Operational promotion: a low-cost growth hacking experiment from 0 to 1!

With the disappearance of the demographic dividend and mobile Internet traffic, the growth environment of a product has completed the transformation from extensive growth to lean operation. When a new track emerges, there may be more than a dozen players competing at the same time, such as the chicken-eating game, shared bicycles, shared power banks, etc. in recent years.

In the context of product homogeneity and convergence of business models, products need a more refined operation and marketing system to help them grow rapidly and stand out from the crowd.

The growth hacker method is precisely the core of this refined operation system. Its main features are to drive growth with data and experiments, focus on the entire user life cycle, productize the growth mechanism, and integrate growth into the product.

Below I will use the "method + example" approach to explain how a small product can use the growth hacker method from 0 to 1, start growth experiments at low cost, and gradually embark on the path of refined operations?

To make it easier to understand, let me describe the main functions of this product I have previously handled.

Product description: A B2B mobile transaction and social tool serving buyers and sellers of bulk plastic products.

Upstream factories/traders and downstream factories can publish supply/purchase information anytime and anywhere, and push it to cooperative customers or other customers in the product.

Buyers and sellers can communicate about market conditions in a timely manner and make inquiries and negotiate prices through voice, text and other means. Once an agreement is reached, the order can be generated and the contract signed online.

Buyers and sellers can manage their personal connections, organize existing customers, peers/colleagues into groups, and establish new personal connections.

First, let’s take a look at the core canvas of the growth hacker methodology to establish an intuitive impression, as follows:

Next, I will explain it step by step, first the method and then the practice. (If it is too long, you can just look at the method selected in the box)

1) Find the “North Star Metric” for Product Growth

Definition: An important indicator to measure whether the core value of a product is experienced by users.

Function: Provide guidance and unify cognition; clarify priorities and guide actions.

method:

Framework: What is the core value of the product? What core problems does the product solve for users, allowing them to experience value? Which indicator can be used as the basis for judging the results?

Self-examination: (User) Can this indicator reflect the user's activity level? (Team) Does this metric make it easier for the team to understand and communicate? (Enterprise) Can this indicator reflect the operating conditions of the enterprise?

After communicating with leaders and team members, we reached a consensus that as a tool product for trading assistants, its core purpose is to solve a series of problems existing between buyers and sellers in the chemical and plastics trading process, and to facilitate the formation of orders between the two parties. Its measurement indicators must be those related to trading orders.

However, there is some controversy and discussion among the three alternative indicators: "total amount of completed orders", "total number of completed orders", and "number of inquiries/quotations". The reasons for each are explained below:

Indicator 1: Total amount of completed orders

Reason: As a transaction assistant tool, its core value is to facilitate transactions. The total amount of completed orders can reflect the overall operation of the product and show whether users have experienced the value of the product.

Indicator 2: Total number of completed orders

Reason: The unit price of raw materials is relatively high. Even if there are only one or two transactions, the total amount of the completed orders will be very high. Since it is an assistant tool, it should emphasize the frequency of users using the tool, and the order quantity can be more direct.

Indicator 3: Number of inquiries/quotations

Reason: Inquiry/quotation is a prerequisite for forming an order. At the current stage when the data is not good, we should first focus on increasing the number of inquiries/quotations. Only by increasing the number and success rate of inquiries/quotations can we improve indicators related to order completion.

Indicators 1, 2, and 3 can all reflect the user's activity level to a certain extent, but indicator 3, the number of inquiries/quotations, can only reflect the activity level of the first half of the business process. There may be situations where no transaction is generated after multiple negotiations. Therefore, indicator 3 is out.

Indicators 1 and 2 can facilitate team understanding and communication. In terms of reflecting the business conditions of a company, the "total amount of completed orders" wins because the total amount can better reflect the scale. Most importantly, the total amount of completed orders = the total number of completed orders * the average unit price of completed orders. The "total number of completed orders" can be decomposed from the "total amount of completed orders".

Therefore: We have found the North Star indicator of the product: the total amount of completed orders

2) Build a “growth equation” for product growth

Definition: A simplified mathematical formula that contains the main variables that affect user growth or profit growth.

Function: It helps to grow more comprehensive, simple and structured thinking.

method:

1) Define the North Star metric as the output variable of the equation.

2) Draw a path/step map from user experience to product value.

3) Find the corresponding measurement indicators for each step as input variables.

4) Combine input and output variables to construct a growth equation.

Self-test:

1) Try to decompose each variable until it cannot be decomposed any further.

2) Are there any repeated additions or partial omissions between variable data?

First of all, the North Star indicator of our trading assistant product has found the "total amount of completed orders"

Next, we list the main steps and corresponding key indicators for users to go from knowing nothing to experiencing the core value of the product , as follows:

1) Download apps → App downloads

2) Registration and certification → Registration rate

3) First inquiry/quotation (negotiation) → First negotiation rate

4) First order → First transaction rate

5) Repeated negotiations and order formation → Average negotiation rate and average transaction rate of old users

Then, we combine these core variables and, if necessary, further decompose them to arrive at the growth equation for our product:

Total amount of completed orders

= Total amount of completed orders of new users + Total amount of completed orders of existing users

= (Total number of orders completed by new users * Average amount of orders completed by new users) + (Total number of orders completed by existing users * Average amount of orders completed by existing users)

= (number of app downloads * registration rate * first-time negotiation rate * first-time transaction rate * average transaction amount) + (number of existing users * average negotiation rate of old users * average transaction rate of old users * average transaction amount)

3) Establish a key indicator monitoring dashboard

Definition: A series of charts or systems that monitor and record product channel data, user behavior data, and profit data.

