Using Analytics to Create Personas and Win the Moments That Matter

Relationships—professional or otherwise—don’t happen overnight. If you’re lucky, you build a foundation with someone overtime through meetings, emails, or LinkedIn. Each touchpoint, you gather bits of information, provide value to the other party, and form long-lasting relationships.

In a sense, this is exactly how your brands build lifetime relationships with their customers. The foundation starts with a solid analytics architecture, which enables brands to create unified customer profiles. These profiles gather data from various systems and touchpoints to form a single view of an individual. With a platform like Adobe Analytics, brands can stitch experiences across devices and channels, perform real-time marketing activities, and generate advanced reporting—all with the goal of adding value to the customer relationship.

Download the Complete Customer Experience Report to learn how to create an unforgettable experience that will engage shoppers and improve revenue.

The next step in creating an unforgettable experience is leveraging unified data across online and offline customer touchpoints to craft personas and inform personalization efforts. The datasets housed in customer data platforms (CDPs) allow analysts to create data-driven insights that brands can use to improve the personas created by marketing teams and the experiences created by content teams. There are two main hurdles brands face when taking this next step in the personalization journey:

1. Tying together data-driven personas created by the marketing team with journeys surfaced by the analytics team.
2. Prioritizing the explosion of content needed to create personalized experiences.

Hurdle** 1: Creating Data-Driven Personas

There are typically two ways marketing departments interact with analytics teams to develop personas. Option one is the analytics team verifies existing personas and defines targeting rules to reach the persona through digital channels. Option two is involving the analytics team early in the process to help the marketing team create personas from scratch. From our experience of helping brands develop personas, the second option is the preferred route. But let’s dive into each scenario.

Refining Existing Personas

Brands often have long-standing personas they or external agencies created. Brands or partners typically give them whimsical names and broad descriptions that cover key attributes and buying behaviors. Specific targeting rules rarely accompany personas. An analyst would need to gather all personas and create business rules to understand the size of the group and how to target them.

With broad persona definitions, analysts can develop targeting rules that are restrictive enough to effectively personalize the experience for a group that shares common attributes, while large enough for a brand to invest in pursuing those users.

For example, “Sarah the Nonfiction Loyalist” is an account holder at an online book store with an interest in nonfiction. An analyst translates the persona as someone with a customer ID and purchased three nonfiction books in the past six months. Using the quantity and the time frame, analysts identify each “Sarah Nonfiction Loyalist.” Based on the number of users in this group, they determine its percentage of the total customer base and the percentage of revenue this group generates. If it’s 15% of the customer base and produces 40% of revenue, it would be well worth pursuing.

Creating New Data-Driven Personas

Ideally, analytics is involved in crafting personas for personalization from the start. Rather than bringing a preconceived persona tangibly to a digital environment, they can create personas based on insights and advanced analytics. In this scenario, an analyst can pull together all data sources that marketing would want to use in defining personas and employ algorithmic efforts such as clustering.

Clustering uses machine learning to identify groups of customers with similar attributes and behavior. Maximizing intergroup similarities and intragroup differences forms tightly woven personas that relate to personalized content. Layering analytics with user research and marketing knowledge results in well-informed persona definitions with pre-established targeting rules. This ensures that marketers can deploy relevant marketing campaigns to clusters based on shared interests and actions, as well as forecast the size of clusters and their impact on revenue. A data management platform like Adobe Audience Manager will make sense of your brand’s data and automatically identify new audiences to target and how to convert those customers.

Hurdle** 2: Prioritizing Content for Personalized Customer Experiences

Winning brands understand that the business goal of personalization is to tailor the experience for a user (or group of users) that will change the outcome. With that in mind, prioritizing which personas to develop content for should be prioritized by the group’s likely response to a personalized experience. We’ve identified two efforts to prioritize efforts.

First, many brands adopt a simple acronym for persona development and prioritization: ADAMS or Accessible, Differential, Actionable, Measurable, and Substantial. Analysts use these questions to qualify personas.

• Accessible: Can you reach each group effectively and on what channels?
• Differential: Are the groups truly differentiated, and if you change the experience, will it really alter the outcome?
• Actionable: Do you have products and content that meet the core needs of each group?
• Measurable: How can you measure the effectiveness of your efforts?
• Substantial: Is the group large enough (in terms of size or revenue share) to warrant the expense of personalization?

Second, once you identify each persona, shift attention to the journeys each of these personas typically takes. Keep in mind that some moments in the customer journey simply matter more. Using analytics to find influential moments and prioritize those efforts will help you excel in the moments that disproportionately influence outcomes.

For example, many brands use data science techniques, like propensity models, to identify whether or not a particular user is likely to buy within a specified time window based on their persona affiliation and user-specific customer data. Marketers use that information to optimize onsite interactions, marketing campaigns, email frequency, and promotions. These efforts maximize marketing return on investment by influencing the influenceable during the consideration phase of the journey.

In our upcoming blog, we will discuss how brands can utilize cross-channel marketing orchestration to present a single and compelling message to personas at their unique moments of influence.

If you’d like to speak to a customer experience expert at Blue Acorn iCi, feel free to reach out to use here. For more tips and information about customer experience, subscribe to Blue Acorn iCi’s Monthly Digital Digest here.

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