During Tech a Break, we had the opportunity to learn from two prominent speakers in the industry. First is Supreet Sethi, a software platform architect from Tokopedia and Marvin Mahadharma Muditajaya, the Commercial Lead for Telkomsel MSIGHT. Marvin showed us how we can obtain many insights from our customers especially with the development of technology adoption nowadays. The growth poses a prediction that there will be 57% more phones in this world than the number of people in 2018.
Play with Big Data
Consequently, there will be a large amount of data which can be utilised, this data is called Big Data defined by 3V: volume (large size), variety (diversity of data), and velocity (fast changes in the data, being highly dynamic).
What does the Big Data look like? Demographic information, such as age, gender, household expenditures, prepaid or postpaid, is common and relatively easy to obtain. User behaviour (e.g. call, sms, top up, payment, download, roaming, location, and browsing behaviours) can be used to complement the common data.
Telkomsel utilise the Big Data using segment profiling as follows:
- Conducting a survey via Telkomsel Customer Care to all respondents
- Create a study based on Telkomsel Mobile Consumer Behaviour
- Apply the model/formula for entire Telkomsel Subscription which have a same characteristics
- Validate the result with Re-survey the respondents
Telkomsel tried to segment its users by crossing their consumption and mobile behaviour to get higher accuracy, higher precision, and positive impacts to the right ROI. The example of consumption behaviour is credit card usage, household expenditure, and other data that Telkomsel can get from the user survey. Meanwhile, mobile behaviour is the data that Telkomsel can get internally such as call behaviour, top up behaviour, download behaviour, or browsing behaviour.
The importance of joint both the data is we can create more targeted audience for our marketing activities. For example we want to make the campaign which objective is to increase the awareness, traffic, and sales. We can make audience segmentation based on age (18-35), Socio Economic Status (SES) based on consumer’s monthly household expenditures or top up behavior, credit card users based on SMS containing “Credit Card” keywords, and e-commerce active based on application they installed on their phone. By doing advanced profiling, we as marketers can take better and faster decision making, achieve higher business performance in terms of budgeting and efficiency.