Retailers that have tried out every possible method to enhance operational efficiency instantly are now trying to find new ways of becoming the market leaders.

Today, the most efficient method is price optimization: pricing is the quickest way to make sure that companies receive the highest ROI. This is the case since to optimize marketing, logistics, or CRM domains; multiple departments would need to be engaged, significantly delaying the process.

How Retailers Craft Prices

First of all, price optimization needs both historical and competitive data. A lot of retailers use tools that collect data manually (offline prices) and automatically (online prices). Regardless of how they choose to gather data, retailers are finding it difficult to analyze all of it considering how much data there is nowadays.

Retail managers typically rely on expert-based pricing, when they base their pricing decisions on their gut feeling, instead of data-driven pricing. They struggle to set the most optimal prices while reaching their KPIs. As a result, they lack time to evaluate the effects of pricing decisions and, therefore, cannot steer clear of going through the same failure once again in the long term. The same goes for successful decisions; retail teams don’t end up taking advantage of them.

Retailers also struggle with the fact that they don’t have a database where all the data is pooled into one location, holding information about every move that the retailer has ever made. As a result, it takes much more time to engage a new manager.

Those problems have caused retailers to reach a point where they can’t expand on the competitive market any longer. Instead, they search for ways that will help them leave their rivals behind.

Let’s go through one situation where artificial intelligence optimizes pricing.

How You Can Use AI to Optimise Pricing

Managing your data

In the beginning, companies need data whenever they are taking on machine learning algorithms. Those algorithms need both historical and competitive data to make strong pricing recommendations as well as sales predictions. Also, that data can’t span any less than three years, it’s got to be quality, the structure needs to be good, and it’s got to be fresh and all in one format.

Current data. Even though retailers already gather a bit of data, in many cases it’s just not good enough for AI. It happens since it could be stored in other locations and formats, the structure could be poor, or it could just be old. Occasionally, different business departments are in control of various data, making it complicated to extract. Therefore, the first thing that you’d need to do is structure the data and then put it all into a single format. Should the retailer not have enough information about some products, prices, or transactions from the past, companies could either restore it via an AI simulation or buy.

New data. Retailers can use an in-house or a third-party tool to gather competitive data or to put both of them together. When you need to select a data provider, there are three steps that we suggest you take:

  • Test out the quality of product matches for various categories of products;
  • Outline the criteria for quality data collection as well as delivery to make sure that the provider will continue abiding by them;
  • Secure the transparent tracking of the quality of both matches and data delivery.

Take into consideration an AI-powered optimization platform

Retailers have the choice of creating an in-house algorithm, or they can try to find a separate AI-provider. There may be a few that would rather have the internal system for many reasons, one of which would be the fear of their commercial data going out into the public. The thing is, though, this type of solution needs a lot of human, financial, as well as infrastructural resources to build an IT system, teach and keep up with the algorithm, and track how it’s doing.

As a result, the latter may be the better choice in the long run as they are much less expensive than internal solutions and they also keep retailers from engaging an entire IT team that will support them on a daily basis. Besides, there are systems available that ensure retailers commercial data security.

Regardless of whether it is in-house or outsourced, a price optimization solution takes advantage of neural networks to make predictions about sales as well as to offer recommendations that can assist companies with reaching their goals. It’s got many advantages in comparison to expert-based pricing:

  • It can evaluate a lot of data, the amount of which is unmanageable for humans.
  • It’s much faster than managers.
  • It holds onto every piece of data, even information about its successes as well as failures. That type of method lets companies easily scale as well as onboard new managers.
  • It learns about the nonlinear interrelations amongst items to offer counterintuitive pricing recommendations. Managers would never be able to suggest that kind of information.
  • It makes transparent data-driven pricing as well as promotional recommendations. These are then tracked and torn apart if needed.
  • It keeps retail managers from doing everyday jobs so that they can instead focus on high-level decisions.

Introduce a pilot

It’s typical for retailers to question how effective SaaS solutions are for a couple of reasons:

  1. They don’t know how the system operates; they don’t understand the logic that it uses, or they can’t figure out its recommendations. In other words, they look at the system merely as a “black box.”
  2. AI-powered recommendations seem counterintuitive. For instance, they may suggest selling at a price higher than that of a rival.

With a pilot, though, systems can show how effective they are in achieving a business goal, wiping away any concerns that the managers may have.

Before any pilot, the algorithm has to be trained based on the retailer’s current historical and competitive data. It takes in each data point to make predictions. At the same time, it learns from any mistakes that it makes. As soon as the predictions are precise, a pilot can commence.

As soon as it’s done and the results are acceptable, the entire algorithm can begin to work and scale out accordingly.

Conclusion

Retail companies have come to a point in their business lives where they require innovative ideas that will help them boost their operational efficiency within the competitive market which.

The most feasible strategy is price optimization fueled by machine learning algorithms. It doesn’t need much time nor massive investments in comparison to other solutions. Also, it is fast, capable of handling a lot of data, makes data-driven recommendations and lets managers focus on more strategic jobs.

The biggest struggle that retailers face when taking on AI is the quality of data and whether or not the data is in the proper format. In addition, managers are just not ready to listen to the recommendations from the algorithm before understanding the logic behind it. A pilot project comes in handy in this situation; it can help prove how effective the algorithm can be for the business management, getting rid of any trust issues that were there before.

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