Optimize your promotions with AI

The multi-channel contact strategies put in place by the brands allow them to gather as much information as possible about their customers. The aim is to offer them services and/or products that meet their expectations. However, the collection of information is not enough; it must be associated with its processing, in order to collect (and analyze) reliable data, necessary for the development of an effective marketing strategy. Although information processing is an activity that most often falls under the responsibility of the staff, it is generally very difficult for them to take on because of its time-consuming nature. Therefore, artificial intelligence (AI) meets the needs of modern companies. Let's find out how it can be used to optimize sales promotions.

Definition of key performance factors to boost sales

• the quality of the data collected (garbage-in, garbage-out) on the clientele;
• the use of static models, which do not facilitate data modeling
• insufficient appropriation of the solutions by users and managers; and
• the difficulty of different departments in the company to collaborate on a self-learning tool.

These failure factors can be transformed into key performance factors if companies decide to make some changes in the way they conduct their promotions. These changes should cover 4 dimensions: data, modeling, ownership over time and collaboration.

Data

In order to develop a good promotional strategy, a company must make sure that it masters the business processes and local specificities. To do so, it will have to equip itself with powerful processing software, capable of correctly indexing data and eliminating inappropriate shortcuts and "one-size fits all".

Modelling

Static models must be replaced by a self-learning tool. Indeed, integrating an AI into a sales promotion strategy makes it easier to process large volumes of data. The use of continuous learning technology will also allow the company to improve its predictive reliability.

Appropriation in the long term

To optimize your promotions, it is best to opt for solutions that are simple to implement. It is even ideal to train the different actors so that they appropriate the techniques quickly and durably. In addition, it is advisable to use impactful dashboards adapted to key uses.

Collaboration

AI offers agile solutions to boost sales

In 1956, an American scientist by the name of Marvin Lee Minsky stated that AI is: "the construction of computer programs that perform tasks that are, for the time being, more satisfactorily accomplished by human beings, because they require high-level mental processes such as: perceptual learning, memory organization, and critical reasoning".

From this definition, it is easier to understand the importance of AI technology in the implementation of a promotional strategy for the benefit of a company's customers. Indeed, the use of a computer algorithm for information processing allows companies to fully exploit the potential of data, but also of their teams to finally reach this famous break-even point; which was still impossible a few years ago with the various promotional programs set up thanks to tools such as PEA (Post-Event Analysis), TPO (Trade Promotion Optimization), TPM (Trade Promotion Management), etc.

In concrete terms, a solution based on AI will allow to:

• save time in the accomplishment of tasks thanks to a clear and fast analysis of data;
• improve decision making;
• increase ROI and anticipate trends based on consumer behavior;
• boost sales and margin by stimulating volumes and orienting the mix;
• etc.

Ultimately, a company's use of AI in developing promotional offers ensures reliable projections for future plans. However, the algorithm that best suits this type of application must be used. Machine learning can be a rather interesting solution.

Machine learning: definition and workings

Machine learning is an AI technology that gives a machine the ability to learn by itself without explicit programming, using statistical probabilities. The objective of machine learning is to teach a computer, for example, to analyze and process complex information in order to make accurate predictions. This science is quite modern since it meets the current needs of companies in terms of marketing strategies.

How does machine learning work?

Supervised learning induced by the use of machine learning technology consists in teaching an algorithm to make predictions on unavailable or future data. This is called "predictive modeling". However, the algorithm must first be fed with labeled data to analyze. Thus, the theory that Big Data is intimately linked to the functioning of machine learning is totally founded.

If labeled data tells the model the recurring characteristics it must identify, unsupervised learning data forces it to identify and extract its characteristics from itself. In both cases, it is necessary to prepare the data well in order to prevent the training from being biased and the results from being affected.

In concrete terms, a computer system using this predictive AI, which is machine learning, will learn to discover repetitions or recurring patterns in one or more data streams. This learning will thus improve its performance in the execution of a specific task. The goal is to draw more or less precise predictions based on statistics.

Machine learning: an effective solution for optimizing promotions

Machine learning at the heart of data processing

Promotions have been part of the corporate culture for many years now. All sectors of the retail industry have adopted this practice to meet the demands of the market. It is not the most reliable way to build customer loyalty, but if the company knows how to do it right, it can reap great benefits.

According to experts, the success or failure of a promotional campaign will be determined by the strategy implemented by the company. Generally speaking, in such a process, it is important to ensure that the pricing is done on an objective basis and that the service to be offered meets a specific need. Considering the amount of data a company has on its customers, one might think for a moment that it is easy for a company to define a good pricing or inventory management policy. In reality, it is much more complex than it seems.

The difficulty already appears in the choice of the promotion to set up. Should you systematically offer discounts, prizes, an additional free service or organize flash sales? If the company opts for a discount policy (sale), should it increase or reduce the promotional intensity? You have to rely on the context, but also on the profile of the customers to know which type of promotion to adopt.

For example, for a company specialized in retail and wishing to offer discounts on products, machine learning will either perform a discriminant analysis in order to determine the ideal choice of products for a particular type of profile or a data partitioning to determine similarities in customer habits and thus accurately predict a purchase act on a product.

The machine learning algorithm is effective for price optimization

The implementation of a pricing strategy depends on the automated processing of data. From this data, the machine learning algorithm will be able to define a good pricing strategy. The data we are talking about here are:

• the product stock (category, packaging methods, brand, etc.);
• the different points of sale (size, location, assortment, etc.);
• information on the competition around the sales outlets (density, nature, etc.);
• economic factors (exchange rate, inflation rate, average salary in different regions, etc.);
• the company's commercial goals (optimization of margins, units sold, turnover, etc.);
• etc.

Machine learning for a comprehensive sales promotion strategy

Today, it is almost impossible to do without this technology. In addition to data processing and pricing, machine learning technology also serves other needs. It prevents the risks associated with the implementation of a sales promotion strategy. These are mainly:

• cannibalization (between products, over time and on multiple channels): this phenomenon occurs when a company competes with its other offers by lowering a price;
• Deterioration of brand image: the manufacturing defect of a product benefiting from a promotion or abusive promotions are factors that can lower the brand's reputation;
• loss of loyalty: promotions on products that do not correspond to consumers' needs tend to cause them to lose interest.

Thanks to its algorithm, machine learning ensures the personalization of promotional offers for customer loyalty and predicts the appropriate pricing strategy according to the existing product stock to avoid cannibalization.

Machine learning technology promises a bright future for marketing

For example, when developing a promotional offer, machine learning analyzes customers' buying habits, their website browsing behavior and their socio-demographic profile. Retailers can now offer each customer the right promotional mechanics.

This selective approach to promotion has the advantage of preserving the company's margin without sacrificing its turnover. Another advantage of this practice is that the competition is not able to decipher a promotional strategy based on a selective approach.

In conclusion, AI is beneficial for companies. It allows them to analyze a large flow of information in order to have reliable statistical data to develop their marketing strategy in order to optimize their turnover.