Many e-commerce companies face significant obstacles when it comes to decision-making analytics. These challenges can make it difficult to stay ahead of the curve and meet customer demands. There are two primary challenges for e-commerce business analytics systems.

One system challenge is data integration. With data spread across multiple systems, integrating and analyzing it can be a daunting task. This can result in multiple disconnected data sets with no way for them to act in concert, making it hard to get a complete view of the business.

The other system challenge is the inability to analyze data in a timely manner to make an actionable decision. It’s important to keep in mind that decisions have a window of relevance. If it takes too long to analyze the data and the subsequent decision is made too late, the resources placed on the analysis are wasted and the opportunity is lost.

Given this, two essential features are required for data analytics systems:

1. Data integration: E-commerce companies often have data spread across multiple systems, making it challenging to integrate and analyze.
2. Timely analysis: E-commerce business leaders require timely analysis to make timely decisions, but many struggle to access and analyze data quickly enough. 

Now that we’ve covered basic system requirements, let’s explore e-commerce decision-making challenges.

E-commerce companies must manage inventory across multiple channels and understand inventory levels and trends to ensure adequate stock levels and avoid stockouts. Inventory can be complex and challenging to track.

Customer segmentation is a critical aspect of e-commerce, but many companies struggle to collect and analyze customer data effectively. Without a deep understanding of customer behavior, it’s challenging to optimize offerings to meet customer needs and preferences.

Promotion is required to generate traffic to e-commerce websites. There are many methods for promotion and they all require resources. To grow your business with these promotional methods in a systematic way, it is important to understand the return on investment for these methods to determine the best set of methods and their configurations.

Overall, these decision-making pain points can impact an e-commerce company’s ability to compete effectively. By addressing these obstacles and utilizing data and analytics effectively, e-commerce companies can make better decisions, enhance operational efficiency and increase customer satisfaction.

The following is a summary of e-commerce decision-making challenges: 

1. Inventory management: E-commerce companies must manage inventory across multiple channels, and understanding inventory levels and trends is crucial for ensuring adequate stock levels and avoiding stockouts.
2. Customer segmentation: E-commerce companies must personalize their offerings to meet customer needs and preferences, but doing so requires a deep understanding of customer behavior.
3. Promotion management: E-commerce companies must manage promotional activities across multiple channels and systems to drive traffic to their websites. 

The following sections address these analytics platform requirements and decision-making pain points in more detail and explore how Fusion Analytics can address these challenges.

Data Integration

Data integration is a significant challenge that many e-commerce businesses face. These businesses often have multiple systems in place for inventory management, sales tracking, customer relationship management, and more. However, these systems are often unconnected, making it difficult to integrate data and gain a complete view of the business.

If the data from these systems remain disconnected, it is very difficult to form a complete view of the organization. This makes growing the business through decision making prohibitively expensive. For example, questions like “How is promotion impacting inventory levels?” or “How are website visitors responding to new product bundles given the current promotional strategy?” are very difficult to answer. 

As businesses grow, they may add new systems to their technology stack, further complicating the integration process. For example, if a business decides to start selling on a new marketplace, it may need to integrate with that platform’s API, which creates a new data source to integrate into the analytical process..

Fusion Analytics was designed to address these challenges. A fundamental benefit of Fusion Analytics is its ability to ‘fuse’ data together from multiple systems. Simply use its data importer to connect to various data sources (databases, APIs, files, etc) and load the data into Fusion Analytics. Once the data is loaded, use the ‘Relation Manager’ to fuse the tables together to act as one data set. 

Here is a simple example. In this case, we loaded data on tickets sold and the orders made through an experiential tourism e-commerce website. We used the relation manager to fuse these tables together using the ‘order id’ columns of the tables.

Figure 1: Screenshot of Relation Manager Fusing Two Data Tables

Data integration is essential because it enables businesses to make better decisions. By having a complete view of the business, businesses can identify trends and insights that may not be visible in individual systems. This can lead to better decision-making, such as identifying which products are selling well and which ones need to be discounted to clear inventory.

