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Data & Storytelling - What Now?

Data Analysis has been the buzz word this decade. Data Storytelling will be the next big influencer in Value creation.
Find out how Gramener is shaping the Data Storytelling landscape.

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Data & Storytelling - What Now?

  1. 1. Hello Data People Hola Personas De Datos डेटा लोग 你好 數據人 नमस्कार
  2. 2. Data & Storytelling - What Now? Sundeep Reddy Mallu @sundeeprm /sundeeprm
  3. 3. My struggles with new tasks during the Pandemic 51 46 42 40 38 35 32 30 30 29 18 13 28 21 29 28 10 24 16 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16 W17 W18 W19 Time(inminutes) Week Count This chart shows the average time it took me to complete sweep and mop the house floor each day. The X-axis has time it takes daily to complete the task (averaged at week level). The Y-axis has Week count. Week 1 starts on 25th March 2020. I had hoped (wrongly so) to beat the expert time. I couldn’t translate my body agility on to this task. I struggled with the nausea of what needs to be done, treacherous learning curve. What’s unusual More than 70% of time I took longer than an expert (20 minutes) For the rest, I gave up or done part of the work  Expert time (20 minutes)
  4. 4. Data storytelling is a critical skill for data scientists, analysts & managers 4 Stories are memorable. They spread virally People remember stories. They’ll act on them. People share stories. That enables collective action. For people to act on analysis, data stories are critical. But analysts present analysis, not stories We present what we did. Not what you need. You need to know what happened, why, & what to do. Narrated in an engaging way. As a story. We’ll learn how do that in this session. Storytelling has a 30X Return on Investment Rob Walker and Joshua Glenn auctioned common items like mugs, golf balls, toys, etc. The item descriptions were stories purpose-written by 200+ contributing writers. Items that were bought for $250 sold for over $8,000 – a return of over 3,000% for storytelling! Mario – The Baker Mouse Original price: $.50. Final price: $62.00. Story by – Megan O’Rourke When I was a child, my mother used to keep Mario on a shelf near the oven. Sometimes I would play with him. She told me that Mario was magic; in the night, he made muffins light as manna and delicate as silver. If you happened to sleepwalk into the kitchen, you could eat the muffins, but they disappeared by morning. …
  5. 5. Solving a Business Problem Stage 1- Identify Business Problem Define the problem statement by understanding: • What is the business need and desired outcome? • Who will benefit? • What is the impact? • What is the success criteria? Stage 2- Translate to Data Problem • Breakdown the problem statement into multiple use- cases • Connect each use case with a data set • Understand any limitations on data sources- Internal and External? Stage 4- Translate to Business Answer • Stitch insights from individual use case to create a story • Connect data story to help in better decision making • Measure success Stage 3- Arrive at a Data Answer Target each use case with data through: • EDA and transformation • Modelling • Generating insights Data Storytelling Data Storytelling
  6. 6. You have data. Now what?
  7. 7. DO IT: Who is the audience for your analysis? ❑ Role: _____________ Be specific. “Head of sales”, not “executive” ❑ Example name: ______________ Name a real person. “Jim Fry”, not “any sales head”. Different people want different things from the same data. Given sales data: • The Board: “Predict next quarter’s sales” • Product head: “Which product grew the most?” • Sales head: “Did we meet our target?” They are not interested in each others’ questions. Who is your audience? They determine the story
  8. 8. DO IT: Write it in this structure “[Person, Role] is in [situation], and faces this [problem]. By taking [action], she can drive [impact].” Example Stacy, the Marketing head, person, role must create a region-wise budget, situation and doesn’t know the region-wise RoI. problem By prioritizing the region, action she can maximize ROI. impact For each person, answer the following questions: 1. What’s their situation? 2. What problems do they face? 3. What action can they take? 4. What is the impact of this action? What is their problem? That defines your analysis
  9. 9. Here are three examples in real life 9 Purchasing Commodities Cargo Delay Customer Churn Person, Role Adam, the purchasing head of a leading European brewery Cris, the operations head of a leading US airline Ravi, the marketing manager of an Asian telecom company Situation Had plants that purchased commodities from several vendors. Discounts were low. Number of weekly orders were high. Had an SLA to deliver cargo from the flight to the warehouse in under 1.5 hours – 15% lower than their current best performance. Found that the cost of replacing customers was thrice the cost of retention. Problem But he didn’t know which plants and commodities were a problem. Every plant denied it. But she didn’t know what were the biggest drivers of this delay – people, assets, or type of cargo. But he didn’t know which customers to make offers to in order to retain them. Action By consolidating vendors and reducing order frequency, By adding resources only to the largest levers of delay, By predicting which customer was likely to churn, Impact They could increase their discounts and reduce logistics cost. She could reduce turnaround time with the lowest spend. They could tailor a retention offer and reduce re-acquisition cost.
