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.

Historical sales data and regular sales data enrich the recommendations related to accessories and complementary products. These sales data files are not used for personalization (e.g. they are not used in the case of post-purchase tactics).

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.

Yes, in most cases. For mobile apps using web technologies (e.g. HTML/JavaScript in a browser or embedded in a native shell) our JavaScript API is directly applicable for collecting data and delivering recommendations. For fully native apps, you could use our web service API directly.

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.
• 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 Interactions program).

You do. If you are using VCE, you can include great-looking recommendations as part of the email creation process. If you prefer to use your own HTML code, you can use the Email Widget Designer to integrate the recommendations in your email.

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. The links served up in the Predict widgets are the links that the you have provided in the catalog, i.e. they are your own links. Concerning search engines, Google have been executing Javascript and crawling our links since at least 2011.

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.

All the JavaScript snippets are asynchronous. No element of the Predict infrastructure can slow your website or affect user experience in any negative way, even in the case that the Predict service is completely unreachable for the visitor (e.g. due to network issues). The worst case scenario is that Predict widgets will not be displayed, without affecting the rest of the page.

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.

On the Live Events page, times and dates are always reported in UTC, and not in your account time zone.

No. Make sure that you upload product images that do not use Chroma subsampling. This compression technique is not supported and your image will not be shown in recommendation widgets.

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.

The actual user experience will be determined by all the other elements in your website (i.e. when they start calling the recommender and how long it takes to load the product images from your server). An additional note is that our Javascript API is fully asynchronous, which means it will in no way hold up the loading of your website, even if there is some network connectivity problem between the visitor and our servers.

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 or regions 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 their activity and check the contents of their shopping cart. This activity is stored in the web behavior fields.

A cart is considered abandoned when:

• The cart contents have been modified.
• The cart still contains items.
• There has been no purchase since the changes to the content.
• There have been no browsing interactions for 30 minutes (i.e. 30 minutes after the visitor has left the website).

If one of the cart items was purchased, the remaining items are not considered to be abandoned.

The relevant web behavior fields (predict last abandoned date, etc.) are updated every 3-4 hours. 

Emarsys offers the following out-of-the box solutions to cart abandonment:

• Predict cart recovery
• Abandoned cart Tactic
• Abandoned cart programs in Interactions
• Fast Abandoned Cart Tactic

All the web behavior fields are updated in cycles that run every 3-4 hours.

• Fields with session and purchase data are updated with new values, if any events have been registered since the previous update. If not, they are left untouched. This means that the predict last session fields, for example, will always contain data from the last session no matter how long ago that was.
• Fields with abandonment data are emptied of their values if the criteria for abandonment (see above) have not been met since the last update.