[Big]-Data Analytics for Businesses SESSION 1 Five Key Takeaways - - PowerPoint PPT Presentation

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[Big]-Data Analytics for Businesses SESSION 1 Five Key Takeaways - - PowerPoint PPT Presentation

Apr 09, 2024 453 likes •650 views

Theos Evgeniou; Professor of Decision Sciences [Big]-Data Analytics for Businesses SESSION 1 Five Key Takeaways 1. It is now possible to make evidence based , data driven decisions in increasingly more areas 2. Analytics does create value , in

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Theos Evgeniou; Professor of Decision Sciences

[Big]-Data Analytics for Businesses

SESSION 1

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Five Key Takeaways

1. It is now possible to make evidence based, data driven decisions in increasingly more areas 2. Analytics does create value, in multiple dimensions 3. There is more value in combining diverse data 4. Key Business Performance (KPI) Measurement facilitates coordination and change

  • 5. Technology = Change
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Respondents to our survey are from a wide range of industries and from all regions in the world…

Respondents per Industry

n = 479

Region of Company’s Headquarter

n = 479 Africa 3% (16) South America 4% (19) Middle East 7% (33) Asia Pacific 13% (63) North America 18% (88) Europe 54% (260) 9% 5% 4% 11% 21% 19% 13% 10% Transportation Technology Health Energy Telecom Public Sector Media & Entertainment Consumer & Retail Professional Services Real Estate Financials Industrials

Source: Strategy&/INSEAD Demand Analytics survey (August 2014)

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… representing companies from <$50 million to >$20 billion, and are primarily occupying an executive role

Company Size (Revenue)

n = 479

Respondents per Role

n = 479 8% 8% 5% 6% 8% 6% $10B - $20B $5B - $10B $1B - $5B $500M - $1B $100 - $200M $50 - $100M $200M - $500M > $20B 35% < $50M 11% 13% 28% (132) 14% (68) 13% (62) SVP/VP Employee C-Suite Member 9% (42) Board Member 14% (68) Specialist 3% (14) General Manager Other 14% (67) Consultant Director Manager Project Manager

Source: Strategy&/INSEAD Demand Analytics survey (August 2014)

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Companies with a leading Analytics capability are demonstrating statistically higher performance levels

Company performance vs. Capability level of Demand Analytics (Mean score)

n = 451 Excellent Average (n = 174) Lagging (n = 108) Poor Above average (n = 123) Leading (n = 46) Average Level of Demand Analytics capability (vs. competitors) Company performance (vs. competitors)

Note: Company performance and level of Demand Analytics capability are self-reported by respondent Source: Strategy&/INSEAD Demand Analytics survey (August 2014)

50 respondents

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Above average DA performers typically outperform their average peers by ~1.5x on sales, margin, profit & TSR

Average company performance levels in past three years

Company performance (vs. competitors) 10% x1.5 6% Averag e 15% Below Averag e Above Averag e 9% 6% 4% x1.4 6% 9% x1.4 2% 20% 8% 11% X1.8 Sales growth n = 264 Margin growth n = 127 Profit growth n = 200 Total Shareholder Return n = 75

Note: Company performance is self-reported by respondent Source: Strategy&/INSEAD Demand Analytics survey (August 2014)

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Within each of these five categories, on average two to three different types of analysis are performed by leading companies

Source: Strategy&/INSEAD Demand Analytics survey (August 2014)

Digital Analytics Customer Analytics Marketing Analytics Sales Analytics Consumer Analytics

Average no. of analysis

3

Average no. of analysis

3

Average no. of analysis

2

Average no. of analysis

2

Average no. of analysis

3

Product and service bundling & offer

  • ptimization

48%

Customer profitability & lifetime value modeling

46%

Demand forecasting

46%

Pricing elasticity modeling & discounting

  • ptimization

41%

Survey & questionnaire design

48%

Digital pathway analysis & website optimization

46%

Cross-sell, upsell & next- best-offer modeling

46%

Market mix modeling & media budget optimization 33% Price laddering & category management

