MK99 – Big Data 1

Big data &

cross-platform analytics MOOC lectures Pr. Clement Levallois

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Integrating data in a multichannel environment

1. Data: you don’t get it on tap

2. A multichannel environment increases data fragmentation

3. The business stake of data integration – why should we care?

4. Practical aspects of data integration

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Example: Customer data.

Source: UNICA Corporation, in Multichannel Marketing, by A. Arikan (2008).

1. Data: you don’t get it on tap

Take away: data is fragmented by nature. You construct customer profiles by joining and assembling different sources of data into a meaningful synthesis.

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2. Data gets even more fragmented in a multichannel environment

• Basics: – Distribution, information and ad channels keep diversifying

• POS, print, TV, radio, outdoor posters, mobile apps, mobile sites, emails, SMS, APIs, social networks, search engines, e-commerce platforms, e-commerce websites, blogs, content channels, …

– Connections between these channels intensify and complexify

• Social TV is TV delivered with Internet services, user profiles created on one platform are imported on another, orders taken online can be picked up on a variety of POS, ads circulating through one channel replicate on other channels, …

– Underlying technologies evolve and fragment quickly, across channels

• Cookies, SaaS, APIs, retargeting, HTML, Android, etc.

– The expectations of customers on the quality of service elevate (realtime, seamless experience)

-> Business stake: how to manage the complexity of this multichannel environment to deliver value to the market?

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Example: French bank Societe Generale, up to early 2000s

POS

Outdoor

Call center Radio, TV, Print

media

One-way communication, analog. 1. No digital data collected

Two-way communication, analog. 2. Little (but important) digital data collected

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Mobile app

Twitter account POS Youtube channel

Google Plus

Online banking

LinkedIn profile

Facebook page Instagram

Call center

Outdoor

Print media, including online version

TV (including online TV)

Example: French bank Societe Generale, in the 2010s

One-way communication, analog and digital. 1. Digital data collected in large volumes

Two-way communication, digital. 2. Digital data collected in large volumes

Two-way communication, analog. 4. Little (but important) digital data collected

Two-way communication, digital. 3. Digital data collected in large volumes

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Example: Societe Generale, in 2020?

Check the presentation on APIs to understand the stakes of this shift. 1. Very large (extensive?) amounts of digital data collected.

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The fragmentation of channels

before today

Source: “Multichannel Marketing Ecosystems”, by Stahlberg & Maila 2014). Chapter 1.

Data integration now: - Offers more

opportunities to create value and differentiate from your competitors

- But is harder to manage

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Multichannel = disintegration of data • The fragmentation of channel means the fragmentation of your data.

• Data about your customers, products and campaigns is scattered in

different places (channels). You don’t know precisely:

– Who your customers are, how they behave with you

– How your products are perceived, purchased and used

– What are the results of your campaigns

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3. Business stakes of data integration 1. Providing a consistent customer experience across channels

– How to provide the right services on the right channels – How to integrate the experience across channels (seamless, enriched experience)

2. Managing communication campaigns – Which campaigns for which channel(s)? – How to coordinate campaigns across channels? – Which budgets should be spent, and how to spread them across channels? – How to measure the results of each campaign and the global result?

3. Generating actionable insights for business – How to increase brand awareness, – How to segment the customer base, – How to improve the retention rate, – How to better measure default risk / fraud risk / …, – How to develop new products / services / channels

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The pre-condition to achieve this goals

We should be able to have a picture of all different channels and how they connect.

This is hard.

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4. Practical aspects of data integration

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Organizational culture

Business culture

Software Application

Data type

Infrastructure

Organizational culture

Business culture

Software Application

Data type

Infrastructure

Servers in Canada

Clicks

NoSQL DB

Web agency

Startup mentality, data driven, fast to react

Servers in France

Number, duration and subject of customer calls to call center

ERP

Call center: B2B services

Mature industry, not data driven

Example: Your company has a website

generating clicks from your customers. Your customers can also use your call center. How do you integrate these two datasets about yours customers?

?

Call center

Web agency

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The layers of data integration 1. Organizational culture

– Attitudes towards data must be compatible – Organizations / execs don’t have the same sensitivity to the priority of data projects.

2. BU – How will different datasets be made compatible / convertible? (clicks and calls?) – What is the revenue sharing agreement on data, if any? – What are the acceptable levels of investment to generate / curate / share data?

3. Software application – If the two parties agree to share data, how to do the sharing work in practice? (see video on APIs)

4. Data type – Are datasets of a textual, numerical type? Are they time series, if so what is the frequency? Can identifiers be reconciled?

5. Infrastructure

– The volume of data, the place it is stored, and the servers and connections available: do they permit the integration to take place?

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Options to manage the integration

Option Pros Cons

Delegate to IT department - Resources already there. - Conformity to existing procedures is assured.

- Innovation levels remain low - Ad hoc solutions

External provider – domain specialist - Experimented on data integration for a single domain of expertise

- Cover all layers of data integration

- Knowledgeable of your business needs

- Coordination costs

External provider – - Large scope of data integration, across domains of expertise

- Coordination costs - Diseconomies of scale? - Lack of / costly customization

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The jargon of data integration

DMP Data Management Platform

CRM Customer Relationship Management

DSP Demand-Side Platform

SSP Supply-Side Platform

ERP Enterprise Resource Planning

ETL or API

ETL or API

ETL or API

ETL or API

: you should research the meaning of these terms.

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Next steps

• Watch the video lecture on APIs

• Go through the reading list

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This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com)

Contact Clement Levallois (levallois [at] em-lyon.com) for more information.