Introduction. In this post, I’m exploring the usefulness of personas in digital analytics. At Qatar Computing Research Institute (QCRI), we have developed a system for automatic persona generation (APG) – see the demo. Under the leadership of Professor Jim Jansen, we’re constantly working to position this system toward the intersection of customer profiles, personas, and analytics.
Three levels of data. Imagine three levels of data:
- customer profiles (individual)
- personas (aggregated individual)
- statistics (aggregated numbers: tables and charts)
Which one is the best? The answer: it depends.
Case of advertising. For advertising, usually the more individual the data, the better. The worst case is the mass advertising, where there is one message for everyone: it fails to capture the variation of preferences and tastes of the underlying audience, and is therefore inefficient and expensive. Group-based targeting, i.e. market segmentation (“women 25-34”) performs better because it is aligning the product features with the audience features. Here, the communalities of the target group allow marketers to create more tailored and effective messages, which results in less wasted ad impressions.
Case of design and development. In a similar vein, design is moving towards experimentation. You have certain conventions, first of all, that are adopted industry-wide in the long run (e.g., Amazon adopts a practice and small e-commerce sites follow suit). Many prefer being followers, and it works for the most part. But multivariate testing etc. can reveal optimal designs better than “imagining a user” or simply following conventions. Of course, personas, just like any other immersive technique, can be used as a source of inspiration and ideas. But they are just one technique, not the technique.
For example, in the case of mobile startups I would recommend experimentation over personas. A classic example is Instagram that found from data that filters were a killer feature. For such applications, it makes sense to define an experimental feature set, and adjust if based on behavioral feedback from the users.
Unfortunately, startup founders often ignore systematic testing because they have a pre-defined idea of the user (à la persona) and are not ready to get their ideas challenged. The more work is done to satisfy the imaginary user, the more harder it becomes to make out-of-the-box design choices. Yet, those kind of changes are required to improve not by small margins but by orders of magnitude. Eric Ries call this ‘sunk code fallacy’.
In my opinion, two symptoms predating such a condition can be seen when the features are not
- connected to analytics, so that tracking of contribution of each is possible (in isolation & to the whole)
- iteratively analyzed with goal metrics, so that there is an ‘action->response->action’ feedback loop.
In contrast, iterative (=repetitive) analysis of performance of each feature is a modern way to design mobile apps and websites. Avoiding the two symptoms is required for systematic optimization. Moreover, testing the features does not need to take place in parallel, but it can be one by one as sequential testing. This can in fact be preferable to avoid ‘feature creep’ (clutter) that hinders the user experience. However, for sequential testing it is preferable to create a testing roadmap with a clear schedule – otherwise, it is too easy to forget about testing.
Strategic use cases show promise. So, what is left for personas? In the end, I would say strategic decision making is very promising. Tactical and operational tasks are often better achieved by using either completely individual or completely aggregated data. But individual data is practically useless at strategic decision making. Aggregated data is useful, e.g. sales by region or customer segment, and it is hard to see anything replace that. However, personas are in between the two – they can provide more understanding on the needs and wants of the market, and act as anchor points for decision making.
Strategic decision aid is also a lucrative space; companies care less about the cost, because the decisions they make are of great importance. To correctly steer the ship, executives need need accurate information about customer preferences and have clear anchor points to align their strategic decision with (see the HubSpot case study).
In addition, aggregated analytics systems have one key weakness. They cannot describe the users very well. Numbers do not include information such as psychographics or needs, because they need to be interpreted from the data. Customer profiles are a different thing — in CRM systems, enrichment might be available but again the number of individual profiles is prohibitive for efficient decision making.
Conclusion. The more we are moving towards real-time optimization, the less useful a priori conceptualizations like target groups and personas become for marketing and design. However, they are likely to remain useful for strategic decision making and as “aggregated people analytics” that combine the coverage of numbers and the details of customer profiles. The question is: can we build personas that include the information of customer profiles, while retaining the efficiency of using large numbers? At QCRI, we’re working everyday toward that goal.