Retail is changing at breakneck speed. The e-Commerce market is growing increasingly fast. Consumers are getting more demanding and sophisticated, as they expect personalized customer experience. Also, by using a variety of online tools, buyers have more means to instantly compare what different retailers have to offer. The amount of data retail teams need to process to set the optimal price is surging and becoming unmanageable for humans.
The retail market is embracing artificial intelligence and its core element, machine learning, to optimize pricing. For example, Amazon, which accounted for nearly half of the U.S. e-Commerce market, or $258.22 billion, in 2018, has come so far as to outsource a big chunk of the pricing process to AI. Its recommendations engine generates 35% of the company’s revenue by setting optimal prices based on customers’ purchasing history. It is easy to calculate how much artificial intelligence lets the U.S. giant earn.
What Makes Algorithms Beneficial?
To deliver such results, the algorithm factors in patterns, processes massive amounts of data, develops a sort of intuition by taking in all the experiments, both successful and failed, that businesses paid for. Unlike humans, the machine understands when the market changes; it does not have “bad” days or hard weeks. The algorithm always offers the optimal solution for every situation, knows what outcomes the pricing and promo decisions will have, and predicts the short-term future. As machines take care of routine tasks, retail managers can finally switch to crafting a balanced strategy and ensuring a rewarding customer experience.
Why Are AI-Powered Solutions Not Yet Ubiquitous?
In its recent study, Deloitte states that despite heavy funding and optimistic forecasts, machine learning has not enjoyed much traction yet. Citing its 2017 survey of U.S. executives, the company indicates that no more than 62% of businesses using cognitive technologies or at least knowing what they are “had five or fewer implementations or the same number of pilots under way.”
Machine learning requires three elements to solve real-life business tasks:
- High-quality data;
- A well-oiled infrastructure; and
- A trained and perceptive team.
These three factors make it difficult to deploy AI-based solutions fast. However, according to the same Deloitte report, the number of AI applications will grow by four times by 2020.
What Data Do Retailers Need In Order To Benefit From Algorithms?
The machine learning solution learns from historical, competitive and customer data spanning at least one year. The algorithm needs to “live” through every transaction and see the fullest picture of the customer journey for every product. If the data is of low quality or something is missing, the system will not work properly: the algorithm will not be able to calculate the right price and offer the right price prediction of sales or margins.
At the same time, the algorithm does not require the full body of data that retailers have. What the retailer deems important may be irrelevant for the solution. However, to identify what the system needs and what factors influence sales, it is essential to collect all the data in the right format.
How To Adopt Algorithms Effectively
Data preparation is considered responsible for over half of the success of the deployment of an AI-based solution. However, for businesses, the data is the beginning of the process. After the data is collected and structured, retailers need to take three steps to utilize machine-learning successfully:
- Choose an approach and build a model. Retail is an extremely complicated industry that depends on many variables, such as promotional and marketing activities, assortment and pricing, among many others. Each of these domains has hundreds of nuances that the system will have to take into account when predicting prices and sales. Therefore, retailers need to understand not only their business goals but also the steps that will benefit their business the most. Knowing what goals businesses have helps to identify the data algorithms retailers will need to work with.
- Roll out the infrastructure. The algorithm itself is not the answer to everything. To process massive amounts of data every day, the system needs a proper infrastructure powered by several levels of checking and notifications, which help to avoid errors in vital decisions. An in-house solution seems to be the most feasible option as it protects confidential data. However, only a small number of companies can deploy, maintain and update such a sophisticated system. As machine learning algorithms are becoming more affordable, external providers can be an option.
- Develop and deploy the solution. This requires full engagement of the whole team of a retail enterprise. Quite often, managers either fear losing their jobs to AI or refuse to use the recommendations provided by AI as they do not understand its logic. Retailers can overcome this barrier with the help of a pilot: it proves the efficiency of the solution and wins the trust of the team.
To win in the modern retail market, businesses need to be agile, as it is the most feasible way to build a rewarding customer experience and thus to increase revenue. The capabilities of machine-learning are increasingly growing, and AI-powered solutions are becoming more affordable to a broader circle of retailers.
These solutions allow businesses to do two things:
- Set optimal prices based on objective historical and competitive data while factoring in seasonality, customer behavior and any number of other parameters, including business goals.
- Enhance their managers by allowing them to switch from routine tasks to strategic ones, which significantly benefits companies.
Without this technology, retailers are likely to be deprived of their market share in favor of those able to cater to their customers’ needs in real time.
Alexandr Galkin is CEO & Co-founder of Competera, price optimization software for enterprise retailers looking to increase revenue and stay competitive. He is a Forbes contributor, speaker at IRX, e-Commerce and RBTE conferences.