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You’re reading the first chapter of the six-part guide, “How agencies use AI to increase their impact and develop new areas of expertise”.

Agencies at the forefront of AI-optimized marketing

AI is making huge advances in every field, in every corner of the world. On the medical front, for example, AI-optimized solutions have reduced false negatives and false positives in breast cancer screening and mammograms by 9%. And the Look to Speak application uses AI to give people with speech and motor impairments a voice, helping them to express themselves by selecting phrases on their phone using simple eye movements.

On a different, smaller scale, AI can also help solve new challenges in marketing, where the goal has always been to engage customers in a relevant and respectful way to drive results. This is becoming increasingly difficult as today’s consumers interact with a multitude of touchpoints that manual approaches can no longer handle.

Developing skills to increase efficiency

Every marketing strategy is shaped by human judgment and ingenuity, including the ability to leverage AI technologies.

AI helps agencies amplify their expertise to meet business objectives and drive future growth. It’s a competitive advantage worth seizing, backed by statistics: almost two-thirds of agencies say that adopting AI and machine learning is an important or absolute priority for consolidating and developing skills.

“The key to success lies in the ability to create multidisciplinary teams that blend agency profiles with the client’s ‘field’ teams, combining technical and business profiles.”
Jérôme Petit, Managing Partner of Artefact and Retail Practice Lead

Artefact, in partnership with Google, leveraged data and AI to enhance customer satisfaction for the Carrefour brand. One of the actions taken by Carrefour and its agency was to target food waste in the bakery and pastry sections of its hypermarkets in France.

To address this challenge, the Carrefour and Artefact Group teams started with data from sales receipts generated by over 200 hypermarkets in France. Every day, this data was collected, cleaned and enriched with external sources – such as calendar data, for example – to build up a sales history over several years. This collection resulted in thousands of configurations for a single day, depending on assortment, product prices, promotions, etc.

This data was used to train supervised machine learning models based on decision trees, to determine the relationships between the target variable (future sales) for each product and explanatory variables (promotions, cannibalization, etc.).

The algorithm’s recommendations were first tested in pilot stores, solely on viennoiseries (Vienna-style pastries), to obtain feedback from field teams. This mix of skills was essential. As Jérôme Petit explains, “The bakery-pastry department professionals were able to explain their trade and their needs, as well as contribute their vision, in order to guarantee the success and adoption of the solution in actual practice”. Their feedback was used to improve the models, before the solution was deployed across the entire hypermarket bakery-pastry range.

Scaling up proved very successful. Every month, tons of viennoiseries and pastries are saved from being scrapped. At the same time, sales are up thanks to fewer stock-outs at the end of the day.

The acceleration of Carrefour’s digital transformation was made possible by the creation of comprehensive, expert data teams within the company, and the deployment of data platforms in all countries where the group is present.

AI clearly allows agencies to deliver higher-value services while enabling brands to achieve better results, regardless of their level of digital maturity. It’s a great way of strengthening the brand-agency relationship, which has already proven its worth.

A recent American study supports the notion that when this relationship is considered at the level of overall brand strategy, and not just at the operational level, brands tend to have better-performing marketing.

Specifically, the study reports that the most successful marketing services work with more agencies on average than those that perform less well. They are also more likely to involve their agency partners in the overall marketing strategy, while services with “average” results see their relationship with agencies as mere campaign executors.

41% of agencies now use performance-based remuneration models.

Incorporating AI-optimized solutions can also enable agencies to commit to business outcomes and thus be preferred in tenders.

Increasingly, marketing executives base their decisions on strategic impact and demonstrated commercial results, rather than staff size or the number of hours allocated to campaign management. To meet this demand, agencies are revising their traditional fixed compensation and fee models for more flexible, growth-focused models. As a result, 41% of agencies now use performance-based remuneration models.

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