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Are Predictive Journeys moving beyond the hype?

4 minutes, 42 seconds read

Predictive Analytics is disrupting the business-consumer dynamic. To improve engagement with their customers, organizations have begun identifying potential segments (predictive audiences) that are likely to convert with them. Modelling data to learn about the potential ‘new’ customer, their preferences and spending behaviour has already proven demonstrably higher conversion rates and lower churn rates. In fact, the market value for these types of services is expected to touch $12.4B by 2022.

As we transition into a semi-connected world supported by global IoT sensors and devices, the real-time analysis of past and future-probable events is evolving business actions more prescriptive in nature. Every touch or interaction triggered by an individual customer is a data point that is captured, stored and examined for insights. Data is an interminable asset that continues to grow exponentially while storage likewise is getting cheaper each year. With nearly infinite cloud computing and scaling it becomes much easier to process these extremely large amounts of data.

But, are customer journeys actually getting better? Are these journeys still reactive? How much of the world has moved to a predictive-first approach? and, has it really helped CXOs address their business goals? Let’s evaluate the state of real-time predictive trends that are being put to use by global enterprises. 

First, let’s look at some easily identifiable use cases that have some verifiable results.

  • Identity Resolution — understanding the individual persona consistently and accurately across -domain, -device and -channel, while maintaining stringent privacy compliance. This approach typically gives you a singular view of a potential customer. (ex: LiveRamp, Full Contact)
  • Customer Journey Data Integration — data integration transcends the siloed view of traditional web analytics. For these multiple integrations like web, mobile app, email, social media, CRM, call centre, device, etc. are essential to understand customer flow across channels. (ex: FirstHive)
  • Customer Segmentation and User Experience Recommendations — It is done using clustering models to perform highly accurate segmentation creating micro-segments and tracking each customer as they shift from one segment to the other. (ex: Lattice-Engines)
  • Personalization — It marks which marketing campaigns, channels, touches, and behaviours users are responding to, and contributing to a business outcome, using a machine learning-based attribution. (ex: Everage)
  • Lead Scoring, Prioritization & Allocation — It helps identify which leads will convert, churn and which customers will buy one or more products for a cross-sell or upsell. (ex: Mantra Labs LCA, Pardot
  • Automating Prediction & Rule Setting — Use automated machine learning for predictive modelling. Enables rapid iteration cycles. (ex: Nokia, DataRobot)

The total number of journey interactions the world over is an unquantifiable number. It is predicted, though, that there will be nearly 2MB of data created by every individual in 2020, every second. With all this data to go around, why are companies so invested in them? It’s because customer experience has become the number one marketing activity of 2019, and will continue to rank highly over the next five years. 

In fact, Gartner predicts by 2019 more than 50% of organizations will redirect their investments to customer experience innovations. For SaaS enterprises, there is a lot to gain. Research indicates CX initiatives can double an organization’s revenues within 36 months, and this extra share will come from the customer’s wallet. Good CX will create real value for your customers, which means they will spend more.

According to Accenture, 87% of organizations agree on traditional experiences no longer satisfy customers. To counter this, Businesses are now investing in customer journey management. Interestingly, insurance (39%) is showing the highest adoption rates outside of retail (42%). The tech industry comes up third behind them at 7%. 

Customer journeys are orchestrated into three: Acquisition, Conversion and Growth. Majority of journeys are identified as growth journeys (64%), and typically run for nearly 34 months on average.

Has it made a difference in Experience?

Yes, and there’s data to support it.
The predictive journey allows businesses to place real-time marketing bets on the behaviour of the customer. We don’t have to look any further than the example of Netflix and its impressive predictive recommendation system. Almost 80% of the content watched on Netflix is attributed to recommendations. A robust predictive analytical engine working behind the scenes is able to perform two critical aspects of the customer life cycle: Needs forecasting and churn reduction. The system is estimated to save Netflix at least $1 billion each year in customer retention.


What about the Impact to Business Goals?

The short and long answer is yes.
According to a salesforce study, the key to building highly personalised journeys begins with predictive intelligence. The report found on average, predictive intelligence recommendations influenced 34.7% of total buys. The lift in conversion rate within the first 36 months is around 23%, which is significantly high. Imagine what 23% more in conversions can do for any business. The real value from predictive intelligence is that it gets more intuitive with time. After 36 months of implementation, there is 40.3% more influence in revenue from this technology.

Continuous Predictive Learning Model
Continuous Predictive Learning Model

For future engagements, customers want businesses to proactively reach out to them and offer them tailored products and services that will be highly relevant to their needs. On the other hand, businesses prefer to study their consumers by looking at their data under the strict regulations enforced in data privacy laws — because it will certainly avoid long term risk to their business models. The results are clear: A predictive journey is the only way forward. 

Mantra Labs is an Insurtech100 company creating AI-first products and solutions for the evolving digital enterprise. To learn more about how we are using predictive journeys to create the Internet of Intelligent Experiences, reach out to us on hello@mantralabsglobal.com

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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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