11 Ways Data Analysis Can Boost Your Bottom Line

11 Ways Data Analysis Can Boost Your Bottom Line

It is best not to calculate everything. Concentrate on a few macro, actionable metrics, like revenue per customer or the cost to acquire new customers. Every action you take should be to affect one or more of these metrics. Measure before and after to see if you changed anything.

To get beyond demographics, location and other standard user metrics, use session or event data to further segment and identify user groups. Their behaviors may be unique to how they interact with your product, which could help predict future actions.

Integrate data from internal and external sources, like social media, to get a 360° view of the customer. If someone tweets immediately after a purchase on your site, you want to know whether they’re saying good or bad things about the product.

Real-time, in-session analytics make a huge difference. They help drive new revenue with customized offers and recommendations, targeted advertising, and custom user paths. Real-time analytics can also help save costs with fraud detection, network monitoring and inventory management.

Study all your data, not just a sample. You must be able to search, analyze and visualize granular transactional data and web and mobile data on a massive scale. This will give you the true, full picture.

A single analytics product cannot solve the data and analytics requirements mentioned in the previous. Find complementary tools to co-exist with your organization’s analytics infrastructure, especially ones that don’t require wholesale retooling of the infrastructure and personnel’s skill sets.

Examine data across all time-horizons, such as historical, current and predictive, especially for automated decision-making. This helps expose time-related variability, like seasonal effects.

Multidimensional statistical analyses, like regression analysis, market basket analysis and other mainstays of advanced analytics reveal correlations quickly and better slice and dice your data.

Simulations can help you quickly test models and assumptions. You need to perform free form, what-if analyses to forecast alternatives without having to define rigid data models upfront.

A/B test assumptions about your products and marketing campaigns and engage in other controlled experiments to gain insights quickly. This testing will lead to better-informed decisions.

Share across your entire organization, not just at the executive level. Empower employees on the front lines to make better day-to-day decisions with the right analytics. For example, give customer support staff real-time information on what the customer did with a product just before he or she called the support line.