---
title: "Growth Hacking, Brand-Effect Integration, and Private Traffic: Understanding the Logic Behind Marketing Buzzwords"
description: "From growth hacking to private traffic, these are hot terms in the internet industry since last year. Brand-effect integration is a slogan loudly touted by internet and e-commerce companies in recent years. This article helps you understand the logic behind these marketing buzzwords."
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published: "2019-06-12"
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# Growth Hacking, Brand-Effect Integration, and Private Traffic: Understanding the Logic Behind Marketing Buzzwords

> From growth hacking to private traffic, these are hot terms in the internet industry since last year. Brand-effect integration is a slogan loudly touted by internet and e-commerce companies in recent years. This article helps you understand the logic behind these marketing buzzwords.

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From growth hacking to private traffic, these are hot terms in the internet industry since last year. Brand-effect integration is a slogan loudly touted by internet and e-commerce companies in recent years. This article helps you understand the logic behind these marketing buzzwords.
**The core of growth hacking is A/B testing + data validation**
Let's first talk about growth hacking. The core model of growth hacking is AARRR. I have written about this model in several articles, so here I will emphasize the most critical point.
Compared with all previous consumer journey models, whether AIDMA, AISAS, SICAS, SIPS, or AIPL, the most essential difference of AARRR is that each stage is a consumer behavior variable—measurable consumer behavior.
Acquisition is behavior, Activation is behavior, Retention is behavior, Revenue is behavior, and Referral is behavior.
Since they are behaviors, it means that every step of the interaction between brand/product and consumer can be quantified through data, thus enabling precise control over consumers and providing accurate direction and guidance for brand/product marketing.
In contrast, in models like AIDMA, AISAS, and AIPL, only some stages are consumer behavior variables, while more are consumer psychological variables.
For example, in AIDMA, Attention, Interest, Desire, and Memory are psychological variables, while only Action (purchase) is a behavioral variable.
In the internet-era AISAS model, measurable consumer behaviors have significantly increased. In this model, only Attention and Interest are psychological variables, while Search, Action (purchase), and Share are behavioral variables.
Looking at Alibaba's AIPL model, Awareness and Interest are psychological variables, while Purchase and Loyalty are behavioral variables. You might ask why loyalty is a behavioral variable—because the metric for measuring loyalty is the number of repeat purchases.
(Of course, I cannot understand the logic of Alibaba's model: consumers form "awareness" first, then develop "interest"? According to common sense, consumers should first be interested in a product, then be willing to learn about it.)
From an academic perspective, psychology and behaviorism are closely related and overlap; behaviorism itself is a school of psychology.
But from a marketing perspective, the biggest difference between behavioral and psychological variables is that behavior is quantifiable and objective, while psychology is elusive and subjective.
Behavior can be directly measured and reflected in data; psychology can only be inferred through behavioral data and consumer interviews. For example, if a person likes giving speeches and participating in various gatherings and activities (behavior), you can infer that this person is extroverted, confident, and sunny (psychology).
As the saying goes, "You can draw a dragon's scales but not its bones; you know a person's face but not their heart." Another saying goes, "In judging lust, consider deeds, not thoughts; if you consider thoughts, no one is perfect." To determine if someone is lecherous, rather than guessing what they think all day, it's better to check their browsing data to see if they frequently visit certain indescribable websites.
Understanding a person through their behavior is more reliable and accurate than understanding them through their psychology.
Because behavior is explicit, while psychology is implicit. Behavior is a direct variable, while psychology is an indirect variable.
Growth hacking is built on the foundation of big data of user behavior, dividing consumer behavior into the five stages of AARRR, then examining the data performance of each stage to validate whether product design and marketing campaigns are accurate and effective.
Its methodological principle is essentially A/B testing + data validation: first test, then validate and compare through consumer data performance in the five AARRR stages, and use data results to optimize products and marketing.
For example, if you shoot two versions of an ad, A and B, and launch them separately for validation, and if version A significantly improves Acquisition while version B shows no significant change, then through Acquisition behavior data, you have tested and validated that version A is superior to version B, so you should use version A for the ad.
Growth hacking is actually an extension of agile product development processes into the marketing field. Test—validate—retest—revalidate, and then find the means that best stimulate growth.
**New marketing is based on behaviorism,**
**while traditional marketing is based on psychology**
All brand theories and most marketing theories we know today are built on psychology. Brand talks about awareness, recognition, association, esteem, and personality—all psychological terms. Marketing management talks about needs, positioning talks about mind—all within the realm of psychology.
Today is an era where data is on everyone's lips. The fundamental difference between big data-era marketing and traditional marketing is that traditional marketing is based on psychology, while new marketing is based on behaviorism.
So, what is big data? Essentially, it is behavioral statistics.
In the past, when we did marketing, before proposing core strategies, we could only understand consumer psychology and purchase motivations through questionnaires, interviews, and focus groups, and after ad placement, we would evaluate communication effects through market research.
We all know the problem of inaccurate market research. On one hand, the sample size is limited, so it's hard to say the conclusions are definitely accurate; moreover, the larger the sample, the higher the cost, and many startups cannot afford the research costs.
On the other hand, consumers often say one thing but mean another when answering questions. For example, in many product surveys, consumers will answer: "I would consider buying." But when it comes to actually voting with their money, it's a different story.
Because when they say they would consider buying in a survey, it may be due to face-saving or not wanting to contradict the researcher. It's hard to gain insight into consumers' true inner thoughts through surveys.
What advertising agencies call "insight" is actually guesswork—using empathy and putting yourself in others' shoes to guess what consumers are thinking.
