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Personalised offers

In today’s highly competitive and connected environment, modern businesses can no longer survive with generic, static online content. Furthermore, marketing strategies using traditional tools are often expensive, hard to implement, and do not produce the desired return on investment. These systems often fail to take full advantage of the data collected to create a more personalised experience for the user.

Surfacing offers that are customised for the user has become essential to building customer loyalty and remaining profitable. On a retail website, customers desire intelligent systems which provide offers and content based on their unique interests and preferences. Today’s digital marketing teams can build this intelligence using the data generated from all types of user interactions. By analysing massive amounts of data, marketers have the unique opportunity to deliver highly relevant and personalised offers to each user. However, building a reliable and scalable big data infrastructure and developing sophisticated machine learning models that personalise for each user is not trivial.

Ofertas PersonalizadasNos ambientes altamente competitivos e interligados da atualidade, as empresas modernas já não podem sobreviver apenas com conteúdo online estático e genérico. Para além disso, as estratégias de marketing que empregam ferramentas tradicionais são, muitas vezes, dispendiosas e difíceis de implementar, sem produzir o retorno do investimento desejado. Estes sistemas raramente conseguem tirar o máximo partido dos dados recolhidos para criar uma experiência personalizada para o utilizador.User ActionSimulationPersonalized OfferLogicSystem ResponseQueueUser ActionQueueEvent HubStream AnalyticsPower BIMachine LearningCold StartProduct AffinityUserDocument DBProductOffersReferenceProduct ViewsOffer ViewsCache Update LogicAzure ServicesRaw Stream Data12354

Disclaimer

©2017 Microsoft Corporation. All rights reserved. This information is provided “as is” and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third-party data was used to generate the solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licences in order to create similar datasets.

Ofertas PersonalizadasNos ambientes altamente competitivos e interligados da atualidade, as empresas modernas já não podem sobreviver apenas com conteúdo online estático e genérico. Para além disso, as estratégias de marketing que empregam ferramentas tradicionais são, muitas vezes, dispendiosas e difíceis de implementar, sem produzir o retorno do investimento desejado. Estes sistemas raramente conseguem tirar o máximo partido dos dados recolhidos para criar uma experiência personalizada para o utilizador.User ActionSimulationPersonalized OfferLogicSystem ResponseQueueUser ActionQueueEvent HubStream AnalyticsPower BIMachine LearningCold StartProduct AffinityUserDocument DBProductOffersReferenceProduct ViewsOffer ViewsCache Update LogicAzure ServicesRaw Stream Data12354

User activity on the website is simulated with an Azure Function and a pair of Azure Storage Queues.

Personalised Offer Functionality is implemented as an Azure Function. This is the key function that ties everything together to produce an offer and record activity. Data is read in from Azure Cache for Redis and Azure DocumentDb, product affinity scores are computed from Azure Machine Learning (if no history for the user exists, then pre-computed affinities are read in from Azure Cache for Redis).

Raw user activity data (Product and Offer Clicks), Offers made to users and performance data (for Azure Functions and Azure Machine Learning) are sent to Azure Event Hub.

The offer is returned to the user. In our simulation this is done by writing to an Azure Storage Queue and picked up by an Azure Function in order to produce the next user action.

Azure Stream Analytics analyses the data to provide near real-time analytics on the input stream from the Azure Event Hub. The aggregated data is sent to Azure DocumentDB. The raw data is sent to Azure Data Lake Storage.

  1. 1 User activity on the website is simulated with an Azure Function and a pair of Azure Storage Queues.
  2. 2 Personalised Offer Functionality is implemented as an Azure Function. This is the key function that ties everything together to produce an offer and record activity. Data is read in from Azure Cache for Redis and Azure DocumentDb, product affinity scores are computed from Azure Machine Learning (if no history for the user exists, then pre-computed affinities are read in from Azure Cache for Redis).
  3. 3 Raw user activity data (Product and Offer Clicks), Offers made to users and performance data (for Azure Functions and Azure Machine Learning) are sent to Azure Event Hub.
  1. 4 The offer is returned to the user. In our simulation this is done by writing to an Azure Storage Queue and picked up by an Azure Function in order to produce the next user action.
  2. 5 Azure Stream Analytics analyses the data to provide near real-time analytics on the input stream from the Azure Event Hub. The aggregated data is sent to Azure DocumentDB. The raw data is sent to Azure Data Lake Storage.

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Demand Forecasting and Price Optimisation

Pricing is recognised as a pivotal determinant of success in many industries and can be one of the most challenging tasks. Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions. Expanding product offerings adds further computational requirements to making real-time pricing decisions, thereby compounding the difficulty of this already overwhelming task.