Expert Interviews: Data-driven Customer Experience Insights by Marc Preusche (DEPT)
Imagine you can inspire every single user of your website with relevant content. You only show him or her elements of your website that really interest him and are relevant in his current situation – without prior interaction with your website and even though you don’t know him. At the same time you act 100% GDPR-compliant, because personal data is not necessary!
That sounds impossible? It is not! Exactly this is enabled by Situationalization, one of the most innovative methods to individualize and optimize your digital customer approach. We will explain what situationalization is, what advantages it brings for your business and in which areas it is already being used successfully.
You are probably asking yourself by now: “How exactly is this possible?” – In order for you to really understand situationalization and its central assumptions, we first want to shed light on the concept of the situation and thereby prove to you: The current situation, in which a visitor to your digital channels finds himself in, allows many conclusions to be drawn about his specific needs.
A situation is a “series of circumstances in which one finds oneself” or a state.
In addition, the situation often also describes the place or the surroundings.
In the digital context, the situation of the user (also called user situation) is defined by factors such as his location (city, country or region), the device and browser used, the referrer (the page he came from), the current year or time and weekday of his access as well as the weather. This information is called situational data.
But what does this data have to do with the needs of the user? – The influence of the situation on shopping behavior may not sound intuitive to you at first. But think about your own shopping habits in the supermarket. If you go to the supermarket on a cold day in December at 8 a.m. on a Monday morning, you quickly buy your breakfast and a hot coffee. In July, on the other hand, you’d rather buy a cold Coca Cola or an ice cream. On Friday evenings you might get a bottle of wine and chocolates for your date and at noon on Saturdays you do all your weekly shopping and are possibly more open to browsing for inspiration.
You are always the same person, but in different situations you behave differently and have varying interests. The corresponding usage/user situations can be recorded and made usable in the digital context with the help of situational data: The device and browser used, for example, say something about your personality; time and origin of your access say something about your current needs. The following graphic helps to better understand the method.
This knowledge can be used profitably for the individual optimization of your digital channels: Based on the situational data, digital touchpoints (online shops, apps, news portals, etc.) can be individualized and continuously optimized for each individual user. Based on the current users’ needs, which can be derived from the situational data, different content can be presented to each individual user – individually and situationally relevant. This requires disruptive technologies (e.g. the ODOSCOPE platform) and intelligent real-time analysis methods such as correlation or prescriptive analyses. Only this way, the situational data can be evaluated and made usable at the very moment the page is loaded. This method is called situationalization.
Situationalization is a method for the optimization of digital channels, which enables businesses to display each user the most relevant content with the highest conversion or purchase probability in real time.
Does situationalization contradict personalization efforts, which have been used by companies for years? Definitely not. Both approaches can help companies with conversion rate optimization. Moreover situationalization helps to eliminate the weaknesses of personalization.
Personalization holds a big dilemma: Every user always wants to see the most relevant content in an online shop, news portal or app. And, of course, companies also want to display the most relevant content in order to keep users on/in their website/app and increase conversions (purchases, ad clicks, etc.). But most websites unfortunately know only 20% of the users before the first click – so personalization alone is quite inefficient. There are many reasons for this: first-time visitors, GDPR, script blockers and much more. Therefore, personalization can usually only be used for 20% of the users. 80% of the users receive a website according to the “one size fits all” principle and bounce more often.
Personal data serves as the basis for personalization. Their usage requires an opt-in by the persons concerned. Since the GDPR, many users have become significantly more sensitive to the disclosure of their data, making the opt-in process a higher hurdle for many companies. The user experience suffers as a result!
Situationalization solves precisely this problem: The situational data used for this purpose does not require an opt-in and will be sent for every session. Furthermore, data such as device, time, date, referrer or anonymous IP do not allow any conclusions to be drawn about the identity of the visitor. Who exactly the person sitting in front of the screen is, is completely irrelevant for situationalization. This innovative method is fully GDPR compliant and allows you to optimize your digital channels for 100% of your visitors.
But how can the two methods be combined?
Although both methods have many differences, they can still be combined very well. This allows the advantages of personalization (precise data on purchase history, user behavior, individual interests, etc.) to be combined with the potential of situationalization. The combination of the two methods delivers great results and addresses each individual user as individually as possible.
A good example of this is the individualization of product recommendations using a combination of the two. In this way, the recommendations of Asambeauty, for example, can shine by recommending the optimal products for everyone. These are calculated for each individual visitor in his current situation – making use of both situational data (in the example: operating system, referrer, weekday and weather) and personal characteristics (in this case interests: skin care or hair care). For example, a young woman entering asambeauty.de on a Sunday evening via Instagram in search of a trendy face cream will be recommended completely different products than a user who enters the platform on Thursday afternoons via Google and clicks on hair care products.
