Data, Data, and Data! In today’s data-driven world, eCommerce brands are swimming in a sea of information. But how can they harness this data to achieve tangible business results? From drafting blueprints to final decision-making calls, data plays a critical part in our lives. And we cannot deny the fact that data is a gold mine for established and soon-to-be venturing brands in the eCommerce and Qcommerce space. The new age of retail is pushing brands to invest in data analytics, and it is the only way to drive your brand to the next level.
If you are a novice to data analysis, it can be a bit overwhelming. It is for that reason that we are here to explain how data analytics works. Data analytics aims to analyse a company’s data to find meaningful insights that lead to better decision-making. When businesses leverage their data holistically, they can make more informed decisions, increase profits, attract more customers, and optimise their processes. In an e-commerce business, data analysis can help you gain insights into customer behaviour and then use that information to guide your marketing and product development efforts.
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Data Analytics
With the advent of e-commerce, there is a lot of data available for businesses, but there are also challenges. How do you use this data to make your brand better? How does it help you make decisions? Let’s take a look at how it helps in contributing towards eCommerce growth.
Brands must first identify the key metrics that matter most when diving into e-commerce analytics. Generally, analytics experts recommend the segmentation of the data so that you can see how your performance has changed over time.
Analysing your sales and performance data can be an uphill task; a digital shelf analytics solution helps you automate this process. For instance, dashboards display data like price MAP variation, category visibility, best-seller share, and more—making strategic planning easier. You can use data to create, eliminate, or focus marketing activities on the most relevant target audience.
The Competitive Edge of Data Analytics
In the eCommerce world, where multiple brands often sell identical products, competition is fierce. For instance, a simple search for a mop brush on Amazon may yield dozens of sellers. Data analytics can reveal why some products perform better than others, highlight potential pitfalls, and identify areas where competitors excel. For example, companies using advanced analytics are 2.6 times more likely to have a significantly higher ROI on their marketing campaigns, according to McKinsey. Data clubbed with predictive analytics tools helps draw conclusions with confidence.
Before moving forward to how a digital shelf analytics tool can help brands scale their businesses. Let’s take a look at the challenges faced in gathering and utilising data effectively.
- Data Overload: Brands struggle to sift through massive amounts of data to find actionable insights, leading to inefficiencies. Our data analytics tool does the work for you.
- Fragmented Data Sources: With data scattered across various platforms, getting a holistic view of customer behaviour becomes challenging.
- Data Accuracy: inconsistent or outdated data can distort analysis, leading to poor decision-making. The digital shelf analytics tool allows you to customise the dates as per your needs.
- Privacy Concerns: Maintaining compliance with regulations like GDPR while collecting data can be complex.
Why should brands take advantage of digital shelf analytics solutions to improve their performance on eCommerce platforms?
Not all e-commerce brands are able to gather, process, and use data to identify opportunities and assess the size and scope of opportunities for their business. A digital shelf analytics solution brings structured data on consumer purchase behaviours to help you identify the various KPI metrics, improving sales performance. Analytical insights have become the must have for every brand/seller to stay ahead in this cutthroat competitive environment.
Let’s take a look at the different avenues of data analytics that can help you to increase sales:
- Product page optimisation
Product page optimisation is a little-known art; though all the product pages should be optimised, very few are. This is where customers get to interact with your brand, but if they are not performing well, what would you do?
Data can help you figure out by crawling all the data at once, and you can analyse where you are falling short. It also assists in identifying whether the product title doesn’t have the relevant keywords, whether the images are mobile-optimised or not, and whether your product feed contains all the content or not. Performing regular content audits is important to identify quickly, compare, and address product display page content shortcomings. You can use it to generate scorecards that show if your product information is correct and complete to improve discoverability and buyability and to identify gaps that should be closed.
80% of shoppers conduct online research before actually buying a product. Meanwhile, 88% of consumers trust product reviews and ratings as much as personal recommendations (Invesp).
Ensuring that your product content meets platform requirements and consumer expectations is critical. Non-compliance can lead to penalties or reduced visibility. This is where our digital shelf analytics tool jumps in; it tracks and ensures that all product content is up-to-date and compliant with platform guidelines. Compliance tracking is vital for avoiding costly mistakes and maintaining a positive brand image. With the right tools and information, you can see what changes need to be made to optimise your product listing and ensure that every detail is considered.
- Price and Promotion optimisation
Studies reveal that 80% of customers prefer to compare prices before making the final decision. So, you know how important it is to strike a balance between your price and keeping customers satisfied so that they keep coming back to you.
