Marketers are going to have to become more scientific to take advantage of Gen AI, says Zappi’s Steve Phillips.
Over the past 20 years, technology has levelled the playing field between the big brand giants and the start-ups, disruptor businesses and challenger brands. From the simplification of the manufacturing process and digital advertising to e-commerce and the ability to sell anywhere in the world, technology and the consequential leaps in distribution and cost savings have helped smaller brands compete.
Now we have a new game-changer in town – Generative AI. It’s not just changed the game; it’s changing how we play – and faster than we can learn the rules. But what’s of particular interest here, is how this technology is impacting the way brands create competitive advantage. For AI isn’t levelling the playing field for all brands, it’s doing the opposite.
AI is massively advantageous to the big brand players and that’s because of one thing – their data.
Data assets are business assets
Large brands have large pools of data – often in multiple streams and across disparate data sets. The secret to success in an AI world is to ensure that data is of good enough quality and quantity. To optimise the impact of AI, those random databases dotted around an organisation need to be gathered in one data stream. When they are treated as one asset, with consistency of quality, they offer a significant competitive advantage to those organisations because AI is only as good as the data it’s fed; garbage in, garbage out.
This recognition of how vital asset data is for a business has led to some interesting arguments about how it will impact businesses. For instance, some people talk about the need to put data on the balance sheet – placing a value on the data that sits within a company’s systems; while others argue that every business should have a chief of data or chief of AI sitting on the board.
Personally, I feel these are diversions; how data appears within a P&L is less of an issue than how it is organised by the brand, the investment put into its quality and how it is brought together to be optimised as one asset. These are what will really make a difference; for the big players with huge data assets, this is where their competitive advantage will emerge.
So, the race is on for the large brands to manage their data assets more productively. What sounds so simple is very difficult to achieve in practice – aligning data sets is not an easy thing to do. But while other elements of generative AI are relatively straightforward – a basic model sits underneath with a user interface on top – all the strength lies in the middle layer, the data layer.
Becoming a vital data stream
Within organisations, each department will want a quality data stream feeding into the whole business’s data asset. Marketers take note. Customer insight needs to be a core data stream; currently, it often is not.
A large company will know a lot about its customers. Different data streams will identify: what customers have bought; what parts of its website they’ve visited; their billing cycle. All this data can show customers’ preferences and behaviours. What consumer insight data adds is the ‘why’, and this is what Gen AI models thrive on.
Historically, it’s been all too easy to gather this data, use it once or twice, and then ignore it. But if you approach it as an asset, pulling all those streams together with AI, a new world of personalised communications and recommendations opens – exactly fitting the profile of that customer.
Of course, consistent quality is crucial. A big brand with multiple data streams is only as good as its least good data. Because if everything is pulled to create a combined view of the customer and the market, the poorest quality data will pollute everything else.
With predictive modelling, organisations can determine where they're going next, their next strategic investment, and what competitors they're looking at. The planning that’s possible because of that data puts big businesses at a significant advantage over the SMEs lacking that data set.
How SMEs can respond
What are the options for the smaller players? If they don’t have their own data, they will increasingly have to buy it in from syndicated sources. We will see new data sets being offered by providers which allow smaller businesses access to large data pools. But because any SME will be able to buy it, while it will give them information, it won’t offer the same competitive advantage as a distinct data asset. As ever, the smaller players will have to carve out their own advantage through their agility and the speed at which they can adapt to changing markets.
The structure of all businesses will have to adapt to reflect these changes. More power will lie in the hands of the IT, data and data science teams. Marketers will change the way they work as more creative output comes in-house.
It is an opportunity for marketing to become more scientific, which will gain it more respect in the boardroom. This trend has been emerging for some time, but AI turbocharges it.
