Optimizing your articles for autoreplies

Attention: Zendesk has renamed our bot capabilities. Answer Bot is now Zendesk bots, Flow Builder is bot builder, and Article Recommendations are autoreplies. For more information on this change, see this announcement.

Restriction: For customers on legacy Support Suite plans, or the standalone Zendesk Support + Guide package, autoreplies with articles are called Article Recommendations.

Autoreplies with articles allow you to automatically suggest relevant help center articles via email and web form in response to a customer support ticket.

There are a number of ways you can write and format your articles to work best with autoreplies. You may find that you can't or don't want to use all of the suggestions in this article, but applying just one or two can greatly improve your autoreply results. Presenting customers with better recommendations can increase the autoreply resolution rate, which is the percentage of enquiries resolved by autoreply from the total enquiries where it offered suggestions.

This article contains the following topics:

• Structuring and organizing your information
• Using segmentation and labels

Related articles:

• Best practices: Preparing to use article recommendations
• Best practices for using labels for article recommendations
• Zendesk Bot resources

Structuring and organizing your information

One area where you can make a difference is in the structure and organization of your articles. The suggestions in this section include both customer-facing and behind-the-scenes decisions.

Creating titles for your articles

Article titles are considered when determining whether an article is a good candidate for solving a customer's problem. If an article's title closely matches the text of a support request, it's more likely to come up as a suggestion in an autoreply.

To take advantage of this, you should give your articles titles that are similar to how your customer might phrase their support request.

Try formatting your article titles in one of the following ways:

• As a question: "How do I reset my password?"
• As a simple, active phrase: "Resetting a password"

Narrowing an article's focus

Wherever possible, an article should cover a single, specific topic. How this impacts your articles depends on how you present information to your customers:

• Issue/resolution articles: If you prefer to write your articles around issues and resolutions, try to focus each on a very specific issue with a single resolution. For instance, rather than having a single article covering multiple, related billing issues – payment options, requesting a refund, and challenging a charge – write three articles, one for each issue. If possible, offer a single resolution for each issue as well. This way, you can guide your customers to the resolution you prefer, and if necessary, link to other articles that may offer alternative resolutions.

• Process articles: The same general principle applies to articles built around processes and procedures. Say, for example, you need an article walking a customer through an account set-up process, which involves two main tasks: Creating an account and selecting account options. You could have a single article covering both tasks, since they are both part of the account setup process. However, breaking it up into two articles, one for each task, will help the bot identify the article that best matches your customer's needs, and help your customer locate the applicable information without having to wade through unnecessary text.

Writing article introductions

The first 75 words in an article are weighed more heavily than the subsequent text. By including as many related keywords and context as possible in an article's introduction, you will boost the accuracy of your suggested articles.

For example, if you're writing an article about your company's return policies, don't include a preamble about how you understand that sometimes a shirt is the wrong size or that everyone makes mistakes. As charming as that might be, it gets in the way of evaluating the article’s content. Start your articles cleanly and precisely: "We accept returns for a full refund within 30 days of purchase. After 30 days, you can request a store credit," then get into the details.

Using segmentation and labels

The power of article labels really comes through when you have a situation where you can segment your customers when tickets are created. A good example of this is a game developer supporting iOS and Android platforms. The developer has two channels for incoming tickets: one for iOS issues and one for Android. Using the trigger conditions, you can create two autoreply with articles triggers (one for each channel) and then use article labels to serve up only iOS or Android articles where they make sense. So, stop for a second and really think about how you could best segment your customers and create useful conditions around them, then serve them the best articles possible! This is described in more detail in the best practices for using labels with autoreplies guide. For information on working with triggers for autoreplies, see Creating new triggers for autoreplies.

Even if you are not using customer segmentation, labels have a huge impact on autoreply results. Use them thoughtfully. For example, if an article is about a topic that does not tend to generate customer questions, you might omit the label you've added. This helps create a smaller, more curated pool of articles when making suggestions.