Home » Analytics » Customer Insight – the Tacit Truths and the Misunderstandings

Who are working as marketer mostly know what customer insight means: Tacit truth – their understanding of customers and their behavior, even if they do not speak it out. But what if customer insight is different from the research or customer data you have? How can you gain insights of your customers and apply them to improve your business? What are the common mistakes and misunderstandings about customer insight? Furthermore about insight and its application, what can we do? This article will help you answer these questions.

* In this article, I would like to use the words “customer insight” or “insight” instead of the word “implicit truth” to keep it short.

What is Insight and its characteristics </ h2>

Source: freepik

Customer insight is the customer’s behavioral and trending interpretation based on the data that we have about them so we can execute actions to improve product quality, service, and sales revenue so that both parties (brand and customer) benefit.

Some characteristics of customer insight:

1. It is not an obvious fact. If it is obvious, it is not called the implicit truth. For example, based on Google Analytics, you know that 70% of visitors are between the ages of 18 and 24, so you infer most visitors to your website are young people. This is so obvious that I can not call this insight.

2. Do not just rely on one type of data. You need to combine multiple sources, multiple metrics, multiple data, multiple categories to create the correct insight. For example, if you just look at the high bounce rate index (the number of people visiting the site and exiting without interacting) on a site that judges that the page content is not good then it may not be accurate. Because it can provide very full and useful content so visitors coming to read the content is satisfied, there is no need for them to search or see more information so they leave the web right away, making a high bounce rate. However, if you use both the bounce rate and the time on page index for evaluation, it will be more accurate. If the bounce rate is high and the visitor’s time on page is high, ie the page content is good. If the bounce rate is high, but the time on page is low, the page content actually has a problem. Therefore, combining multiple metrics will help you find accurate and deep insight. 

3. Basing on that insight to take the practical action.  If it is just theory can’t be applied or testified, then it is not insight. For example, your company has two business segments: B2B (business customers) and B2C (retail customers). The B2B segment is working well, but you need to improve the B2C sales. After researching the B2C group, you come to the conclusion that retail customers love to recommend services to their friends and receive commissions. From there you need to set up a referral system to help increase the number of visitors and revenue. However, the fact is that the system is not feasible with the company’s current condition of: human resources (needing too much manpower to manage the system), costs (investment to build referral system ) and time (6 months to complete the system and put to use); The outlook can not be transformed into a concrete and practical action (no matter how good or bad it is) is not insight, but in the end is just opinion and thought.

4. The above action if being executed must have the ability to convince the customers to change their behaviour. For example, you find that after buying a laptop, customers often buy additional computer mouse. From the insight of this customer segment, you can place the mouse product you want to sell right next to the laptop product to increase the purchase rate – & gt; if your customers buy both products, it means you successfully change their buying behavior (buy 2 instead of 1).

5. Behavioral change must be beneficial for both parties: the brand and the customer. For example, buying a computer mouse and a laptop brings value to the buyer and It gives seller higher sales, which means that both parties are gaining profits.

Each customer will have very different thoughts and behaviors and we need to see things far more than data or numbers. We also need to understand that customer insight will change over time, in terms of technology, time, season, age, and so many other factors. If you just analyze and judge based on old behaviors, then you will not shed new light on customer insights. The insights you have about your customers will gradually become outdated and inaccurate.

How to build customer insight?

Source: pinterest

Understanding what insight and its characteristics are, you can now start building customer insight basing on that and apply it to your business. This process consists of three steps:

– Data collecting

– Interpreting / analyzing data to make insight

– basing on the insight to give the actions

We will go through each step in detail as below:

1. Collecting data

As noted above, insight comes from data, and with digital marketing the data comes from:

– Website: sessions, time on site, bounce rate, etc.

– Mobile applications: screen views, time on screen, downloader information, etc.

– Social networking: followers, like, share, comments, etc.

– Search / Display Ads: impression, clicks, conversion, CTR, CR, etc.

– Email: open rate, click rate, CTR, abuse / spam rate, not open email list, etc.

– SMS: number of sent SMS, open rate, fail-to-send phone number list, etc.

– Online survey

These are just some common channels and not all. Insight can also come from other data sources such as:

– Sales: information from CRM, order tracking file, contract, etc.

– Customer care: information from call center, hotline, web chat

– POS: information from the system at the point of sale

– Reviews, comments from customers

– Market research

Just some common channels, not the whole. Next we go through the way to be able to derive meaningful insights from the data.

2. Interpreting and analyzing data to create insights

Once you have the data, you need to understand what these data mean and then look for the correlation between the pattern of some indicators with the goal of the customer (better experience) as well as your goals (sales).

