Data driven marketing: generation and value of data in marketing

[muskanhossain]

Digital marketing has the great advantage of not only its ability to deliver and segment audiences, but also the ability to view real data available during and after the activity. It is the future of marketing.

PWC - PricewaterhouseCoopers reported in 2019 that data production was at 2.5 quintillion bytes of data per day, with 90% of this volume having been produced in the last 2 years alone .

Data production has never been so large and its estimated value in 2020, considering the personalized data category, would reach 1 trillion Euros . The main function of data is related to monetization strategies. Check out the reference .



If you think this approach is a result of the digital world, you are mistaken. William Edwards Deming, one of the masters of quality management, said:
Without data you are just another person with an opinion.


This evolution in marketing enables analyses based on analytical data, providing security in decisions regarding strategy adjustments, testing, improvements, monitoring and personalization of communication for business growth, and most importantly, it shows what works and what doesn't.

By using a rational approach, data driven marketing does paraguay whatsapp data disqualify insights ; in fact, it maximizes the generation of new conclusions through the development of scenarios and contingencies, as it transforms real-time data into resources for qualitative and practical decisions.

The greater the reach of digital visibility points, the more insights will be possible. Therefore, take the opportunity to check out this content on the 9 Strategies for Success in Digital Presence


Stages of Data Driven Marketing Adoption
Stage 1 : work on metrics based on reach and visibility (Followers, links, visits);
Stage 2 : analyze communication indicators along with the tools used to contact leads and customers: engagement, involvement, brand health, communication and customer retention (sessions, views and interests, user experience, bounce rate, exit points, clicks, health, opening and clicks on emails);
Stage 3 : deals with business sustainability metrics , i.e., the financial gains resulting from the quality of the work developed (Profitability, NPS-Net Promoting Score, MRR-Monthly Recurring Revenue, LTV-Life Time Value, Cohorts-Audience Harvests, MAU-Monthly Active Users);
Stage 4 : Sales results , CAC and conversion, revenue and ROI (Leads, conversion, revenue, CAC-Customer Acquisition Cost, ROI-Return of Investment);

Read this exclusive article on how to increase the open rate of your email marketing campaigns .



The 4 Big Challenges of Data Driven Marketing

1. Integration of different data sources
The first step is to have access to data from each platform that can be measured.
It includes the parameterization of URLs, ads, tracking and conversion pixels, cookies, codes and manual, native or API integrations necessary to access data collected about consumer behavior and journey on their social networks, applications, platforms, websites, e-commerce, blogs, satisfaction surveys and service channels.

The ultimate goal of data-driven marketing is to generate sales through positive shopping experiences, generating revenue predictability and average return on investment.

Data processing tools depend on the work carried out at the beginning of planning. They will be the basis of information for the development and improvement of products and services.

When we think about the use of data, artificial intelligence is a mandatory resource for maximizing and connecting different sources of data. So take the opportunity to learn more in this exclusive content: Artificial Intelligence in Digital Marketing .
The promotion, communication and especially validation strategy of acquisition channels can greatly increase your results. Find out more in this content: Place and Promotion in Digital Marketing .


2. Selection and prioritization of available data
The amount of data available can be challenging for managers and analysts, as it is so large that it demands an applied mindset in capturing metrics, transforming them into qualitative indicators, interpretation, suggestions and corrective actions. And of course, always leaving vanity metrics aside.

The flow of this process goes through 3 steps:
1. Metrics : are data measured separately in defined situations;
2. KPIs: deal with the mathematical formulation of metrics for presenting results;
3. Insight: is the development of hypotheses and scenarios in response to an identified situation.



3. Ability to convert data into insights
Data analysis must involve a 360º view of the teams and managers involved, as certain results may be consequences of themes positioned in internal layers of organizations.

As a marketing manager, you have the obligation to navigate between areas and understand the challenges that interact with the public's points of contact with the brand. Negative experiences are triggers for detractors regarding the work carried out.

An experience must be measured to be classified as positive, neutral or negative , depending on the scale used.

X = UX + CX + BX
UX: User Experience is the interface used by customers to access the brand;
CX: Customer Experience , deals with context, consumer experience and moment.
BX: Branding Experience are the points of contact between customers and the brand
Use analytics data combined with analysis of customer satisfaction responses, because at the end of the day, the positive experience in the consumer journey is what will generate results.



Furthermore, data-driven marketing is a daily process mentality, as Peter Drucker - Considered the Father of Modern Management - said:

What can be measured, can be improved.


4. A new performance mindset
The implementation of an analysis-based mindset promotes discussion between different points of view, scenario management, constant testing, personalization, monitoring of numbers in real time and growth, that is, only qualitative gains for the entire marketing process.

Marketing Data Driving is asking the right questions to achieve the desired goals and presenting the facts objectively . Each analysis should be followed by a recommendation, which will always be measured and presented in the next report. It is an evolutionary accountability.
The next step is to use automated resources. It is the second step after the integration and visualization of data and its results.

Delivering the right ad to the right audience, as well as prior knowledge to optimize predictability of results .
It favors the use of dynamic ads to identify consumer response behaviors in relation to campaigns.
With so many tests and adjustments, it is essential to create a playbook to document the history of actions for access by new team members and queries;
Now that you know what to do, choose the tools, view the information and work on generating and distributing the knowledge applied in all stages of marketing planning and operations.