Situationalization by ODOSCOPE
The ODOSCOPE platform makes exactly this possible: With its help, both personal and situational data from different data silos can be merged. Classic examples of such silos include systems for Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) or Product Information Management (PIM).
The most important differences between the two methods for optimizing the user and customer experience can be found in the following table.
|Online shops know only about 20% of their users at first glance.||Online shops know the situational data of 100% of their users before the first click.|
|GDPR makes personalization considerably more difficult, especially with regard to 3rd party data.||Situationalization is 100% GDPR compliant and requires no opt-in.|
|Personas are usually formed on the basis of historical purchases and are relatively rigid.||Situation-based personas develop with every click in real time and therefore change depending on the situation.|
|Personalization does not take into account that even known users have different buying behaviors – depending on their particular situation.||Situationalization also takes into account the current situation of known users and automatically adapts the digital channels.|
Situationalization became possible only through the evolution of digital analysis methods. In the context of situationalization, automated decisions about the placement of content and elements of digital channels are made and implemented in real time – i.e. within a few milliseconds while loading a page.
Before such high-tech software and hardware solutions were widespread, data was often only available with a time difference between their collection and evaluation. Until recently, large amounts of data could only be combined and evaluated overnight with classic Business Intelligence (BI) solutions. In concrete terms, this means that today’s data could only be used profitably tomorrow. In summary, BI only enables a review, i.e. an analysis of historical data.
Thanks to the further development of Business Intelligence into a disruptive Operational Intelligence (OI) technology, data can now be collected and evaluated in real time. OI is a novel technology that allows an unlimited number of rapidly changing live data sets to be stored, updated and analyzed. This is made possible by the In-Memory Data Grid (IMDG), an innovative database structure that sits completely in memory instead of on the hard disk and is distributed across several servers. Therefore it is possible to perform hundreds of thousands of data updates per second.
Based on this real-time analysis of large amounts of data, Operational Intelligence is also able to make data-based decisions within milliseconds. Here, too, the classic BI is not enough: Because it usually only makes it possible to uncover connections in the past – conclusions and forecasts about the future must then be made by human analysts. The solution is präskriptive Analysen, which calculate the best alternatives for action and implement them directly.
This requires a high computing power of the servers for the execution of self-optimizing algorithms. The prescriptive analyses of the algorithms answer the question: “What should ideally be done to achieve a certain goal?” and initiate the necessary measures – if desired, even fully automatically. That way you can achieve and even surpass your goals with the help of situationalization!
The process of situationalization
Data collection and tracking:
Companies continuously collect usage and interaction data in different data silos (e.g. web analysis tools, PIM, ERP).
Consolidation of the data:
The raw data of these silos is regularly imported into a central platform and forms the historical database for situationalization.
As soon as a user accesses the digital touchpoint, his relevant situational data is transmitted. These are recorded and compared with the existing database. Using correlation-based real-time analyses, the so-called statistical siblings of the current visitor can be determined for each individual page element – i.e. the historical users who are most similar to the current visitor (real-time clustering). In this way, a user is perceived in all its versatility and addressed perfectly.
Prescriptive Analyses & Self-Learning:
Prescriptive analyses calculate in real time which page elements are most relevant for the current user. The calculated page elements with the highest conversion probability are automatically displayed – in less than 20 ms. All reactions of the user to the displayed content are then stored anonymously and integrated into the database. Thus, the analysis can be continuously refined and optimized – and also the database is constantly updated and improved.
The situationalization of content, products and information offers some advantages over other optimization methods such as personalization.
Important advantages of situationalization are
Situationalization is revolutionizing the user experience in many industries and at the same time also enables significant added value for (digital) providers. Customer and company satisfaction therefore go hand in hand, because situationalization enables a win-win situation: the relevance for users increases and companies can achieve significant uplifts in their KPIs.
|E-Commerce||Display individually relevant products, search results, recommendations and much more.||Conversion and revenue uplifts of 20% and more|
|Publishing||Display of individually relevant content such as articles, videos, picture galleries, etc.||Significant uplifts in retention time and page impressions and thus higher advertising revenues|
|Games||Display of better items depending on situations||More sales of digital items, longer retention time, more advertising revenues|
|Out-Of-Home Advertising||Advertising on truck rear walls depending on the situation, e.g. the nearest road service area like in Germany done byRoad Ads)||More relevant advertising messages, higher awareness|
|IoT||A cross-linked refrigerator could order other products depending on the situation, e.g. sparkling wine for New Year’s Eve.||Adjustment of purchases to the season, higher customer satisfaction|
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