Many organisations believe data is the direct path to achieving competitive advantage. The thinking goes something like this: The more customers we have, the more data we can gather, and that data, when analyzed, allows us to create a better product that attracts more customers. Then our increasing mountain of data will eventually marginalize our competitors because now we can take advantage of a network effect. It seems a plausible story, however recent research done by Harvard Business Review says otherwise: “In most instances people grossly overestimate the advantage that data confers.”
Every organisation gathers data in some form or another. From website analytics, product purchases, time in stores or customer attrition to macro-market trends and industry analyses.
The key question is: Are we gathering data in a way that we can leverage to create competitive advantage?
Without the right type and level of data, it is difficult to make good decisions. The data are at risk of being perceived as whole lot of words or numbers that don’t mean anything to the people in the organisation who need to use them. They need to be translated into insights that the organisation can relate to. Data contributes usefully to a story and are the foundation for organisational change. Once the foundations exist, analysing what it means for the organisation becomes an engaging process.
When learning from one customer translates into a better experience for other customers and when that learning can be incorporated into a product fast enough to benefit its current users, customers will care about how many other people are adopting the product.
HBR 2021
Machine-learning algorithms can provide insights into an organisation’s “data mountain”. Large amounts of data analysed in useful ways can inform what is likely to happen. For example, Google Maps is useful because so many other people use it too, and the more traffic data the software gathers from them, the better its predictions on road conditions and travel times. For those who are interested in diving deeper into this topic, Prediction Machines is an excellent primer into how machine learning works with data. A practical caveat: whatever technology you use to conduct the analysis, it is always relevant to include humans who understand both the data and your business to sense-check the output.
The data also have limitations. Data are relevant to a particular place and time. They cannot actually tell the future (predictions aren’t always reality!). Over time too, their value depreciates. Data are the building blocks with which to make decisions, however more is needed to achieve advantage.
How Netflix uses data to deliver a personalised customer experience
Netflix relies on large amounts of data and analytics to deliver a customized product to its subscribers. Using AI enabled pattern recognition, Netflix is able to better understand their customer’s behaviours and provide tailored content for specific customer segments. Like many organisations, at first Netflix segmented its customers based on location and demographics, but as its data grew the company was able to take advantage of more advanced analytical models. Now, there is enough information to see that many customers who liked the new Jason Bourne film also liked The Queen’s Gambit and Bridgerton. Customers are segmented based on their preferred content and receive recommendations related to their specific segment (or “taste community” as the company refers to it). Applying advanced recommendation algorithms enables Netflix to identify previously unknown segments (e.g. Bridgerton watchers might also quite like classic films) New Netflix subscribers gain the benefit of many other previous watchers because they get an increasingly personalised experience due to data used well. This gives Netflix an enormous competitive advantage over other entertainment companies because it is difficult to copy and is remarkable in its accuracy. Netflix is now closer to an analytics company than a media company. Every customer experience is informed by the right data.
Data can help you achieve competitive advantage
In our paper The Best Next Move we describe how to achieve competitive advantage through adjacent market positioning. What is an adjacent market? A market that is close enough to access using your existing resources and does not require large-scale investment or risky restructuring to move into. Normally, the organisation has an existing resource (like a customer base) and can leverage this to exploit a nearby, or adjacent, market. This could be by offering existing customers a different value proposition, for example when McDonalds started offering its customers salads instead of burgers. It could also happen through developing core capabilities and assets to access adjacent customer groups, for example when robotic packing machine company Fibre King developed a smaller, less expensive “Little Packer” targeted for craft breweries and food producers. Quality data can help reveal these opportunities faster.
CapFeather applies The Best Next Move methodology to define market growth opportunities. Stages of the process can include: customer segmentation, analysing customer behaviours, product and service design, customer experience innovation, service ecosystem design, and activation and retention strategies. Data and insights are necessary to inform every stage.
Is data the new competitive advantage? The answer is “Yes, and…”
1. Organisations need to first gather data that is useful for the organisation to make decisions with.
2. People who need to use the data are able to access it and create models with it. There are an enormous array of technologies that can help you access and make decisions with data, including AI enabled decision-making.
3. That data then becomes the foundation of the strategic design process.
4. Data-enabled learning then informs ongoing customer segmentation, personalisation, market-testing and innovation efforts.
A last thought: to succeed in ongoing innovation based on data, data needs to be liberated out of the IT or data science department. When it is easy to access and facilitates decision-making, the whole organisation benefits.
Sarah Daly is undertaking a PhD at the Queensland University of Technology investigating the role of trust in the adoption and diffusion of AI based innovation. She is also the Operations Director of CapFeather, a customer strategy and innovation consulting firm. Sarah quite liked Bridgerton and enjoys Netflix’s ensuing recommendations.