Why customer analytics matters and how to get started

The rise of the internet and eCommerce specifically has provided consumers with an explosion of information available to them. Demographic data, transaction history, advertising preferences, channel usage, social media interactions and more – all at companies’ fingertips, withholding more information on their target customers than ever before.

In this context and for some practical insights, we caught up with the expert team at Data Cubed on how to get started with customer analytics and why it matters. Data Cubed says:

Many companies are failing to unlock the data they have and are missing out on the rich opportunities of tapping into it.

Customer and brand loyalty are in decline – and brands can no longer rely on it to retain their customers. Across all markets, the customer has more knowledge. More knowledge of your brand, more knowledge of your products. And more knowledge of your competitors. As we all know, knowledge is power, so customers are demanding more and they’re fickle when it comes to switching.

To stand a chance at retaining your customers and their loyalty, you need to understand them – so that you can get them, grow them and keep them. But before you start on a quest to get closer to your customers, you need to follow a process. It’s not enough to simply know you want a customer data dashboard. To win at the data game, you must follow the right steps.

Imagine if you could:

• Identify your most valuable customers so you can target more like them
• Learn when a customer might leave you so you can put in plans to retain them
• Boost sales to your current customers by anticipating and meeting their needs
• Compare the costs and effectiveness of your sales and marketing campaigns
• Discover the best time to target a particular demographic
• Forecast future business threats and predict future behaviours.

All of this is possible when data is extracted, analysed and visualised in the right way.

Alone, metrics like page visits, satisfaction scores and email open rates generate little value to a business; they are unlikely to drive change and inform future behaviour. But together, the trail of data that customers leave us can be woven into a richer picture – helping us drive our product, pricing, marketing and sales strategies.

ADLIB: So, what key pieces of wisdom can you share for those looking to start measuring their customer analytics?

Data Cubed:

Ask the right questions

Think about the questions you want to be answered – the ones you might be asked by your stakeholders, distributors and customers. These could be:

• How can we grow?
• How can we cut costs?
• How can we improve our customer service?
• How can we become more efficient?

And what are you trying to achieve? If you’re exploring your data, you’ll know if your primary goal is to find new customers or to keep and build on the ones you’ve got. Focus on this goal first and foremost.

Track your performance

There’s nothing worse than knowing you’ve done a great job without being able to prove it. So think about how you can measure your success. Consider your business priorities and your key performance indicators.

These could be based on revenue, sales, costs, what your customers need and want, and/or how satisfied they are.

Focus on the performance indicators that matter, so that the use case will have a real-world impact on your business, and not just look pretty.

Think about your users

• Who will use your data tools? You need to design solutions that work for them.
• A tool that’s designed for the Board and C-Suite will need to be very different from a tool that’s designed for a hands-on operations manager.

So think about how the user will use the tool and the questions that they will want answers for.

Will they want to explore the data or just see the end results? Will they access it every day or just now and again? How often will they want to see updates? How far back will they want to see historical trends?

The answers to these questions will affect the design of the data tool, so it’s important to understand the expectations and requirements of your business users.

Tell a story with your data

People love a good old story. It provides context, insight, interpretation — all the things that make data meaningful, and analytics more relevant and interesting.

Think about your users, and the trends they’ll want to see. Then start building your story:

Imagine if we could identify areas of underperformance or untapped business opportunities. Imagine if we could identify areas for potential revenue increase. Imagine if we could predict future business performance.

It’s crucial to understand what your business users will be interested in so that you design a tool that’s useful and valued by them.

Thanks so much for sharing!