For example, you see that mobile customers visiting the website currently have a lower conversion rate to purchasing than desktop ones. You draw the insight that maybe your current mobile version of the website is not very good for the user experience, and if you can improve it, it will contribute to higher revenue. </ Em>

We will find that the satisfaction from the experience of customers when buying products or services will directly or indirectly bring revenue to the business. Therefore, not all insights must necessarily be directed toward generating immediate revenue, but sometimes just focusing on improving the user experience, then they will be more likely to return or introduce new customers.

3. Basing on the insight to give the action

Once insights have been generated from the analysis of the data, you can now begin to take action that can help you get closer to your business goals. This is the time when insights interpreted and analyzed from the data must be reconciled with the factors outlined in section 1 to ensure that they are truly accurate and appropriate for you to apply.

Actions created from insights will vary according to your desired goals as well as industry characteristics, company situation, market situation and trends. So there will not be a standard or template for this.

Some common applications of insight

Source: pinterest

As noted above, although there are no common standards, here are some of the applications that you can do with customer insights generated from the data and some examples to illustrate the ideas:

1. Evaluating the impact: Helping businesses understand how what they do impacts on customer behavior and also predicts customer responses to changes being proposed.

For example, before adopting a new pricing policy, you can rely on insights collected from sources to evaluate how the application of this price could be responded by the customer ( positive or negative). And based on the insight from this you can adjust the price more appropriate before the market.

2. Increasing the Lifetime Value:  Evaluating the lifetime value of your customers and allowing you to measure many factors such as the cost of having a customer and the percentage of customers who stop using it. service.

For example, based on the insight collected from the customer, you know that the average customer from 15 to 22 yearls old will usually change phones once a year and prefer new, expensive phones. The 23 to 30 years old will change phone every 2 years and not need to be the latest phone model. From that insight the brand decides to launch a new product once a year for customers from 15 to 22 years to buy and at the same time reduce the price of old phone so that the 23-30 year old customers feel like to replace the old phone sooner instead of waiting until next year. This helps increase the lifetime value of the second-cycle customer segment and ensure revenue from the first-cycle customer. 

3. Trend analysis:  Predicting future customer behavior based on past actions and helping businesses understand the likelihood that customers will behave in a certain direction.

For example, based on the data, real estate companies know that July is the time when Vietnamese feel restricted to participating in trading due to their superstition, leading to reducing demand. The company decided at that time to cut advertising costs to avoid waste and offer more attractive promotions to stimulate sales.

4. Cross-sell / up-sell analysis:  Identifying relationships between products and services to understand the best way to combine products. These combined products can then be used for cross-sell or up-sell. Cross-sell: selling related products to customers who have purchased a product. Up-sell: selling a product of the same type but more premium than the product that customers are using.

For example, an English language education service offers products such as flexible adult English courses, including 15 levels with 1 being the lowest and 15 being the highest, Online business courses from a reputable foreign university, IELTS, TOEFL and SAT preparation courses. By analyzing data, the company insists that high-level (10-15) learners of the flexible English language course for adults are more likely to need to apply for online business courses,. while middle-level (6 – 9) students usually register for IELTS preparation courses. This company launches a discount promotion for students in high level classes buying an online business courses and learners in middle class IELTS course. This program led to a 200% increase in the number of courses sold during the promotional period.

These are just some of the commonly used and commonly used applications in business. Depending on the different needs, there will be different applications and approaches.

Going beyond customer insight

Source: conversion.vn

However, the application is not complete, you need to know how the effect of the action is and which data can be drawn from that. There are 2 things you should do:

Commenting

– Efficient or ineffective? How to measure? By what tool? By what indicators? This is something you need to define before implementing and executing activities that come from insight.
– If so, how many percent of effectiveness is achieved and what could be done better?
– If not, what do you do wrong? What actions from insight are unreasonable? Is your insight wrong? Or is the data unreliable leading to mistakes from scratch? You will need to go back each step to find the problem.

Data collected

– What kind of data does the applicable activity generate?
– Based on these new data, what insights can we draw?
– How can new data be combined with old data to create new insights?
– An insight can create not just one but could be more applicable actions. What more can you do?

Conclusion

To sum it up, a “good” insight must be self-evident, based on a variety of data sources that are applicable and capable of changing customer behavior and bringing benefits to both sellers and buyers. To create insight, you need to start by gathering data from various sources and then interpreting the data to generate customer insights. These customer insights can only be called insight if they meet all of the specifications listed above and only then should you apply them to the business to improve efficiency. And after the application, the performance evaluation and data collection will continue to draw new, clearer insights.

This article is intended to give you an insight into the insight and its usefulness in helping you improve what you are doing.

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