18 - 03 - 2022

Collecting data is easy. Activating that data, on the other hand, is hard.

“For most companies, the problem isn’t that they don’t have enough data,” said guest speaker, Forrester Research Vice President and Principal Analyst Brandon Purcell in a recent webinar. “They’ve spent years collecting massive amounts of customer data, but they’re unable to pull it together to create that elusive 360-degree view of the customer — that found asset upon which to build analytics.”

Basically, companies are inundated with behavioral, social, mobile, and environmental data. But they need the right tools and strategies to actually make sense of that information. Only then can they use it to launch personalized campaigns, create better customer experiences, and build retention and loyalty.

“In this age, increasingly empowered customers have complete transparency into their different buying options,” Purcell said. “So the onus is on businesses to understand these customers and anticipate their needs to win, serve, and retain them.”

To help marketers better navigate what Purcell calls “the data doldrums,” we’ll outline three key challenges of data activation — and the solutions you need to overcome them.

Let’s take a look.

“[Companies] have spent years collecting massive amounts of customer data, but they’re unable to pull it together to create that elusive 360-degree view of the customer — that found asset upon which to build analytics.”

Brandon Purcell, Forrester Research Vice President and Principal Analyst


Challenge #1: Creating an organizational model

It’s tough to activate data if you don’t actually know who’s responsible for customer analytics within your organization.

As Purcell noted, it often starts with the marketing team. But over the last few years, many other departments have begun using customer analytics as well, such as sales, product, and customer experience teams. So, how do you create a seamless structure that incorporates each department and lets information flow smoothly?

Forrester identifies three organization models for customer insights:

  • Dedicated or distributed model. Used by 42% of companies, this model allows each team to be separately responsible for its data strategies and decisions.
  • Shared service model. Used by 32% of companies, this model establishes a centralized team for all data decisions.
  • Center of Excellence model. Used by 25% of companies, this model combines a centralized team with dedicated liaisons for each department.

Solution: Build a Center of Excellence

Forrester found that companies with leading and matured customer insights operations use a Center of Excellence model.

A dedicated model leads to redundancies and inconsistencies, since data is split across teams. A shared service model leads to bandwidth and expertise issues, since all decisions fall on one general team. But a Center of Excellence model strikes just the right balance.

“You have a centralized team that’s focused on overall enterprise analytics, but then you have these liaisons who actually sit within the lines of business,” Purcell said. “So every day they are living and breathing marketing’s challenges, trying to address them with analytics. And they’re able to leverage that centralized team, but they also retain that domain expertise.”


Challenge #2: Identifying the right data sources

According to Forrester’s State of Customer Analytics survey, respondents said their top data data challenges were:

  • Ensuring data quality from a variety of sources (41%)
  • Accessing data from a variety of sources (38%)

Again, companies have no shortage of data sources to choose from. But they must be able to identify the sources that make most sense for their analytics goals.

For example, survey respondents also identified their top data sources as:

  • Campaign response data (69%)
    • Ex: clicks, conversions
  • Demographic data (67%)
    • Ex: location, income
  • Transactional data (64%)
    • Ex: product purchase history
  • Anonymous behavioral data (64%)
    • Ex: website activity, content consumption data
  • Digital data (62%)
    • Ex: web analytics

So, 69% of respondents are analyzing campaign response data, but that means 31% aren’t. Meanwhile 33% aren’t using demographic data and 36% aren’t looking at transactional data. As Purcell said, “It just goes to show that there’s a lot of room for improvement among companies in the types of data and the amount of data that they use for customer analytics.”

Solution: Use diverse types of digital data sources.

Leaders in customer analytics use more data sources — an average of 11 compared to just four, which less mature organizations use. They’re also using more diverse types of data, including digital data from social channels and real-time, unstructured sources like online communities, where customers often post about their experiences.

“[This data] gives you a sense of why customers are interacting and transacting in the way that they are,” Purcell said. “Are you delivering on the experiences that they expect? These are increasingly important types of data, especially in the age of the customer when it’s all about meeting and anticipating customers’ needs.”


Challenge #3: Navigating data privacy issues

Compliance should be at the top of marketers’ minds with new data privacy features from Apple and Google, as well as government regulations like GDPR and CCPA. The EU even recently introduced a Proposal for Regulation of artificial intelligence.

It’s evident that businesses have to stay up to date with data privacy rules or risk losing customers and revenue.

“Customers increasingly have to opt into their data being used by companies,” Purcell said. “And any time they don’t opt in, that data goes away or there’s the right to be forgotten in Europe with GDPR.”

Solution: Tie data access to your value proposition

If businesses want to keep collecting data from customers, they have to earn that right. And they can do so by delivering a strong value proposition in exchange for personal information. They can then use that data to build even better and more valuable experiences for future customers.

Businesses also have to consider how they might use data to fuel AI efforts. As Purcell said, “Companies have to start thinking very carefully about the type of customer data that they use in AI and whether or not the outcomes from that model are fair. Because if they don’t, the price could be really high.”

Despite these challenges, it’s important that marketers don’t let the data doldrums get them down. Instead, they can view each obstacle as an opportunity to rewire their customer data strategies and find new, innovative ways to win and retain their audiences.

Why not watch the webinar?

Adapt And Thrive With Customer Analytics.