So it’s time for retailers to enter the game. The trick, however, is to do it just right.
Here’s why pricing store brands is a challenge
Accurate Amazon-like pricing remains a challenge when it comes to launching store brands.
“Retailers need to craft a price which is neither too low or too high. The price should not deter customers who would suspect that something is wrong if the product is too cheap. But it also should be low enough to generate traffic to this particular store,” comments Vladimir Kuchkanov, a pricing solutions architect at the global retail pricing platform Competera.
Kuchkanov adds: “Let’s say you want to sell your own Coca-Cola drink. How much should it cost so that buyers would believe that it’s safe to drink and yet more affordable, but also make sure that it does not eat into the agreed sales share of name brands? You can sell it by 60% or 70% cheaper or, vice versa, make it by 10% more expensive than a name brand (as some companies do). But how can you know exactly what the best price for this particular product is?”
Traditionally, retailers would use A/B testing to play with the prices. If they know that the power of a certain store brand is 60% as compared with a well-known brand, they price such products accordingly. Then they check the effect and scale this logic across all of the offered categories.
Taming private label pricing with AI
Setting the right prices for every private label item so that it maximizes the total profit is a manageable task for artificial intelligence (AI). Retailers like Amazon and the like have been using it for ages to offer just the right prices for their branded products at any given time -- in other words, to fuel their dynamic pricing engines. In fact, AI-powered price recommendations generate some 35% of Amazon’s revenue.
AI is famous for three things:
A huge computational power
The ability to reveal unseen connections and patterns
The ability to bring fast results
In dynamic pricing, AI takes into account the demand elasticity of every item in question in relation to all of the other relevant items. It also provides pricing analysts with data-driven price recommendations to hit three goals:
Maintain or boost margins
Build up customer loyalty
Here's how it works. You feed the AI-enabled engine with necessary data, set restrictions and give it the goals you need to achieve during a certain period of time. AI browses through thousands of scenarios in near-real time and provides you with the optimal option. Market tests show it can bring a double-digit profit uplift to retailers.
Why is it humans cannot do the same thing without technology? Because the amount of data they need to process to set the right prices is unmanageable.
What makes it even more impossible is that usually, pricing decisions need to be quick, as many retailers re-price once a week or even once a day.
To recap, private label products have a significant potential for growth in the highly competitive grocery retail market. However, setting balanced prices for every store brand still remains a challenge for retailers using traditional pricing methods. The option here is to follow market leaders like Amazon and start using AI-led dynamic pricing engines to succeed.