Several studies confirm what most retailers already know: Price is a huge factor in customers’ buying decisions. How can AI help online and offline retailers set prices that attract consumers and still turn a profit?

 

Many factors influence successful pricing – market conditions, general and local demand, seasonality, etc. (And for e-retailers, the variability of each of these factors increases considerably, making setting reasonable and profitable prices that much harder.) Large retailers like Amazon can adjust product prices multiple times per day; for many other companies, this isn’t an option.

Enter AI, wearing its price-optimization hat. An IBM study shows that 79% of retail and consumer products companies plan on using automated customer intelligence solutions within the next year. Why? There are two benefits:

  • Better pricing optimization.
  • Freeing up time to focus on other strategic areas.

AI and machine learning (ML) can improve retailers’ price and promotional activities across channels; in this post, we’ll review some specific ways retailers can solve the pricing puzzle with AI.

4 Ways Retailers Can Use AI and ML for Better Pricing

Finding optimal prices

To set optimum prices, a product manager would need to have analytical and computational superpowers that would allow them to process an enormous amount of data each day. AI can manage this feat of processing handily, providing price guidance based on SKU, product portfolio, point of sale, customer, and channel. Following such data-based pricing suggestions improves profitability, enticing customers while minimizing competition with other items in the portfolio.

Maximizing revenue for niche products

Algorithm optimization is especially helpful for product like private label items that can be sold at a higher price and that set you apart from the competition. It’s also useful for items that other retailers might stock but that consumers don’t buy based on price. For example, you might sell gourmet foods; in such a setting, other factors are usually more important than price point. In both situations, AI can help you set prices that both draw customers in and maximize your revenue.

Analyzing and selecting price points

There’s always some risk involved in changing prices, but AI and ML’s predictive capabilities can help you mitigate it. For example, ML can be used to test promotional and pricing strategies and model their probable impact, including how customers will react to the change. AI can also deliver results for multiple price change scenarios, allowing the retailer to evaluate which option will best suit their needs.

This gives retailers a lot of room to experiment (virtually, that is). It also provides an element of security: whatever option they choose, retailers will know the likely outcome.

Enabling personalized pricing

AI can analyze (in real-time) a tremendous number of variables relating to purchase patterns:

  • Recent and special events
  • Footfall traffic
  • Time/seasonality data
  • Weekend vs. weekdays
  • Weather
  • Customer preference
  • Social media popularity/response

It can also do this on an individual level, analyzing a consumer’s history and preferences and generating personalized price offers.

Solving More than Just Pricing

By scanning through massive amounts of data and analyzing multiple scenarios, self-learning algorithms can help retailers find the most relevant prices for each item. There are many hidden yet dependable factors that are just waiting to be analyzed; these can maximize revenue and sales for the entire product portfolio.

AI also allows retailers to develop specific strategies, e.g. for items that draw more traffic as opposed to those that drive margins. And the more AI and ML techniques are used, the more robust and accurate they become, factoring in shopper sensitivity and competitive flexibility down to the store-item level.

And, of course, there’s also the human element: by taking over time- and labor-draining tasks, AI allows retailers to focus on building stronger relationships with their customers. That in itself is a win.

Authored by: Imran Saeed, Director Analytics at  Absolutdata  

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