#Jang Saetbyeol
2017.12.08
Chapter 5. Big Data as Customer Behavior Forecaster?
Introducing the columns written by member of Amorepacific Group
ColumnistJang Saetbyeol
Amorepacific Amundsen Camp
Introduction
Hello. I'm Jang Saetbyeol. In this column titled Big insight Big !nspiration, we discuss interesting big data practices following the order of our work flow.
We first looked into the process of planning, producing and delivering products to the customers, then in the last chapter we studied big data cases to understand the end user of the provided products or services: our customers.
As noted in the last chapter, today I would like to focus on observing customer behavior as well as understanding and predicting them. If understanding the nature of the customers is simple by using basic data, then comprehending and forecasting their 'behavior' is something far more complex. But at the end, it provides us with valuable insights and fruits.
We first looked into the process of planning, producing and delivering products to the customers, then in the last chapter we studied big data cases to understand the end user of the provided products or services: our customers.
As noted in the last chapter, today I would like to focus on observing customer behavior as well as understanding and predicting them. If understanding the nature of the customers is simple by using basic data, then comprehending and forecasting their 'behavior' is something far more complex. But at the end, it provides us with valuable insights and fruits.
My age doesn't define me
Marketing experts have recently emphasized that traditional marketing based on demographics has ended. Does this mean that demographic information, the tool that helped us understand our customers through data such as age and gender, is now insignificant?
In the hyper-connected society, we are living diversity and exploring the abundance of options. According to a survey of men aged between 18 to 34, the group we doubtlessly assume to be heavy users, only took 31% of total mobile game users. Some data discovered that 40% of the total sales of baby products were made by households without children, while male consumers were suggested as the rising buying power in the skin and body care market. It is the reality we are facing that there is a high chance of which ladies in their 60s or 70s could be those who buy Star Wars VOD on IPTV at home to go against the traditional habit of suspecting young men.
Like in the above cases, existing inertial analysis or grouping of customers could easily lead to a failure in marketing. Now is the time to step up from looking at a cross-sectional consumer profile. Instead, we have to understand them through their behavior to predict their next move or attract them to the direction we have set. And luckily, it has become much easier to observe and analyze customer behavior than before.
In the hyper-connected society, we are living diversity and exploring the abundance of options. According to a survey of men aged between 18 to 34, the group we doubtlessly assume to be heavy users, only took 31% of total mobile game users. Some data discovered that 40% of the total sales of baby products were made by households without children, while male consumers were suggested as the rising buying power in the skin and body care market. It is the reality we are facing that there is a high chance of which ladies in their 60s or 70s could be those who buy Star Wars VOD on IPTV at home to go against the traditional habit of suspecting young men.
Like in the above cases, existing inertial analysis or grouping of customers could easily lead to a failure in marketing. Now is the time to step up from looking at a cross-sectional consumer profile. Instead, we have to understand them through their behavior to predict their next move or attract them to the direction we have set. And luckily, it has become much easier to observe and analyze customer behavior than before.
Friday night fever and no taxi to take me home
This might not exactly fit the type of customer we discuss, but the Owl Bus in Seoul is a great example of solution created through studying consumer behavior. Have you ever tried the night bus in Seoul which the number starts with an 'N'? The so-called Owl Bus was introduced in 2013 to help people return home late night after public transportation hours.
In an effort to design appropriate and effective routes to reduce operational load and maximize convenience for its users, the city's government turned to big data. The city used two types of big data to gain knowledge on their actual behavior. They were five million late night boarding records from the taxi union, and three billion late night telephone call data provided by KT (Korea Telecom – the largest telephone company in Korea). Geographical demand was identified based on boarding records and telephone call data.
However, at the beginning of its implementation, some lines were crowded, while others ran empty. Additional analysis on the boarding data of the late night bus was followed to optimize the line and length of intervals, and now the system is under stable operation. The city government was also able to communicate with its citizens and make further use of big data, and added seven new lines which gave it one of the best big data practices in public sector.
In an effort to design appropriate and effective routes to reduce operational load and maximize convenience for its users, the city's government turned to big data. The city used two types of big data to gain knowledge on their actual behavior. They were five million late night boarding records from the taxi union, and three billion late night telephone call data provided by KT (Korea Telecom – the largest telephone company in Korea). Geographical demand was identified based on boarding records and telephone call data.
However, at the beginning of its implementation, some lines were crowded, while others ran empty. Additional analysis on the boarding data of the late night bus was followed to optimize the line and length of intervals, and now the system is under stable operation. The city government was also able to communicate with its citizens and make further use of big data, and added seven new lines which gave it one of the best big data practices in public sector.
Where should I go? What should I eat? - Don't worry about nothing!
Inbound tourist information technology development / Source : Seoul Tourism Organization
The key to this project was to effectively measure the tourist pattern from foreign tourist roaming data and credit card records used in Korea. In the past, user voices were extracted from direct interview with limited number of actual visitors, but now vivid data collected from all visitors can be reflected in policy.
Combining credit card and mobile data help identify tourist routes, as well as the size of expense at specific areas, which can be used in disposition of information centers and volunteers and content recommendation (e.g. shops, food). Although the data exchange between enterprises is not actively carried out in Korea due to systematic and cultural issues, it is still encouraging to spot piecemeal joint study cases happening in the public sector for the common good.
Gotcha! I got you!
There often are situations where customer problem behaviors need to be identified, restricted or stopped. NCSOFT, the pioneer who opened the era of MMORPG with Lineage a year after its foundation in 1997, proactively adopted big data analysis in 2011 to detect illegal use such as hacking, account theft and workshops.