  10. 10. Filter for big, useful, surprising insights DO IT: Rate each analysis against B.U.S. Filter the analyses using this checklist IS THE INSIGHT BIG IS THE INSIGHT USEFUL IS THE INSIGHT SURPRISING We want a result that substantially changes the outcome. Can they take an action that improves their objective? What should they do next? Is it non-obvious? Does it overturn an existing belief, or bring consensus? Example B U S There are twice as many restaurants in NYC than any other city ✓ ✓  Sales increased in every region except our largest branch, which dipped by 0.1%  ✓ ✓ Increase in rainfall increases the sale of umbrellas, and is the biggest driver of our sales ✓  
  11. 11. Here are the analyses & filters for the problems we saw earlier 11 Purchasing Commodities B U S Cargo Delay B U S Customer Churn B U S The most common commodity was ordered 10 times a week across 2.4 vendors Fragile cargo is a big factor in the delay, with a 20% impact B S Number of inbound calls does not impact churn. S The number of orders is correlated with the number of vendors. Reducing one will reduce the other U Fridays are when cargo is delayed the most Customers who haven’t made any calls in the last 15 days are the most likely to churn B Plant P126 was the plant with the most violations, especially on largest commodity B U Trained staff and forklifts impact delay the most B U S Customers making infrequent calls, recharging small amounts infrequently, are most at risk B U S
  12. 12. Did you get on the BUS (High, Medium, Low)
  13. 13. You have Qualified Insights. Now what?
  14. 14. DO IT: Write your takeaway as one sentence What’s the one thing you want the audience to remember from your story? What’s the one message that the audience should take away? CHECK IT: Verify these yourself ❑ Is it a single, complete, sentence? ❑ Does it deliver what you want the audience to remember? ❑ Will your audience care a lot about this? Close your eyes. Think of a childhood tale. Summarize the moral of the story in one line We easily we remember these stories and their summary as a moral several years later. Close your eyes. Think of a business presentation from last week. Can you easily summarize the message in one line? Stories are designed around a moral. A single takeaway. An “elevator pitch” It’s a one-sentence summary of the most important message for the audience. Start with the takeaway. Summarize your entire story 14
  15. 15. Structure supporting analyses as a tree 15 Example of a business tree Launch sales were 30% less than target due to high competition • Launch sales were projected at $20 mn in the first month, but achieved only $14 mn o Sales in every region were 20-50% lower. o Only Philippines & Korea were on target • Competitors discounted price by 35% - which is unsustainable for them o 80 store discounts increased from 15% to 35% o The maximum sustainable discount is 20% • Stores offered higher discounts saw less than 20% of our target sales Construct a pyramid or tree-like outline • Start with the takeaway at the root of the tree • Add a message that supports the takeaway • Add further details or supporting messages • Messages must prove the first message, and only the first message • Strike off any message that isn’t required to prove or support the takeaway • Add next message that supports takeaway • Add details to prove the second message • Remaining messages for the takeaway • Add details as required Arrange messages hierarchically to prove & support the parent message
  16. 16. Here is the storyline for the analyses we saw earlier 16 Purchasing Commodities Cargo Delay Customer Churn Takeaway Focus on reducing the number of vendors products ICG (in P126), FRS (in P121) and SWB (in P074) for a potential 40% reduction in logistics & vendor cost. To reduce the TAT to 1.5 hours at Airport XYZ, increase the number of forklifts from 1 to 2, and the number of trained staff from 4 to 6 If a customer has not called in the last 5-14 days, and they have made only 1 recharge under $20 last quarter, make them an offer to retain them. Supporting points ICG spend is among the highest, at €6.9m. P126 typically orders 40 times a week, often from 15-20 vendors. The number of forklifts is the biggest driver of TAT. Each forklift typically reduces TAT by 15-30%. The biggest driver of retention is when the customer made the outgoing call. The 5-14 days bucket has the highest variation. FRS spend is €3.2m. P121 orders from 3 vendors 8-14 times a week. Total staff count does not impact TAT. Increasing trained staff has a more tangible impact of ~5-10% per person. Customers who make at most 1 recharge under $20 are 280% more likely to churn than others.
