How Web Analytics Can Enhance the Customer Experiences


Marketers and analysts have utilized analytics to improve customer experience (CX) for so many years that it’s tempting to assume we’ve seen it all. However, the pandemic-era digital acceleration has shown there’s room for organizations to get more CX value from their data insights. In fact, Forrester predicts firms that make data-driven decisions will be three times more likely to outperform their competitors in terms of CX improvement.

Although the greatest benefit comes from proactive analytics, we often see organizations using web analytics for CX reactively. In other words, most companies are looking at site structures, new features, and content after they’re launched and analyzing how customers respond. Typically, we see companies using two types of analytics tools for reactive CX evaluation and improvement.

Traditional analytics tools still have important CX roles

The first is classic clickstream analytics combined with optimization testing. Most organizations track site visitors’ activity to learn which paths are most popular, what content will encourage a conversion, and which parts of the site experience have the highest abandonment rate. When the data reveals a point of friction, A/B testing can identify better alternatives. The second type of analytics tools we see used for reactive CX assessment are visual-friendly optimization tools, such as session replay and heat mapping tools. These are great supplements to the traditional web analytics platforms.

These tools have their place in a CX improvement program. Now, though, as consumers have much higher expectations for personalization and ease of use than pre-pandemic — and as rules around third-party cookies and customer data are changing — it’s time for organizations to get proactive and keep iterating on their CX to get the most value possible from their analytics.

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New approaches for proactive CX enhancement

One emerging area of CX is content intelligence analytics. Content intelligence allows for precise tagging to automatically surface specific content for individual customers or consumer segments. For example, a luxury shoe company might have granular tags for brands and styles as well as customer lifestyle, geographic region, and other attributes.

Then, when a “sneakerhead” visits the site looking for the latest designer basketball shoes, the brand can display new sneaker-related content rather than generic promotions. This strategy requires tagging products and content as well as customers, using machine learning (ML) to enrich those content attributes and create smart segments that evolve as customer interests change.

A customer data platform (CDP) is also essential for content intelligence analytics and other proactive analytics solutions. A well designed and properly integrated CDP can unify customer data from across the company and its web properties to create detailed customer profiles that allow for precise personalization based on individual attributes, regardless of the channel or platform the customer uses. This technology allows for real-time suggestions of next best steps in the customer journey, such as displaying hyper-relevant content.

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Creating a proactive analytics operation

Together, CDP technology, ML solutions, and a robust content intelligence strategy can help companies leverage their data and generate more. Organizations that are ready to start utilizing these capabilities should map out their goals and follow implementation best practices:

  1. Set up a digitally focused analytics team with capabilities to leverage CPD and content intelligence platforms
  2. Empower analytics teams with proper CX measurement tools and support to leverage insights and design process improvements. When the analytics team can help the site retain more visitors, convert more visits, or generate more revenue, they can build on those wins. Empowerment also means allowing analysts to own the process end-to-end, rather than breaking dashboards, tag management, and other steps into separate roles, which simply recreates the silos that most analytics programs try to eliminate.
  3. Enable executive buy-in. When company leaders act on analytics teams’ findings, everyone can see the impact of leveraging data. That contributes to a more data-centric, proactive approach across the board and motivates analytics teams to keep delivering top-quality insights.
  4. Determine KPIs — ideally four or five metrics, such as revenue, conversion rates, and net promoter scores. Ongoing KPI monitoring will show whether CX updates are trending in the right direction and help determine the change in overall customer satisfaction and loyalty. It’s a good idea to review those metrics and adjust them if necessary on a quarterly basis for a clearer CX picture.

Investing in the technology and talent for better CX analytics can help companies outperform their competition and strengthen customer loyalty. They can also deliver insights that enable organizations to take even more new approaches in the future as customer behavior, expectations, and preferences continue to change.

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