Improve Employee Enablement with Personalized Community Recommendations

Employee enablement paves the way for innovation. Enabled employees guide digital transformation projects, develop culture-building initiatives, accelerate knowledge sharing and more. 

The right mix of technology, automation and process enables employees to do their best work. 

That’s why our team developed Recommendations to help employees find the most useful communities within their employee experience platform. 

Here’s why Recommendations are a priority for LumApps, and what you should know about this feature.  
 

Employee Experience is the New Customer Experience

Business analysts and stakeholders are reaching a consensus - to create a successful employee experience, you must implement a customer experience perspective. 

Customers receive personalized, targeted information throughout their buyer journey which is delivered across apps, devices, platforms and regions. Their customer journey is analyzed, scrutinized and reviewed by top decision makers. The customer journey is often one of the top line items for any company budget.

These actions take place because of the connection to revenue. But the most innovative companies are realizing the potential when this thinking is applied to the employee journey. 

Ultimately, The employee experience at your company can affect retention, and culture, in turn  impacts collaboration and productivity. The combination of which has a huge impact on business performance. That’s one of the reasons why the term “employee experience” is twice as popular in 2022 when compared to last year, according to Google Trends. 

As a leading employee experience platform, LumApps understands this fact. Lumies around the world discuss it everyday in internal and external conversations. 

That’s why it’s a priority to evolve the platform with that in mind, starting with smart Recommendations.    

Why Recommendations Rule

To put a spin on a modern-day philosopher's quote, recommendations rule everything around me (and us). 

From Netflix to Google, the clothes we buy, the places we go, and the food we eat. It’s no secret. We live in a recommendation economy. Why is it everywhere? Because it works. 

According to a Meta-commissioned survey, 57% of respondents said the ability to discover relevant products on a shopping website or app was very important or extremely important to them to continue shopping there.

Research by Accenture, found that a whopping 75% of consumers are more likely to buy from retailers if they recognize their name, offer relevant recommendations or remember their purchase history. 

It’s clear that recommendations are important for customers, and this same philosophy applies to employees. Consider the impact of recommendations within an employee experience platform - or any daily-use software tool. 

Knowledge Sharing - Recommendations fill potential gaps with search and outdated information. Recommendations are powered by living data and current usage, items like inactive communities or archived content doesn’t get pushed. 

Adoption - Work tools are only as good as the adoption rate. Recommendations give employees a reason to stay logged in, or a reason to come back later. 

Collaboration - Recommendations can also power collaboration as employees discover ideal ways to use their digital tools, and follow similar habits as their coworkers. 

Introducing: Community Recommendations 

The first edition of Recommendations within LumApps is community recommendations. 

Communities are job-based or interest-based digital communication hubs within a customer’s LumApps platform. 

Community recommendations are powered by the LumApps Data Lake, which allows customers to connect their LumApps data to 3rd party business intelligence tools like Tableau and Google Data Studio. The project is powered by an employee data layer, where all LumApps data is housed. 

This data includes a recording of every action that an employee takes, which informs the recommendation algorithm.  

To go in more detail, the recommendation system relies on an AI and machine learning method based on association rules. This allows the platform to automatically discover relations between entities. 

This is similar to an ecommerce site that recommends peanut butter when someone is going to buy jelly. Based on purchase history, the association rule has determined that peanut butter should be associated with jelly.  

This system, along with filtering and plenty of backend magic, allows recommendations to enable employees. 

How does this work in practice? The intention is to provide organic discoverability. 

An employee in the Philadelphia office might not interact with someone stationed in Bengaluru, but they share similar profiles, shared interests and follow the same communities. Recommendations allow these employees to connect in meaningful ways. 

Community Recommendations is the first recommendation-based feature within LumApps, with things like recommended content planned for the future. 
 

We would love to know more about your goals. How can we help?