5 best practices for transforming the customer journey through analytics – MarTech Today

In this data-driven age, marketers can no longer afford to be analytics-agnostic. Without analytical sensibility, data-driven insights are hard to come by.

The most successful marketers are agile in their approach and use analytics to drive core business KPIs. Even the marketers with non-analytical background are building their analytical acumen to be more effective. The rise of analytical platforms for non-technical marketing teams has empowered CMOs to extract powerful insights from their data.

Here are a few best practices to help marketers integrate data and analytics into their marketing strategy.

1. Be mindful of your data

Modern marketers proclaim that data is the core element of their marketing campaigns, but in the absence of a centralized data repository, there’s a vast gap in knowledge and execution. With so much data at their disposal, marketers are finding it challenging to scrutinize customer and product data across a multitude of consumer interactions and touch points.

The abundance of unstructured customer data is the biggest roadblock for marketers in getting to a decision point. The foundation of strong insights-driven analytics rests on high-quality data and a complete view of the customer journey. Your analytics can only be as good as the data it feeds on.

2. Bring important metrics closer with customized dashboards

Most of the analytical tools offer post-event reporting. But today, customers demand real-time relevance. With the right analytics tools, marketers can access highly customizable and real-time reports based on the marketing metrics that matter most to their business.

Instead of tracking a variety of metrics like the traditional dashboards, marketers can now build dashboards based on specific use cases, so that they can slice and dice reports to identify not-so-obvious patterns. Tracking the right metrics is the key to marketing success.

3. Find the goal of your dashboard

Don’t take a kitchen-sink approach with your dashboard. Tracking too many metrics and dimensions will only complicate your analysis.

Define the use cases that matter most to your business so you can measure the results rather than getting stuck in analysis-paralysis.

4. Choose the right type of visualization

Data is only as valuable as the insights it projects. Data visualization plays a crucial step in finding patterns and extracting meaningful insights. It’s tough to interpret huge volumes of data, but when presented well, data can be acted upon quickly.

It’s as much an art as a science. You need not be a data scientist to analyze your charts to get actionable information. The magic lies in picking the right type of chart for every use case – from the comparison and composition to distribution.

So, when choosing an analytics platform, don’t go for one that offers fancy visualizations or dashboards with default charts to display your data. Instead, choose one that gives you the freedom to choose the most effective visualizations to craft a compelling narrative for your data.

As Randy Olson, AI researcher and data scientist wrote on Inbound.org:

OkCupid wouldn’t be nearly as widely known if its founders didn’t take a step back and think about how they could use data visualization to communicate the compelling stories in their data. Most companies have interesting stories to tell with their data (or others’ data), yet those stories won’t capture people’s attention with walls of text to read through.

5. Choose advanced analytics tools that measure and predict

In this era of real-time and predictive marketing, there is a paradigm shift from descriptive analytics to prescriptive analytics. Modern analytical platforms embed machine learning and artificial intelligence to predict and prescribe.

For example, besides reporting campaign performance, your analytics platform can predict and prescribe which customer segments are the most profitable and what sort of personalized promotion will work for a customer or a segment. The combination of analytics and AI can help marketers make accurate predictions to boost revenue.

Access to data is not a solution in itself; knowing how to analyze that data for actionable business intelligence is the key to making fast and accurate decisions.

In this data-driven age, marketing is no longer gut-driven. Analyzing your campaigns is the ultimate way to extract valuable data and optimize in real time. For example, for every promotional offer you run, you need to track how many people you acquired through the offer to measure the ROI of the given campaign.

Today, retail analysis is not all about the next sale, but it’s about enhancing the whole customer journey.

Opinions expressed in this article are those of the guest author and not necessarily MarTech Today. Staff authors are listed here.

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