data analytics simulationstrategic decision making

debrief businss analytics

1. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
DATA ANALYTICS SIMULATION:
STRATEGIC DECISION MAKING
Debrief Slides

HBP Product No. 4815
This PowerPoint presentation was prepared by Professor Thomas H. Davenport for the sole purpose of aiding classroom instructors in the use of Data Analytics: Strategic Decision Making Simulation (HBP No. 7050). HBP educational
materials are developed solely as the basis for class discussion. These materials are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management.


Copyright © 2016 President and Fellows of Harvard College. This publication may not be digitized, photocopied, or otherwise reproduced, posted, or transmitted, without the permission of Harvard Business Publishing.

2. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Please treat the information in this presentation as
you would a Teaching Note.

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Note to Instructors

3. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Consumer Products Industry
Consumer products are a data-intensive industry, and almost every
company in the industry is becoming increasingly analytical.
Much of the industry’s customer data comes ultimately from bar
code scanners within supermarkets.
Use of data and analysis can help executives in that industry learn
whether they are targeting the right customers, pursuing the right
strategies, operating their business in the right way, and
undertaking the right marketing campaigns and promotions.
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4. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Blue at Kelsey-White
▪ Blue at Kelsey-White
▪Laundry detergent was a key product for K-W in the form of Blue
▪Blue came in several formulations—liquid, powder, and single-use pods
▪Slow pod sales
▪Even liquid too modern for some customers
▪Rising average age of Blue customers
▪Downward drift of Blue’s market share and profitability
▪K-W Vision
▪K-W was behind other firms in its use of data and analytics for decisions
▪New CEO brought new focus on quantitative and data-based decision-making
▪Created Vision, a system for displaying key info about market, financial, and
operational performance
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5. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Three Types of Analytics
DESCRIPTIVE ANALYTICS
▪Descriptive analytics report on what happened in the past. The
format may be a standard report, a dashboard or scorecard (as in
this simulation) or an alert. Although there are not normally
statistical relationships in descriptive analytics, an analyst can
segment the data and explore differences across segments. This
simulation primarily uses descriptive analytics, which are sometimes
also called business intelligence.

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6. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Three Types of Analytics
PREDICTIVE ANALYTICS
▪Predictive analytics make use of statistical models on past data to
predict the future. Once a model is created that fits past data well,
we can use it to predict what may happen in the future, given no
major changes in assumptions or relationships. The forecasting
exercise in this simulation is an example of predictive analytics.

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7. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Three Types of Analytics
PRESCRIPTIVE ANALYTICS
▪Prescriptive analytics are models or analyses that inform people how
best to perform a specific task in their jobs. It is an analytics-based
recommendation for action. The recommendation might involve the
best price to charge for a product, the optimal level of inventory to
hold, or the treatment for a particular patient’s disease. There are
no prescriptive analytics in this simulation.
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8. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Blue’s Competitors
▪Turbo
▪Market leader and highest priced, focused on cleaning power
▪Heavily advertised, with 35% on digital ads likely aimed at “millennials”
▪Success as first to introduce pods and liquid formulations
▪Greatest sales through large retailers (i.e. Walmart/Target), but also in small
urban stores
▪Fresh
▪Second in the market in share and price, focused on fresh, clean smell
▪Stated aim at younger customers, but strong TV focus and during daytime
broadcasts
▪Strongest sales through mid-sized grocery chains and in powder formulation
▪Store brands
▪Compete largely based on price, with lowest prices in the market

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9. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Results: Cumulative Profit
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10. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Results: Cumulative Profit
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11. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Results: Revenue
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12. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Results: Market Share
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13. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
K-W Vision
Historical data, reports, and tools available
▪How much effort did you put into exploring the past data about Blue’s
performance? Was it time well spent? What did you learn from it?
▪What filters made you change your decisions about improving Blue’s situation in
the marketplace?
▪How did you forecast demand? Why is the forecast outcome a range? Would a
specific number be better? What’s the downside of producing too much? Too
little?
▪Would you describe these analytics as descriptive, predictive, or prescriptive?

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14. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Strategy and Analysis
Overall approach and use of analytics/tools
▪What was your overall strategy to turn around Blue’s performance in the
marketplace? What factors did you manipulate in your decisions as a result?
What was the outcome?
▪How big of a role did each of the following play in decisions? Why? What were
the implications of your decisions?
▪Product formulation
▪Product features and positioning
▪Media channel spending
▪Trade channel spending
▪Did anyone look at social sentiment? What did that tell you and how did it
influence your decisions?
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15. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Decision Making
Discussion on specific decisions
▪Did anyone lower price dramatically to gain market share? How did it work?
▪Did anyone try to appeal to a particular geographical region? Which one? How
successful were you with this strategy?
▪Did anyone try to go upmarket with Blue and try to compete with Turbo? How
did that turn out?
▪Did you feel that you were able to get beyond the “what” in your analysis to the
“why” and the “how?”

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16. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Applications of Analytics
Real world application
▪What lessons can you draw about the use of these types of analytics? How easy
is it to use them? What factors might make them more valuable within an
organization?
▪How difficult do you think it would be to assemble and integrate all the data for
a system like this?
▪Can you see any downsides to this type of management? What might invalidate
the data-driven lessons that you learned?
What’s another example of a real world application of
analytics for managing a company?
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17. DATA ANALYTICS SIMULATION: STRATEGIC DECISION MAKING
Key Takeaways
1.Data and analytics are effective approaches to decision-making
2.It is complex to build and use data analysis programs, but the
results make the effort worthwhile
3.There are several different types of analytics, including
descriptive, predictive, and prescriptive analytics
4.It is important to understand the relationships among variables in
past data before making decisions about the future
5.Making decisions that involve multiple variables can be a
complex balancing act


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