Timely Analysis

The ability to make a decision in a timely manner so that the decision can be implemented is critical to any form of decision making. If a business determines that a recent promotional tactic is likely to result in a near-term stockout, that assessment means nothing if it isn’t made in time to order more inventory or reduce the effect of the promotion.

Most business analytics systems employ pre-defined reports for analysis or they are very limited in their ability to integrate data from multiple systems. To address the challenge of timely analysis both abilities are required:

1. Integrate current data from multiple sources 
2. Interactively explore complex analyses in real-time

The best way to understand these requirements is through an example. Continuing with the experiential tourism e-commerce website, we want to know the nature of the promotional efforts for the top selling events during 2023. The following dashboard shows revenue by day, event, and referrer domain. 

Figure 2: Event Ticket E-commerce Dashboard

First, select the dates in 2023 from the ‘Revenue by Day’ widget (see the figure below).

Figure 3: 2023 Dates are Selected in the Timeline Widget

Note how the bar chart widgets adjusted to the filtered dataset. They now only represent data from 2023. Next, select the top selling event from the ‘Revenue from Top 25 Events’ bar chart widget (see below).

Figure 4: Data from Top Selling Event in 2023

The dashboard now shows the revenue over time for this top selling event and the top revenue generating referrers. We can now characterize the promotion behind this event and we can compare with lesser selling events to learn how to make future events sell better through more effective promotion. 

Now we’re interested in how long before the event that customers bought tickets to better understand when digital ads should run. The premise here is if ads can be run before customers naturally purchase, the number of tickets sold should increase since more people are aware of the buying opportunity when they are most likely to buy. 

To answer this question, we need another widget that displays the purchase lead time. With Fusion Analytics, this is easy. To do this, click on ‘Edit Mode’ on the dashboard and then select ‘Bar Graph’ from the widget list (there are currently 17 types of widgets you can create). This displays the following form.

Figure 5: Bar Chart Configuration Form

In this form, we set the title of the widget, set the x-axis to ‘purch_lead_days’, turn on binning with 100 bars, and set the y-axis to the number of tickets for each bar. The modified dashboard is shown below with the same data selected as above. Now we can see customers’ purchase lead time for the best selling event.

Figure 6: Modified Dashboard Showing Distribution of Purchase Lead Times.

Given this data, it appears that running the ad 3 to 6 days before the event will be most effective. Also note the number of sales the day of the event. We may consider a ‘last minute purchase’ digital ad the day of these types of events. We can quickly list upcoming events in Fusion Analytics where this promotional strategy can be implemented. This was achieved through integration of data from multiple sources and the flexible interactive dashboards in Fusion Analytics.

By effectively addressing the challenges of timely analysis, e-commerce companies can make better decisions based on current information, avoid missed opportunities, and ultimately drive growth and success.

Inventory Management

Inventory management is a significant challenge that e-commerce companies face due to the complexity of managing inventory across multiple channels. E-commerce businesses must ensure adequate stock levels to avoid stockouts while also preventing overstocking that can lead to additional expenses, reduced profitability, missed sales opportunities, and reduced customer satisfaction.

Another challenge of inventory management is the need for forecasting future inventory levels. E-commerce companies must have a deep understanding of customer demand and sales trends to ensure that they have adequate stock levels for future sales. However, forecasting inventory levels accurately can be a complex and challenging task.

Additionally, managing inventory across different fulfillment channels can also be challenging. E-commerce companies often manage inventory levels across multiple warehouses, distribution centers, and third-party logistics providers. This can result in additional complexity and logistical challenges that can impact business operations and profitability.

To get a feel for how to address these challenges, consider the experiential tourism e-commerce website discussed above. In this case, we want to track the availability capacity for events (ie, unsold tickets). This gives us a quick view of which events need less promotion and which need more. It also can show us that we may benefit from an increased frequency for particular events. 

