Pricing and Marketing insights: how to create synergies?

Customer is king. And in recent years, some markets have been turned upside down by new entrants that have responded to consumer expectations in new ways, making their way into the rankings of the world’s most successful companies. The discovery of marketing insights – achieved by directly interviewing customers or by making data speak for itself – has become even more critical to the success of retailers. When customer expectations are changing at a frenetic pace, the challenge is to identify, interpret and operationalise them quickly enough to stand out from the competition.

Thus, speaking of pricing management: how do you actually do this and offer the right price to consumers?

Mastering Big Data to gather insights 

Identify internal information sources

There is a real mapping exercise that should not be neglected to identify all the sources of data within the company that can contribute to the collection of insights: feedback from the field, till receipts, frequency of purchases, traffic, feedback from customer service, stock levels, satisfaction surveys, and information on the web about the brand (listening to the « noise » around the brand). We need to think outside the usual business silos.

External sources

By their own means or through the intermediary of service providers, most companies have information on their environment and their customers which benefits from being put into perspective with internal data: socio-cultural monitoring and trend analysis, competitor data, information gathered on the web / social networks on the sector, etc.

Process the insights gathered 

Whether internalised or outsourced, this stage requires two types of skills:

 Data engineering

This involves making data accessible to all teams, which includes collecting and cleansing data, as well as ensuring that information is fed back to the business applications.

 Data science

It is about making sense of the data. Data scientists handle the tools and computer languages needed to rapidly explore large volumes of data, particularly those based on artificial intelligence. They can draw on business knowledge to interpret the data in a way that is relevant to the company. Once insights have been identified, they implement automated means of updating them, or even enriching them and evolving them with machine learning.

Integrating marketing insights into pricing strategy

A stage that is not always well anticipated: the challenge is not only to bring out consumer insights but above all to be able to transcribe them into operational rules.

There are several ways to integrate them

• Transcribe them into product attributes, to refine pricing rules
• Determine them in mathematical form, using algorithms to improve sales forecasts or the performance accuracy of indicators for example
• Include them as information in analysis screens to guide decisions

The benefits of this integration: optimising the shopping basket while guaranteeing customers a shopping experience that meets their expectations.

Possible impacts on pricing strategies: value created for both the brand and its customers

• A more refined and relevant catalogue segmentation:
  • by product typology: a study conducted by our Data Lab for a drugstore chain has made it possible to use an algorithm to distinguish the products that generate traffic in the shop from « complementary » products that are linked to them, and from those purchased independently. This information helps the store know how to adjust price positioning of these products and promotions to produce the targeted effect (attracting customers, increasing the purchase basket, building loyalty through personalised offers)
  • according to price sensitivity: for a tyre company, certain specific brands will be strongly compared on price by customers, such as Michelin in France, while for others, other characteristics are favoured
  • according to the target: McDonald’s, for example, carried out a study to segment its customers, understand their weight in sales and adapt its offers according to whether they are families, people coming with friends, single people, etc.

• Pricing tailored to local customer preferences: a fashion retailer was able to use analysis of its online sales and delivery addresses to determine a ranking of its customers’ favourite products in different cities. Highlighting them in the customer journey and specific pricing offers further boosted their sales.

• More relevant chaining between products: identifying the attributes valued by customers makes it possible to place products on a coherent price scale and to propose prices that reflect this value (a consumer would understand that an organic product is more expensive than a conventional product, but the opposite would give rise to mistrust regarding its quality).

• Designing and monitoring consistent, better-targeted and more profitable promotions across the brand’s channels

What capabilities to look for in a pricing tool?

• Integrate different types of insights according to your needs insight is computed using a multitude of data from e-commerce, point-of-sale such as receipts, loyalty cards, multi-channel customer journey analysis, quantitative/qualitative studies, etc., which are cross-referenced and analysed via different indicators. The frequency of analysis varies. Some strategies require the processing of massive data flows on a continuous basis in order to adapt to market changes. This article provides an overview of retail initiatives to regain margins through data analysis. marketing de différentes natures
• Be able to use insights to refine product chaining insight is computed using a multitude of data from e-commerce, point-of-sale such as receipts, loyalty cards, multi-channel customer journey analysis, quantitative/qualitative studies, etc., which are cross-referenced and analysed via different indicators. The frequency of analysis varies. Some strategies require the processing of massive data flows on a continuous basis in order to adapt to market changes. This article provides an overview of retail initiatives to regain margins through data analysis. pour affiner le chaînage des produits
• Implement accurate pricing rules, based on insights, that can be deployed quickly and widely
• Easily add new pricing rules or new scopes to evolve your strategy with the insights discovered (geo-pricing, product cluster, seasonality, product life cycle…)

Data science now enables companies to identify marketing insights based on data more quickly, and to implement AI models capable of enriching the knowledge of customer expectations according to the new data arriving in the information systems. Taking these consumer insights into account allows for fine-tuned and precise price image management, and for better targeted prices and promotions that are more consistent with the brand’s image and consumer expectations. One thing is certain: investing in research or AI to gather insights is a waste of money if you cannot then integrate them into a pricing tool and deploy the appropriate operational strategies.

Going further with Mercio :

Mercio’s price optimisation software is designed to bridge the gap between marketing insights and pricing strategy. This means that each retailer can express their own business logic in a flexible and scalable solution, while benefiting from a solution developed according to the best pricing practices in retail. Discover the presentation of the Mercio platform in the Our solutions section.

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