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Marketers for companies of all sizes rely on marketing data analytics to drive decision-making, forecast outcomes, evaluate the effectiveness of marketing campaigns, identify market opportunities and potential new audiences, and much more. Marketers really know that data matters. But marketing data analytics can easily be the best support for each team in a company and not only because of the massive volume of data that most companies have to work with but also because there are endless tools, techniques, methodologies, and best practices that can be applied to data analytics in marketing. As marketing data analytics is meant the study of data garnered through all marketing (both online and offline) campaigns in order to understand  aspects such as how a campaign contributed to conversions, consumer behavior, country preferences, creative preferences and much more. The goal of marketing analytics as a practice is to use these patterns and findings to optimize future campaigns based on what was successful.

Few tips to have the best data analytics in marketing

Before starting with the analysis, the strategies, the creation of captivating data visualization, it is necessary to skim the data available and understand what data to use in order to get the best out of our marketing data analysis

Use data discovery in the planning process

You don’t need to be a data scientist or data analyst. You don't have to delve deep into data science to do more successful marketing. Marketers who are beginners into the approach with data could start with website performance data generated by free tools like Google Analytics and Google Webmaster asking you: How many people are converting on your landing pages? How many people have visited by blog? Where are they from? It's the same with the insights of its social media properties: How many people are clicking on your Facebook ads? What percentage of your target audience open your emails?

Not only exploratory data analysis, but also content marketing strategy

Data and creativity go hand in hand more and more. This is why we often talk about data-driven creativity. A complete analysis of data for marketing now has to be linked with solid content strategy. It serves to bring the public closer to the brand. This can only happen through a structure that takes into account the habits, preferences and needs of the public.

Use data from social channels to further define your target buyer personas.

Not only internal reports, surveys or qualitatives and "old " market research methods; with the right tools, Twitter (and other social media) data analysis can help you gain insight into your current social media community, which can be used to help you outline and define your target personas. Applying social media data analysis techniques to a social community opens additional insight into the type of people your social media marketing is attracting and helps you further define their profiles with information such as; what they’re interested in, where they’re located, and more. Consumer intelligence tools , such as KPI6, could help you in defining this personas.

Conduct an industry analysis.

Whether you’re entering an industry for the first time or looking for ways to take your business to the next level, it’s important to conduct regular industry analyses. An industry analysis is meant to help you review various market and financial factors in your industry that affect your business, including evaluating your competition, anticipating emerging trends and crises.

Techniques for marketing data analytics

Once you get more confident with the perceived scary world of data, you could start thinking about different ways to analyze them, avoiding the simplest one, going deep into more difficult but creative marketing analytics techniques.

Predictive analytics before human intuition.

The marketer’s task is to predict what the user or customer will do next, in order to position themselves in such a way as to be there, waiting for those anticipated user behaviors to happen, with a marketing strategy ready to use. To accomplish this, it makes sense for marketers to hone in on characteristics and user behaviors that correlate, or seem to predict certain outcomes. This is the first step in predictive analytics. Many marketing automation systems today are enabled with some predictive capabilities. Perhaps the most basic example is running an A/B test, showing two smaller segments of different versions of a campaign, or using the consumer intelligence and web monitoring in order to understand when consumers are going to change their spending habits or opinion.

Cluster analysis

It is an exploratory tool designed to reveal natural groupings within a large group of observations, segmenting the sample into a small number of groups. To do that you could use surveys or audience analysis tools. Users who show very similar characteristics should fall into the same clusters while respondents with very different answers should be in a different cluster. Cluster analysis can suggest groupings that would not otherwise be apparent, such as the needs of specific groupings or segments in the market. it could be useful in defining the audience for the online and offline campaign, to define the tone of voice to use with them, to launch a new product..and so on..

Select the appropriate data analysis technique for the business case.

There are many well-developed methods available for analysing the different kinds of data that can be gathered. In case you are analysing both qualitative or quantitative data, you should be always focused on the goal of your marketing data analysis, fully understanding the meaning of the main use case that you are trying to deeply analyse.