To implement this solution, we would upload a data table from the event management system into Fusion Analytics. This table would list the event id, the maximum number of tickets and ticket type. This new data can be fused to the existing sales data via the event id. 

Now that the supporting data is in place, we can quickly add a bar graph widget to visualize a ranked list of events by the number of tickets available for each event. Using this new dashboard, we can quickly see which upcoming events need additional promotion or begin planning an increased frequency for frequently sold-out events.

Customer Segmentation

Customer segmentation is the process of dividing a company’s customer base into smaller groups based on specific characteristics such as demographics, purchasing behavior, or geographic location. By segmenting customers, e-commerce companies can gain a deeper understanding of their customers’ needs and preferences, and tailor their marketing efforts to specific groups.

One of the primary challenges of customer segmentation for e-commerce companies is collecting and analyzing customer data effectively. Many small businesses may not have the resources to invest in sophisticated data analysis tools or hire data analysts, making it challenging to gather and make sense of customer data.

Another challenge is ensuring that the segmentation strategy is effective and accurately reflects the customer base. Poor segmentation can result in marketing efforts that are too broad or too narrow, leading to missed opportunities or wasted resources.

Additionally, e-commerce companies may struggle to implement personalized marketing efforts based on customer segmentation. Personalization requires a deep understanding of customer data and the ability to tailor marketing messages and offers to specific customer groups. Many companies may lack the technical expertise or resources to execute effective personalized marketing campaigns.

However, effective customer segmentation is essential for e-commerce companies to compete effectively and drive revenue growth. By understanding their customers’ needs and preferences and tailoring their marketing efforts accordingly, companies can improve customer engagement, increase conversions, and build long-term customer loyalty.

E-commerce companies can leverage analytics to solve customer segmentation problems in several ways. Here are some opportunities:

1. Customer behavior: By analyzing customer behavior data such as clickstream, purchase history, and engagement metrics, e-commerce companies can segment their customers based on their interests, preferences, and purchase habits. 
2. Customer demographics: Segment customers based on demographics such as age, gender, income, and location.
3. Customer value: By analyzing customer lifetime value (CLV), e-commerce companies can segment their customers based on their profitability and potential future revenue.
4. Purchase intent: By analyzing customer search queries and browsing behavior, e-commerce companies can segment their customers based on their purchase intent. 
5. Customer feedback: E-commerce companies can also segment their customers based on their feedback and reviews.

In the case of the example event e-commerce website, we can easily add customer meta-data to segment customers in Fusion Analytics. We can add user account data and fuse this data with the sales data to track customer id and demographics. Web analytics data can be fused with the sales data to infer customer behaviors and purchase intent. Sales data would provide customer value. Given the previous examples, it’s easy to imagine how Fusion Analytics can quickly help e-commerce companies address these challenges.

An interesting application is integrating customer feedback. Customer feedback is most often in the form of text. If it’s not in text form, in most cases it can be easily converted to text. Given this ‘unstructured’ text data, we need the ability to do text analytics. Text analytics is one of the primary features of Fusion Analytics.

The first step is to load the feedback data into Fusion Analytics. The next step is to fuse this new data with the existing data. Depending on the feedback metadata, you could connect the data based on customer id or the product id of the products in the purchase. Once the import and fusing steps are completed, now we can move to text analytics.

It’s always best to start with a well-formed question. In this case, we want to determine if the customer is likely to be an advocate or a great customer of the company. Some of the indicators are:

• Determine whether the sentiment of the feedback is positive
• Look for terms that imply enthusiasm (‘again’, ‘best’, ‘fantastic’, etc)
• Look for terms that imply willingness to share with friends (‘tell’, ‘share’, ‘friends’, etc)

Each of these sets of terms are ideal for a separate dictionary in Fusion Analytics. A dictionary in Fusion Analytics is a configured list of terms related to a particular concept you would like to analyze. Once the dictionaries are created, associate the dictionaries with the column of data that stores the feedback text. Fusion Analytics will then run a set of dictionary match functions in the background. Then modify your dashboard with dictionary match bar charts for each of the dictionaries. Now you have a way to select customers based on their feedback. 

