Search by Algolia
What is retail analytics and how can it inform your data-driven ecommerce merchandising strategy?
e-commerce

What is retail analytics and how can it inform your data-driven ecommerce merchandising strategy?

There is such tremendous activity both on and off of retailer websites today that it would be impossible to make ...

Catherine Dee

Search and Discovery writer

8 ways to use merchandising data to boost your online store ROI
e-commerce

8 ways to use merchandising data to boost your online store ROI

New year, new goals. Sounds positive, but looking at your sales data, your revenue and profit aren’t so hot ...

John Stewart

VP, Corporate Communications and Brand

Algolia DocSearch + Astro Starlight
engineering

Algolia DocSearch + Astro Starlight

What is Astro Starlight? If you're building a documentation site, your content needs to be easy to write and ...

Jaden Baptista

Technical Writer

What role does AI play in recommendation systems and engines?
ai

What role does AI play in recommendation systems and engines?

You put that in your cart. How about this cool thing to go with it? You liked that? Here are ...

Catherine Dee

Search and Discovery writer

How AI can help improve your user experience
ux

How AI can help improve your user experience

They say you get one chance to make a great first impression. With visual design on ecommerce web pages, this ...

Jon Silvers

Director, Digital Marketing

Keeping your Algolia search index up to date
product

Keeping your Algolia search index up to date

When creating your initial Algolia index, you may seed the index with an initial set of data. This is convenient ...

Jaden Baptista

Technical Writer

Merchandising in the AI era
e-commerce

Merchandising in the AI era

For merchandisers, every website visit is an opportunity to promote products to potential buyers. In the era of AI, incorporating ...

Tariq Khan

Director of Content Marketing

Debunking the most common AI myths
ai

Debunking the most common AI myths

ARTIFICIAL INTELLIGENCE CAN’T BE TRUSTED, shouts the headline on your social media newsfeed. Is that really true, or is ...

Vincent Caruana

Senior Digital Marketing Manager, SEO

How AI can benefit the retail industry
ai

How AI can benefit the retail industry

Artificial intelligence is on a roll. It’s strengthening healthcare diagnostics, taking on office grunt work, helping banks combat fraud ...

Catherine Dee

Search and Discovery writer

How ecommerce AI is reshaping business
e-commerce

How ecommerce AI is reshaping business

Like other modern phenomena such as social media, artificial intelligence has landed on the ecommerce industry scene with a giant ...

Vincent Caruana

Senior Digital Marketing Manager, SEO

AI-driven smart merchandising: what it is and why your ecommerce store needs it
ai

AI-driven smart merchandising: what it is and why your ecommerce store needs it

Do you dream of having your own personal online shopper? Someone familiar and fun who pops up every time you ...

Catherine Dee

Search and Discovery writer

NRF 2024: A cocktail of inspiration and innovation
e-commerce

NRF 2024: A cocktail of inspiration and innovation

Retail’s big show, NRF 2024, once again brought together a wide spectrum of practitioners focused on innovation and transformation ...

Reshma Iyer

Director of Product Marketing, Ecommerce

How AI-powered personalization is transforming the user and customer experience
ai

How AI-powered personalization is transforming the user and customer experience

In a world of so many overwhelming choices for consumers, how can you best engage with the shoppers who visit ...

Vincent Caruana

Senior Digital Marketing Manager, SEO

Unveiling the future: Algolia’s AI revolution at NRF Retail Big Show
algolia

Unveiling the future: Algolia’s AI revolution at NRF Retail Big Show

Get ready for an exhilarating journey into the future of retail as Algolia takes center stage at the NRF Retail ...

John Stewart

VP Corporate Marketing

How to master personalization with AI
ai

How to master personalization with AI

Picture ecommerce in its early days: businesses were just beginning to discover the power of personalized marketing. They’d divide ...

Ciprian Borodescu

AI Product Manager | On a mission to help people succeed through the use of AI

5 best practices for nailing the ecommerce virtual assistant user experience
ai

5 best practices for nailing the ecommerce virtual assistant user experience

“Hello there, how can I help you today?”, asks the virtual shopping assistant in the lower right-hand corner ...

Vincent Caruana

Senior Digital Marketing Manager, SEO

Add InstantSearch and Autocomplete to your search experience in just 5 minutes
product

Add InstantSearch and Autocomplete to your search experience in just 5 minutes

A good starting point for building a comprehensive search experience is a straightforward app template. When crafting your application’s ...

Imogen Lovera

Senior Product Manager

Best practices of conversion-focused ecommerce website design
e-commerce

Best practices of conversion-focused ecommerce website design

The inviting ecommerce website template that balances bright colors with plenty of white space. The stylized fonts for the headers ...

Catherine Dee

Search and Discovery writer

Looking for something?

facebookfacebooklinkedinlinkedintwittertwittermailmail

We continue our series on using AI-powered recommendations in the back office. When we built Algolia Recommend, we initially targeted product recommendations for ecommerce customers. But our Recommend API covers many other use cases. This article is about how to improve the design of your online and back-office forms, as seen on a typical website or Salesforce UI.

Filling out painstakingly complicated online forms

For customers and employees, filling out electronic forms requires patience and attentiveness. In the worst of cases, it also requires large amounts of clicks and screen changes and a leap of imagination to find the appropriate action to take.

Employees are the principal victims of convoluted electronic forms. They interact with forms every day, with many different user interfaces (UI) that change from company to company, system to system, profession to profession, and task to task. If employees are lucky, they have developers and designers to create the UI, but numerous requirements and system constraints hinder good design. Often to the rescue is user testing – but this is costly and takes time, and user actions are not always easy to interpret.

