How to Boost Your Online Sales With Machine Learning and Advanced Data Analysis? [Upsaily Launch]

It should hardly come as a surprise that the more saturated the e-commerce market gets, the more difficult it is to save your profit margin from shrinking. With soaring costs of customer acquisition and retention, many shop owners also feel compelled to slash their prices to adapt to their clients’ expectations amidst the ever-growing competition.

Luckily, a way out of this vicious circle can be found with a little help from technology – namely machine learning and advanced data analysis. We spoke about it during E-commerce Berlin EXPO 2018, where we unveiled upsaily – our brand-new customer intelligence system.

When advanced data analysis is missing 

Our team at Unity Group has been lucky to have worked on a variety of large-scale e-commerce projects in Poland, some of which you can find detailed in our portfolio

Even though many of our clients took great care to gather their transactional or behavioural data, later they wouldn’t do much with it, at least not in terms of more advanced analysis or pattern recognition leading to improved customer intelligence. 

We already had known the Nucleus Research stats that every dollar spent on analytics brings $13 of return, so we decided to apply a more methodological approach to data analysis in e-commerce. 

That’s why we acquired a grant to cover our research, managed to get the green light from the clients with the databases of the right size, and what follows is the result of these efforts.

Towards a customer intelligence system 

E-commerce data, if analysed in a methodical way, can indeed tell you a lot about your shoppers. It can reveal not only the triggers that make them buy from you once or twice, but also shed some light on their general price sensitivity and other factors that can help induce even more of their purchases. Not using these insights to your advantage most certainly means that you’re losing out on the revenue. 

At the same time, we all know how difficult it is to find the right analytics talent to fuel your online sales growth. 

That’s one of the reasons why during our research (conducted with scientists of Wroclaw University of Economics and Business), we came up with an idea for a tool that lets you perform advanced analysis and predictions for your e-commerce without having to search for an in-house data scientist. 

upsaily deploys machine learning and big data analytics to identify and adjust your offer and messages to the actual needs and expectations of your clients. As a result, you can surprise them with the right deal, optimise your marketing spend and grow your profit margins. 

Would you go for that massive discount again if you had known that your clients would have bought from you even at a much higher retail price? Yep, sound predictions can also help you to straighten up your pricing strategy, so you can stop offering discounts where they’re not necessary. 

upsaily in detail

During our talk at E-commerce Berlin EXPO we were pleasantly surprised how much interest upsaily aroused. Okay, so we even got some people queuing to talk to us afterwards, so it felt really encouraging. 

Some of the immediate questions were obviously related to the type and amounts of data your e-commerce needs to have in place to be able to use upsaily. 

Our initial research was based on behaviour of few hundred thousand customers, but we also worked on a database of a client with ‘only’ 50k users. We mainly analysed products catalogue, customers demographics and transactions. The most valuable insights were obtained from those data sets. 

In one case, we had access to the transactional history from the last few years. However, we were also able to perform analysis on data from one season only and apply a model for the same period in the next year. But in general, we prescribe upsaily to databases describing at least one full year of company operations. The more data you gather, the better results you can expect. 

Our machine learning models can be also enriched by users’ behaviour on the website, their reactions to marketing messages, as well as transactions from offline stores. 

I like it, so what’s next?

If upsaily sounds like a plan, then make sure you sign up for a free demo. 

Please bear in mind that upsaily is brand new, so it’s still work in progress. We should have a ‘commercial’ version ready by the end of Q1 and until then we’re working hard on refining its architecture to make it scalable. We’re also adding new reports and features.

Stay tuned!

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