---
title: "Senior Data Marketing Expert Wang Zeyun: What User Personas Teach Us"
description: "The following is a transcript of a presentation by renowned data marketing expert, best-selling author, and Ogilvy Group Data Marketing Director Wang Zeyun at the FDIC 2018 China FMCG Digital Innovation Conference, edited by New Distribution. She discusses why ineffective marketing occurs, which consumer data is most useful for improving marketing efficiency, and how to find target users."
author: "王泽蕴"
publisher: "New Distribution"
email: "zhaobo258@gmail.com"
telephone: "+8615854817671"
published: "2018-09-05"
language: "en"
canonical: "https://xinjignxiao.com/en/articles/senior-data-marketing-expert-wang-zeyun-what-user-personas-teach-us-b3bed3ba/"
markdown: "https://xinjignxiao.com/en/articles/senior-data-marketing-expert-wang-zeyun-what-user-personas-teach-us-b3bed3ba.md"
original_source: "https://mp.weixin.qq.com/s/KtRACy03Gm0hoxV1TRnaVg"
translation: "https://xinjignxiao.com/zh/articles/%E8%B5%84%E6%B7%B1%E6%95%B0%E6%8D%AE%E8%90%A5%E9%94%80%E4%B8%93%E5%AE%B6%E7%8E%8B%E6%B3%BD%E8%95%B4-%E9%82%A3%E4%BA%9B%E7%94%A8%E6%88%B7%E7%94%BB%E5%83%8F%E6%95%99%E7%BB%99%E6%88%91%E4%BB%AC%E7%9A%84%E4%BA%8B-b3bed3ba.md"
attribution: "New Distribution — https://xinjignxiao.com/en/articles/senior-data-marketing-expert-wang-zeyun-what-user-personas-teach-us-b3bed3ba/"
usage_policy: "https://xinjignxiao.com/ai-policy.txt"
---

# Senior Data Marketing Expert Wang Zeyun: What User Personas Teach Us

> The following is a transcript of a presentation by renowned data marketing expert, best-selling author, and Ogilvy Group Data Marketing Director Wang Zeyun at the FDIC 2018 China FMCG Digital Innovation Conference, edited by New Distribution. She discusses why ineffective marketing occurs, which consumer data is most useful for improving marketing efficiency, and how to find target users.

The following is a transcript of a presentation by renowned data marketing expert, best-selling author, and Ogilvy Group Data Marketing Director Wang Zeyun at the FDIC 2018 China FMCG Digital Innovation Conference, edited by New Distribution.

Today I want to share with you something related to user personas, from three aspects. **First: Why does ineffective marketing occur? Second: Which type of consumer data is most useful for improving marketing efficiency? Third: How to find target users?**

**The first topic is: Why does ineffective marketing occur?**

Last year, I published a book titled "Don't Do Ineffective Marketing." Often, marketing ineffectiveness stems from brand self-admiration. For the same product or service, what the brand sees and what the consumer sees are often very different.

The key point here is the difference between the brand perspective and the consumer perspective. Let me give you a case: a Sichuan cuisine brand, which in the entire catering industry can be considered the conscience of the industry. Why? The boss said he couldn't stand many catering brands constantly raising prices, thinking it was wrong, so they haven't raised prices for years. Moreover, they place great importance on dish quality and ingredient selection, pursuing quality. But the result? Consumers are drifting away. Young consumers don't go to their restaurants much, and the loyalty of old users, as clearly seen from data, has decreased compared to before. What happened?

Although their biggest highlight is reasonable pricing, when we surveyed their existing consumers, the proportion of consumers recommending them for cost-effectiveness was very low. They've already given up so much profit, but this doesn't form a strong push for me to recommend you. At the same time, people think value-for-money is good, but it's not very important in my decision to choose your restaurant.