Therefore, strategies and creative ideas based on insight cannot achieve precise marketing or quantified results.
**Brand-effect integration is a pseudo-concept**
Speaking of precise marketing and quantified results, we must mention the slogan loudly touted by the internet and e-commerce industries today: brand-effect integration.
"Brand" refers to brand, and "effect" refers to results. The reason we have the opposing concepts of brand advertising and performance advertising today is actually the greatest slander by the internet against traditional marketing and traditional media.
Because it desperately implies that traditional marketing has no effect and can only do branding. Advertising on traditional media can only yield exposure, awareness, and brand image improvement—these intangible things—but cannot bring substantial sales increases.
But does brand advertising really have no effect?
Of course not.
Brand advertising can certainly bring effects (not only communication effects but also sales effects), but the problem is that these effects cannot be measured.
Because in the traditional era, communication and sales were disconnected. For example, if I see a laundry detergent ad on TV today, and three weeks later I go to the supermarket and remember the brand, I buy it. This means the ad influenced the sale.
But it's hard to prove through data that my purchase was influenced by an ad from three weeks ago, because watching the ad and shopping are not simultaneous or co-located; they are disconnected.
Due to this behavioral disconnect and lag in effects in traditional marketing, it's difficult to measure how much impact advertising has on sales, so we can only measure psychological variables through surveys—i.e., communication effects like increased awareness and favorability.
Of course, you can compare sales data over a period after a marketing campaign and ad placement for correlation analysis, but it's still hard to quantify exactly how much advertising contributed to sales.
Because sales achievement is a complex result influenced by many variables, including advertising, price, product packaging, terminal display, terminal promotions, and even competitors' marketing activities.
For example, back to the laundry detergent ad: I saw the ad and thought it was good. At the supermarket, I intended to buy it, but I found another brand was promoting—not only discounted but also giving away a bag of laundry powder—so I bought the other brand.
From the result, the sale was not achieved, but you cannot say the ad was meaningless. It's just that the ad's influence on sales was less than the competitor's promotion; you cannot quantify the ad's effect on sales.
But if the laundry detergent runs a DSP ad, and consumers see the ad, click it, and are directly redirected to e-commerce to make a purchase, then watching the ad, clicking, redirecting, browsing, and purchasing are all connected in one seamless flow. The conversion rate at each step can be analyzed.
This can clearly prove that the purchase was brought by the ad, and the effect of the ad can be quantified, measured, and visualized.
This is actually the fundamental and long-standing problem of traditional marketing: because there is no data on the entire decision-making chain of consumer behavior from viewing to clicking to purchasing, marketing effects cannot be quantified.
Because effects cannot be quantified, it is impossible to precisely validate whether previous marketing strategies and ad creatives were accurate and how to optimize and improve them.
Of course, problems are problems, but you cannot say traditional marketing has no effect and dismiss it entirely. It's just that the effect is very subjective and difficult to visualize.
(It is precisely because effects are difficult to test precisely and the role of advertising in sales cannot be evaluated that advertising agencies have such a hard time; they can only prove their value to clients through the amount of work, thus becoming "creative coolies.")
The classification of brand advertising and performance advertising is inherently wrong; they are not different in results but only in advertising media.
A more accurate naming would be traditional media advertising and digital media advertising, and both have both brand effects and sales effects.
Since the distinction between brand advertising and performance advertising is meaningless, brand-effect integration is also a meaningless concept. Although the slogan is everywhere, without implementation steps and path planning, it becomes just an empty slogan.
Moreover, isn't the fundamental purpose of building a brand to sell better? So why distinguish between brand and effect? Building a brand is for effect!
**Private traffic is old wine in a new bottle**
Since we mentioned that the so-called "brand advertising" in the past cannot precisely measure effects, how can we make the effects of traditional marketing visible? The answer is private traffic.
According to the standard explanation, private traffic is traffic that a company owns and can use for free multiple times (as taught by Sister Dao).
Because traditional marketing essentially buys users from media—whether from CCTV, Baidu, Tencent, or Taobao/Tmall, it's the same. Once the ad campaign ends, the traffic disappears.
Public traffic is one-time, and public traffic is getting more expensive, so companies must learn to create their own traffic and manage their own traffic.
In the past, we could say that ad placement enhanced the brand. Although ads are one-time, brand influence is lasting and can feed long-term sales growth.
But how to quantify this brand effect? It is by depositing the brand into official accounts, personal WeChat accounts, WeChat groups, mini-programs, self-owned e-commerce platforms or apps, and corporate self-media. This becomes private traffic.
A brand is a community composed of consumers and products; private traffic is building your own user camp.
Building a brand is a process of gradually turning public traffic into private traffic, turning users you pay media for into your own fans.
With fan accumulation, brand awareness, recognition, esteem, and loyalty have a real foothold, and these indicators that previously measured communication effects can truly be quantified through fan behavior.
So what is private traffic? It is the brand effect that can quantify user behavior.
Through users' attention, retention, activity, monetization, and sharing, you can solidify brand effects and implement user-centric thinking in brand building.
So private traffic has existed for a long time: managing users is private traffic, brand communities are private traffic, and establishing self-owned marketing positions is private traffic—it's just a new guise.
From growth hacking to brand-effect integration and private traffic, these essentially represent the penetration of big data into all aspects of marketing, shifting from emphasizing consumer psychology to focusing on consumer behavior.
Doing marketing based on consumer behavior changes, and evaluating marketing strategies and brand indicators based on consumer behavior data—this is the marketing principle of the big data era. I believe that future new marketing theories and models will definitely be based on consumer behavior.
Source: Kong Shou (ID: firesteal13)


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