Retailers who have exhausted all the ways of improving operational efficiency are looking for new methods to win the market. Optimizing such domains as marketing, logistics, or CRM requires the engagement of several departments, which may significantly hinder the process. Price optimization may be the most effective solution, as pricing is the fastest way to ensure higher ROI.

How Retailers Set Prices Today 

Before anything else, price optimization requires historical and competitive data. Many retailers use a combination of manual and automatic data collection tools. Data about online prices are collected automatically, while data about offline prices are gathered manually. However, the amount of data is so impressive that it is becoming nearly impossible for retail managers to fully analyze all of it and use everything to inform their pricing decisions.

Managers tend to rely on expert-based rather than data-driven pricing. Torn between the necessity of setting optimal prices and hitting their KPIs, managers lack time to analyze the outcomes of their pricing decisions. This prevents them from avoiding to repeat failures in the future or scaling successful decisions. Another bottleneck is the absence of a unified database that stores information on the retailer’s every move. Consequently, it takes a long time to onboard a new manager.

All of these issues have brought retailers to a point where they can no longer grow in the competitive market, and must start looking for a way to leave their competitors behind. Below are three things to do to start using artificial intelligence for retail price optimization.

How to Start Price Optimization

1.   Manage data

The first thing businesses need when adopting machine learning algorithms is data. To make effective pricing recommendations and sales predictions, algorithms require historical and competitive data spanning no less than three years. It has to be high-quality, well-structured, fresh, and in a single format.

The approach to managing data depends on whether its existing data or new data.

Existing data: Although retailers already collect some data, very often it may be ineligible for AI. It is either stored in different places and formats, badly structured, or simply old. Sometimes, various departments of the business own different bits of data and it is difficult to extract it.

So, the first step is to structure data and convert it into a single format.

If a retailer lacks data on products, prices, or transactions in the past, it can be purchased or restored through simulation with AI.

New data: Retailers can use either an in-house or external tool to collect competitive data or combine them both.

When it comes to choosing a data provider, we recommend taking the following steps:

  • Test the quality of product matches for different product categories.
  • Define the criteria of the quality of data collection and delivery, and ensure that the provider sticks to them.
  • Secure transparent monitoring of the quality of matches and data delivery.

2. Consider an AI-powered price optimization solution

Retailers can either design an in-house algorithm or find an independent AI provider. Some businesses prefer the first option for a variety of reasons, with the most serious of them being the fear of commercial data leak. 

However, an in-house solution requires significant human, financial, and infrastructural resources: rolling out an IT system, training and maintaining the algorithm, and checking its performance.

In the long run, an external ready-to-use tool is cheaper than internal systems and saves retailers from dedicating a whole IT team to support them full-time. Also, there are solutions that guarantee commercial data security available to the retailer.

Price optimization models use the power of neural networks to predict sales and make pricing recommendations to help the business reach its goals. It has several advantages over expert-based pricing:

  • Analyzes enormous amounts of data that are unmanageable for humans.
  • Learns non-linear interrelations between products and makes counterintuitive pricing recommendations.
  • Makes transparent data-driven pricing and promo recommendations that are easy to monitor and dissect, if necessary.
  • Saves retail managers from routine tasks and allows them to make more high-level decisions.

3. Launch a Pilot

It is common for retail teams to doubt the effectiveness of SaaS solutions for two reasons:

  1. They do not understand how the system works or what logic it uses to debug its recommendations, so the system seems like a “black box” for managers.
  2. AI-powered recommendations seem counterintuitive: for example, to sell at a higher price than a competitor.

A pilot is a good way to prove the effectiveness of the tool in terms of reaching business goals and allay the concerns of managers. 

Before any pilot, the algorithm requires training based on the retailer’s existing historical and competitive data. It digests every data point and makes predictions while learning from its own mistakes. Once the predictions are accurate, the pilot can start.

After the pilot is done and the results are satisfactory, the algorithm can fully roll out and scale.

Conclusion

Retail businesses have reached the point where they need revolutionary solutions to help them increase their operational efficiency in a market that is already divided between them and their competitors.

Price optimization powered by machine learning algorithms seems to be the most feasible method, as it requires less time and investment than anything else. Also, it is quick, able to manage huge amounts of data, makes data-driven pricing recommendations, and allows managers to switch to strategic tasks.

Nikolay Savin
Author
Nikolay Savin

Nikolay Savin is Head of Product at Competera, where he helps businesses achieve better results through the merge of data, machine learning, and retail best practices.