How to Outsmart Competitors using Machine Learning

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Peter Drucker once said that the true purpose of a business is to create and keep customers. To make customers see value and to keep them loyal to the brand, it is important to increase the perceived value of the brand’s offerings. Recognizing the value of these offerings enables retailers to guide consumers toward better, and sometimes, more expensive option effectively. Businesses today have great visibility internally into their sales and marketing strategies, but lack in-depth insights especially in terms of product matrix, pricing strategies and consumer buying behaviour. The last few years have seen businesses worldwide increase their competitiveness within most markets by investing more into Competitive Intelligence (CI) programs to gain better visibility into competitor pricing and strategy. Competitive Intelligence is a company’s effort to gather and analyse information about its industry, business environment, competitors, and competitive products and services.

To outrun competition, businesses are partnering with AI/ML data services companies like NextWealth to convert data into intelligence and provide real-time view of their competitive landscape. Having found great value in machine learning algorithms, such partnerships help identify trends and insights from vast streams of data to help make faster decisions that can make businesses competitive.

Price Matters the Most

It is true! The price of a product is what catches the attention of shoppers and makes them buy from a particular retailer or marketplace. While 2020 is cited as the age of value—price is still the king. In the recent times especially, economic growth is slow and affordability is critical in every income bracket in every country and across every age group. Machine Learning creates opportunities that enable anything – from emailing customers when price drops below a certain price to reminding customers to re-order monthly refills!

According to a PwC Report, as much as 60% of shoppers choose retailers with optimal prices. So, what is Optimal Pricing then? Retail prices that do not agonise customers, while maintaining profit margins without cutting the sales of other items in the product portfolio. It is quite common to get confused between dynamic pricing and optimal pricing, given that the two are quite often used interchangeably. While the former allows retailers to dynamically alter the prices of their products to match their competitor’s price, the latter focuses on finding the price that maximizes a defined cost function like profit, considering several factors to suggest price or price range for different scenarios. However, for largescale retailers and bigwigs like Amazon and Walmart, setting optimal prices for thousands of products weekly or sometimes even daily, would require superhuman powers and abilities to enable faster decision-making capabilities. Or simply be equipped with smart machine learning models.

Achieving Optimal Pricing

Pricing strategies have evolved and transformed from applying standard mark-ups to base cost, to being capable of predicting the demand of products or services and finding the best price. AI powered price optimization entails 3 critical steps: 

1. Data Preparation; 
2. Data Enrichment 
3. Build and Deploy Machine Learning based Pricing algorithm.

Preparing and collecting data can be daunting since the data is often unstructured, stored in different sources and has many errors. To help businesses in competitive intelligence, the data for the machine learning model must be complete, high-quality and well-structured. A seasoned Data Enrichment Service Provider will be critical to ensure quality and speed up the data preparation and enrichment process. Whether to outsource or hire an internal team to help gain an edge over competition and set the stage for price optimization is a hard choice and is mostly dependant on outsourcing strategy, security concerns etc. Retailers need to be absolutely sure and know exactly which products are better to use machine learning algorithms or rule-based pricing for. In most cases, products that are offered by all or most competitors generate traffic to online market places since shoppers look for the lowest price for such products and this requires constant competitive prices monitoring. Exclusive products can be sold at a higher price and machine learning algorithms are required to influence demand and alter prices optimally for generating maximum revenue. Products that are offered by competitors, but which do not have to have the lowest price to attract customers can use machine learning to calculate how much the brand affinity allows retailers to raise prices for these particular items.

The NextWealth Advantage

One of the largest retail corporation, who also happens to be NextWealth’s prestigious client, has essentially created a bridge by enhancing the customer’s shopping experience through machine learning. AI removes a lot of the guesswork and manual interference required to come up with pricing strategies. Using customer profiles and purchasing habits, these models can teach themselves to create potential sales that meet all criteria around margins, existing inventory, and repeat business.

There are numerous AI and Machine Learning techniques available for competitive intelligence, especially price optimization. The right choice depends on the data available, industry and partners. NextWealth’s AI/ML data processing and enrichment services have been applied successfully by several companies to drive significant gains to the top and bottom line.

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