You could imagine how this analysis could also be used to determine the perception of your products. Instead of doing the above analysis to find customers to remarket to, use the analysis to rank events based on attributes of customer perception. 

Text analytics is a very powerful tool to make use of valuable text data that would otherwise require lots of valuable resources to process. Think of the number of people necessary to manually read, understand, and operationalize your customer feedback!

Promotion Management

There are several methods available to promote e-commerce websites, including:

1. Search engine optimization (SEO): Optimizing website content and structure to improve search engine rankings and increase organic traffic.
2. Pay-per-click advertising (PPC): Paying to have ads appear on search engines or other websites, with payment based on the number of clicks the ad receives.
3. Social media marketing: Promoting products and services on social media platforms like Facebook, Instagram, and Twitter.
4. Email marketing: Sending promotional emails to customers or subscribers, often with personalized offers or discounts.
5. Influencer marketing: Partnering with influencers or bloggers who have a large following to promote products and services to their audience.
6. Affiliate marketing: Partnering with other websites or individuals who promote the e-commerce site’s products or services in exchange for a commission.
7. Content marketing: Creating and sharing valuable content (such as blog posts, infographics, or videos) to attract and engage potential customers.
8. Referral marketing: Incentivizing existing customers to refer their friends or family members to the e-commerce site.

To use any of these methods to grow your business, you must measure the following:

• The costs of the promotional methods
• The promotional method that created website visits 
• Visitor behavior
• Visitor segmentation

With this data, you can calculate the return on the investment (ROI) in the promotion. From there, you can compare the effectiveness of the methods to make decisions on which promotional methods to use and experiment with how to improve the ROI of your promotional activities.

As we’ve described above, Fusion Analytics provides the means to segment customers. Knowledge of these segments is critical to getting the most from your promotional dollars. Fusion Analytics not only gives you the ability to create effective customer segmentation, but it also gives you the ability to track how well the promotions are working using current data from promotional systems fused with existing web analytics and sales data in Fusion Analytics.

Is Fusion Analytics Right for You?

Each e-commerce operation is unique given the large number of potential business models, business goals, and options for system design. Consider the following attributes of your organization to characterize whether Fusion Analytics is right for your organization: 

• Complexity of products and product bundles: If you have a complex set of product and service offerings, Fusion Analytics greatly simplifies the analysis of comparing the performance of existing offerings and experimenting with new offerings to see how they play in the market.
• Complexity of customer segmentation: If you find that your business could benefit from a more refined customer segmentation strategy, Fusion Analytics allows you to manage your decision-making to discover new customer segments, refine your offerings, refine your marketing, and manage inventory.
• System complexity: If you have a complex set of systems required to run your operations, Fusion Analytics provides an organization-wide view of operations through its data integration features to simplify your decision-making activities.
• Stakeholder complexity: If you have a complex network of stakeholders with varied interests in your organization, Fusion Analytics gives you the ability to keep all stakeholders informed and engaged.
• Oversimplification: If you shy away from complexity in your operations to avoid the more complex decision making requirements, Fusion Analytics would allow you to build a more sophisticated set of operations to grow your business.
• Promotional complexity: If your business requires a complex set of promotional methods and systems, Fusion Analytics can simplify the analysis of these systems so you can make clear decisions to grow your business.

We understand that the problems that you need to solve are likely different from other e-commerce organizations. Know that our consulting services are built into our subscription pricing for Fusion Analytics so we can help you address your specific needs. In many cases, we are able to help customers resolve new questions about their business in minutes.To discuss your specific analytics needs, contact us at info@toplinedecisions.com.