Technology and personalisation simplify shopping and ordering for customers and brings many benefits to brands.
Machine learning and artificial intelligence offer them opportunities to comprehensively understand their customers, handle their goods and products more efficiently and thus improve their business and relationships with customers.
The advent of the pandemic has intensified the transfer into a digital environment and the need for smart solutions and technologies.
Customers’ ever-increasing needs and the dynamics of the e-commerce and retail business itself are putting significant pressure on the quality and availability of services and are forcing competition to reinvent their user experience and services.
Companies have to keep up with the needs and many non-trivial challenges across their businesses – be it better offering, warehouse management, or optimal prices. But how to grasp the challenges and opportunities to scale up or grow the business and satisfy customers at the same time?
For retailers and e-commerce companies, it means accelerating the adoption of smart technologies like artificial intelligence (AI) and machine learning (ML).
Even though these technologies are still at their beginning of impact and growth, they are also big topics and areas that help retail companies to optimise their businesses and solve their challenges. And you should implement them if you want to stay ahead.
To brands, AI and ML technologies help personalise and provide products at affordable prices regarding their business goals and stocks. AI and ML-driven personalisation technologies and solutions offer many advantages and benefits to both customers and brands.
The higher the recurrence is, the higher is the impact and power of personalisation. When a retailer knows their customer, they can evaluate, offer the best possible experience, and build a relationship between the brand and a customer.
How can AI and ML help you with personalisation? We have picked three essential topics most sought by our clients. Discover what aspects of the business on which personalisation and the power of the collected customer data have the biggest impact.
A key area where ML and AI both help is personalisation, which in all its forms is a type of recommendation. From the offer of interesting or similar products to the method of communication with the customer.
Every retailer should collect and analyse as much data as possible because knowing the customer is crucial for many reasons – better sales, relationship management, supply chain management, marketing content, and more.
In this case, machine learning algorithms stand above the data and learn a specific model. Thanks to continuous learning and new data, the algorithms can personalise a retail shopping platform even for the new customer and find new relationships and rules within customer data.
Based on the previous orders, taste, day of the week, and customers’ behaviour, models recommend the right product at the right time. This selective attention enables customers to focus on products they are interested in, and sellers can prioritise and offer the products they want to display.
But recommendations are not just about the taste in food or fashion. To consumers, these engines also consider and suggest a tip, best possibility of delivery, and other information.
There can be tens of thousands of complicated rules with slight nuances. Hand in hand with efficient recommendations, be it at the start of the shopping activity, next offers or check-out up-sell, goes good user interface. Unfortunately, even the best recommendation engine mostly cannot beat bad websites and user experience.
And finally the benefits for both sides. Combining recommendation engines with the following technologies and areas, retailers and e-commerce companies can promote and display products and goods to the right customers, at the right time, for the best price.
This builds a stronger relationship with customers who are happy that the brand knows their needs, knows how to help them, and does everything to save them time. The more satisfied the customers are, the more often they return, give higher ratings, and happier and likable reviews.
The first step to personalisation is most commonly dynamic pricing. This field of the business is crucial because it affects profit, increase of return, customer retention and satisfaction, and waste of goods.
How does it work? Based on market demand and many other factors (internal data, traffic, transactions, availability of the goods and competitors’ prices, taste, behaviour), tailored, intelligent algorithms count the optimal cost, which benefits the seller and customers.
Talking about benefits for sellers, the main point would be to increase the profit by 1,5-3,5% (based on our experience with large e-commerce clients) and the better knowledge of the customers, their shopping routine and behaviour.
The other argument is more efficient stock handling that also means decreased waste of food and goods. Intelligent algorithms ensure that your goods are sold before the expiry date, consider warehouse stocks, and other criteria that help sellers offer, promote, and sell with minimal waste and be the brand customers can identify with.
From the customer’s point of view, the brand knows their preferences, taste and can offer interesting products for attractive prices.
For retail and e-commerce companies operating in various locations and many warehouses, efficient stock handling and a good sales overview are other vital areas closely linked to the pricing itself.
And machine learning and artificial intelligence yield significant and valuable improvements. For example, prediction models can estimate demand for the coming days across all inventory, regarding data about customers’ behaviour, or other data that can shape the demand – weather, season, or holiday.
And retailers can make their sales and marketing more efficient and focused on selected products.
Another interesting topic is B2B prediction and personalisation, where we model the behaviour of suppliers in the supply chain: what and when to order so that the company has everything that needs on time.
Machine learning also helps in the opposite direction. To retailers and sellers, ML and AI help to predict, e.g. the return rate of their products. We developed a quantile regression model for one of our large e-shop clients per product, predicting the return rate for an individual item.
Thus, the seller can detect problematic products, take them off the shelf, or offer early and save return costs. That resulted in cost savings of 92,500 EUR per year.
Last but not least, there is an experience improvement on the customer side. When warehouses and branches operate smoothly, a supply chain is effective, and orders are not mixed up, customers get orders on time and receive what they ordered. And they will return.
Described technologies and solutions help to save time, money and improve many processes from day one. Above, we talked about good personalisation, automation, and the data and interconnection of important systems and processes. How long does it take to implement such technologies into a business?
It depends on the size of the business, amount and quality of the data, product portfolio size, and many other things. But in general, we are talking about a few weeks.
In the case of recommendation, it also depends on which products the company wants to start. Still, it takes between eight and 12 weeks to get such technology into production and tailor every detail needed to provide the best customer experience and business results.
Personalisation is not a new trend, but there is still a lot of space for improvement in many areas. And it is more than clear that it will last because one survey after another shows that many customers who do not receive personalised experiences are quick to shut doors.
So let’s take this reality as a good start point for better knowing the customers and more efficient business. At the end of the day, and with the right technologies, this reality can turn into every retailer’s advantage.
Jakub Šmíd has a Ph.D. in AI & data mining. He is a passionate machine-learning tech lead at Blindspot Solutions, heading a team of machine learning engineers that focuses on discovering the needs of retail and e-commerce clients and delivering innovative and functional solutions.
Blindspot.AI is a dynamic and globally operating software company based in Prague, delivering comprehensive solutions based on machine learning, artificial intelligence, and optimisation principles. In 2019 and 2020, the company ranked among the fastest-growing companies in Central Europe in the prestigious Deloitte Technology Fast 50 ranking.
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