To deal with organized actions collecting unfair profit through hundreds of accounts running auto-hunt programs, the company used log data. Based on the log listing the entire play by all users, the company was able to operate a model that detects users with repetitive actions or abnormal exchanges or selling and successfully tracked down the problem accounts.
To deal with organized actions collecting unfair profit through hundreds of accounts running auto-hunt programs, the company used log data. Based on the log listing the entire play by all users, the company was able to operate a model that detects users with repetitive actions or abnormal exchanges or selling and successfully tracked down the problem accounts.
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A workshop accused of abnormal play / Source : Naver
They even analyzed the network by using combination of data such as the zones or channels used by game players as well as other players they contacted to catch suspicious ones as well as the group behind them. It was possible to establish infrastructure for the big data over a short period since game log accumulates immense volume of data.
A staff whose team participated in the analysis said the team was able to discover numerous sites of the so-called 'workshop.' Big data is now essentially used in the game business, but NCSOFT was able to stand up as a leader by proactively using big data to find and catch illegal users who threaten fair play and the market. For the first time in the company's history, NCSOFT has reached the sales amount of over KRW one trillion with the success of Lineage M which had launched last June. It looks like the company is looking at a bright future.
A staff whose team participated in the analysis said the team was able to discover numerous sites of the so-called 'workshop.' Big data is now essentially used in the game business, but NCSOFT was able to stand up as a leader by proactively using big data to find and catch illegal users who threaten fair play and the market. For the first time in the company's history, NCSOFT has reached the sales amount of over KRW one trillion with the success of Lineage M which had launched last June. It looks like the company is looking at a bright future.
Call my name
Some practices induce specific actions from its customers and use this as part of viral marketing instead of simply observing or predicting their behavior. Customers get to join the brand's marketing activities without noticing it. Here's an example from Starbucks.
Lately, Starbucks has evolved from its original coffee shop business to lead digital transformation in its sector through practices such as Siren Order or e-gift. Among the new ideas is 'Call My Name,' a smart viral campaign that generated customer action. As the brand that sells culture and experience, Starbucks has maintained a system where staff call out the customer by name. (In Korea, they use the order number on the receipt, but in most global stores they call you by the name you told them with the order.)
Lately, Starbucks has evolved from its original coffee shop business to lead digital transformation in its sector through practices such as Siren Order or e-gift. Among the new ideas is 'Call My Name,' a smart viral campaign that generated customer action. As the brand that sells culture and experience, Starbucks has maintained a system where staff call out the customer by name. (In Korea, they use the order number on the receipt, but in most global stores they call you by the name you told them with the order.)
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Starbucks Call My Name service / Source : Starbucks Korea
In the Call My Name campaign, if you register a nickname as you please through the website or application, they call you by that name when your order is ready. I guess a lot of you have already experienced this. This has started out as an ordinary customer service, but later a large number of people started to share their unique nicknames. List of funny names used in Starbucks are easily found on the internet. I personally remember 'The Coffee Bean Mania.'
Starbucks witnessed a tremendous response and active participation by its customers and is continuing the campaign on a regular basis. It became popular by sharing interesting names on official social media accounts along with photos and funny ideas voluntarily shared by its customers.
Starbucks witnessed a tremendous response and active participation by its customers and is continuing the campaign on a regular basis. It became popular by sharing interesting names on official social media accounts along with photos and funny ideas voluntarily shared by its customers.
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Starbucks Call My Name photos shared on the internet / Source : Naver Blog
Catch before they leave
The most important objective of studying customer behavior might be to predict the signs of consumer churn. This is not widely mentioned due to confidentiality, but this is the reason why most companies are using big data in HR management. Finding out employees with a high possibility of leaving, or foretelling this at the time of hiring could be critical in terms of efficiency in HR.
No.3 carrier in the US, T-Mobile / Source : Google
T-Mobile suggested customized services and benefits to the customers with signs of churning before they terminated their plans. As a result, the number of customers churning per quarter has dropped to a half. If you have canceled a mobile plan or credit card before, you might as well have experienced the intense defense strategy by the termination defense team. If customers were provided with some of these benefits at the right time, wouldn't it reduce the defection rate? After all, it must be easier to notice the delicate change in the air and try your best to your love than to change your mind after you hear a goodbye.
In conclusion
The process of collecting data customers had created or left like the foot print, and using this to understand and induce them all leads to one question: "What benefits them?" Applying a short sighted strategy to increase monthly sales or daze customers will eventually result as a backfire in terms of long-term engagement between the company and the customer. Call My Name by Starbucks was a fun viral campaign, but it was also a great example of communicating the company's philosophy of valuing sympathy with their customers.
We are able to understand the unspoken words when we face somebody. It is because the mind is revealed through action. Isn't this be the reason we are observing customer behavior? Recording, reading and grasping a clear understanding of customer behavior seem to be a way for us to reach 'what customers want.'
In the hope that big data would be our weapon to read customer minds as well as the behavior, today's story finishes here. I will come back with more cases led by interesting big data practices. Thank you.
We are able to understand the unspoken words when we face somebody. It is because the mind is revealed through action. Isn't this be the reason we are observing customer behavior? Recording, reading and grasping a clear understanding of customer behavior seem to be a way for us to reach 'what customers want.'
In the hope that big data would be our weapon to read customer minds as well as the behavior, today's story finishes here. I will come back with more cases led by interesting big data practices. Thank you.
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