Price optimisation basically utilises data points that include historical sales data, operation costs, and SKU’s. With analytics tools, you can identify the bigger picture and monitor the competitor pricing as well. You can also track the pricing pattern of authorised and unauthorised sellers, and this data helps ensure a controlled pricing and promotion strategy and helps close loops.
Discounts and promotions are proven methods for boosting sales. IOS reports state that shoppers make the most of their purchases when any sorts of promotions are ongoing. But what most e-commerce retailers know is that discounting products is not as simple as it sounds—you need to track your promotions to analyse their impact on the business, identify trends, and make informed decisions on optimising them for maximum ROI.
By analysing data, you can identify any promotions made by any seller or platform on your SKUs, compare those promotions to the ones you had planned, track whether your authorised seller has the buy box, and compare pricing and promotions across the competition, platforms, and sellers to identify trends.
- Keeping track of inventory
Stockouts are the worst nightmare for any e-commerce business. The number of times a retailer has to deal with out-of-stock situations can be devastating. The situation can also be frustrating for customers who would have otherwise bought the product but cannot because it is out of stock, and later influences them to switch to the competition brands. The deactivation or removal of products from listings is also standard among e-commerce platforms when products are not in stock and can decrease sales.
Analysing data can be used to forecast the likelihood of an out-of-stock product. With more data, the prediction becomes more accurate. E-commerce retailers can use this knowledge to prevent stockouts of products that are likely to be out-of-stock by taking proactive measures like substituting goods or increasing manufacturing capacity.
With our in-house tool, Kinator, you can track on-shelf availability across platforms, sellers, and locations. Also, send a custom alert for each out-of-stock item with your authorised sellers and separate out-of-stock Power SKUs.
- Tracking of visibility metrics
We all know how it goes; if you are not on the top search result pages, that means no visibility, which equals sales stagnancy, and the research also posits that two-thirds of clicks come through products that appear on the first page of search results. Ideally, your action plan would be to appear at the top as a brand manager, but manually tracking your performance and your competitors’ can become a tedious task. Here’s where data can be a big boon for you.
With consolidated data from all channels, you can see how many of your SKUs show up in the Top 3, 10, or 20 search results for important search keywords, how many of your SKUs appear on Page 1, and how many are at the top of their respective categories. You can identify which competitors hold key visibility spots by tracking trends in your share of shelf over time.
- E-Advertising
It is essential to track advertising metrics for several reasons. If you use the right advertising channel to attract customers, you must have a proper tracking system to measure each campaign’s success. It isn’t straightforward to know which campaign is doing well if you don’t track it properly. What if you stop one campaign and start another, and you didn’t track it? If your business is suffering because of that, you would not be aware of it until the next month when you compare your sales with last month, resulting in a loss of revenue.
Media assets: A combination of powerful words and pictures can make a powerful impact on your audience. Since the market is so competitive, it’s important to find an edge over others. A successful banner ad can do wonders when it comes to increasing your CTR rate and, thereby, conversions.
Data analytics can help you analyse all important metrics of an advertisement on major eCommerce platforms, such as measuring KPIs against brand goals and category benchmarks. mapping stock, campaign balance, and campaign creation error, and analysing performance at the campaign, ad type, and portfolio levels. With this, you can brainstorm and make a data-backed decision to increase maximum ROI on your campaign’s expenditure.
- Market Basket Analysis
Retailers use the market basket analysis technique to understand the products that are often purchased together. Data analytics helps them identify which items can be promoted as “package deals” or “bundles.” It also can be referred to as association analysis, which is an analysis of the association rules that exist between two or more purchase events.
Let’s take an example to understand better; suppose you found out that one of your SKUs is slow-moving (e.g., toothbrush) and not bringing you sales, but according to the trend pattern, you bundled that product with a fast-moving product (shaving cream). This can help you increase your conversion rate as well as move up your inventory.
Let’s dive deeper, shall we? Creating a basket of complementary products, such as the ones that go together but aren’t similar. (Oil & flour). These interesting combinations can be found through analytics by identifying the shopping pattern. This unexpected combination falls under the umbrella term of data mining, which uncovers meaningful correlations between different products according to their co-occurrence in a data set. Which we talked about, called affinity or market basket analysis.
- Regression analysis
Regression analysis helps to understand the association between the dependent and independent variable(s). It helps you identify patterns and relationships over a period of time. For example, regression analysis can help you determine how much sales are influenced by the marketing budget.
