Ever since IBM invented the first hard drive, businesses have tried to get more and more out of the data they collect. Big names such as SAP and Oracle helped them integrate their systems and put their data into databases to generate reports on that data. Data in this era was siloed and had limited applications for end-customers.
Managing data was always a challenge, particularly for startups and emerging companies. Businesses began gradually warehousing their data so they could access and manage their customer and product data all in a single place. This meant they could analyse more complete data sets across multiple silos.
Data analysis was still limited to addressing what happened in the past and was unable to offer predictions or information on future trends. Computing limitations also meant that by the time reports were created, they were often out of date.
With the proliferation of consumer-focused technology, the amount of data generated increased exponentially. Small businesses, who traditionally struggled when it came to customer engagement, were now able to interact through web and digital channels, reaching their customers in new ways with pop-up ads, CRM-driven email campaigns, push advertising and customer tracking becoming mainstream. These same small businesses turned to big data analytics to predict customer behaviour, track what they were most likely to purchase and generate leads to push products they thought customers might want.
This has resulted in a huge amount of data being collected and generated which is stored in ‘data lakes’, which ultimately become ‘data swamps’, as the data stagnates without being used or refreshed.
Big data had its own limitations: we’ve all been followed around the internet by a pair of trainers we’ve already bought, and we’ve all had Amazon recommend products that are more suited to the niece you bought a present for that one time. The algorithms are only as smart as the data they are fed by, with no intelligence or understanding of context or the customer they are analysing.
Big data also hinted at the promise of true personalisation, but was doomed to failure, because how could you personalise something based solely on analysis of large anonymous data sets with no true context about the individual?
The next big breakthrough
AI, machine learning and deep learning offer the next big breakthrough in the use of data.
Startups and small businesses who can intelligently apply machine learning or artificial intelligence are increasingly finding themselves at the forefront of understanding the intricate behaviours and individualised preferences of their customers within a specific context.
What we’ve seen with the myriad applications of these new technologies is the ability to solve business pain points and develop deeper, more accurate insights into who our customers are.
In technology terms, we’ve moved from collation in silos, to integration, to analytics, beyond template-based systems to true personalisation of data and the ability to drive intelligent end-to-end outcomes for the customer.
A design flaw
Even though governments have legislated that customer data technically belongs to the customer businesses have not invested in technology that allows customers to have the same use and utility out of that data. They have been more interested in the information they need.
For example, when you restructure your mortgage you are faced with the same mortgage form you filled in the first time. When you deal with your energy company and you ask if you are on the best plan they can’t tell you, even though they have all the information available.
Why don’t banks proactively move your money to an account where it will earn the best interest rate? Why can’t your insurer track whether your cover is sufficient as your circumstances change?
As data systems have evolved over the years, they have not been designed to be customer-centric, intelligent or contextual.
An opportunity for startups
It’s time this changed and it is an opportunity for startups. Much is being written about the personal information economy and the intention economy which provides customers with use and utility over their own data. This enables small businesses to transform their customer service and value proposition allowing them to not only serve their customers better but operate more efficiently.
For the first time, it is technologically possible for the information power imbalance to be resolved in favour of the customer. Most startups already know that getting to know their customers is essential. Adding an AI layer to these data systems will enable them to move from providing a helpful product or service to an essential one.
Nobody wants the Groundhog Day of filling in endless forms and providing the same information over and over again. Nobody enjoys the stress of being bounced around call centres, waiting hours only to explain your problems multiple times to different departments.
Customer loyalty stops when you find out your telco or Pay TV provider is offering new customers a better plan than the one you, a loyal customer, have had for the past few years. Businesses don’t plan to provide those kinds of negative experiences, however, legacy processes and systems have perpetuated this. The advantage many startups have is they are developing these legacy systems now and can implement processes early so they never fall into this trap.
Again, AI is an essential element here.
Practically, customers taking back the power of their own data is not going to be achieved by the customer owning and maintaining a huge repository of their own data to share back with businesses. It’s going be achieved by the creation of new intelligent enterprise customer relationships based on end-to-end outcomes producing mutual value. This is where the real opportunity for startups lies.
In the age of intelligent data, startups will be rewarded for thinking about what the customer needs from their own data before they think about what the business needs. Where incumbent institutions have failed, startups have the power to be nimble, adapting quickly to harness their data insights and tailor their offerings accordingly to suit their customers’ needs. This model also enables the creation of customer-centred ecosystems, where data becomes the invisible oil driving true end-to-end outcomes for customers.
It’s about empowering a business’s data to intelligently work for people, not just for the business.
In an era where substitutes are within easy reach, being customer-centric is no longer enough.
Customer focus, customer obsession even, is the way forward, and AI is the essential tool businesses should be looking at for establishing a competitive advantage.
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