Well-optimized internal company stores can be amazing drivers of employee engagement, recognition, and retention. And there is factual evidence that companies with a highly engaged workforce are more profitable – as this Gallop study suggests, higher engagement means an average of 21% higher net income. Engaged employees are not only more productive, but they also tend to stay longer with the company.
However, a well performing company store simply needs strong data analytics to support it. Otherwise, you are unable to read into the complex patterns of employee behavior and what motivates them, risking your store being ineffective and more of a burden than an asset. But how do you organize your data analytics properly?
This and many other relevant questions we are going to answer in this article – we will dive into how data analytics for internal company stores differs from standard mechanisms as well as what metrics you want to track and what conclusions you should be making.
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What Is Data Analytics? Is Data Analytics Different For Company Stores?
Data analytics is the process of collecting, processing, and interpreting raw data to extract valuable insights. And what is data analytics in the context of internal company stores? Well, it tends to be somewhat different to typical e-commerce metrics, since their main goal is not to produce revenue, but to make the team more engaged.
This way, you’re not only tracking all your transactions, but also analyzing employee behavior and always assessing reward preferences so that the store keeps being relevant.
Why Optimizing Your Online Company Store is Essential?
Simply put, without optimization, your company risks running a store that feels irrelevant, outdated, or overly complex, which leads to reduced usage and missed opportunities for team engagement. But there’s more:
Increasing Employee Engagement
Employee engagement is closely tied to the perceived value of the company store. If employees feel that the rewards are underwhelming or hard to redeem, they will simply be less likely to use the store. Data analytics can show you which rewards tend to generate the most enthusiasm, so that you focus on what matters most to your team.
Aligning the Store with Corporate Culture
The items in your store should ideally also reflect the values and goals of your business. If you place a lot of emphasis on employee wellness, your store should offer rewards connected with these themes – fitness memberships or ergonomic equipment are good ideas.
For this purpose, analytics can help you find out which items are most and least frequently chosen – if they show low engagement with health-related rewards in a company promoting wellness, it may be time to reconsider the reward selection.
Budget Allocation
Lastly, understanding which rewards are most popular allows you to allocate your budget more effectively. Data analytics can help you see where to invest – otherwise, you might spend on rewards that employees don’t care about, resulting in wasted funds.
The Role of Data Analytics in Online Company Store Optimization
There is little surprise that data analytics plays a pivotal role in making your company store the best it can be. For example, by analyzing the data, you may find that 70% of employees who meet their goals redeem rewards immediately, but the remaining 30% don’t engage – a typical scenario where the numbers show you where the room for improvement is.
More specifically, however, data analytics helps your internal stores in the following ways:
Refining the Rewards Selection
First of all, analytics can help you identify which types of rewards are most popular with specific employee segments. You will be able to see that, say, employees in the IT department gravitate towards e-learning courses, while those in HR prefer wellness-related rewards – and make certain conclusions on the viability of certain items for certain demographics..
Finding User Friction Points
Analytics can also be used to detect user friction points – areas where the employee experience breaks down. This way, if analytics show that a high percentage of users are abandoning their carts during the redemption process, it means there’s an issue with either the store’s interface or the clarity of the redemption steps.
Predicting Future Trends
And with predictive analytics, you can even try to forecast future behaviors based on current and past data. During certain times of the year (holidays, major company events, etc.) employees may be more inclined to redeem rewards – and you can use that. You can prepare your store in advance, so that the most popular items are well-stocked and relevant promotions are also timed correctly.
Key Data Analytics Metrics for Optimizing Company Stores
With internal online company stores, you must understand that the most important metrics will not be your usual suspects – you need to focus on behavioral data rather than quantitative:
Employee and Manager Participation Rate
The employee participation rate measures how actively employees engage with the store – browse, redeem rewards, or just log in. Low participation could indicate that employees see little value in its offers or are unsure of how exactly it works.
Similarly, manager participation is vital, as their engagement can often set the tone for overall employee usage. So you should encourage managers to participate more actively in the rewards program and distribute reward points or incentives to their teams.
Reward Redemption Rate
Monitoring this metric helps you adjust the available rewards – a low redemption rate often clearly indicates poor reward options or overly complicated redemption processes. For example, if your analytics show that certain high-value rewards are almost never redeemed, it could suggest that the point thresholds are set too high.
Engagement Frequency
Tracking engagement frequency allows you to assess how often your employees are interacting with the platform and to introduce features that encourage more frequent visits. For instance, you can introduce various gamification techniques such as time-limited bonuses to increase that interaction frequency.
Time Spent on Platform
With this metric, you need to keep in mind that bigger doesn’t always mean better – excessive time spent in your store may actually hint at certain design inefficiencies. If users are spending too much time browsing and not redeeming, it could mean that the navigation is unclear, or that users are struggling to find relevant rewards.
User Satisfaction Metrics
Make sure to combine the above quantitative analytics with qualitative data (such as user surveys or direct feedback) to get a fuller picture – after all, employee satisfaction should be your number one goal. Simple surveys can be integrated into the platform to ask employees how satisfied they are with their rewards experience and what can be improved.
4 Things to Keep in Mind While Doing Data Analytics for Your Company Store
From our experience, here are some of the key points to keep in mind when introducing and performing data analytics for your company store:
Balance Automation with Human Oversight
While automation can help you collect and process data, human oversight is essential for proper interpretation, especially in a more nuanced case of online company stores. Your team needs to contextualize the provided information within the broader company culture and your long-term store objectives.
Consider Different User Groups
Different departments, job roles, and even geographic locations might interact with the company store differently – and analytics should be segmented to understand these differences. For example, your sales team might prefer high-mobility rewards (like business travel accessories) while tech employees may lean toward gadgets or e-learning.
Mind the Timing of Rewards
You must know that employees are more likely to engage with the company store during specific periods, e.g. end-of-year performance reviews or some company-wide initiatives. That’s why you need to analyze the timing of reward redemptions to prepare for surges as well as dips – for instance, you might want to introduce time-limited rewards during slower periods to encourage more consistent usage throughout the year.
Use A/B Testing
Finally, A/B testing is another powerful method for online store optimization. You should invest time in testing different store layouts, reward categories, or point systems on small segments of the user base. This way, you can gather data about which configurations work best and reduce the risk of large-scale changes that might negatively impact your engagement.
Conclusion
Overall, the role of data analytics in online store optimization is hard to overestimate – it helps you look into the deeper patterns of employee behavior and fine-tune your store’s offer. Not to mention, relevant data can assist you in improving the user experience, and aligning reward programs with your company culture.
At the same time, setting up your data analytics might be easier said than done – while you may do it internally and succeed, it’s still highly recommended that you partner up with an experienced company store setup team like Brandscape. We will provide expert assistance in making your company shop one of the biggest positives in your employees’ corporate life experience.