Find your best analytical dashboard

There is no limit to data collection … the more you have it, the better you get it! Even if the company has got a CRM full of information or a proprietary database, it is always possible to make more accurate and high performing analyses by enriching the proprietary data in possession (first-party data) with third-party data. In this way, it is important to understand that to run sophisticated campaigns or strategies, the use of different types of high-quality data is essential– specifically, 1st and 3rd party data. Each type provides a unique value and serves a specific business goal. When combined, the data types create a holistic view of the customer and build a database that can provide actionable insights for the entire organization. But, what is the difference between them?
  • 1st party data includes personally identifiable information such as name, email, and mailing address.  This data is most often collected from the website or other registration points, stored in a Customer Relationship Management system (CRM), and includes information from past and current customers. It is data that the company owns. (e.g. minutes watched on an hourly basis for streaming on-demand services)
  • 3rd party data is anonymized and aggregated from a variety of sources. This data consists of user behavior including interests, patterns, hobbies, and preferences. It can enrich an already existing audience segment, but it can create entirely new audience segments for marketers. These segments are valuable because they are based on identifiers beyond demographics such as loyalty, intent, shopping behaviors, and brand association. (example: interactions of the posts published on social media during the day to promote new releases)
Adding contextual data (e.g. open data, financial data, consumer data, movements, device distribution, digital divide on the territory) to the analyzes allows you to extract new information that could be decisive in the strategic phase. Having a Consumer-centric Data Room is essential because consumers are the lifeblood of a business and to be successful you need to know them as thoroughly as possible. Moreover, it is also necessary to have continuous knowledge of consumers because they change their behaviours more and more frequently, or completely change it in the event of a crisis.

Arrange all data in one place and happy analyzing

It’s all about playing with data, to be curious with them, and to keep looking for it until it will show us the best results. To make this more catchy, it is possible to use Data Visualization analysis tools which give you an interesting and quick visual look of the information that can be activated for your business. Data visualization plays a crucial role in the entire business intelligence dynamic. In today’s time, with increasing amounts of free-flowing data, efficient BI tools are very important to make the most of the knowledge that hides in raw and unprocessed data. Data visualization is a pictorial representation of a given set of data. A text-based data is visualized graphically in the form of charts, graphs, tables, infographics, maps etc. With the help of visualizations, new insights and hidden patterns in data can be detected. The information necessary for a business can be various and can come from various sources, they can come directly from the consumer, or can be collected in an unsolicited way. Having a holistic view of the data really means never stopping monitoring information, constantly searching for it in new sources and testing new possible crossings between the data, because you know, it is only by continuous experiments that the best results have been obtained. Data Visualization has become a fascinating area in the Big Data era, with the challenge of representing data in large volumes. The main players of data visualization tools could be Microsoft Power BI, Tableau or Data Studio.
  • Tableau
Tableau is one of the leading software of the data visualization market, enabling various types of graphs creation, charts, dashboards, stories, maps and other elements without programming. It includes various functions of descriptive and inferential statistics with analytical charts generation. Allows other tools integration such as Excel, SQL, SAP, Amazon, and others.
  • Microsoft Power BI
Microsoft Power BI is a powerful data visualization tool and a very popular one. It is a cloud-based software which is well known for its easy-to-use functionality for data preparation and data visualization. Power BI comes with a lot of visualization features such as custom visualizations, creating visualizations using natural languages, having Cortana personal assistant etc. Microsoft Power BI offers connectivity to a wide range of data sources such as Oracle, IBM, SQL Server, Salesforce, Google analytics, Azure DevOps, Excel, text files, JSON, Zendesk, Mailchimp etc. In addition to this, integration with big data sources is also easy with the help of direct connections using web services.
  • Data studio
Data Studio is a  free tool from Google that lets users make custom reports with data from Google’s marketing services and external sources. Data Studio is Google’s reporting solution for users who want to go beyond the data and dashboards of Google Analytics. The data widgets in Data Studio are notable for their variety, customization options, live data and interactive controls. Data sources include Google products (Analytics, AdWords, Search Console, Sheets, YouTube, etc.), database connectors, file upload, social media data and “community” connectors to popular marketing services. As seen, there are many ways, techniques, tools, ways of thinking that you could involve in the process of creating a successful marketing analytics. Any marketing team can not only analyze their current strategy, but also the potential one, using innovative techniques such as predictive analysis or improving the performance on their user cluster, carrying out a cluster analysis in order to know not only the interests and socio-characteristics demographics, but behaviors, media touchpoints etc. Whether it is an advertising campaign, a product launch, or internal analysis, all these techniques can be accompanied by refined data visualization tools, to make your marketing data analytics spectacular and effective!
Article updated on: 09 August 2023
Talent Garden
Written by
Talent Garden, Digital Skills Academy

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