39%

Customer experience research & modeling

43%

Email campaign

  • ptimization

43%

Customer acquisition and activation optimization

41%

Market structure, brand portfolio & architecture

  • ptimization

30%

Sales agent & commission analytics

30%

Customer satisfaction & customer advocacy modeling

41%

Social media, mobile & text analytics

43%

Customer loyalty analytics &

  • ptimization

41%

Contact center analytics & cost optimization

28%

Assortment planning & analytics

24%

Needs-based segment. & development of value propositions

37%

Behavioral segmentation & profiling

39%

Response & purchase propensity modeling

33%

Marketing attribution models 22% Assortment planning & analytics

20%

Qualitative research, ethnography & social listening

35%

Content testing & user experience optimization

39%

Churn modeling & attrition prevention optimization

28%

MROI of paid, owned, & earned media channels

20%

Sales territory design

20%

Price–product architecture models

28%

E-commerce optimization

28%

Advanced micro segmentation & profiling

24%

Contact agent analytics

17%

SKU rationalization & product delisting

20%

Identification of unmet needs/white space

24%

Design of recommendation engines

26%

Win-back modeling & offer

  • ptimization

22%

Retail site selection

7%

Conjoint & discrete choice modeling

20%

Affinity analysis & market basket optimization

17%

Most used

Least used

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Visualization is key: Business Sphere

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There is a big potential in combining diverse data…

The main types of data analyzed

0% 20% 40% 60% 80% Transactional data Customer Relationship management data Social media data Log (e.g. internet/web) data Unstructured data (documents, video, images) Structured survey data Sensor data % of respondents Efficient Not efficient

= (somewhat) inefficient in using data analytics = (somewhat) efficient in using data analytics

  • 30% analysed data from just ONE source
  • Over 50% analysed data from TWO source’s
  • Less than 20% analysed data from MORE THAN TWO source’s

BUT

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Do you harvest multiple (unconnected so far) data sources?

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Bringing the capability above average typically requires considerable & yearly investments to build the capability

Source: Strategy&/INSEAD Demand Analytics survey (August 2014)

25% 2% 5% 14% Average

Level of DA capability (vs. competition)

Above average No investments made Leadin g Laggin g behind 52% 36% 9% 8% Minimal (only time/resources) 23% 44% 52% 19% Small / Ad-hoc 34% 70% Considerable & yearly to build capability

Investments made in developing DA capabilities over the past three years?

n = 434

What is your current Demand Analytics capability level (vs. competition)?

n = 479 Level of DA capability (vs. competition) 23%

Leadin g Above average Laggin g Behind

36% 26% 6%

I don’t know Average

10%

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PERFORMANCE METRICS

Greg Linden at Amazon created a prototype to show personalized recommendations based on items in the shopping cart. While the prototype looked promising, “a marketing senior vice- president was dead set against it,” claiming it will distract people from checking out. Greg was “forbidden to work on this any further.” Nonetheless, Greg ran a controlled experiment, and the “feature won by such a wide margin that not having it live was costing Amazon a noticeable chunk of change. With new urgency, shopping cart recommendations launched.”

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What performance metrics do you use?

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A Common Trap: IT + OO = EOO

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Firms with these leading Analytics capabilities have put distinct enablers in place – processes, data and expertise are key

Best in class Minimal Qualified participant

DA Expertise level/Skill- set DA resource dedication

Average

Accessibility & Quality of data Defined DA processes

Enabler level (mean-score) Enablers for Data Analytics Capability Above average Lagging behind Leading

DA tools & technique s DA embedmen t in strategy Leadership perception & drive of DA Alignment to customers

DA capability level

Statistically significant difference

Source: Strategy&/INSEAD Demand Analytics survey (August 2014)

Most important Least important

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Digital Maturity: Standardization

  • 1. We have reached an efficient level of technology standardization

and infrastructure sharing across our organization;

  • 2. We have effectively standardized administrative processes (e.g.,

HR, finance, purchasing) across our organization;

  • 3. We have effectively standardized core operational processes

(e.g., supply chain, manufacturing, operations, sales, customer service) across our organization;

  • 4. We are effective at sharing standardized data (e.g., product,

customer, partner) internally – i.e., among individuals within different parts of the organization; and

  • 5. We are effective at sharing standardized data (e.g., product,

customer, partner) externally – i.e., with key partners (e.g. suppliers, customers, other partners).

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Digital Maturity: Integration

Internal data integration Our information systems allow us integrated access to . . .

  • 1. . . . all customer-related data (e.g., service contracts, feedback)
  • 2. . . . all order-related data (e.g., order status, handling

requirements)

  • 3. . . . all production-related data (e.g., resource availability, quality)
  • 4. . . . all market-related data (e.g., promotion details, future

forecasts) External data integration

  • 1. Data are entered only once to be retrieved by most applications of
  • ur channel partners.
  • 2. We can easily share our data with our channel partners.
  • 3. We have successfully integrated most of our software applications

with the systems of our channel partners.

  • 4. Most of our software applications work seamlessly across our

channel partners.

Roberts and Grover, 2012