Data: The coal of the fourth industrial revolution (Part One)
In the next instalment of SQLI’s series on future-proofing digital services, we talk to David Ellis, Managing Director of data analytics consultancy Station10.
Data use has become one of the most important cogs in the wheel of innovation, allowing brands to transform their businesses, make better-informed decisions, create dynamic, personalised experiences and increase customer loyalty and sales.
In just a handful of years, data analysis has evolved from a tool most likely to be implemented in the retail industry to countless others, with Gartner expecting 65 per cent of B2B sales organisations transitioning from intuition-based to data-driven decision making by 2026.
However, many eCommerce brands are still struggling to utilise their customer data, effectively.
In part one of this two-part series, David discusses the evolution of data and how businesses are improving their data management.
Where does the eCommerce industry currently stand on data collection and analytics?
We are going through the fourth industrial revolution and data is really the coal of that. It's a really interesting time to be involved in this area doing what we're doing at Station10.
This phase began about 15 years ago, with retailers leading the way due to their extensive customer data. They embraced analytical and data science techniques to gain insights and understand their customers at scale, with supermarkets being a prime example.
Now, though, we are seeing more tech and financial sectors getting involved and it's starting to be applied in a B2B environment. These industries are becoming proficient in terms of how they're using data to understand their customers and target them more effectively, adopting tools like Customer Data Platforms. So, it's no longer the preserve of retail eCommerce. We are seeing a growth in the overall field.
How has data use evolved?
From an analytical perspective, we have what could be called traditional AI, which seems almost outdated given the rapid advancements. This approach predicts customer behaviour, identifies patterns early, and leverages these insights for business value.
Eight years ago, we were asked by a supermarket to predict loyal customers before they became loyal. We managed to identify patterns of behaviour that were predictive of a future loyalty. What was interesting is that we could recognise these traits in a customer's very first shop.
Many brands have this idea that loyalty happens after many transactions, but we spotted particular patterns before someone had actually bought anything.
The supermarket used to focus on basket size and average order value. But real data analysis showed this might be frustrating customers, which led to a significant shift in their digital strategy.
There’s now a lot more focus on predicting future habits, a practice that has become mainstream over the last eight years. Today, we use a technique called Causal Inference, which analyses multiple influences to determine a particular outcome.
To use an analogy, your data might show a correlation between shark attacks and ice cream sales, but the real factor could be rising temperatures driving both. Causal Inference helps pinpoint the actual influences behind data patterns—a concept that won the Nobel Prize for Economics in 2021.
There’s now a lot more focus on predicting future habits, a practice that has become mainstream over the last eight years. Today, we use a technique called Causal Inference, which analyses multiple influences to determine a particular outcome.
Where does Gen AI fit in?
Aside from what I’ve called traditional AI, we have this Supernova - Gen AI - which has just exploded over the past two years. Now, when everyone talks about AI, they mean Generative AI, but I think we're still trying to find the most valuable use cases of these.
It also has bigger implications on how these could impact the workforce. There are very polarised views on its future.
In eCommerce, Gen AI is largely speculative when it comes to data; the use cases are still emerging. But in areas like cancer diagnosis, the combination of pattern matching and Gen AI is groundbreaking, allowing for early detection and personalised treatment plans for each patient.
How are brands improving their managing and use of their data?
Many organisations are recognising the true value of their data, and are more open to figuring out where they currently stand - that’s where we come in. It has been exciting to see businesses go from ‘testing the waters’ to becoming highly proficient. Some are even diving into real-time predictive modelling to improve segmentation and customer targeting.
Over the last 18 months or so, we've seen greater maturity around Customer Data Platforms (CDP) - and how they integrate with analytical platforms.
You may have your predictive insight about your loyal customers that aren't loyal yet. How then, do you target them more effectively? Do you change your creative or go down the suppression track? You may not need to advertise to one segment of customer because you know they are more likely to be loyal, so you can focus on others.
We’re at a tipping point where effective data strategies are becoming essential, not optional. As more companies leverage CDPs to truly understand and engage their customers, the pace of transformation across the industry will only accelerate.