Yes. On the Historical Sales Data page you can jumpstart the recommender by importing historical data, but also regularly import sales data from offline sources.
Yes. If you are comfortable using Google Tag Manager, you can do the integration this way. Having said that, you will still need to review the Predict documentation to make sure you are sending all the required information. You will also have to check that all the correct scripts have been added in relation to various events and the required input variables included.
However, in this case there are additional details to cover relating to user identification, tracking, etc. Before committing to such a project, please discuss the specifications with the Emarsys Predict team.
No. Each Emarsys client’s data and analysis results are stored separately. Even crowd behavior from similar industry verticals is treated confidentially. We take data security very seriously and mean it when we promise not to use your data for other than your own benefit.
Unlike the Email Personal Recommender, which will show products that a contact might be interested in buying depending on their online behavior, the Post-Purchase Recommender widget will show products which are specifically correlated to their last purchase. As an example, let’s suppose they just browsed your website and ended up purchasing a digital camera. While Email Personal will show more digital cameras (according to your online behavior, you’re interested in cameras), the Post-Purchase Recommender will show memory cards, camera cases, etc.
Therefore, as a best practice:
- Email Personal should be part of routine newsletters and email retargeting (as shown by the Email Retargeting blueprint in the Automation Center).
- Post-Purchase should be included in emails which are going out around a day or so after a purchase was made (which can also be triggered by an Automation Center program).
By default they are using mostly the same signals/models, but on a different schedule. On the website these recommendations are displayed in real time, meaning that they change as the session progresses and each new page is loaded. In contrast, email recommendations are calculated nightly, so that the email content is stable and does not change while the recipient is reading it.
This is a built-in feature of Predict: stock availability is controlled through the catalog. Items that are temporarily out of stock or no longer offered should still be included in the catalog, but the field Available should be marked as FALSE. These products will not be displayed in the recommender widget, but will be included in the analysis.
Yes. Predict only collects behavior data using anonymized customer IDs, and it and anonymizes visitor cookies. No personally identifiable information is managed or handled within Predict.
Yes. There is a limit of 1 MB for product images in the Email Widget Designer; larger images will not be displayed. In addition to this, image dimensions should not exceed 1280 x 1280 px or the image will not be displayed.
The Live Event View shows all images the same way as a browser does, so please make sure the images look good in the Email Widget Designer, too. The Email Widget Designer shows the actual email recommender image, exactly as it will render in an email.
No. In a single Emarsys account we can only put one Predict dashboard and can only connect the CMS to one Predict account. With Website Recommender there is less of an issue with multiple predict accounts, obviously.
Nothing! If there really are such regional tendencies, since all visitors are interacting with the same recommender engine, the engine will learn from this and reflect these differences automatically. And because individual customers get the PERSONAL recommender widget, which uses personal browsing history, this whole issue is not relevant for the personal email and website recommendations.In other words, this is precisely how Predict is designed to work and there is no need to customize the engine or the data-collection in this matter. All localization options in Predict are related to managing languages, and differences in stock/availability.
This depends on what you mean by ‘response time’. Our server-side latency for answering a web recommendation request is below 1 millisecond at the 99th percentile. The client-side response time will be determined by network latency – as our servers are located in Ireland, this works out to be around 10-40 milliseconds for most of Europe.
Our uptime on the current AWS infrastructure averages 99.998% over the year (12 minutes downtime in total).
Recommender widgets display information from the product catalog – whatever is passed on from your database is displayed in the widget. Therefore there are no language issues here. Even if your online business is international, and offered to users in different countries and under different domains (e.g. international mystore.com site, and localized mystore.co.uk), this is not a problem.
Multi-domain integration uses a single, aggregated product catalog that contains all the localized variants of the catalog data for each item ID. Localized data (product names, prices, etc.) need to be added to your main store catalog export. The Emarsys Predict Dashboard and supporting documentation are currently only available in English but will be translated into all Emarsys languages soon.
When a visitor browses a site we track them and check the content of their shopping cart. If there are items in the cart and no purchase has occurred by the end of the day, the cart is judged to be abandoned (in a future release we plan to offer the option to reduce this period to one hour). If multiple items are added to the cart and only some of them are purchased, this is not counted as an abandoned cart. However, the remaining items will be again regarded as abandoned the next time the contact browses the site without making a purchase.