How Data Aggregation Helps Multi-Brand Companies

AI & Machine Learning|[wtr-time]

How Data Aggregation Helps Multi-Brand Companies

BlueConic customers understand they must recognize customers at every journey stage and orchestrate individualized experiences across lifecycle stages if they want to own the relationship with them and achieve their desired business outcomes.

Translation: They know they must leverage the single customer view they build in BlueConic to deliver a mutually beneficial customer experience and accelerate business growth.

But what about BlueConic customers with multiple business units, each of which has their own BlueConic tenant? How can these multi-brand companies gain a single view of their shared customers to improve each brand’s customer engagement strategy?

The answer is Aggregated Data Manager (ADM): BlueConic functionality that enables multi-brand organizations to sync first-party data from their ‘sub-tenants’ into ‘super-tenants.’

Why data aggregation is essential for multi-brand companies’ success today

Data aggregation is the process of unifying data (e.g., customer data) that is stored in multiple systems and sources. This merged view is then made accessible to all relevant stakeholders across an organization (i.e., specific brands and/or departments).

In the case of first-party data for prospects and customers, different systems and sources typically have their own distinct ways of identifying individuals who engage with a company.

By aggregating disparate customer identities for the same individual into a single identity in a centralized system, like a customer data platform (CDP), teams across a given business can better understand that person’s historical interactions, purchases, interests, and behaviors.

In other words, data aggregation essentially amounts to creating a single customer view.

Where data aggregation gets tricky for companies today, though, is creating this holistic, comprehensive view and making it available to multiple brands within the company.

Why would multi-brand organizations need to aggregate customer data from each individual brand’s tech stack and data environment? There are several reasons, including the ability to:

Aside from these tactical advantages to aggregating first-party data, this supercharged single view of the customer offers a strategic benefit for businesses as well:

  • Each brand can more confidently utilize their first-party data, knowing they have an even more robust and complete set of consented data for prospects and customers.

As long as this view is explicitly designed to elevate each brand’s marketing and customer experience efforts and streamline work for their growth-focused teams (not just marketing and CX, though; more on this shortly), they — along with their parent company — can realize consistently better (and scalable) business outcomes.

How ADM helps multi-brand BlueConic customers’ data-driven decision-making

Here’s how BlueConic customers with multiple brands, regions, business units, or a combination of all three use ADM to better understand and engage with their customers:

  • In BlueConic terms, data aggregation is the process of integrating data from individual-level customer profiles in multiple brands’ BlueConic tenants into a single super-tenant.
  • This super-tenant features data for unique customers (those who’ve only bought from one brand) and shared customers (those who’ve bought from at least two brands).
  • The sync of first-party data between the sub-tenants and super-tenant happens at a scheduled interval (usually once per week). When a customer exists in multiple BlueConic tenants and has a unique profile in each tenant, those profiles are consolidated into a single profile in the super-tenant.
  • When a particular customer has a profile in just one brand’s BlueConic sub-tenant, that first-party data simply carries over into the company’s overarching super-tenant.

(Discover the technical details of Aggregated Data Manager on our Knowledge Base.)

Each brand can then use this new, more comprehensive single customer view to better utilize their data. Specifically, BlueConic can be leveraged at the super-tenant level to:

  • Build and deploy AI workbench notebooks to create new profile scores (or enhance existing ones for customers), such as our out-of-the-box customer lifetime value model
  • Enable each brand to build richer segments based on those scores; create more useful dashboards (e.g., segment comparison); and glean more intricate customer insights
  • Connect new systems and sources (notably, business intelligence and data visualization tools like PowerBI) to export the new segments, insights, and/or profile scores to other components of their tech and data ecosystem, all while respecting each customer’s privacy and consent settings

These efforts at the super-tenant level lead to enriched profiles on the sub-tenant level.

Each brand can then send first-party data from the super-tenant to their own BlueConic tenants and use the augmented profiles to engage customers in smarter, more creative, privacy-centric ways (e.g., promote products and/or content that align with their most recent interests, preferences, and behavior).

What’s more, they can work in unison with other brands with whom they share customers on collaborative engagement strategies to ensure they don’t inundate customers with redundant, excessive, or ill-timed messaging or experiences.

The marketing teams for two or more brands under the same corporate umbrella can even work together to identify customer overlap as well as upsell and cross-sell opportunities for those individuals (e.g., joint holiday or seasonal promos).

single customer view

Data aggregation key to unleashing growth for multi-brand businesses

Many enterprise companies with multiple sub-brands now have a wealth of first-party data. Those with a pure-play CDP like BlueConic are able to extract substantial value from that data (see: increased revenue and/or operational efficiency).

But that data’s potential in terms of accelerating business growth for each sub-brand (and, therefore, enterprises at large) will remain limited until it is aggregated into a centralized location and made available to each sub-brand’s growth-focused teams.

Let’s consider an example of a multi-brand retail organization with BlueConic:

  • Each brand operates in its own niche retail space (e.g., one specializes in outdoor wear; another in athletic gear and accessories; and another in activewear for kids and teens).
  • At first glance, there may not appear to be significant overlap in terms of customers. But with an aggregated view of the profile data in each brand’s BlueConic sub-tenant via ADM, it becomes evident there are several thousand shared customers.
  • Aside from shared customers, there could also be shared ‘households.’ Customers who live together may have purchased items at different brands’ ecommerce and/or brick-and-mortar stores (e.g., one person buys camping attire from one brand; another person buys sneakers for their child from another brand).
  • Data that shows customers’ buying patterns, preferred store locations, online behavior, and overall interactions across brands can be sent back to brands’ sub-tenants.
  • From there, they can use this ‘bonus’ data to strengthen their predictive modeling, multi-dimensional segmentation, analysis, and cross-channel activation efforts.

First-party data collection and utilization are now table stakes for all companies today, multi-brand or otherwise. But it’s especially vital for organizations with multiple business units.

The more data brands can access and use to carry out growth initiatives and key programs, the better they can optimize their engagement efforts and realize their desired ROI.

Learn all about Aggregated Data Manager and how your multi-brand business can benefit from BlueConic. Request a demo of our pure-play customer data platform today.


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