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Enabling more convenience and simplicity is one of PayPal’s priorities as the online payments company focuses on using data to deliver on customer priorities.
According to the company’s Europe, the Middle East and Africa (EMEA) director of analytics Davide Cervellin, previously a head of EU analytics at ecommerce firm eBay, successful companies are looking to excel at “de-cluttering” the wide range of choices available to consumers, focusing on curation.
“Delivering simple and convenient services is true for eBay, where I did extensive work on proving the negative impact that exposing customers to too many options were detrimental to sales conversion.” Cervelin tells Computer Weekly.
“Tools like machine learning and artificial intelligence help organisations do this at scale, creating individual marketing profiles and delivering content in an extremely customised way, making sure each customer sees just about what they need to see – and have high propensity to buy,” he says.
Looking back at what was important at his previous organisation compared to his current agenda, Cervellin says PayPal is a little different in that it’s a payment service, but data is crucial nevertheless.
“The goal at PayPal is to profile a simple to use, fast and reliable service, that makes customers feel secure with the payments they make,” he says.
“Over time, we added services such as Free Return Shipping, which is an incredible service not many people know about. Customers can have PayPal cover for the cost of return shipping, providing them with a real full refund if the sale went wrong or they were not satisfied with the product they bought.”
Measuring and optimising
One of the main areas of focus for Cervellin and his team is to constantly observe the market, which means analysing competitors and customers. “Our job is to highlight patterns and see where we should focus our actions, investments and resources.”
Without mentioning PayPal specifics on interesting insights obtained through data analytics, Cervellin uses the initiatives carried out in his previous role to illustrate how such efforts can generate tangible results.
“At eBay, we did a lot of work proving that a simpler, cleaner way of displaying the results of a research had significant impact on conversion, which has led to the creation of a new set of pages that also perform very well in SEO terms,” he says.
“That effort was originated by customer research, continued by business analytics who proved the value of making the change, and then deployed by the engineering and product teams.”
“We constantly measure and optimise, but we also update our predictive models so our segments and profiles are built on the latest knowledge we gained”
Davide Cervellin, PayPal
All projects at PayPal are driven by data, says Cervellin. This includes campaign setup and optimisation, to ensure that customers are segmented in a way that maximises the impact of promotions, tools and services.
“We constantly measure and optimise, but we also update our predictive models to make sure our segments and profiles are built on the latest knowledge we gained,” he adds.
“By putting data at the center of decision making, we ensure that even bad decisions become useful as we can dissect them and understand what went wrong, avoiding the same mistake in the future.”
“Many technology executives are a little blasé about the ‘big data hype’ and often stress the need to move the focus beyond just the technology,” says Cervellin.
But how is that possible if the underlying tech is a key enabler and constantly in evolution? He points out many decision makers embrace big data “because it’s a trend of the moment, not because they understand what it really means.”
“The problem with embracing big data is that, for analytics and data-based decision to truly work and have the impact they can have on the business, executives need to release a bit of the power they have and trust things that they often do not fully understand,” he says.
“That is why so many people begin big data transformation journeys but so few manage to complete these projects successfully: it’s far too hard to trust machines and algorithms to make decisions that, until a few years ago, were made by experts.”
However, Cervellin points out that doesn’t mean data is right and instinct of experience is wrong – but undoubtedly, data is objective while instinct and experience are subjective.
“There are benefits in relying on objective criteria to make a decision. Firstly, everyone can understand the premise as data is a common language and, secondly, objective criteria allows for easy analyses afterwards, as well as a clear set of expectations before the decision is made,” he says.
“That means executives need to give away some of the power, and that may make some feel a little less “solid” in their roles – which is why big data transformation journeys fail if they do not have blessing at the highest level.”
Dealing with suppliers
As data becomes the bedrock of decision making at practically any business, technology suppliers want to claim a slice of that pie. But how are they managing to cater for the needs of buyers within analytics? Cervellin says most technology providers tend to focus on their product rather than the problem their customer needs to solve.
“One customer’s problem may be the same as the next one, and that’s when the sale goes well. But often that isn’t the case and also when issues arise,” he says.
The high level of fragmentation and diversification of data tools and services available in the marketplace also makes it difficult for customers to identify which might be right for them.
“Often times the customer is not even fully aware of the problem he has, so how can he think of a solution? I believe successful suppliers need to move away from pitching their tools and services and focus on the customer problems first,” says Cervellin.
“This may mean the customer may not need the tool or service at all, but it would help suppliers to build trust and reputation – which would help them whenever their tool happens to be the right one.”
A new business reality
When it comes to preparing staff for a data-driven business reality, Cervellin believes people and culture are the two keywords, with the former facilitating the latter.
“Companies need to hire the right people who can help them make the transition to data-based decision making in a painless way, ensuring the proposed solutions, the tools, the processes, are designed with the company good in mind,” he says.
In the process of bringing analytics champions in, Cervellin deems it essential that the first hire of any data transformation process needs to be at a C-suite level. “This will allow for the journey to be explained to employees in the jargon they are used to, making collaboration much easier and increase the chances of getting to the other side.”
“I also strongly advise large companies to have a data person with a profile like mine sitting on the board, to ensure it can add value to the process instead of just being at the receiving end of it.”
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Cervellin says the ability to tailor customer offerings by taking data-driven decisions will remain the highlight for organisations for some time to come. “The capacity that Amazon, Facebook and Netflix have of knowing what we want even before we do is a fascinating expression of what is on the other side of a data transformation journey.”
“These companies were blessed to have been created at a time when tracking everything was already possible, but that does not mean more traditional businesses cannot be disrupted by embracing the fact a data-based decision is never a bad decision,” he says.
“That is why Amazon has Alexa, Apple has Siri and so on: it’s not about turning the lights on with your voice, it’s about them being in your homes, listening to what you say and do, then providing you with a better, more customised service.”