate elasticities using a/b tests

Price of Weekend Hybrid increased

Sales of weekend hybrid dropped Sales of DIGI increased Drop in sales of Weekend  high price elasticity Substitution between Weekend and Digi  cross price elasticity shows the two products are close substitutes Subscription revenue grew by 4% Total expected value of sales grew by 3% Everyday hybrid sales did not change

CASE STUDY: ACQUISITION SALES OFFERS

CONTROL VARIANT

Step 3: combine a/b tests with machine learning to personalize offers

33% 19%

0% 5% 10% 15% 20% 25% 30% 35% Top propensity to subscribe decile Random sample

A/B Test results : Ratio of sold continuous subscriptions to samples in premium article paywall

Machine learning to identify cookies with high purchase propensity A/B test in premium article paywall: fixed term trial vs subscription Targeting high propensity cookies brought a 6% increase in total subscription value and a 10% increase in sales volumes

CASE STUDY: ACQUISITION SALES OFFERS

Data and analytics drive decisions

CASE STUDY: ACQUISITION SALES OFFERS

Teamwork! Never stop learning, reiterate and get new ideas Decision making is easier if there is evidence from data and research. Impact of decisions easier to measure

THANK YOU!