Analytics to understand customers' behavior patterns

Customer behavior analysis it's important because it helps businesses to understand how users interact with a brand, now it can be done in a more objective and real way using Big Data management techniques

Friday, September 10, 2021

Customer behavioral analytics is the process of collecting and analyzing data, using technology tools such as Big Data, machine learning and location intelligence, help gain long-term insights of the average purchase value, customer lifetime value and users interaction with a brand, enabling companies to incorporate data-driven business strategies that facilitate decision making and revenue maximization.

Also read: "Supply chains predictive analytics benefits"

This type of analytics is already used by streaming platforms such as Netflix and Spotify, which are constantly striving to learn more about users' tastes and preferences in order to personalize and improve their experience.

How do they do this?

This technological tools record data on customer behavior, buying habits, favorite products, etc., this is how businesses can predict which products the customers might be interested in and when they might want to buy another one.


"With a location Intelligence analysis based on spatial data, it's possible to locate customer concentrations within an establishment and identify behavioral patterns."

Equipped with this data, buying behavior patterns that are typical of a target audience are established, producing unified reports that answer questions such as:

  • What is the most common time of purchase?
  • What are the main obstacles to completing an order?
  • What aspects of functionality and design were misunderstood by the user?
  • In which areas businesses should focus on to bring tangible improvement to your customers?

You may be interested in: "How to increase customer dwell time?"

Feedback of a product in real time with the help of machine learning, allows you to adapt services to the preferences of each customer as they use it.

How does the implementation of these tools help businesses?

  • By understanding customer habits and motivation, they provide a unique perspective of online and physical stores, creating indicators of how customers see a business.
  • By identifying and anticipating customer issues, such as product, store or service design failure, they maximize purchase conversions. 
  • Maximizing marketing campaigns, allows companies to find potential customers and target them with effective advertising, based on patterns of purchase, transaction behavior, locations and favorite products, optimizing operational costs.
  • Segments target audiences into groups based on their characteristics, including location, gender, age, status, education and occupation, determining the average search history, purchase frequency, value, urgency and shopping experiences of customers.
  • It allows businesses to create a competitive analysis that consists of analyzing competitors' products, strategies and performance to gain competitive advantage.

At PREDIK Data Driven, we build customized customer research solutions that are based on big data, machine learning and geomarketing, helping our clients gather information and turn it into actionable insights for decision making.

Do you need customized solutions for identifying customer behavioral patterns? Contact us!









this site is protected by reCAPTCHA and Google's privacy policy and terms of service.
Need assistance? Contact us
(506) 4001-6423


More on this topic

Branch expansion with predictive analytics

March 2022

Predictive location, and foot traffic analytics reveal valuable information that helps retailers to choose the right location when setting up geographic expansion strategies.

How are predictive analytics models used to determine the optimal location for a new facility?

How to increase foot traffic in clothing stores?

November 2021

Retailers can apply location intelligence techniques and foot traffic analytics to understand consumer mobility patterns, measure foot traffic at each store, understand the performance of their outlets, and estimate competitor turnover.

The correlation between foot traffic visitation, sales, and the success of retail apparel franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.

Business Planning with Big Data

November 2021

With Big Data management techniques, companies can optimize their strategic business planning, by taking advantage of market and companies' data.

Big data has emerged as a powerful tool that organizations can use to leverage data-driven decision making for better strategic planning, determine which market niches of their products, are growing or shrinking, obtain traffic data of their stores or website, determining where they come from, what kind of devices they use, dwell time, and foot traffic patterns to help analyze which promotions and efforts are successfully driving their business.

Retail: Benefits of Data Monetization

September 2021

More and more retail organizations are monetizing their data to increase revenue, boost productivity and optimize costs, enabling effective leveraging of assets, technology tools and external information to generate better results.

In the retail sector, data monetization is about making better informed decisions, increasing revenue and reducing costs from access to different types of stored, categorized and accessible data.