using big data to find out what women want - john lervik, cxense

Cxense Using Big Data to find out What Women Want

Dr. John M. Lervik – Founder, Cxense

MATH

Women have gone online

To search

To socialize

To shop

To learn

To stay up to date

They do many things online…

Big Data

Big Data is the “new oil”: But only a few players use big data

And only to their own advantage

Capture real-time data from any device, incl. mobile and tablet apps

Does not rely on 3rd party cookies

In-depth user profiles are created, containing the user’s interest, intent, and context, combined with 1st and 3rd party data

Semantic content profiles for each individual page by analyzing and extracting keywords, categories, companies, people, locations, sentiment, etc

Gain real-time insight: Content semantics, traffic patterns, and audience segmentation & reporting

1. Understand your audience/customers – Develop individual user profiles, meaningful segments and get real-time

feedback on content consumption 2. Increase digital engagement

– Boost engagement, page views and loyalty by personalizing content 3. Convert users to subscribers and buyers

– Segment your audience and target those most willing to pay for content 4. Increase digital advertising revenue

– Introduce high-value targeting for display ads – Increase click-through rates for performance ads – Deliver native advertising

Big data can help businesses…

Sample Case Studies:

BBC & Styria

WHETHER IT’S A MORNING COFFEE OR A MIDNIGHT SNACK… CATERING TO YOUR NEWS NEEDS WHATEVER THE TIME OF

DAY

Regular user Bus: 10 mins (3G… sometimes) Tube: 20 mins (Intermittent wifi) Morning takeaway Key stories + tech + something to talk about in the office Evening takeaway What’s happened + analysis + something to share with friends + remove ”content noise”!

Regular user

The casual user Bus: 40 minutes Checks Facebook many times a day Uses her phone on the bus travelling to and from work Wants to be entertained and likes to share stories with like-minded friends

Casual user

Lands on a story page from a Facebook post

Casual user

Because she came from Facebook, it is likely she wants more light stories, not more stories from the US & Canada index

3 Austria, Croatia, Slovenia

CORE MARKETS

daily newspapers weekly newspapers

magazines

radio, tv

newsportals, marketplaces, content & community, services

28 mio. UNIQUE VISITORS

e-commerce

Content personalization & individualization

● Mixed recommendations: contextual, trending, behavioural, collaborative filtering ● Trending videos or trending articles from a specific channel to funnel traffic ● Noise reduction: Remove articles the individual user has read, or proven not to engage with ● A/B testing of configurations

Cross-Portal content recommendations

Responsive recommendation widget

Mobile recommendation widget

Result: Significant increase in user engagement

Start with content personalization

+111% increase 2013 +50% increase by April, 2014

Product recommendations and promotions

● Provides user with timely and relevant offers

● Digital replacement for printed leaflets

Recommendations on homepage

● Behavioural and collaborative recommendation - based on user data from all Styria websites

● Trending product

● Special offers which expiries in the next hours

By capturing, analyzing and acting upon user data, you can not only find out what women want but you can also give it to them, instantly.

So what do women want?

Women want you…

Women want you…

to know what they want…

Women want you…

to know what they want… without having to tell you.

Women want you…

to know what they want… without having to tell you.

John M. Lervik, PhD Founder, Cxense john.lervik@cxense.com