Simulation of consumer behaviour with Gen AI
The challenge
Targeted, data-driven management of marketing budgets is essential to ensure the long-term competitiveness, brand awareness and sales of consumer goods. Manufacturers must keep pace with ever-changing market conditions and respond to growing consumer expectations in order to remain competitive. Key initiatives include the roll-out of campaigns and promotions, sales promotions, new product launches, customer loyalty programmes and much more. The advancement of innovations and a pronounced flexibility in advertising investments are decisive factors for increasing a brand’s market share.
Our response
The development and use of synthetic consumers in the consumer goods industry is an innovative approach that enables companies to run simulations around the expected consumer behaviour. Synthetic target group profiles can be used in simulation environments to mimic customer behaviour predictions in different scenarios. This approach enables consumer goods manufacturers to better understand products and optimise marketing strategies.
Through synthetic consumer profiles based on real customer data, realistic and data-driven simulations of consumer behaviour are generated to make better business decisions. This risk-minimised approach to product testing, marketing strategies and target group identification helps consumer goods manufacturers to better understand consumers and optimise budget allocation.
The key POINTS
Data Management
Our solution includes the integration of extensive data sources, including demographic information, purchase history and cycles, purchase channels, customer loyalty information, pricing and promotional data as well as competitive data. This data is stored in a central data repository and is prepared for Gen AI-supported analyses.
Simulationen
Intelligent simulation applications enable consumer goods manufacturers to test various scenarios and marketing strategies virtually. The tools use synthetic consumer profiles to simulate customer behavioural tendencies, such as the probability of purchase in various situations. Factors such as product placement, prices, advertising and competition can be considered.
Usability
The solution offers user-friendly interfaces to quickly capture important information, interact effectively with the application and make informed decisions. Users can start simulations, adjust parameters, visualise results and generate reports.
Adaption
The Gen AI models and algorithms are regularly updated with growing data volumes and increasing requirements in order to keep pace with changing market trends and customer preferences.
The Use Cases
Target group identification: The solution uses advanced algorithms and AI techniques to precisely segment customer target groups. Behavioural patterns are analysed to determine which customer groups are most likely to buy a particular product or which marketing approaches are most effective. This enables companies to target customers more effectively and run personalised marketing campaigns.
Product development and testing: The synthetic customer profiles created by Gen AI are based on extensive data analyses. Companies can test their products virtually in various scenarios to find out how they are perceived by different customer segments. For example, the acceptance and purchasing behaviour can be simulated when a new product is launched. This makes it possible to make product improvements even before they are launched on the market.
Market forecasts: With the help of intelligent market forecasting algorithms, future market trends and developments can be predicted in the shortest possible time on the basis of the simulation results. These forecasts can help to make strategic decisions, e.g. with regard to production capacities, stock levels and marketing activities. This helps companies to strengthen their competitive position.
Our objective
Synthetic consumer profiles open up new opportunities to make data-driven decisions and utilise marketing budgets more efficiently. Realistic expectations and forecasts for the success of marketing activities are provided based on dynamic simulation scenarios. Brand owners gain a better understanding of consumer preferences and behaviour and recognise market trends at an early stage. This allows resources to be better allocated within the company in order to increase competitiveness and market success. The use of synthetic data minimises risks in product tests and marketing strategies. Companies can draw on realistic scenarios without involving real customers.
Would you like to take your marketing to a new level with synthetic data? Then please contact us.