Data Utilization in Service Personalization

Three Indonesian startups, namely Blibli, Cakap, and Super, sat down together and shared insights about the use of AI in the creation of highly personalized products/services (hyper-personalization) for their business.

Different from last year's event, this time a discussion panel The Big Leap which was held by e27 together CleverTap review the topic "Engagement Playbook Indonesia: Harnessing Automation and AI for Hyper Personalization".

As an introduction, hyper-personalization generally known as a marketing technique that is highly targeted and personalized to customers by utilizing data effectively real-time.

This strategy is widely used to build relationships with customers through products/services that suit their needs and preferences. Through this strategy, business owners can increase sales and user retention levels.

The three speakers, namely the VP of CLM Marketing Blibli Fanky Mulia, Chief Growth Officer Competent Margarita Tan, and Co-Founder & CEO Super Steven Wongsoredjo talked about the use of data to develop products in his different business sectors ecommerce, edtech, and social commerce.

Simplification and utilization

There were several important notes obtained from the panelists' presentations regarding automation and personalization, and their relevance in their respective businesses.

Super CEO Steven Wongsoredjo highlighted the importance of simplification in personalizing the services they develop. This is because the target market is in rural areas which have different consumer behavior compared to those who live in urban areas.

This simplification is reflected in the way Super carries out its user acquisition strategy and its efforts to encourage use of its services. This strategy can be executed through the data they collect, for example recent purchase transactions, products purchased, or costs spent on shopping.

"One of our challenges is building a strategy scalable that relevant for them. Users in rural areas do not use the device mobile devices which is expensive, the data package purchased is also not large. When we build features, it sucks up their data quickly. They will drop, they will churn. Therefore, simplification is very difficult, but important for us. Once you make things simpler, we can understand them," he explained.

Meanwhile, VP of CLM Marketing Blibli Fanky Mulia observed the personalization aspect from the technological aspect. Automation can indeed help scale up, but he sees technology as just that tool. In fact, it is important to focus on utilizing data into something insight worth.

"If insight results are wrong, this will encourage wrong decisions. Being too philosophical in utilizing AI will also not lead you to the target you want to achieve. Personalization is about utilizing existing data. If the data being utilized does not achieve ROI, for example, it might be the right decision to close it. Personalization is not a magic potion," explained Fanky.

Priority

The increase in digital behavior has contributed to the data explosion in recent years. Business people are required to understand customers better by utilizing the data they have. However, in the midst of this data explosion, is it relevant to mention the term "too much data"?

Cakap Chief Growth Officer Margarita Tan believes that learning is a long-term process that will not stop. As long as the process is still ongoing, data will still be needed to get to know customers and provide services that suit their needs.

"There's no such thing as too much data So far there is no need to incur costs. It's just a matter of priority when this data will be utilized. This data can be reused for other opportunities. Every marketer can choose which ones can be pursued and which ones can be followed up again," he said.

Meanwhile, Fanky actually provided a different point of view from the technical side. According to him, the more data you have will certainly influence the cost aspect. The reason is, data requires storage space (storage) the big one. This is where it is important to be able to select data that can be valuable.