Artificial Intelligence

AI-powered price optimization: how to allocate tasks between the software and pricing analysts

traditional data warehouse

The short answer would be to make AI responsible for routine and free up managers for value-adding, creative and strategic tasks. The article is a guide on how to do it in the best way possible.

Artificial intelligence and its core element, machine learning, are here to change things. They are expected to bring unprecedented capabilities to humans, optimize external and internal business processes, and boost consumer demand. It is valid for a variety of industries, including retail. In fact, retail is among eight sectors which are poised to benefit from AI the most.

Why should you start with price optimization?

In retail, everything revolves around the customer. The whole multi-level, robust and extremely complicated machine of retail exists to reach the only goal — to make the shopper buy. Prices are a major lever retailers pull to lure buyers into spending. The price of a product is the first thing customers notice and care about the most. Therefore, pricing should be among the priorities when it comes to optimizing retail businesses.

Advanced analytics is already boosting retail pricing. Look at Amazon or Walmart, which have been using such cognitive technologies for pricing optimization for years. It is very likely that some of your competitors are already introducing intelligent automation in their operations. However, just the fact of adopting pricing analytics software is insufficient. You should use it right to leapfrog your competition.

Getting you pricing analysts and processes AI-ready

It all starts with the end-user, which is your team. The better you educate your employees, the higher the ROI you will get. Retailers are already using machine learning to design what may be called “the retail employee of tomorrow”  that will be more capable, effective, and creative than ever before.

However, to reach this stage, pricing managers need to accept AI’s help. Retail teams are often not ready psychologically for using price optimizer software. For the most part, managers like any other human prefer sticking to habitual and comfortable things which they have been already using for years. Letting intelligent automation is their lives requires the full-time involvement and support of their top management. Team preparation consists of three parallel steps and can be done via a series of workshops.

Learning to use pricing software

Just like getting familiar with Slack or Excel, pricing analysts need to master the art of performing key operations with the help of the AI-powered tool. These include learning to launch repricing, set goals and restrictions, order new products, and track the software’s performance, among many other tasks.

Interpreting and applying AI’s price recommendations

Self-learning algorithms are utilized where there is a need to process large data sets fast and reveal patterns unattainable for humans. For example, setting optimal prices requires analyzing billions of data points, sifting through millions of pricing scenarios, seeing hidden relationships between such factors as products’ elasticity of price and demand (and dozens of other parameters), and suggesting the best pricing strategy to hit a particular business goal like increasing sales or revenue. In other words, the dirty work is AI’s job.

What pricing managers need to do is to feed algorithms with relevant data, set goals and limits, and track AI’s performance. They should also course-correct the machine, if necessary. It should work like this: you tell the algorithm, where you want to get, and it gives you the optimal way to reach your goal.

Very often, pricing teams cannot debug the logic behind every algorithmic price suggestion. This leads to their refusal to apply such recommendations as managers are afraid to fail their KPIs. However, with AI, employees need to stop tracking the performance of every product in the portfolio and see the big picture. If the overall revenue and sales are growing the way they are supposed to, then everything is fine.

Learning to trust the machine requires time and real proof, which brings us to the next step.

Testing the effectiveness

Launching a market test to allow pricing managers “to play” with price optimization tools is a necessary step in adopting AI. Once your analysts experience the boost of their effectiveness, as well as of the quality of their pricing decisions and the overall financial performance, they start using the software more often (and the great thing about AI-powered algorithms is that the more you use them, the better they get).

The results of employing price analysis software can be stunning — from increasing revenue by 16% to boosting sales by 24.7%.

All in all, retailers need to accept that AI is already here — and it is changing retail significantly. Using cognitive technologies in pricing can be a wise option to entice customers and defeat your competition. But AI adoption needs to be done right, which means preparing your team thoroughly. Preparation includes turning pricing analysts into proficient users, teaching them to trust the algorithms by showing them real-life results of AI-backed price optimization, and educating managers on how to understand and apply algorithmic recommendations.

It is as simple as that: the more AI-ready your team and processes are, the higher the revenue you get.

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