Luo Ming had never imagined, not once, that he would one day share an electric scooter with someone else—and that he’d be this happy doing it. This completely new experience filled him with an immense, fleeting sense of contentment.
It felt as if a firework had suddenly burst open in his heart. He couldn’t help but start dreaming about the future—all those wonderful things he’d never experienced before, he wanted to experience them all with her.
Lin Shuwen had a wild, carefree side to her too. Noticing the elderly folks exercising in the neighborhood staring at them, she sped up even more and let out two joyful whoops.
“Luo Ming! Are you having fun?” Lin Shuwen asked loudly.
A faint smile tugged at the corners of Luo Ming’s mouth. He’d initially planned to respond quietly, but since she’d asked so loudly, he decided to match her energy. “Yeah, it’s fun. I’m really happy.”
“As long as you’re happy, I’m happy too!” With that, she braked, turned her head slightly to look at him, and suddenly asked, “Luo Ming, do you… do you get the feeling that we’re eloping?”
Luo Ming couldn’t help but laugh at her words. “What, you want to elope with me?”
“I don’t want to elope with someone who can’t even ride a bicycle!”
With that, Lin Shuwen made a funny face at him. “(~)”
Before Luo Ming could react, she twisted the throttle again, and the scooter shot forward once more.
One lap around the neighborhood wasn’t enough for Lin Shuwen—she drove them straight out onto the streets.
Half an hour later, the scooter was just about out of battery, so she finally turned around and headed home.
As soon as they walked through the door, Lin Shuwen headed straight for the bathroom, and soon the sound of rushing water could be heard.
Luo Ming sat on the living room sofa, staring absentmindedly at the TV.
About forty minutes later, Lin Shuwen pushed the door open and stepped out.
Hearing the door open, Luo Ming turned his head to look at her, fresh out of the shower.
Her hair was dripping wet, beads of water falling from the tips. She was wearing a rather oddly designed nightgown.
The nightgown was black and white. Luo Ming looked more closely and realized it was a sika deer design—yellow with black spots, and it even had a hood with little antlers on top.
His gaze drifted downward, scanning her from head to toe, and finally stopped at her delicate, porcelain-white feet.
Luo Ming wasn’t a foot fetishist—if anything, his thing was legs.
But Lin Shuwen’s feet, nestled in her slippers, were certainly eye-catching. Not only were they fair and soft-looking, but they were also remarkably small and dainty.
As if sensing his gaze, her ten round, adorable toes fidgeted slightly.
Luo Ming immediately looked away.
“Stop pretending, I’m not blind. You pervert!”
“To be fair, I’m really not interested in your feet.”
“Bet you I don’t believe you.”
“Bet you do.”
“Hmph!”
Luo Ming scratched his head awkwardly. Back when he was inside Lin Shuwen’s body, he could look at her however he wanted, but back then he had absolutely no desire to do so. Now, though, he could barely tear his eyes away. How strange.
“Luo Ming, let’s continue our conversation from this afternoon—about big data.” Lin Shuwen couldn’t be bothered to get hung up on these little things with him; after all, he’d already seen every inch of her.
“Sure, go ahead and ask.”
“You mentioned earlier that our company’s big data is about providing services to users, right?”
“That’s right.”
“So how do we make money?” Lin Shuwen asked, looking puzzled.
“Let me give you an example, and you’ll understand. Suppose one day you’re shopping on an app, looking for something, but you just can’t find what you want.
Then, out of nowhere, another shopping app automatically recommends the exact item you’ve been looking for. How would that make you feel?”
Lin Shuwen thought for a moment before answering. “Two feelings, I guess. One is that the other app really gets me and is super useful. The other is that it might be invading my privacy—otherwise, how would it know what I want to buy?”
“Right, most people have those two reactions. But would you actually uninstall the other app because you felt your privacy was invaded?”
Lin Shuwen shook her head. “Honestly, I’d probably enjoy the convenience too much.”
“And the truth is, most users feel the same way. If this happens a few more times, they might even start to depend on that app.
On that foundation, if one day that app suddenly recommends something you don’t really want, the chances of you clicking on it are still much higher than before—and even the likelihood of you buying it goes up. Humans are creatures of habit; it’s only natural.”
“Then here’s the question, Wenwen. Why would that shopping app sometimes push products that have nothing to do with a customer’s big data?”
Lin Shuwen thought for a moment and came up with an answer. “I guess it’s because merchants on the app pay to have those products promoted?”
“Exactly. That’s the main revenue stream after using big data to serve users—advertising fees.”
“Wait! Luo Ming, how does the shopping app’s backend even know what product a user wants?
Unless the user searched for it before. But even then, that search data belongs to the portal sites. We can’t buy it, and even if we could, the cost would be sky-high.” Lin Shuwen frowned.
“No, Wenwen, you’re wrong. Even portal sites have a hard time pinning down exactly what a user wants. Just because someone searches for something doesn’t mean they actually want to buy it—wouldn’t a guy ever search for stockings or high heels?
Big data doesn’t actually need to rely on any external software. Our data comes from the users themselves—or more precisely, from the user’s ID.
Take the shopping app again. How does it figure out what a user needs? It’s actually pretty simple—through the user’s delivery information, which shows up in real-time on their phone.
By tracking that delivery data, the app can collect relevant information, see what the user has recently bought, and then, based on their overall shopping patterns, predict what kinds of products they might need. Finally, it delivers those precise recommendations.”
“What about first-time users?”
“That’s even simpler. Just push based on age and gender. For example, if I know you’re a 23-year-old woman, I’d recommend cosmetics, trendy bags, and other things that appeal to young women.”
Hearing this, Lin Shuwen finally understood Luo Ming’s vision—and she was also astonished by his ideas and approach to big data.
“Of course, what I just said is only a rough outline. The actual execution is far more complex, and the amount of data analyzed is massive.
Big data is ultimately just prediction—and predictions are never 100% accurate. All we can do is try to improve the precision.”