During the research, there was a focus group interview in a one-way mirror room. The moderator interviewed consumers one-on-one, while we and the client observed from the observation room. On the first night, the client couldn't sleep. One user ate at this brand four times a week on average, a heavy user. When asked why he came so often, he said, "Because my company has had a daily meal allowance of about 40 yuan for over a decade, and it's still 40 yuan now. We're in Wangfujing, and within this allowance, the only place nearby where I can eat well is this restaurant."

Another consumer expressed concern for the brand. On one hand, she didn't want it to raise prices for selfish reasons, but on the other hand, she felt the company might die if it continued like this, with such low profit margins. Importantly, if she invited friends to dinner, she definitely wouldn't choose this place. Because first, the environment is average, and then when the bill comes, it's 50 yuan per person. Friends would think, "Do you even respect me? I came all this way to eat with you, and it's only 50 yuan per person."

So we surveyed the important factors influencing consumers' dining-out decisions. The first is taste, but that's a basic need, so it's a default option that must be present. Besides taste, the most important factor is the environment and atmosphere.

Especially for young consumers, post-90s, many go for the topic. I can travel a long time to a restaurant; they need to satisfy many intangible things, like creative restaurants, themed restaurants. But for this company with a low average order value, because profit margins are too low, they've never been able to upgrade the environment and atmosphere.

During interviews, some said, "I'm indeed dissatisfied. Their tablecloths look five years old, the air conditioning is dripping, and the tableware placement and quality lack aesthetic pleasure." We can see that for dining out, what the consumer sees and what the brand sees are different things.

Moreover, we saw that in 2017, among the eight major cuisines, all were growing except Sichuan cuisine, which was declining. Why? The overall impression of Sichuan cuisine is unhealthy, oily, poor environment, noisy, and spicy. So this doesn't align with the requirements of current consumer upgrade. They want dining out to be healthier, lighter, and more pleasant.

Under this premise, we found that this brand has strong capabilities. If it can transform its image and upgrade properly, it could be an opportunity. It could not only leverage the market but even change people's old, wrong perceptions of this cuisine. Because authentic Sichuan cuisine isn't heavy oil and heavy spice; it emphasizes a hundred dishes with a hundred flavors.

The difference behind this is the difference between the brand perspective and the consumer perspective.

As marketers, we hope to find the direction for strategy, which is the intersection of these two circles. This intersection we call the marketing perspective under a data-driven thinking system.

Why is consumer research so critical in this era? Because it's hard to truly stand in the consumer's shoes through intelligence alone.

**The second topic today: In the process of creating consumer personas, what data is most critical?**

When we create user personas, we aim to narrow the focus as much as possible, not broaden it. Because in the context of overall marketing upgrade and consumption upgrade, no matter how much money you have, your marketing budget is limited. When we do user research, we pursue focus. Only when we narrow the focus to a very small group can marketing funds be spent efficiently.

I've seen clients who confidently say, "Teacher Wang, I think our brand has a wide market, targeting all young people." But that's the biggest problem. If your brand targets all young people, you can't choose accurate content channels, and you don't know what to say to influence all young people.

To focus the group, when we study consumers, we collect three types of data:

**First, metadata.** Metadata is also called social attribute data, such as gender, age, location, income, etc.

**Second, behavioral data.** Behavioral data is all data that leaves behavioral traces. For example, many people took a taxi here today, from where to where, how much it cost—these leave traces. Also, how much you spend on Taobao each month, who you follow on Weibo, your daily online/offline times, what you watch on Douyin—all data that leaves traces is behavioral data.

**Finally, attitudinal data.** Attitudinal data explains the reasons behind user behavior. This type of data is what I want to share with you: it's the most critical type of data when we do user personas and user insights.

Why? Because attitudes are more important than behaviors, and sometimes behaviors and attitudes are not consistent.

You might want to argue: Why aren't they consistent? I'm someone who likes rock music. If you open my NetEase Cloud Music, my playlist is all rock—behavior and attitude are consistent. Yes, often they are. But there are many cases where they differ. Is it possible that a girl who is very appearance-focused has a boyfriend who isn't good-looking? Of course, it's possible. There are many reasons. Although she's appearance-focused, for various considerations, she ended up with a not-so-good-looking boyfriend, and she's satisfied.

