Data Monetization Pt.1

Analytics - Netflix

Netflix uses analytics to gain a competitive advantage

• It knows what is being watched by all consumers

• When someone pauses, rewinds, fast-forwards

• What is being searched

• What device is being used

• When the viewer leaves content

Netflix’s “data-driven” culture

• decisions about original content

• what films to license

• what shows to recommend to viewers

• what colors and images to use on its website

Caesars Entertainment

Caeser’s entertainment is the worlds largest gaming company more than double revenues by collecting and analyzing customer data

• They cater to all their wants and needs → customers will spend more money → increase in revenue per customer

Capital One - how did they succeed?

Became one of the industry’s (financial services) big players by using analytics

• Created new products/services that appeal to new customers and strengthen their relationship with existing customers

• Addressed “niche markets” which might not be attractive to big players because of a small number of potential customers

Why use Analytics?

The use of analytics to gain competitive advantage is that many industries offer similar products and use similar technologies

• Analytics helps companies differentiate themselves!

Analytics enables…

fact-based decision-making

• reduce bias

• increase accuracy

• make decisions easier to judge

Business Analytics Definition

Using tools and techniques to turn data into meaningful business insights

• data → tools and techniques → business insight

Descriptive Analytics

• summarize raw data and make it easy to interpret for humans

• used to understand at an aggregate klevel what is going on in the company

  • historical insights

Predictive Analytics

• Provide estimates about the likelihood of a future outcome

  • Forecasting customer behavior and purchasing power

Prescriptive Analytics

Attempt to quantify the effect of future decisions before they are made

• Using prescriptive analytics to optimize production, scheduling, and inventory in a supply chain

Analytics - Categories

What should we do?

What might happen?

What is happening?

Data and the importance

• Data is the “oil gig of the digital economy”

• Data has economic properties that enable it to be leveraged in ways other assets cannot

• when you consume data it does not get used up

• when you use it, it often generates more data

• it has low inventory and transit costs (compared to other assets)

How is the role of data shifting?

From serving as a secondary asset that supports decisions to being a primary asset that businesses can productize(make into a product) and sell

Data challenges

Data buyers brush off the potential value of data

• data cannot be fully disclosed prior to purchase

• data needs to be analyzed to discover the FULL VALUE

• data buyers don’t know the creation, processing, storing, and distribution of high-quality data is a large cost for the data provider

• lack of trust and security concerns

Data sharing 1.0

Companies share minimal data, only when it is REQUIRED, to proceed with transactions, and solve large problems, and follow regulations

Data sharing 2.0

Sharing COMPLEMENTARY data assets and capabilities to create new value propositions

Data monetization

When companies convert data and analytics into financial returns

What is data monetization?

Process of using data to obtain quantifiable economic benefit

• Internal or indirect methods include using data to make measurable business performance improvements and inform decisions

• External or direct methods: data sharing to gain beneficial terms of conditions from business partners, information bartering, selling data, or offering products and services

3 main data monetization approaches

• Improving processes with data (to create returns through operational efficiencies)

• Wrapping core offerings with analytics features (to increase a product’s price, wallet share, market share, or customer loyalty)

• Selling information solutions

What is market share and wallet share?

Market share: a company’s portion of total industry sales

Wallet share: how much an individual customer spends on one brand

How can you improve processes?

• The most immediate way to monetize data

• Financial returns can be generated by using data to create operational efficiencies

• Putting data and analytics in the hands of employees who make important decisions

If you cannot measure the impact of indirect data monetization….

It is hard to claim that it is “monetization”

• Direct data monetization:

  • data → revenue

• Indirect data monetization:

  • data → improved business performance → economic value

Can improvement in the hiring process be measured?

• Organizations needs evidence that the “data-driven” intervention caused at least part of the improvement

  • E.g. Changes can be caused by new recruiters, better software, changes in software, etc…

Wrapping

Enriching products/services using data analytics (dashboards, reports, alters, suggested next step, or automated action)

• Differentiate product/services; enhance value proposition

Banks have been wrapping their products/services with analytics

Fitness wrist bands such as Fitbit offer basic analytics for free and premium analytics for a fee

OneTouch Glucometer offers an app rich with analytics

4 design characteristics for data wrapping

Wrapping (4 A’s)

Anticipate

Advice

Adapt

Act

Anticipate

The wrap understands in advance the customer’s need

Advice

The wrap supports evidence based decision-making

Adapt

The wrap meets the customers need in a tailored manner

Act

The wrap performs an action that benefits the customer

Information Offerings

Represent the hardest way to monetize data because they requires a unique business model (e.g. becoming an information business)

Information Offerings 3 phases

1) Data

2) Insights

3) Actions

Information Offerings Consumption Path (9)

Data Offerings

Represent the foundation for the information offerings consumption path

• Requires data acquisition platforms

• Requires mechanisms to understand the data after it is sold because the buyer is alone in analyzing it and using its insights

2 categories of data offerings

Raw data: data with no cleaning, transformations, or enhancements

Prepared data: data that has been transformed, enhanced, cleansed, etc.

• Usually prepared to be used for specific purposes

Insight Offerings

Reports and analytics that directly support processes and decisions

• Reports and analytics must be useful and easy to use

2 categories of insight offerings

1) Reporting (descriptive): dashboards, visualization tools

2) Analytics(prescriptive and predictive): tools that use algorithms, stat modeling, and machine learning to discover new insights about the data

Action Offerings

Help customers act on insights

• 2 categories

  • Process design: consulting services and on-site support

  • Process execution: process automation and outsourced solutions that execute tasks on behalf of the client based on insights

Indirect data monetization

Using data internally to improve processes

• Employee referrals are hired faster and tend to stay longer

Direct data monetization

Selling data, insights, or actions

• selling anonymized data to a labour-market research company

• selling reports explaining which recruitment channels produce the best employees

  • Selling action: advise companies on how to improve their hiring processes using hiring data and analytics

Data monetization