In this article, we show how you can create buyer personas automatically for your (and/or your competitor’s) digital assets such as websites and mobile applications. You can use the deeper insights and intelligence thus gained to market, sell and serve your customers better.
A foundational step for defining and delivering exceptional digital customer experiences for a business is a deep understanding of who its digital users are, their intent and their journeys.
Traditionally, aggregated data from web/mobile analytics tools (such as Google Analytics, Adobe Analytics, Mixpanel, etc.) have been used to understand digital users.
Utilizing such data for deep understanding of users and their behavior is often challenging for the following reasons:
By definition, user/buyer personas are fictional representations or composite views of audience segments based on various factors. They include inputs from customer demographics, behaviors, motivations, goals, data of existing customers, data from competitor’s customers, research, etc.
In an era where privacy of users is at the forefront, as is evident through ad blocker trends and increasing privacy related laws across the world, focusing on group behavior using fictional representations such as personas, rather than on actual individuals, enables business owners to meaningfully leverage data from their digital user segments to understand their jobs/tasks to be done, while honoring individual privacy.
Personas are used by organizations to market, sell and serve customers better. Humanized digital views using personas help businesses understand their digital users and their interactions better, deliver exceptional customer experiences and power growth.
Personas help businesses with better customer relationships, marketing strategies, usability and consistency.
Some of the functional roles (with use-cases in parenthesis) that data-driven personas can be used by, include:
Specifically, in marketing, personas can be used to improve a variety of use-cases, such as:
Personas are currently created primarily by qualitative methods such as user research that involves interviewing or surveying users, prospects and/or customers. While such methods provide depth of insights such as motivations and challenges/pain points, they are neither easily scalable to millions of data points nor amenable to frequent, near real-time updates. As a result, persona creation tools today (such as Hubspot’s Make My Persona, Userforge, Xtensio, etc) are primarily limited to templates or presentation/visualization/collaboration tools that rely on inputs from the user surveys/interviews.
A viable alternative is to use quantitative methods that enable frequent updates and data inputs at scale as complementary means to creating user/buyer personas. In an era of rapidly changing user behavior, “live” personas that are updated frequently are needed to understand shifts in consumer behavior, their evolving needs over time and detect anomalies/changes as they happen.
Such quantitative data-driven approaches enable rapid generation with frequent updates of personas and use of data at scale. The resulting humanized views can help answer simple questions such as:
Further, as machines are used to crunch data to create personas, industry specific insights can also be derived using deep libraries of domain specific intent.
Delve AI offers the world’s first software platform to automatically generate buyer personas for a given website/mobile application, business or industry from digital data. Personas can often be generated in minutes.
Compared to the conventional means of understanding users using surveys/interviews and/or analytics, Delve AI leverages advancements in artificial intelligence and machine learning to deliver insights that are deeper, easier, and more human.
Sources of digital data used as input to generate personas include:
Data from such sources are typically provided to the platform via ongoing programmatic access (using API/feed integrations, for example), as dimensions/metrics and may include historical/projected data.
Further, input data may optionally be filtered by one of many attributes to create narrower segments, including:
A single persona can be generated for the entire audience (i.e. without segmentation). A preferred approach though is to create personas segment-wise with:
Figure 2 shows the detailed view of a sample persona generated. Attributes of persona generated, as shown, can either be inferred or be directly abstracted based on data. Attributes inferred and displayed may include:
Attributes analyzed and displayed when generating personas may include industry specific insights based on views/searches or other interactions. Figure 3 shows examples of inferred attributes such as apparel type and color for Apparel & Fashion industry.
Figure 4 provides an outline for a solution that can be used to generate personas automatically from digital data.
As shown, depending on the sources of data used to generate personas, such a platform regularly:
Enrich: Augment data for deeper user context, with external/generated data sources/models, such as:
Learn: Inferred insights using machine learning may include:
Segment: Behavioral attributes used for automated segmentation may include:
Humanize: Abstractions for human assimilation and follow-up may include:
Another variant may include a step for visitor group identification before generating personas, based on profile, intent and behavior. As shown in Figure 5, users may be automatically classified into one or more of the following groups:
|Traditional analytics-based solutions||Persona generation solutions|
|Data transformation||Raw data||Augmented/enriched data|
|Process||Aggregate and tabulate||Learn and abstract|
|Representation||Attributes/dimensions + metrics||People|
|Visualization||Dashboards using numbers||Personas/journeys|
|Goal||Short term trends/movement||Longer term strategic insights|
Finding buyer personas is certainly not easy. Currently, they are created primarily using qualitative methods based on user research. This can involve interviewing or surveying users, prospects and/or customers. While such methods provide depth of insights such as motivations and challenges/pain points, they are neither scalable to millions of data points nor amenable to frequent updates. As a result, persona creation tools today are primarily limited to templates or visualization tools that rely on inputs from the user surveys/interviews. Accordingly, improvements to the automatic creation of buyer personas are desired.
In this article, we have shown how personas can be generated automatically for your and your competitor’s business. With a platform like Delve AI, no code is needed and results are often available in minutes. You can use these personas to serve your customers better and grow your business.