From a data analysis perspective, having a not-good-looking boyfriend is behavioral data. But if you apply the previous logic, since you have a not-good-looking boyfriend, you shouldn't have high requirements for appearance. With simple causation, when recommending products, I'd say, "Although this thing isn't good-looking, it's delicious." Would that move you? Impossible.

Let me give another example: a lipstick brand. Last year, its online sales showed that 50,000 users bought more than 10 lipsticks of this brand within a year. These are definitely heavy users. Generally, what do companies do? They immediately get these people's contact info and send them ads weekly, saying, "Dear, we have new arrivals, come buy quickly!" Annoying, right?

Some smarter companies hire data firms to pull out the characteristics of these 50,000 people, summarize them, and say, "Look, these are the common traits of my target users. Design ads and place them according to these traits to attract more such users."

This logic sounds fine, but it's actually very problematic.

Because, do these 50,000 people buy more than 10 lipsticks a year for the same reason?

This is important; it's likely different.

So we did a survey asking, "Why do you buy so many lipsticks of this brand in a year?" The results showed that many bought this lipstick not because they liked the brand, but because they just graduated, had no money, and their job required daily makeup. Buying lipstick was a necessity, and this brand was the cheapest first-tier brand they could afford, so they had to buy it. But this brand doesn't want to be seen that way; it wants to be like Chanel. So this answer shows: first, you've discounted too much historically, and your promotional tone lacks class, leading people to think you're cheap. Second, these people shouldn't be your target consumers; they're wrong. If you use them as prototypes for ad content and channel selection, you'll waste a lot of marketing budget.

This is a model called the **Brand Cognition Model**. Through this model, let's discuss the importance of attitudinal data. First, all brands can satisfy consumers' functional benefits. When I buy an air conditioner, I first buy the cooling function. But if a brand only fulfills functional benefits, you'll find loyalty is poor, there's no pricing power, and if a competitor next door looks better, consumers will run away in a minute.

So, besides functional benefits, companies must also satisfy consumers' emotional benefits. We know McDonald's slogan targets every generation of young people, and the ad copy is emotional. Why doesn't it say, "You can buy fast food anywhere"? If it said that, it would be functional. It says, "I'm lovin' it." What emotion does this convey? It's the attitude of young people: I'm young, I'm willful, I do me.

For young people, seeing this slogan, they feel an emotional resonance with the brand. I'll like you, and we'll be willing to be more loyal and prefer you because I like you. So when we help brands with positioning or consumer research, exploring emotional benefits is very critical. If your brand can resonate with consumers on values, then consumers will be more stable and long-term loyal, and the brand's pricing power will be strongest, like Apple. In Q4 2017 data, Apple alone captured 86% of global smartphone profits, with iPhone X alone accounting for 35%. Note, this is not shipment volume, but profit. Why do you buy this expensive brand? Because people like Apple's values.

What I've said is all about attitude insights, and the conclusions behind this mean loyalty and pricing power.

In addition to the above, we also focus on studying the push and pull factors at several key nodes in the consumer purchase journey.

First, motivation trigger: from a user not planning to buy to having a purchase intention—why, what happened?

The second node is competitive win: the consumer plans to buy, but compares you with other brands, and after careful consideration, decides to buy your brand. You won? But how?

The last is loyalty formation: I bought your product, felt great after using it, and am willing to continue buying, repurchase, and recommend to friends. What happened behind this?

You see, behind these key nodes, we're studying user attitudes.

**The last question: How to find target users?**

I've found many brands fall into a misconception: they think existing users equal target users. That's not true. We once helped a high-end nail salon create a persona. They said, "I want to enter O2O; which group should I target?" After research, we found that first, their existing users were fine, high quality: women around 35, high income. But we also discovered another group: men around 35, mainly in entertainment, marketing, etc., high income, mid-to-senior positions, who are gay.

