Latest news:

You are currently not logged in

Log in

SUBSCRIBE
FREE NEWS BRIEFS
Get breaking news delivered

E-commerce

Why internet retailers need a data scientist

Online retailing is a crowded marketplace; being able to collect data, analyse it, and act upon it can significantly improve your competitiveness.

Being an internet-based business makes it relatively simple to collect vast amounts of data about customers. You can track their behaviour while they browse your site, understanding which items they look at the longest, if and when they abandon their shopping carts, and what journey they take to get to their ultimate purchase. Internet retailers that can interpret this data will be able to make smarter business decisions, competing more effectively in a tough marketplace.

Data can yield important insights such as which products to offer at what price points and at what time of the year. This means you can predict more accurately what products will sell more, when.

To get these game-changing insights, you need to be able to use data effectively. That means putting in place the right people and tools to maximise the value of your data.

The 2016 Teradata ANZ Index showed that the number of organisations considering hiring a data scientist has risen by seven per cent in the past year to 21 per cent. A further 19 per cent will either access data science skills externally, or will develop those skills in-house. That means 40 per cent of organisations plan to access data science skills, in some way, to help them make sense of their data.

For internet retailers, this number is interesting because it demonstrates just how many companies fully appreciate the potential value of data to make a real difference in the competitive race. Becoming truly data-centric is therefore essential to your ongoing competitiveness but it will be difficult, if not impossible, if you don’t have ready access to data scientist skills.

A data scientist takes your data analysis from adequate to transformational. They have the skills and knowledge to manipulate and analyse data in new ways, always with your organisational objectives at the forefront.

This disciplined approach to data analysis is crucial because the vast quantities of data available, coupled with myriad possibilities for analysis, can be distracting to inexperienced analysts. If you’re not absolutely focused, your analysis can end up following data down a rabbit hole, wasting time and money pursuing insights that won’t actually move the needle in terms of your competitiveness.

Not only can a data scientist more readily identify the right questions to ask of the data, they can also help identify more sources of data. This delivers a broader view of the marketplace, and can include consumer sentiment from social media as well as machine learning from the Internet of Things.

Ultimately, the goal of a data-driven organisation is to push through from the simple collect-analyse-decide cycle to become a sentient organisation. Sentience, or autonomous decision-making, is the ideal state for organisations of any size and in any industry because it removes much of the potential for human error in decisions.

Sentient organisations can constantly listen to, analyse, and make automated business decisions based on data, at a massive scale, in real-time. Businesses that can do this can significantly outperform others in their industry by making the right decision before their competitors do, giving them the first-mover advantage.

Businesses must go through five key stages to reach sentience:

  1. Data agility: a balanced, decentralised framework that enables a mix of workloads and data types.
  2. Behavioural analytics: asking different questions to gain new insights, considering behaviours instead of just transactions.
  3. Collaborative ideation: working together to pool data and insights to get a better view of trends and challenges.
  4. Analytic applications: smaller, self-service apps that let users reproduce insights, letting more people within an organisation leverage data to create analytical outputs and insights.
  5. Autonomous decision-making: leveraging predictive technologies and algorithms to look at anomalies, reducing the amount of time spent sifting through dashboards and mountains of data to make decisions.

To reach the fifth stage, businesses need to combine the right mix of people and technology. By putting smart, qualified data scientists in place and supporting them with powerful data analysis solutions, online retailers can gain a strong competitive advantage, which can contribute directly to the bottom line.

Alec Gardner is Teradata’s general manager for advanced analytics in Australia and New Zealand. 

Share article: 1
No Comments | Be the first to comment
+-

Comment Manually

No comments

SUBSCRIBE
FREE NEWS BRIEFS
Get breaking news delivered