Data Optimisation

Why is Data Optimisation important?

1. Enable data-driven decision-making

To compete in an increasingly data-driven market, organisations need to act quickly and confidently to avoid threats or capitalise on opportunities. If you have access to key information in real-time, but your competitors have to wait days or weeks for those insights, you’ll have a significant advantage.

Data coming from separate marketing CRM and billing platforms will probably be in different formats, be missing key data points, or have conflicting information. By optimising this data, you harmonise the two data sets. And by analysing this collated data as a whole (known as ‘big data’), you’ll have access to insights that weren’t possible before.

For example, after optimising their data, a business manager realised that customers aged under 40 were more likely to pay their bills late and default on payments. As a result, they came up with the idea of offering interest-free credit to under-50s. This increased their revenues overall and expanded their share of the market.

2. Improve business efficiencies

Data isn’t just about what’s happening outside your organisation. By optimising data that originates from inside, you uncover possibilities for efficiencies too. For example:

• Productivity. By optimising its data, business analysts at a finance company were finally able to stop wasting time manually wrestling with data. With the added time and confidence, they were able to run experiments on how to reduce spending and reallocate resources.
• Reduce costs. By pooling together data sources into a data warehouse, a private hospital was able to get a bird’s eye view of their overall IT hardware performance. This data dashboard gave them the insight to spot inefficiencies, forecast spend accurately, and create performance benchmarks.
• Energy efficiency. By collating and optimising its data, a supermarket brand realised there were large differences in energy consumption between its stores. This presented an opportunity for store managers to exchange best practices in how to become more energy efficient.

3. Maximise sales

Understanding your customers helps you to maximise their value, and in turn will boost revenue. But too often, valuable customer data is locked away in spreadsheets and systems that aren’t connected (AKA data silos).

Data from browsing behaviour, customer logins and qualitative customer service queries can all be collected to form one comprehensive picture. This picture can show you who your customer is, what they value, and what their experience of your brand is. With this level of insight, you can analyse and improve your customers’ experience, leading to increased sales.

In addition, consumer attention spans are at an all-time low. There are more advertisements, social media distractions and new competitors than ever. To cut through this noise, businesses need to use increasingly innovative methods. One way companies are achieving this is through personalisation. Examples include:

• Timely offers based on browsing and purchase behaviour. E.g. Amazon’s ‘People also viewed’ function.
• Retargeting ads and customised physical products. E.g. Facebook’s retargeting ads model.
• Customised marketing communications. E.g. ASOS using date of birth to give people 10% off on their birthday.

These examples are all made possible through building a consistent, accurate data profile on your customers and their behaviour.