When we presented this, the client stared wide-eyed, saying, "You're wrong. We never see any men in the store." Why? How did we conclude this? When we analyzed their existing users and the mainstream trends in the nail market, we found very strange behavioral data: this store's average order value was 1,500 yuan, very expensive, but their existing users showed a very regular pattern. Despite the high price, they went every two or three weeks. But regular nail users don't have strong regularity.

Why is it that the more expensive, the stronger the periodicity? Behind this, it actually reveals two groups with completely different lifestyles. If a person is willing to get their hands done every two or three weeks, their hair is probably also well-maintained, and their skin condition should be good. So these are people who have high requirements for their overall appearance and are willing to spend time, energy, and money to be refined. Does such a person distinguish between male and female? It doesn't distinguish; men are fewer, but they exist. And the group I just mentioned is particularly high among men. More importantly, you don't see men in offline nail salons. They have needs, but their needs are unmet. Going into an offline nail salon, they'd face strange looks.

Later, we interviewed typical users. They said, "I want to find a professional to do my nails, do hand care, apply nourishing oil, but I can't go into offline nail salons; people think I'm a pervert." So for this group, if we launch O2O nail services, the conversion rate would definitely be high.

So how did we find this group? Let me briefly share our model.

The model on the right is an important one from my book "Don't Do Ineffective Marketing" published last year, called the **Persona 3C Nine-Grid Model**. Through three channels, it ensures you don't miss important consumers. First, the left circle: first, brand existing users; second, category influencers; third, competitor influencers. There's also a fourth direction in the figure, called new positioning radiation group. The fourth circle depends on the company's actual needs; sometimes it exists, sometimes not, depending on the situation. The first three are mandatory. The union of the people circled in the first three is what you use for further user research in persona creation; the intersection is the heavy typical users.

When we create personas, as you can see in the right figure, the horizontal axis is the 3C: brand, category, competitor. The vertical axis is the metadata, behavioral data, and attitudinal data introduced above. We don't use this model just to fill in blanks. When we study people, we study the users on these three lines separately. After summarizing and cross-comparing the data pairwise, we can derive key insights.

Creating a persona isn't about drawing a little person with age range, income, hobbies, etc. In fact, the persona plans we create for each brand look different. The only evaluation criterion for a persona is whether it helps derive strategy with direct causal relationships. Like the stories I shared, they all end with clear lines. Many of you might be clients; you don't need to create personas yourself; you can hire a third-party company. If you give me a bunch of data about what this group looks like, with many dimensions, it looks rich. But that's not a good persona; it's unqualified. All qualified personas, after these data, must tell me: for me at this moment, what does this mean, and what should I do next? Personas aren't blind; they must start from your own business problems and goals, and the result must give clear, actionable execution recommendations.

That's what I shared today. Let me briefly review: First, why ineffective marketing occurs, especially for consumer brands; most ineffective marketing comes from brand self-admiration, and we need to see the difference between brand perspective and consumer perspective.

Second, when we study users, the most critical data is attitudinal data.

Third, I introduced a model to help brands create personas, select the right users, and understand them: the Persona 3C Nine-Grid Model.

Finally, let me leave you with a sentence. Data marketing involves many fields; personas are just a small part of our work. But for every topic, I end with the same sentence: In this era, data-driven thinking is a basic quality that every marketer needs to possess. Thank you. My name is Wang Zeyun. I hope my sharing today is helpful to you. Thank you.

Click "Read Original" to see more highlights from the 2018 FDIC China FMCG Digital Innovation Conference...

-END-


---

## Copyright and AI use

This article is sourced from New Distribution. Search, quotation, summarization, and model training are permitted, but every use must credit New Distribution and retain the canonical source URL.

Contact: zhaobo258@gmail.com · +86 158 5481 7671