Function : Understanding data is the foundation of growth and an important basis for measuring growth results.

method :

1) Use a hierarchical and step-by-step approach, first defining the few most important events to track, and then gradually adding secondary events.

2) After defining basic events, build a basic user behavior funnel and record the retention rate/churn rate of each step.

3) Gradually establish a graphical monitoring mechanism with the North Star indicator, key growth indicators (acquisition, activation, retention, profitability, recommendation), segmentation indicators (related to key behaviors and important retention), and important user groupings (channels, new and old users, specific behaviors) as the core.

Due to limited initial costs and a small number of users, we only count the three most important behaviors of new users from their first contact with the product to the final order formation every day, namely "successful registration and authentication", "initiating negotiation" and "creating order".

At the beginning, the amount of data was small, so we first used an Excel table to do the simplest statistics (if conditions permit, you can directly use user behavior data analysis products such as Zhuge io, growingio, and Sensors). The table header is as follows:

After all these were prepared, we carried out a week-long pull-in activity and recorded the data from the above form every day. We found that the number of app downloads gradually increased, but the registration rate and consultation initiation rate were very low, with a registration rate of only 45% and a first-time consultation rate of only 40%.

Based on the problems reflected in the data, we conducted a qualitative survey to understand the reasons behind them. We specifically selected two types of users who made different choices (insisting on registration and giving up registration, initiating consultation and giving up consultation) for interviews.

The registration rate is low. According to research, most of the users who give up registration are salesmen, who generally think that registration is too troublesome (because our product is aimed at bosses and salesmen of B-side enterprises, in order to ensure the accuracy of the target, users are required to fill in company information and upload business license certification when registering).

Most of the users who successfully registered are bosses, and their company’s business license is available on their mobile phones, so not much time is wasted.

The first consultation rate is low. After investigation, the main reason is that after the user successfully registers, the page the product jumps to is the homepage "My Supply/Purchase Information" page. As a new user, there is no release record, so when faced with such a "blank page", they will feel confused and don't know what to do.

4) Design growth experiments

Definition : Guided by data and using an experimental approach, we systematically look for the most cost-effective opportunities at each stage of the user life cycle and achieve growth goals through rapid iterative experiments.

Function: Make growth results measurable and methods repeatable. Make growth more targeted, scientific and rigorous, and improve efficiency and effectiveness.

method:

1) Set growth goals 2) Find areas of focus 3) Generate growth hypotheses 4) Prioritize hypotheses 5) Develop growth experiments 6) Analyze experimental data 6) Apply experimental results 7) Generate new hypotheses (loop)…

See the figure below:

Our overall growth goal is our North Star Metric: total transaction value;

Based on the quantitative data after the online promotion and the qualitative data from the survey, our focus area is to improve the experience of new users, and the key lies in increasing the registration success rate and the first consultation rate;

Based on the decomposed small goals, we organized the team to conduct brainstorming and let everyone come up with their own ideas. Some experimental hypotheses are as follows:

Experimental hypothesis to improve registration rate:

• Simplify filling in the company information page information items. You only need to fill in the company name. Users can choose whether to upload the business license;
• The step of filling in the company information page is placed at a later stage, and the user only needs to fill it in when initiating a negotiation operation;
• Using OCR recognition technology, you can upload the business license to identify the corresponding company information and automatically fill in the corresponding fields without filling in;
• Strengthening guidance documents and informing companies of the necessity to improve their information will help enhance the trust of trading companies, increase the probability of transactions, etc.

Experimental hypothesis for improving the first negotiation rate: After successful registration, users will be directly redirected to the public supply/purchase information page, allowing users to view it directly and initiate negotiations directly. On the personal management homepage, add a shortcut button of "Go to the trading area" with guidance text such as "You haven't posted any information yet, go and see the quotations of other companies first" etc.

After brainstorming, we ranked these hypotheses based on the principle of cost-effectiveness and decided on the following two experimental plans:

• Experiment to increase registration rate: Simplify the information items on the company information page. Only the company name is required. Users can choose whether to upload the business license.
• An experiment to increase the first negotiation rate: After successful registration, users will be directly redirected to the public supply/purchase information page, allowing users to view and initiate negotiations directly.

After a week of development and adjustment, and after the launch, we collected statistics and found that the registration rate increased by nearly 30% and the first consultation rate increased by nearly 15%. It is obvious that our experiment has achieved certain results.

We then built on our success by optimizing the pages and button copy for public supply/purchase information pages to continue increasing the first-time negotiation rate.

Conclusion:

I have not elaborated in detail on the key 2A3R growth funnel of growth hackers and the growth strategies that should be adopted in each stage of the user life cycle (acquisition, activation, retention, revenue, and recommendation). First, we did not perfect the entire product data monitoring mechanism in the early stage and only monitored core indicators as a starting point. Second, space is limited. I will find an opportunity to share my thoughts with you.

Finally, there is no silver bullet for growth, no one-size-fits-all method. Only by making reasonable hypotheses, conducting experiments based on a deep understanding of the product and users, getting feedback from the experimental results, and learning from the feedback can we achieve long-term and sustainable growth of the product.

Author: Mr. Mu Xin

Source: Mr Mu Xin