If you follow retail news, you’re aware of different tips and tricks retailers around the globe use to keep customer engagement high. They create cashier-free stores, self-driving shops, and use new engagement techniques in old-fashioned offline stores. But all these are lame attempts as long as the price is still a prevailing reason to buy stuff for 60% of shoppers.

That’s why modern retailers need an intelligent dynamic pricing strategy. Although new ways of collecting and processing data greatly help them with that, most specialists still use manual crawling techniques, Excel Spreadsheets, or simple price tracking solutions like Price2Spy/Prisync.

The aftereffect of such an approach is obvious: only 39% of retailers succeeded with useful insights.

Price adjustments made for the whole front store’s inventory in just a second, in response to real-time demand, is much more effective than those set manually with all the human mistakes. It is where machine learning steps into the room, giving retailers an option to optimize not only prices, but also business strategy, costs, and managers’ efficacy.

Keep reading to find out how exactly a retailer can employ strategic pricing and outperform his competitors.

Step 1: Errorless Data Collection

The very first thing a retailer needs to handle for strategic pricing is data. Before any pricing decision can be made, clean and useful data needs to be collected.

Before the rise of technology, data collection in retail was straight forward:

1980s — hire a person who will visit a competitor’s store and manually collect all data needed;

1990s — build custom crawler and collect all the data you need;

2000s — create more tools to allow a crawler to get past a competitor’s protection tools, and collect data from different marketplaces as well;

2010s — find and utilize a solution that will suit all of the above.

Nowadays, most items on the list are senseless. Custom crawler creation is too complicated, so the main goal for retailers is to find an appropriate solution that will act behind the described scenarios, and wisely use the machine learning techniques to:

Check itself for mistakes and double check data quality (anomaly detection to avoid parsers errors);

Automatically avoid website protection with self-learning human behavior imitation;

Inspect, clean, transform, and deliver data;

Produce data crawlers automatically.

All profitable and loss pricing decisions depend on the data quality. Auto crawlers, with a help of computer vision and machine learning algorithms — instance based learning — automatically extract structured items from the web page faster and more properly than by manual methods:

1200% crawler initialization time reduction, from 12 min per item for manual collecting to 1 min with auto crawler;

~70% cost reduction for auto crawler creation in comparison to the manual programming;

anomaly detection algorithms show deviations in the way crawlers work.

Dynamic pricing tools

To get started with strategic pricing, retailers can use one of the following solutions:

Competera is an advanced retailing solution based on a best-in-class data IQ and competitive intelligence family of products to gain leading edge pricing, promotions, inventory analysis, and management and optimization.

Wiser (QuadAnalytics) helps automate price monitoring, reprice your products based on customizable rules, and get actionable insights into current market trends. For enterprise-only retailers and brands.

Market Track is the leading provider of retail promotion. It offers both retailers and manufacturers competitive price monitoring, Excel-based reporting with custom filters, and more.

Upstream Commerce is a configurable, scalable, and accurate retail solution for price management automation and high data accuracy.

Step 2. Advanced Data Analysis

The next stage in handling a dynamic pricing system is data analysis that should lead retailers to correct and quick decisions based on direct price suggestions or recommendations.

To get more insights out of the collected information, there’s a need to visualize it. Good visualization provides the possibility to quickly grab all deviations and react on them. All the tools which we suggested for strategic pricing have this feature. Let’s take a closer look in the case of Competera Price Management.