Why Personalize the Experience?
The digitally native generations (Millennials and Generation Z) expect personalization as proof of the brand caring about them and not treating them just as ATMs. In 2017, a study showed that more than half (56%) of clients were ready to spend more on a customized product or experience. Following that logic, marketers acknowledge the importance of personalization, but under a third of companies have reported they have the necessary data and know what to do with it. Data – this is exactly what is needed and in large quantities, however, it should also be logical and organized.
Personalization is based on creating profiles, or user personas from the anonymous data of real clients. Combining the information to come up with a behavioral pattern is the cornerstone of website personalization, and it all starts with profiling. Even traditional marketing used demographic and sociographic information to create market segments. This trend has not died, it has just been adapted.
Companies aiming to craft unique experiences should be ready to gather data like:
- Demographics (age, gender, location, studies);
- Sociographic data (employment status, title, salary, interests, hobbies, political parties, religion, associations, and groups);
- Buying history (retrieved from credit cards and loyalty programs);
- Social media activity, making a clear distinction between channels and measuring sentiment towards the brand, usually from reviews;
- Activity on the target website and those of competitors;
- In-store activity, if applicable.
It might seem far-fetched and difficult to get all the information listed above, but this is just a minimal list to get started in Big Data systems like Hadoop.
Gathering data is just the first step in creating models that can benefit first the customer and then the business. As all Big Data powered machine-learning processes, this one relies on iterations. Massive amounts of information are cleaned, correlated or analyzed in other ways to distil patterns or signals, as they are called. These are categories that refer to different user characteristics. Signals are further processed by algorithms to extract recommendations and action courses. The results are sent to the users as responses, and a new cycle of recording begins, that will correct the aberrations of the first.
At each step, new incoming data is converted into signals and compared to the existing library, generating recommendations or personalization of actions. If the signal matches the current user to a particular profile, usually up to a degree, the machine produces a response that would fit best that profile. The biggest problem and algorithm failure happens when the existing user matches more than one pattern, or a pair can’t be found (usually the user is an equal mix of more profiles).
For each model, it is advisable to follow the academic research technique: formulate the hypothesis, gather relevant data that is linked to the central question, test your assumptions and discuss results. Repeat the procedure as many times as necessary and focus on one issue at a time. To speed up the process, gather data that is relevant to many problems.
Big Data Strategies for Personalization
Collect Relevant Data
Taking Big Data into consideration as a viable business strategy is the first step. This includes additional dimensions like selecting the best data for your goals, cleaning data to make it ready for analysis, capturing new data points and giving a real-time response. Putting in place systems like pixel tracking, cookie gathering and re-targeting are all part of this initial set-up. Just in case you were wondering, this is why you saw a promoted post on Facebook for a product you were talking about with a colleague.
Train Employees to Understand Data
The next step is creating the framework for your Big Data initiative, working on the corporate culture, analyzing the customer journey through the sales funnel to identify real insightful streams from white noise. Looking at each step of the process and the drop-offs can give you valuable action points. For a website, this means looking at analytics. Trace behaviors, identify entry points, evaluate bounce rates, and pinpoint drop-offs.
Create a multi-disciplinary team to generate working knowledge from data. You don’t need a data scientist on site, there are enough vendors that offer Big Data analysis packages. It is more important to make sense of their findings by relating it to your business context, as advised by specialists in web development from Iflexion. Work with user experience and user interface experts to create different layouts. Test each design against user personas. If possible, dynamically adjust your page for each user.
Look at the Context
Context is also important for the client, understanding their situation helps your company create a unique experience that is relevant to their particular circumstances. The tool to be used here is sentiment analysis, an approach that scans the information put by the client on social networks, either related to the brand or in general and configures the website accordingly. Imagine a person who is a fan of a particular TV series and uses hashtags linked to that in their daily conversations. Priming the site with fan merchandise, if available, increases the chances of sales.
The most radical change Big Data will bring in website personalization is the transformation and gradual elimination of the search function. This has already become a fraction of what search queries were before. From complex SQL database interrogations, now just typing a few characters in the omnisearch box yields relevant results. As Big Data creates more relevant recommendations, users will be presented with what they need right away. This includes not only the solution to their primary pain, or the product they needed, but also accessories or related information, making the search box redundant.
One size fits all websites were good enough in the year 2000, but are no longer an option today. The only way to remain relevant is to adjust your content dynamically to the user. The competitive advantage will belong to those companies which transform the pull models based on a user’s search into push models created from the user’s profile.
It is a scary thought that the Internet is slowly turning into a psychic with a crystal ball, but it is reality and considering human nature, usually convenience prevails to security concerns. Otherwise people would not have 123456 as a password anymore.
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