7 steps to transform into a data driven organisation

Step 1: How to discover your business value with data

Do you know what your business value is? And, no, I’m not talking about how much $$ you can rake in if you were to sell it.

No, the business value that I’m talking about has more to do with the value you provide your customers, your stakeholders, board or investors, or the value that you provide your employees - beyond remuneration. 

It’s the kind of intangible value that makes people choose your organisation, products or services over another’s.

To put a definition on it, the value I’m talking about is ‘the net benefit that will be realised by the customer of a project (product or service) and can be measured in monetary or non-monetary terms.’ 

If we take Apple for example, their value lies in delighting their customers and making their life easier with a wide range of products and apps that seamlessly integrate with each other. 

How then does your organisation add value to your customer’s lives, which in turn adds value to your organisation? To find out, look to your data.

 

The value you offer customers

We often talk about how data can help you understand your customers better, how it can help drive personalisation or targeted marketing programmes, matching current products or services to a person. 

However, data can also help create a deeper awareness of customer's needs and pain points, and help you understand what they actually want or need so you can better align your product development or customer service processes to meet them.

This will require you to look beyond the usual data sources like CRM, marketing, or engagement data to achieve this, and look at more complex analytics projects such as customer churn modelling, sentiment analysis and NPS programmes. 

The data that these types of programmes uncovers provides invaluable insights into what your customers actually think about you, and what you may need to improve on.

New call-to-action 

The value of your products 

As well as customer sentiment, understanding product or purchase data can help you understand the value they offer your customers. Which products or services are your best sellers – and which ones are your worst? More importantly, how does this compare to your competitors or market share, and has this changed over time? 

A good example of this is if your product is digital – such as a SaaS product - you can look at usage data to get an understanding of how people are using it, and which features they are or aren’t using. 

Are they taking the same journey from the log-in page to a key feature page as what you had designed, or are they taking a different route because it isn’t obvious or intuitive? This information is then an opportunity for you to improve the UX. You’ll also get an understanding of your most popular features and those not being used allowing you to focus development resources to improve those with the most demand.

 

The value of new opportunities

If you’re looking to expand your product or services offerings, expand into a new market, or fill a new niche you’ll be wanting data to back up your decisions. 

Data will help you understand market trends and help you identify the opportunities where you can improve your products or expand to fill a need. 

It will also help you identify the opportunities within your organisation where you can streamline processes, automate repetitive tasks and improve employee engagement. 

Improvements to products, services and processes that are backed by data are more likely to succeed and add value to your customers, your employees – and your company, than those based on gut feelings or assumptions.

 

The value of more accurate predictions

If you can predict what’s going to happen you’re able to put in processes to prevent it, or profit from it. A common type of predictive analytics is customer churn modelling – where you use the behaviours, actions or traits of customers who’ve already left to predict which of your current customers are likely to leave now, or in the near future. Once you’re able to identify (aka predict) who’s likely to leave, you can then act on keeping them on-board.

Another use-case for predictive analytics is assessing the risk associated with a particular investment or new product development to determine whether it will be beneficial or profitable. Comparing the conditions of the present with historical data you can identify risk factors and make a decision that mitigates this risk and ensures success.

Modern data organisations know how to harness the power of their data, to extract insights that deliver value to their customers and their organisation. By using data to influence the decisions you make, you’re basing changes and improvements on quantifiable evidence, rather than a gut feeling or assumptions.  

If you’re in the early stages of transforming to a modern data organisation and wondering how you can get started, check out step 2 where we investigate why the agile methodology works by using an iterative approach to implement new systems or develop new products.

New call-to-action

Subscribe to the blog