What is Data-Driven Marketing?

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It is a marketing technique that focuses on utilizing data and decides on measures based on data evidence.

Originally, data-driven means visualizing obtained data (especially big data), analyzing it in an integrated manner, and using it to solve problems, predict the future, make decisions, and develop plans.

In other words, it is a method of comprehensively analyzing big data such as sales performance and customer information and using it for marketing purposes.

Why Data-Driven Marketing is Necessary

Customer behavior is becoming more complex.


・Prevent unnecessary costs.

When a company implements marketing strategies, it must plan and develop initiatives based on a targeting model for the individual businesses that will become its customers.

However, as individual hobbies and tastes president email database become more diverse and models become more complex, countless options arise at each stage of attracting customers, turning them into prospects, customers, and loyal customers.

Therefore, to achieve better results, marketing strategies can be implemented based on data rather than intuition.

When it comes to data-driven marketing
Developing a marketing strategy
Selecting marketing indicators
Creating a roadmap for implementation
Implementing measures and regular reviews
You need to follow these steps.


Of the four steps, the most important one is selecting marketing indicators.

This indicator will be the following:

① Brand recognition
Brands cannot be measured by financial indicators, so they are measured by non-financial indicators such as "recognition rate" and "likability" in surveys.
② Test drive
Can customers take a trial action, such as test driving a car?
③ Cancellation rate
The rate at which customers cancel a service.
④ Customer satisfaction (CSAT = Customer Satisfaction)
How satisfied are customers with the service?
⑤ Offer acceptance rate
The rate at which customers respond to offers (requests) such as campaigns from companies. It is necessary to evaluate the operational efficiency of marketing campaigns.
⑥ Profit
Calculated as "sales revenue - cost". Did the campaign, discounts, and promotional expenses contribute to sales and profits as a result?
⑦ Net present value (NPV = Net present value)
"PV (present value) - cost"
Used when comparing each campaign measure. When the cost of the campaign is subtracted from the value of each measure, what is actually profitable?
⑧ Internal Rate of Return (IRR)
The return on investment when implementing a campaign or measure
⑨ Payback period
The period until the invested cost can be recovered. The shorter the better
⑩ Customer Lifetime Value (CLTV)
An essential indicator for measuring the value of a customer. In business models such as subscriptions and SaaS, if a customer cancels a service that costs 10,000 yen per month after an average of three years, the LTV of that customer is 10,000 yen x 36 months = 360,000 yen.
Since some customers have high LTV and some have low LTV, it is good to be able to adjust the special and normal treatment depending on the customer.
⑪ Cost per Click (CPC) The cost
per click of a web ad
⑫ Transaction Conversion Rate (TCR)
Transaction = purchase on an EC site. 13. Return on Advertising Spend (ROAS) - This is like the advertising version of ROI (Return On Investment) 14. Bounce Rate - The rate at which users who visited a website left without visiting any other pages
15. Word of Mouth Amplification Coefficient (WOM) - This is the coefficient of how much word of mouth was generated online







Once the set indicators have been implemented, regular review will be necessary.

Holding regular retrospective meetings not only enables more effective marketing but also leads to the accumulation of marketing skills and knowledge within the company.

3 steps to implementing data-driven marketing
Understand the required knowledge and skills
Establishing a system to be prepared
1. Understanding the required knowledge and skills


The following six areas of knowledge and skills are required:

Business Knowledge
Marketing Knowledge
Logical thinking
Knowledge of data analysis methods
Knowledge of databases and data processing
Knowledge of statistics
Because data-driven marketing focuses on utilizing data, it requires not only knowledge of business and marketing, but also the ability to think logically about data, as well as knowledge of data analysis and statistics.


2. Prepare for the disaster

Even if you are able to analyze the data necessary to implement data-driven marketing, if your organization does not have a system in place to utilize data-driven marketing, you will not be able to execute the plan formulated based on the data.