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? • Subscription revenue Ambitious and conflicting beat all time records targets: Accurate pricing and • Number of digitally productization decisions Revenue maximization active subscriptions higher across product portfolio than ever + are key • Total number of Subscription growth subscription grew 2%

HS has a framework for maximizing both revenue and subscription growth Price analytics • Consumer preferences • Price optimization • Price elasticity • Pricing research • Portfolio • Cross price through conjoint optimization elasticity studies • Value analysis Consumer research Decisions CASE STUDY: ACQUISITION SALES OFFERS

CASE STUDY: ACQUISITION SALES OFFERS Step 1: collect insights through consumer research and develop hypothesis 1. Respondents’ demand curve relatively flat. Can offer price increases % of customers chosing product at price X be profitable? 25% 20% 2. How the offer price of one product affects 15% Mini demand of other Digi Weekend hybrid products? 10% Everyday hybrid 5% 3. Can we personalize 0% offers? Current offer Current offer +5% Current offer +10% Full price

CASE STUDY: ACQUISITION SALES OFFERS Step 2: validate elasticities using a/b tests Price of Weekend Hybrid increased CONTROL VARIANT Sales of Sales of DIGI weekend increased hybrid dropped Everyday hybrid sales did not change 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%

CASE STUDY: ACQUISITION SALES OFFERS Step 3: combine a/b tests with machine learning to personalize offers A/B Test results : Ratio of sold continuous subscriptions to samples in Machine learning to identify cookies premium article paywall with high purchase propensity 33% 35% 30% A/B test in premium article paywall: 25% fixed term trial vs subscription 19% 20% 15% Targeting high propensity cookies 10% brought a 6% increase in total subscription value and a 10% 5% increase in sales volumes 0% Top propensity to subscribe decile Random sample

CASE STUDY: ACQUISITION SALES OFFERS Data and analytics drive decisions Teamwork ! Decision making is easier if there is evidence from data and research. Impact of decisions easier to measure Never stop learning, reiterate and get new ideas

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