Control network access by integrating customer understanding and digital analytics

Digital analytics is no longer just for public experiences like websites. Modern organizations are adopting an integrated approach to all interactions, enriching both customer profiles and aggregated behavioral analytics.

However, where more mature organizations thrive is extending the same thinking to different areas of the overall user experience. For example, companies like American Airlines and Amazon are looking at different interaction models to better understand what, why, when and how people are doing.

This strategy he does two things.

First, it helps these companies understand how many customers are using all platforms and experiences to support their overall business goals. This information helps in decision making and prioritization.

Second, this strategy helps modern organizations understand how individual users are grouped together so that companies can customize experiences based on behavioral data and attributes.

Plan for prescriptive digital analytics

While not necessarily new, as the enterprise-wide deployment of machine learning (ML) and artificial intelligence (AI) platforms eclipses digital platform integration of these models, both of these ways of using digital data It is currently attracting attention. ML and AI present great opportunities for many companies to mature as they acquire data.

Too many organizations focus on KPIs and metrics instead of what they want to learn. In other words, they are descriptive rather than prescriptive. Before building a relational model based on a taxonomy of questions, it's a good idea to reverse the planning process to focus on the questions you want to answer.

For example, consider the following questions.

• What features do your customers use most often?
• Are your customers likely to use features in combination? what are you?
• How often do customers use each individual feature?
• What are the most commonly used attributes for each major feature flow?

but specifically focused on features used within the product. This level of planning shifts the paradigm away from "what did the feature do". The answers to each question support different business teams across the organization, facilitating information sharing and data normalization to support the organization as a whole. Most importantly, you can take advantage of this more dynamic way of planning the collection and use of modern digital analytics across your organization. The user experience team will have a better understanding of the overall usage flow associated with each objective than a single point in time. How can I purchase a ticket through your website or mobile experience?

Does it make a difference if I'm logged in or out?

What about using apps on mobile phones, laptops, and tablets? Data architects can get more data to inform models and adjust leverage as needed to increase profitability. Drive high performance. For example, automatically adjusting the price of a product in real-time can influence purchasing behavior and likelihood of a sale.

Merchandising

Teams gain a better understanding of inventory management. This helps you better plan your purchases with vendors, understand the impact of margins on dynamic pricing, and identify other buying signals. These features help traders better predict sales and work with management to ensure the success of the investor's business.

Technical His lead will be better able to prioritize work related to value-based deliverables while executing releases to maximize team utilization. Also, start preparing improvements related to increasing your sales potential at the category or feature level, removing waste from the experience, and making it easier for your customers to purchase your product or service.

Conclusion

If you want to be a modern analytics company that bridges the gap between customer expectations and business execution, you need to understand how to move from reactive to predictive. You can then evolve from an insight-providing organization to an action-providing organization. This level of sophistication will improve customer satisfaction and reduce customer acquisition and service costs as you begin automating relevant experiences internally and across channels. Achieving this, however, will require an evolution in the role that analytics and related technologies play in collecting, analyzing, and disseminating behavioral information.