With advertising analytics based on big data techniques, marketers can quickly determine the effectiveness of each channel and thus adjust their strategies, enabling them to run hyper-targeted campaigns, choosing the right ad content for the appropriate ad networks.
What is it?
This type of analytics refers to the use of data and technology tools that help companies and marketers effectively monitor their marketing efforts to ensure that campaigns are targeted to the right audience and use the right channels for effective communication.
In order to optimize advertising costs and maximize revenue, marketing leaders are using spatial data to create geofences in specific areas, allowing them to reach audiences that are more likely to become potential customers.
Thanks to predictive modeling and artificial intelligence (AI), marketers can now leverage these tools to segment audiences beyond traditional parameters and build a more accurate profile of consumers.
By using AI, it is possible to segment audiences at more granular levels and identify which ones are most valuable to marketing objectives.
Location Intelligence is transforming the way brands communicate with their current and potential customers, maximizing marketing campaigns and optimizing costs.
Location analytics leverages and enhances marketing campaigns by providing faster and deeper insights to your customers.
Footfall analytics helps to make critical operational and strategic decisions for any type of business, improving conversion rates, maximizing sales, optimizing costs and increasing brand positioning.
Thanks to mobility data, retailers can get a deeper insight into their business by analyzing changes in sales volumes and the consequences of fluctuations in footfall levels inside and outside stores. At the same time, they can measure the effectiveness of marketing campaigns, providing a clearer picture of what really works for a target audience.
Mobility data analytics are transforming the way commercial strategies are defined in the retail business, and supermarket chains are no exception.
Understanding what consumers think, what they want and what they do is critical for companies in the retail sector. This is where Big Data tools play an important role, as it is possible to measure the affluence at a location and customer behavior, among other aspects. With this information, it is possible to model the performance of points of sale and estimate the turnover of competitors or potential locations.
New data management methodologies now allow retailers to take advantage of even the smallest piece of information to generate valuable insights that help optimize their marketing and customer loyalty strategies.
What promotions do we do to get more customers to the point of sale?
The process of data monetization, a concept that until recently was present only in conversations between technology experts, is now one of the recurring topics in strategic meetings at the management level in companies.
What is Data Monetization?
The concept of data monetization refers to the process of extracting, cleaning and analyzing the millions of data generated within a company, with the purpose of obtaining a benefit or economic value. This value can range from using the information to create performance indicators of the company's own business and use them to optimize processes and make better strategic decisions, use it as input in the creation of other products or services, market it to third parties, share it with business partners, among others.
Customer analytics along with Big Data tools, predictive modeling and geospatial data are used to understand consumer needs, analyze price sensitivity, and the general behavioral patterns that customers follow when choosing products or services.
The analysis of customer information offers great advantages in strategy development, as customer interactions, consumer response and behavioral patterns, among others, can be monitored and predicted. This information is useful for increasing customer acquisition, profitability and long-term customer loyalty.
The use of big data and predictive models has become one of the greatest successes for the sector, thanks to these, it has been possible to take proactive measures based on real-time data and predict future trends.
Using the right models and calculating historical data, it is possible to predict customer behavior, sales growth, changing consumer behaviors and/or market trends; helping retailers to stay ahead of the curve in order to compete effectively and gain considerable market share. It also enables leaders to set precise goals for their business.
Consumers today have access to information anytime, anywhere, including what, where and when to buy, how much to pay, among other things. This makes it increasingly important to use consumer-focused data analytics to predict how they will behave when interacting with brands.
The goal of consumer analytics is to create a single, accurate customer view to make strategic decisions about how to acquire, identify and retain them. The better the understanding of consumer buying patterns, the more accurate the prediction of consumer behavior and journey when purchasing a product or service.
Geolocation intelligence is accurate contextual information about the location and movement of people anywhere in the world.
The potential that this type of data has in the context of business decision making in the retail sector is invaluable. It includes demographic information about the retail outlets themselves, nearby businesses, competitor insights, customers, suppliers, among others. With this data it is possible to know consumers in depth: who lives in a given area, what is their socioeconomic position, what type of housing they live in, what businesses they visit, at what times, on what days, what preferences and tastes they have, among others. This data can be complemented with sociodemographic details to deepen the analysis and provide a wealth of information about the populations.
More and more companies are turning to predictive analytics to optimize their processes, achieve better business results and increase their market share.
Organizations use internal predictive analytics to forecast trends, understand and predict customer behavior, improve performance and drive strategic decision making.
Predictive models are statistical tools that use machine learning supported by Big Data mining to predict and forecast likely future outcomes with the help of historical and existing data by inputting multiple parameters.
They can be used to predict virtually anything containing existing data, in every sector imaginable, from ratings of any program, a customer's next purchase, credit risks, decision making among others.
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