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Customer Engagement Strategies For Gen Zs in Insurance

Indian market is a multi-headed Hydra that confounds in more ways than one. Being the world’s largest democracy and the most diverse country has resulted in a level of stratification that most countries would be unable to fathom. The tiered expectations and a shift in customer demographic are pushing insurers to rework the Customer Engagement Strategies For Gen Zs.

Tier 1 customers hold businesses to an extremely high standard, often on par with global companies operating out of mature ecosystems like the UK, USA, et al.

Tier 2 customers on the other hand are more rustic in their ways of seeing but actively seek the kind of novelty and flair that their Tier 1 counterparts crave. This cohort also strikes a fine balance between modernity and tradition when it comes to customer engagement expectations, e.g. would prefer talking to a live agent instead of a bot.

Tier 3 customers continue to operate on a major time lag, i.e. fully digital touchpoints do not work and software can be a catalyst for change only insofar as they remain invisible in the interactions that Tier 2 customers have with businesses.

Use Cases:

Given the democratized access to generative AI technologies, insurers would do well to incorporate them in each and every facet of the customer experience, right from purchase, all the way to fraud detection. That being said, regional differences could be accounted for in the following ways:

Tier 1: Metro cities require a comprehensive customer experience approach that never rests. Highly personalized chatbots that operate on context, slick user interfaces that are built to minimize friction in service, and proactive communication (via reminders, automated calls, etc.) are strategies that insurance providers could start using.

Tier 2: Given the relatively less frenzied environment in Tier 2 cities, it would make more sense to devote a sizable portion of the budget towards a digitally-enabled physical office. Incorporating the usual technologies to extend reach, while also maintaining a team in these geographies would give it that added human touch that Tier 2 residents usually appreciate.

Tier 3:

For Tier 3 cities, technology ought to recede into the background and do all the legwork that humans did earlier. A more committed implementation of predictive analytics would be needed as Tier 3 residents don’t have as much of a digital footprint as their Tier 1 and Tier 2 counterparts do. 

Phygital v. Digital

Ensuring stickiness and retention amongst Tier 1 GenZ customers will require a domineering digital play. Establishing multiple touchpoints across popular and emerging platforms would be a non-negotiable strategy. 

Tier 2 customers on the other hand would do well with a digital play with a slight mix of physical touchpoints which could include a singular office in the arena, primarily for servicing and support activities. Customer engagement would require a localization effort, in terms of language as well as distribution.

Tier 3 GenZ members would require a full-fledged phygital strategy where the role of digital engagement would purely be limited to the realm of convenience, by way of sharing documents, essential information, etc. Establishing reasonably spacious offices, coupled with outdoor advertising would be the only way to be ‘taken seriously’ in such geographies.

Next-gen Engagement Models

Both AdTech and MarTech are evolving at a rapid pace, to the point where the cost of implementing experiential engagement strategies is decreasing with each passing year. Audiences in Tier 1 areas will be more receptive to AR/VR engagement that can allow Insurers to integrate physical locations with a slick, digital experience. 

The current ecosystem could even allow for engagement strategies built on the metaverse. These, however, will need to be restricted to upscale commercial/residential areas for maximum effectiveness.

Tier 2 and Tier 3 geographies, on the other hand, are not yet primed for such innovations. The balance between physical engagement strategies, i.e. having a team on the ground, hosting events, and actively reaching out to younger customers in collegiate environments ought to be in favor of the physical, with digital-only being an enabler.

There can be no one size fits all customer engagement strategies. The only way forward would be to carefully select an engagement mix and deploy it dynamically to get the attention of GenZ customers.

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10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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