The Importance of Statistics & Analytics for Business

The Importance of Statistics & Analytics for Business

In this lesson, you’re expected to learn about the need for statistics in business and how it can be used to uncover insights to help guide decisions.

“Personal data is the new oil of the Internet and the new currency of the digital world.” 

– Meglena Kuneva, European Consumer Commissioner 
Source: Bain / World Economic Forum Report (Personal Data: The Emergence of a New Asset Class)
With the explosion of the internet and the digital world, people are constantly leaving behind a digital trace that reflects their behavior.

Hence, now there is more data than ever, and with a much higher resolution.

For example, before when we looked at a country, we could just analyze it at a high level with aggregate measures such as GDP per capita. However, now we can look at the industrial composition of a country at a very minute level.

We can agree that money is something we exchange for goods and services. So if we accept this definition, ask yourself the following question: Have you ever paid with your data for a service or good?

The answer is probably yes, you are constantly doing it! Have you ever provided your email and other personal information to enter a draw for, let’s say, an iPad? If you did, you were paying the ballot with your data.

Or think about Facebook. This platform provides you a service where you can talk and share content with your friends without paying a single dollar. How are you paying for that service? The value of Facebook is so high because it has tons of data of millions of people and knows how to turn it into insights and monetize them.

Need for Statistics & Big Data

Nowadays, companies have more data than ever, but data alone has little value.

Hence, the challenge is to process, merge and analyze these datasets to turn them into insights that can help a company make better decisions.

And this cannot be done without Statistics and Big Data.

[Optional] Business Analytics – Turning Data Into Insight
Watch this 3-minute video to learn more:
Importance of statistics in business
Target Case Study

Retail chain Target realized that when people become parents, they become loyal customers of their preferred store, and they start buying all the products they need at the same store.

Thus, Target’s strategy was to detect future parents at that crucial moment before they have a baby and turn them into loyal buyers.

Once detected, they could start sending parents coupons for baby items at the right time. But the challenge was to estimate who was pregnant and when. For this, statistics and data analysis were essential.

A company like Target has this data, as they capture lots of information of their customers who are identified by a Guest ID number.

Hence, a team led by Andrew Pole started analyzing the behavior of people just becoming parents and how their buying habits changed. They identified a series of products that people bought more frequently when pregnant.

“[Pole] ran test after test, analyzing the data, and before long some useful patterns emerged. Lotions, for example. Lots of people buy lotion, but one of Pole’s colleagues noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester.

Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date.”

Source: The New York Times
The result was that the team found 25 products that when analyzed together could predict whether someone was pregnant and of how many months.

Hence, the company used this model to decide which customers should receive coupons for baby items and when.

The model was so effective that the following incident happened at a Target store in the US:

A man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation.

“My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again.

On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”

[Optional] Target Your Shoppers – Retail Predictive Analytics
Read this article to learn more about Predictive Analytics:
Key Takeaways

1) If we have the right data and the capabilities to analyze it and turn it into insights, one can make better decisions in business. 

In this case, the retailer had a model that anticipated pregnancy and used it to increase customer loyalty. The model was so good that it could anticipate pregnancy before the father got to know about it himself.

2) When making business decisions with data, managers must be careful. 

Knowing a lot about your customer can obviously be an advantage. However, people do not like to feel like their privacy is being invaded.

Jim Rohn Sứ mệnh khởi nghiệp