by Nick Gaubitch, Research Director, Pindrop
Today the algorithm is the point of origin for the majority of start-ups. Just one of many recent examples of this is London company Butternut Box, who recently reached £1m in investment thanks to an innovative solution born out of an algorithmic process.
This isn’t a standalone case by any means. A unique algorithm is an important element for any business looking to make a name for itself. In an industry which is becoming increasingly competitive, this is especially true.
Having a unique algorithm is a good start for a new business, but competing against enterprise behemoths such as Facebook and Amazon, who now have the resources available to crush competition and steal customers, it’s simply not enough. It’s therefore vital to build large datasets to compliment algorithms, which data must then be analysed and crunched by machine learning technologies to provide valuable outputs. Failing to do this will quickly end a start-up’s ability to grow and develop.
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Below we discuss why it’s almost impossible for a modern-day start-up to succeed without the machine learning capabilities required to analyse data sets.
It’s all about the data
Applying machine learning capability can provide valuable outputs, from ensuring a customer experience is unique, to streamlining the supply chain. Machine learning, accompanied by a large data set, is crucial for today’s new businesses.
Here at Pindrop, we’ve clocked over 400 million calls this year, ensuring access to a wealth of valuable data. Our patented Phoneprinting technology can detect which calls are fraudulent or legitimate, then feed this back to a tool that can track repeat offenders, categorise fraudulent cases and create fraudster profiles. This enables the naming and shaming of real-life fraudsters who are targeting consumers across the world.
Having access to such a large dataset means suspicious behaviours can be quickly picked up and patterns formulated to build a bigger picture, and inform clients in real-time when fraud is detected. This data can also be used to hinder a fraudster’s activity, help customers avoid fraudulent breaches, and use it as a proof-of-concept when talking to prospects.
In our 24/7 connected world, it’s increasingly the case that a business is only as good as the data it has access to. For a modern-day business to succeed, they need access to vast amounts of valuable data, which allows them to tailor their approach to customers and business goals.
It’s all very well having a unique solution in the market, but without access to data how will a business tailor their product to specific customer needs and adapt to changing market demands? Start-ups looking to continue their growth must also look to issues such as:
• How do you keep consumers up to date with new products and innovations?
• How do you follow up with customers who have made an initial purchase or interaction?
• How do you ensure customers stay loyal to your brand?
All of these questions can be answered in one simple way, utilising the data that customers willingly share when registering on a website or buying products to tailor their experiences. A start-up that’s able to build a data profile of their customers and implement this into their algorithm to create a tailor service is certainly on to a winner.
This data can be run through machine learning programs which reveal valuable outputs. This is what puts a start-up ahead of the competition. Not the base algorithm, or the amount of data stored, but the ability to process this data and utilise it effectively.
Keeping data safe
As start-ups begin to accumulate more data from their customers, it is of course crucial that they keep in mind the nearing launch of the General Data Protection Regulation (GDPR). When GDPR comes into force, data protection will have to be incorporated into the core of all business procedures, products and services across all channels, and all employees will have to be aware of their obligation to protect consumer data across channels including the phone.
This is why GDPR needs to be viewed as a way for companies to introduce a robust data protection strategy that protects call and voice data as well as all other data that runs through digital channels.