The aim of marketing is to know and understand the customer so well the product or service fits him and sells itself. – Peter Drucker
With the recent rise of big data and BI tools, data is becoming more accessible to professionals than ever before. Behavioral Analytics is a significant part of the big data rise, which is becoming much more common and affordable. But exactly what is Behavioral Analytics and why can’t my current web analytics tool deliver the same results?
Social media is just one channel companies are using to build consumer profiles and purchasing habits, using data analytics. By knowing what a consumer wants, behavioral analysis specialists, with the forward thinking help of data analysis technology developers, are turning a once descriptive practice into a predictive one. Make no mistake, the results are already signaling a game-changer for businesses looking for more insight on customer preferences and behavior.
In a word, behavioral analytics leans on consumer attitudes and emotional tendencies, and leverages both to spur behavioral change and drive them toward products and services that feed those attitudes and tendencies.
A company that has really made this approach a way of life is mySupermarket, a leading online supermarket providing a new type of shopping experience by enabling its users to compare prices and shop online from all the main retailers – on one platform. Their mission is to help users save time and money, while offering the best possible shopping experience. With a customer base of five million monthly grocery shoppers, the real challenge mySupermarket faced was encouraging online purchasing, analyzing user behavior and spotting emerging trends.
Behavioral analytics enabled mySupermarket to have the capabilities it needed to understand user segments with an emphasis on ROPOs – shoppers who Research Online, Purchase Offline – and Omni channels, like responsive mobile/web design. This also enabled mySupermarket to drill even deeper with different segments inside its ROPO, analyzing behavior all the way to the user level. This lead mySupermaket to target specific audiences with tailored offerings and resulted in increased conversion.
It’s not just the public sector using behavioral models to provide better service – Uncle Sam is doing so, too. Last year, the state of Indiana recently rolled out a data analysis campaign for the Indiana government designed specifically to improve service outcomes for citizens, based on what consumers say they like and don’t like, and on what they use more in terms of government service.
For companies who want to emulate what the State of Indiana did on the behavioral data front, you’ll need to look at what you’ve been doing differently.
How can you start applying this today?
In the past, traditional descriptive-based web analytics provided businesses with data on page views, click-through rates, total time spent on a web site, and bounce rates. All that data, in turn, gave company analysts the information they needed to tweak search engine optimization and search engine marketing datasets, and to clarify and streamline overall firm marketing efforts. While this information is critical for reporting on the areas and metrics to which you should paying attention, behavioral analytics can help give you guidance on what to do next.
Path widget helps users analyze consecutive events performed by a user, or a cohort, which is a specific group of users. It provides the ability to optimize a process by showing you where your customers go and drop off between various events. For example, CoolaData’s Path is based on events, which means that any event you’re tracking can be a stop in the path. This is what makes Path analysis extremely flexible and powerful. You can basically track any process. For example, a user logs in and continues to navigate through a shopping experience or a game. Additional events are registered as the user navigates and creates a path.
The cohort widget is an advanced analytical tool which enables you to classify users into groups according to their actions or behavior. A user cohort is formed by a group of people who generate the same two consecutive events during a set period of time. Cohort Analysis pulls your data from eCommerce platforms, web applications or digital gaming providers and breaks it into related groups of people, rather than examining everybody as one cohesive unit. It allows you to identify interesting groups and compare between them, so that you can optimize service offerings to specific groups of people.
You’ll mostly be using funnels to analyze your user’s success in completing a certain flow or process. With funnel analysis you can identify the problematic steps in your flow, understand where users are churning and see the effects of improvements you integrate into your processes.
Traditional analytics is just not enough anymore – it’s old data that tells analysts what’s already happened.
With behavioral analytics, company data managers can decipher and leverage the navigational flow and additional actionable decisions a consumer makes digitally, and dynamically upgrade online content as needed, based on current customer behaviors. If regular business analytics is about the who, what, where and when, behavioral analytics focuses on the why and how. Or in other words behavioral analytics makes it easier for professionals to catch fraud before it happens, increase sales, decrease costs, analyze revenue trends, improve customer experience and satisfaction, build better products and so on.
Behavioral analytics looks at humanized patterns for things such as keyword searches, navigation paths and click patterns.
Understanding customer-employee interactions is not enough to drive business value and improve customer experience. For example Path analysis helps you analyze your users navigation on your platform and Cohort analysis helps you group users by certain criterias and revue churn rates.
By merging traditional digital analytics and behavioral analytics, public and private enterprises can “get ahead” of the customer, and base digital content delivery on what customers want to see – and more importantly, what to buy.
All based on predictive, instead of descriptive, behavior data analytics.