Regression analysis will benefit businesses that rely on repeat customers, and eCommerce is such a business.
In e-commerce, regression analysis is used to understand the impact of various independent variables (stock, price, and visibility) on the dependent variable (sales). By identifying the relationship between independent and dependent variables in advance, you can predict future sales. For example, if you offer discounts or cashback on a specific product category, knowing which category will get maximum traction can help you serve more customers and increase the conversion rate.
Another example for easier understanding is if a brand manager has chosen to increase product visibility as a marketing strategy, then regression analysis will help evaluate the impact this decision has on sales volumes. Because visibility is an independent variable and sales is a dependent variable on it.
- Best Seller Share tracking
Amazon bestseller comes in the orange-like ribbon shape, and you can see that on the product detail page itself, it works on category level and not on the product level; when you have this badge, it means that your product is selling the most in that category. Since best seller rank is updated on an hourly basis, there are certain things you need to keep constant track of, such as;
- Keep on updating your product content with relevant keywords
- Maintain your Amazon account health.
- Inventory management
- Creating attractive media campaigns
- Maintain a positive set of customer reviews.
All these areas need constant tracking, along with tracking your competitors’; it also provides data on how many days you are holding the top spot in a particular category, your overall rank, and much more helpful information. And while we all know how BSR directly impacts sales velocity, identifying these metrics will be a boon for you to stay on top and sustain your position for the long run. Maintaining a top BSR can increase a product’s sales by up to 65%, as per a study by Marketplace Pulse.
Impact of Data Analytics on Consumer Behaviour
Understanding consumer behaviour is at the heart of successful eCommerce strategies. Data analytics provides brands with the tools to decipher complex consumer patterns, helping them predict trends and personalise shopping experiences. By analysing data such as browsing history, purchase frequency, and product reviews, brands can tailor their offerings to meet consumer expectations more precisely. For example, a study by Accenture found that 91% of consumers are more likely to shop with brands that recognise, remember, and provide relevant offers and recommendations. This personalisation is made possible through data analytics, which enables brands to anticipate consumer needs and preferences.
The Role of AI and Machine Learning in eCommerce Analytics
Artificial intelligence (AI) and machine learning (ML) are transforming the eCommerce landscape by enhancing data analytics capabilities. These technologies allow for real-time data processing and the ability to uncover patterns that may not be immediately apparent to human analysts. AI and ML algorithms can predict future sales trends, optimise pricing strategies, and even automate inventory management. For instance, Paxcom’s Kinator uses AI to offer predictive analytics, helping brands forecast demand, optimise stock levels, and personalise marketing efforts. According to a report by Deloitte, companies that leverage AI-driven analytics see a 60% improvement in decision-making speed and accuracy.
The Future of eCommerce Analytics
The future of eCommerce analytics lies in the integration of more advanced technologies such as AI, ML, and natural language processing (NLP). As these technologies continue to evolve, we can expect even more sophisticated tools that can analyse vast amounts of data in real-time, providing brands with deeper insights into consumer behaviour and market trends. For example, NLP can be used to analyse customer reviews and social media mentions, helping brands understand consumer sentiment and identify areas for improvement.
Additionally, the rise of omnichannel retailing will require more comprehensive analytics solutions that can track consumer behaviour across multiple touchpoints, from online shopping to in-store purchases. This will enable brands to create a seamless shopping experience and increase customer loyalty. Paxcom’s DSA tool is positioned to support this shift by offering cross-channel analytics, helping brands stay ahead of the curve.
How can Paxcom’s digital shelf analytics solution help?
The company’s digital shelf analytics tool, Kinator, combines any available data into a very visual and interactive format to help brands make strategic decisions. A customised dashboard that is accessible anytime, anywhere, features the ability to filter by channel, category, brand, focus SKU, timeframe, city, and more. You get access to high-end information about the consumer’s behaviour, and you can then tailor your marketing and sales strategies to meet their needs, resulting in increased sales.
Unlock Your Brand’s Potential with Data Analytics
Data analytics isn’t just a trend—it’s the future of eCommerce. By leveraging the right tools, brands can optimise their product pages, adjust pricing strategies, prevent stockouts, detect unauthorised sellers, and drive more sales. Paxcom’s Kinator offers a comprehensive digital shelf analytics solution that helps businesses track crucial KPIs, make data-driven decisions, and thrive in the ever-evolving eCommerce landscape.
Ready to take your brand to the next level? Explore Paxcom’s Kinator and start optimising your digital shelf for maximum sales today. Reach out to us at info@paxcom.net for more information.
