As the production of data is on the rise, companies are becoming more oriented to manage their efforts and make decisions based on them. Each like, each download and each click leave a trail that can be measured and converted into valuable information to make business more efficient.
Despite the fact that data is one of the most precious assets of the 21st century, it is necessary to have the right skills in order to get the most out of it, something that is still in short supply, according to the report Big Data and AI Executive Survey 2021 from NewVantage Partners: only 39% of companies manage data as an asset and only 24% have developed a data culture within their organizations.
Without a doubt, there is an opportunity for growth and improvement. After all, becoming a data-driven organization is a transformation process that does not happen overnight. Now, how can companies take advantage of data and make better decisions?
It is critical to identify the most effective data analysis techniques, which will contribute to the efficiency of a strategy and to anticipate opportunities.
Techniques for data analysis
Big Data Marketing
Also known as the Marketing Mix Model (MMM), it is an advanced data analysis technique that is carried out to determine, for example, User behavior and to design strategies to attract them with the ultimate goal of increasing sales.
The result of this data processing and analysis technique can lead to various applications:
- Develop personalized marketing strategies. Being a tool that helps to better understand customers, it is possible to create communication strategies adapted to tastes, location, the stage of the funnel where the customer is, and other types of data.
- Detect and avoid customer losses. Consumer behavior responds to patterns that can provide useful information to find out which elements of the purchase path boost conversion or not.
- Segment customers properly. Thanks to Big Data Marketing, much more precise consumer profiles can be found and go beyond the place of residence or gender. Through the analysis of large volumes of data, profiles associated with the purchasing behavior of a product can be generated.
- Analyze and measure the results of the actions carried out. It important to anticipate, measure and evaluate the evolution of strategies and the fulfillment of objectives. The idea is to check the effectiveness of the actions being taken and establish possible improvements on the go.
- Identify new market trends. The information provided by this technique allows to measure the state of health of the brand and analyze the competition.
In addition, it is important to clarify that in order to make effective use of Big Data in marketing, it is necessary to use specialized BI (Business Intelligence) programs and a database with updated and verified information.
Reach, Cost and Quality (RCQ)
This approach is a way of using data and structured judgments when there is not much information to analyze and seeks to reveal the impact of a specific activity in a specific context. It is used to measure, for example:
- Engagement quality
- Customers reached or conversions
- Cost per lead
As its name implies, this analysis technique helps to predict results more accurately, to plan for unknown scenarios and to identify business opportunities. Thanks to these models, information can be obtained in order to apply it in:
- Fraud prevention. Predictive analytics evaluate the actions of a company’s network to detect irregularities that could imply a risk. As cybersecurity becomes a major concern for companies, this analytics is becoming more relevant.
- Risk reduction. The results serve as a basis to determine, for example, the creditworthiness of a client and assess the probability of a customer to make certain purchases or to pay a debt.
- Improve operations. Predictive models have the ability to forecast inventory and manage resources based on it. This technique is widely used in airlines to set prices or in hotels to anticipate occupancy levels.
- Marketing campaigns. Based on data that predicts the probability of purchase, companies can publish online ads or carry out retargeting campaigns to increase sales more effectively.
How it is done? Basically, it is a process that uses data, statistical algorithms, and machine learning technology to make highly accurate predictions. Historical data is typically used to build a mathematical model that captures trends and is then used with current data to project what will happen next.
This method for data analysis is related to omnichannel, that is, the multiple ways through which a customer can purchase a certain product or service. This is a rule or set of rules that determines how a sales and conversion value is assigned to each touchpoint within the consumer journey.
This type of analysis is a great ally to manage digital marketing resources efficiently and obtain a higher conversion rate. It mainly applies to digital touchpoints, allowing you to know what works and what needs to be optimized, such as emails, websites, Google ads, etc.
Time series analysis
Time series analysis is a set of statistical techniques that allow, in addition to studying and modeling the behavior of a phenomenon that evolves over time, forecasts of the values that will be reached in the future.
This technique can be used for various purposes: forecasting the number of subscriptions, sales figures, visits to a website, among others, in order, for example, to be able to set realistic growth goals.
Now, to make better decisions based on data, it is essential to take into account that the information obtained from different sources should go through consolidation and validation processes in order to provide greater accuracy and veracity.
Platforms such as Conciliac EDM allow you to centralize, automate and standardize all the processes that involve data processing, such as integrations, reconciliations, validations, consolidations, extraction and transformation, among others, obtaining an indispensable solution for the generation of various strategies based on data.
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