How AI-driven recommendations can improve electronic form UI design

In this article, we imagine Algolia Recommend as automating the user testing and UI design process. It can capture combinations of actions and build models around those combinations. These models, when fed regularly with employee typing and button clicks (captured as analytics data), can very quickly generate such recommendations as:

  • What fields to place on the same screen
  • Which fields should be adjacent
  • How many questions to ask on a single screen
  • How to reduce the number of screens and actions for a given task
  • Whether to use guided navigation (wizards) or allow free entry 

Two API calls

In a previous article in this series, we discussed how Recommend relies on two APIs:

  1. Our Insights API sends events and captures user activity and analytics, such as clicks and conversions. When enough events are sent, Algolia Recommend can build models that feed the second step.
  2. Our Recommend API learns about the most frequent combinations. Recommend can help you discover the combinations of actions a user takes while using any user interface, especially a form. 

Note that Recommendations can find more than frequent combinations. Equally powerful is the notion of related entry fields. Combining related fields opens up new possibilities of user design that we don’t discuss in this article. 

Rearrange and reduce the number of fields using recommendations

The general idea

First, you need to capture the fields users tend to modify together. Then, Algolia models that data. The models allow the Recommend engine to compile a complete list of frequently used fields together. With that report, your designers can start to rearrange or remove fields on a single screen or rethink how the fields are distributed across workflows and systems.  

For example, airlines already know how to ask only three questions to start a reservation process: airports, dates, and the number of passengers. Unfortunately, most companies don’t know which three questions to ask, so they splatter 10s of questions on the screen, hoping users will figure out for themselves the appropriate fields. Intranet applications with only 10 fields are a rarity – it’s more like 15 to 30. 

Let’s see how Recommend can simplify form interactivity.

The code to send user activity and system events

Send an insights event that tells the Recommend engine that text fields 1, 3, and 8 were modified together:

convertedObjectIDs(
  'on_save_form',
  'ui_forms_index',
  ["field1_id", "field3_id", "field8_id"]
);

Getting the recommendations

Return fields associated with Field 1 on Screen 1:

$recommendations = $recommendClient->getFrequentlyBoughtTogether([
  [
    'indexName' => 'ui_forms_index',
    'objectID' => 'field1_id',
  ],
]);

Two things to note here:

  1. While the name of the API method is frequently bought together, the word “bought” can be thought of as any action that captures two or more objectIDs during any given event. The event we defined here is the “on_save_form”. However, as seen below, we could have used “during the user session”. 
  2. This particular code is about tracking only one screen. We’ll extend that to multiple screens next.

What you can learn from these recommendations

Here’s a report that you can generate from this use case:

Target Field Frequently Edited Together
Field 1 Fields 3, 8, 11
Field 2 Fields 3, 9, 20
Field X Fields a,b,c, ..

With a little pivoting and cross-referencing, you can discover patterns that suggest different placement of the fields. Maybe you’ll put the frequent field pairings adjacent to each other.

Improve the distribution of fields over multiple screens using recommendations

Continuing with the previous example, let’s add several screens. You’ll need to change the data to reflect the larger context of more than one screen. To do this, you’ll need to make only one change:

  • Add the field’s screen information

The code to send user activity and system events 

convertedObjectIDs(
  'on_save_form',
  'ui_forms_index',
  ["screen1.field1_id", "screen2.field1_id", "screen2.field2_id"]
);

Getting the recommendations

Return fields associated with Field 1 on Screen 1:

$recommendations = $recommendClient->getFrequentlyBoughtTogether([
  [
    'indexName' => 'ui_forms_index',
    'objectID' => 'screen1.field1_id',
  ],
]);

What you can learn from these recommendations

As you can see, now we know that field 1 on screen 1 is often combined with modifications on fields 1 and 2 on screen 2. You’ll need to perform that extra parsing to distribute the correct screen classification.

Going further: what’s to stop us from capturing patterns across multiple systems? You’ll just need to specify the “system” in which the field exists. However, the real trick is the event: the “on save” event typically doesn’t work across multiple systems. We could use a known endpoint in a system workflow, but we’ll use time instead.

Streamlining the workflow and efficiency of filling out forms across multiple systems

The solution proposed here is to use a session timeout

The code to send user activity and system events

convertedObjectIDs(
  'session_time_out',
  'ui_forms_index',
  ["system1.screen1.field1_id", "system2.screen1.field1_id", "system2.screen2.field2_id"]
);

Getting the recommendations

Return fields associated with Field 1 on Screen 1 of System 1:

$recommendations = $recommendClient->getFrequentlyBoughtTogether([
  [
    'indexName' => 'ui_forms_index',
    'objectID' => 'system1.screen1.field1_id',
  ],
]);

Conclusion

This is only the beginning of how you can use AI-driven recommendations to analyze user activity. I am sure you can think of more. This is the beauty of it all: recommendations are a generic tool that can be adapted easily with imagination and some thoughtfulness.

About the author
Peter Villani

Sr. Tech & Business Writer

linkedinmediumtwitter

Recommended Articles

Powered byAlgolia Algolia Recommend

AI-powered recommendations in the back office
product

Peter Villani

Sr. Tech & Business Writer

Increase basket size & value and build customer loyalty with Algolia Recommend and Analytics
ai

Luigi Castellano

SR Product Marketing Manager

Recommendations for developers: the complete how-to, what-to, and where-to guide
engineering

Peter Villani

Sr. Tech & Business Writer