  17. 17. You have a story Let’s present it
  18. 18. European brewery identified €15 m cost savings after consolidating vendors A leading European brewery’s plants purchased commodity raw materials from several vendors each – and had low volume discounts. Plants also placed multiple orders placed every week, leading to higher logistics cost. When plant managers were shown the data, they objected, saying “That’s not always the case.” Or, “That’s the only way– no one else does better.” Gramener built a custom analytics solution that sourced their SAP order data, automatically identified which plants ordered which commodities the most from multiple vendors – and when. It showed how each plant performed compared to peers – shaming those with poor performance. With this, they identified savings of €15 m — which the plant managers couldn’t refute. €15 m 40% savings potential identified annually vendor based reduction identified 18
  19. 19. Global airline reduced cargo turnaround time by 15% with scenario modeling A global airline company took up a service level agreement to deliver cargo from the flight to the warehouse in under 1.5 hours. This target was 15% lower than their current best. Several factors affect cargo delay across airports. Availability of forklifts, staff size, cargo type, part shipment, and many others. Altering any of these is expensive and takes long. Gramener built a visual analytics solution that showed where cargo was delayed. We built an ML model that identified the drivers of delay (forklifts, trained staff), and the impact of these on turnaround time. What-if scenario modelling helped pick the optimal combination that reduced TAT. This allowed the airline to reduce the turnaround time by 15% from 1.76 hrs to 1.5 hrs. The worst- case turnaround time also reduced by 34% from 2.9 hrs to 1.92 hrs. 15% 34% cargo turnaround time reduction (from 1.76 to 1.5 hrs) reduction in worst-case turnaround time 19 Evening Morning Night Fri Mon Sat Sun Thu Tue Wed FAH N70 RPP TDS ZDH 20-40% 40-60% 60-80% <20% Full Recovery times are neutral during the evening and morning shifts (mornings are slightly worse), night times are the best. Recovery times are worst on Fridays, and best on Saturdays & Wednesdays. Specifically, Friday mornings are particularly bad. So are Thursday mornings. The FAH product category has the best recovery time, while ZDH is much worse. However, RPP on Sundays is unusually slow. Part shipped products tend to perform worse than full-shipments. Specifically the <20% and 40-60% part-shipments. This is especially problematic for ZDH Product category Part shipment Weekday Shift This slide is best viewed in slideshow mode. The animations tell a story that isn’t obvious on the static version.
  20. 20. Telecom company saved 66% customer acquisition cost by predicting churn A national telecom provider had a churn rate of over 10% a month. Thanks to low switching cost, their entire customer base churns within a year. The cost of replacing each customer was thrice the cost of retention – provided the customers could be identified with some confidence. Gramener used the customer profile, transaction data, payment data, service log data, and other related information to create a series of classification models. These predict whether a customer will churn one month in advance. The simplest model – the decision tree (shown alongside) – reduced the cost of attrition by 39%. A second, more robust machine learning model increased this to 66%. This model only missed 0.6% of customers and incorrectly spotted only 2.5% of customers. 66% 99.4% reduction in customer re- acquisition cost potential churn customers correctly identified 20 OUTGOING CALL N 0 - 4 15+5-14 Y RECHARGE AMT > $20 NY YN > 1 RECHARGE N N Y 3.2% 3.6% MISSED WASTED 4.0 COST PER CUST. 39% IMPROVEMENT Decision Tree MODELS
  21. 21. Pick a format based on how your audience will consume the story 21
  22. 22. Pick a visual design based on the takeaway 22 Deviation Change- over-Time Spatial Ranking Correlation Part-to- Whole Flow Magnitude Distribution
  23. 23. Annotate to explain & engage. Use four types of narratives Remember “SEAR”: Summarize, Explain, Annotate, Recommend 23 0 5,000 10,000 15,000 20,000 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Marks # students Teachers add marks to stop some students from failing This chart shows Class 10 students’ English marks in Tamil Nadu, India, in 2011. The X-axis has the mark a student has scored. The Y-axis has the # of students who scored that mark. Large number of students score exactly 35 marks Few (but not 0) students fail at 31-34 marks What’s unusual Large number of students score 35 marks. Few (but not 0) students score between 30-35 Only some students get this benefit. Identify a fair policy that will be applied consistently. Summarize the visual in its title Don’t describe the chart. Don’t write the user’s question. Write the answer itself. Like a headline. Explain & interpret the visual How should the user read it? What do you say when you talk through it? Explain what the visual is. Then the axes. Then its contents. Then the inference. Recommend an action How should I act on this? You need to change the audience. (Otherwise, you made no difference.) Annotate essential elements What should the user focus their eyes on? Point it out, or highlight it with colors Interpret what they’re seeing – in words. This is a bell curve. But the spike at 35 (the mark at which students pass) is unusual. Teachers must be adding marks to some of the students who are likely to fail by a small margin. No one scores 0-4 marks
  24. 24. In summary, here are the 9 steps to go from data to a data story 24 Who is your audience? They determine the story What is their problem? That defines your analysis Find the right analysis to solve the problem Filter for big, useful, surprising insights Start with the takeaway. Summarize your entire story Add supporting analyses as a tree Pick a format based on how your audience will consume the story Pick a visual design based on the takeaway Annotate to explain & engage. Use four types of narratives
  25. 25. Thank You! @sundeeprm /sundeepreddym