This is a sample scenario that we want to reproduce:
Meet John. John wants to buy a new car. During his lunch break he uses his iPad to look at a manufacturer website and finds a model he is interested in. He engages with the content but then his lunch break ends! He leaves the site and continues with his working day.
The following weekend, with more time on his hands, John returns to the website. John is recognised as a returning visitor and the data held on him is interrogated. It is known that he did not sign up for the manufacturers newsletter on his previous visit so the landing page is personalised to prompt him to sign up. Also, the vehicle that he was interested in is given prominence on the site. John follows the prompts and signs up to the newsletter.
One week later John receives an e-mail, but he is busy with his work and he doesn’t open the e-mail before the evening. By this time he is really tired and doesn’t click through to the website from the newsletter. He puts down his iPad and goes to bed.
Based on John’s previous behaviour, the next newsletter is sent to him on the evening. John opens it and as it focuses on the vehicle he has already expressed an interest in, he clicks through to the website. The website recognises him and displays information about where and when he can test drive his preferred vehicle, along with an incentive to do so. John follows this link and books a test drive for the next day.
These are the steps to perform all the actions described inside the scenario:
Indeed all the integrations are not super simple and they strongly leverage API calls between the various modules, but now you have an idea on how to perform such personalization.