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Before the arrival of product analytics, many online stores showcased their products based on what they wanted to sell. Specifically, they pushed low-demand products on splash screens or promotional pages. And while initial results showed a temporary spike in sales, interest didn’t last. It turns out that pushing products to uninterested buyers doesn’t change the fact that nobody wants them.

Marketers who understand push and pull marketing know which one works better online. In the digital marketplace, people actively search for what they need. They don’t want to be told what to think, like, or buy.

Today’s digital marketers use product analytics to understand what customers do want. This game-changing tool looks at how customers engage with the brand’s products and solutions. With product analytics, marketers can monitor and track the behaviors that define a user’s engagement. The collected data is then analyzed, interpreted, and converted to insights that can improve the product further. In effect, this tool helps brands learn what people like or dislike and adjust accordingly.

Instead of highlighting what brands want to sell, online stores focus on what products buyers are interested in. But how can you predict buyer interests? By using product analytics to better define your customer journeys.

product analytics

Why Define Your Customer Journeys

The customer journey is what buyers go through as they look for a solution to their problem. By meeting them through the various stages, brands stand a better chance to earn their trust.

  • Awareness: In this stage, the buyer learns about the brand and its product. The brand must be a fundamental match with the buyer’s needs to qualify for the next stage.
  • Consideration: Consideration is where the customer performs further research into the brand to see if its solution fits the problem. Buyers will consider the brand’s history, reputation, and core values. They’ll also read reviews and testimonials from fellow consumers to see if there are authentic positive experiences.
  • Conversion: If everything goes well, the buyer will proceed with the sale and establish a relationship with the seller. In this stage, the retailer should ensure that the buying process is as pain-free as possible. Otherwise, the chances for disengagement will go up.
  • After Sales: Once the sale is made, sellers must now retain their new customers. Loyalty programs can help maintain high engagement levels. But superior after-sales care and helpful customer service are also equally crucial in retaining customers.

Brands should be able to identify which stages their customers are currently in at any moment. More importantly, they should take the time to learn how customers engage at every stage. For instance, what makes buyers arrive at their online doorsteps? What makes them stay engaged?

In addition, brands need to analyze whether their sales process matches customer expectations in terms of accessibility and ease of use. Finally, brands must make sure that customers always have easy access to informative resources and helpful customer service.

Using Analytics To Enhance Product Discovery

Learning what works and what doesn’t along the entire customer journey requires diligent data gathering. Anecdotal reports from customer service calls may be able to identify unaddressed problems. But nothing beats the precise information provided by analytics.

Product discovery is the process of looking deep into customer problems and creating the right solutions. How do you know they’re the right solutions? Easy: if the customers are willing to pay for these solutions, they’re definitely the right ones.

business owner showing product analytics metrics

While market and product research can help you identify the problems and suggest answers, analytics can further refine ideas and deliver custom, targeted solutions. This includes diving deep into consumer surveys, product reviews, and customer feedback to gather insights. As a result, marketers and site developers will know more about customers’ pain points and desired solutions.

Note that product discovery isn’t just about delivering a product. It also refines the sales journey to give customers an intuitive and hassle-free experience when using the product. Thanks to quantitative data, you can learn more about what users like and dislike about your solutions.

Improvement Is a Continuous Process

Continuous product discovery enables you to apply changes that further improve the customer experience. When faced with a myriad of suggestions on how to change or improve your product, you’ll also need analytics to identify priorities.

Data can help you identify the tasks that promise the greatest improvement so you can prioritize their development. Once updated, you can continue using metrics to see how these changes improved your product’s performance.

Democratizing Analytics Data: Analytics Data for Dummies

Before the introduction of automated tools, digital merchandising was a difficult process. Marketing and merchandising teams pored over spreadsheets of the brand’s inventory to determine which items needed special attention. Replacing highlighted products when they sold out had to be done manually. And with new products coming in and obsolete ones being phased out, updating the online product became a full-time job.

Thankfully, technological improvements have led to the development of automated merchandising software. Such programs help automate the process of which products get featured and where.

Of course, automating the process might be helpful for merchandisers, but it still isn’t intuitive enough for customers. More importantly, the insights produced by analytics often remain confined in silos reserved for the organization’s top decision makers.

Democratized Product Analytics = Faster, Better Decision-Making

Making product data more widely available across your business gives your brand teams a consistent, customer-focused basis when developing their merchandising strategies. Why confine your data to your analysts and data scientists? Your end users are actually customer-facing teams such as sales, marketing, and merchandising.

Data democratization opens up information so end users can use it to achieve their objectives. For instance, front-line personnel can continuously adjust strategies based on current customer sentiments. Democratization also lets users bypass higher-level clearance for basic decisions. Think removing out-of-stock items from display rotations or increasing the visibility of popular items.

Making analytics data more accessible can also improve customer experiences. By tracking how customers navigate the site, brand teams can identify the friction points that stop a sale from happening. Whether it’s a slow-loading page, a delayed payment approval process, or repeated requests for payment information, democratized analytics can empower even lower-level operators to cross-reference the data with customers and come up with suggestions for improvement.

managers discussing product analytics

Tracking the Right Product Analytics for Every Journey

When it comes to product merchandising, what metrics can identify pain points and successes that lead to positive changes? After all, determining which products to highlight in a catalog is a subjective task. The items that attract a specific group of shoppers could turn off other audiences.

Merchandising tools that also include social metrics such as conversion rates and click-through rates can help sellers create dynamic catalogs that feature the most popular or trending products. Items that garner positive ratings on social media or those with the most likes and shares can automatically appear as featured products for customers identified as frequent social media users.

Similarly, product analytics can dive into a customer’s purchase history, preferences (size, color, brand), and average purchase value to present products that are available in their preferred size or color. The site can also highlight cross-sell or upsell options to bump the customer purchase value a little higher.

Churn rates, average session duration, and cart abandonment rates can help you see where customers are dropping out of the buying process. These metrics can point to fixes like seamless checkout and payment processes that can make the purchase journey both easier and more enjoyable.

Key Takeaways To Consider

Product analysis enables brands to continuously learn about their buyers. As the customer continues to evolve, brands can reshape their product solutions and storefronts to conform to their changing needs. In short, online merchandising that’s designed to pull customers into trusting them instead of pushing unwanted products will keep customers coming back for more.

But improving the customer experience isn’t limited to providing solutions to customer’s problems. It also means offering a user-friendly storefront that makes the purchase process a pleasant and hassle-free experience.

Finally, product analytics can best help if data gets shared throughout the organization. When front-line workers have access to metrics such as customer sentiment, they can initiate improvements in the storefront themselves in real time. Of course, to accomplish this, they’ll need modern software that incorporates automated tools and powerful analytics.

Smart Merchandiser Helps You Succeed

Visual merchandising software such as Smart Merchandiser has the tools and features to ensure your storefront displays what your customers want. It incorporates powerful product analytics to track customer sentiment so you can adjust merchandising behavior accordingly. This includes featuring products based on the current season or popular trends. The software also removes sold-out items from the display rotation to keep customers from getting disappointed.

Learn how Smart Merchandiser can automate your operations and increase your organization’s productivity and revenue. Sign up today for a free demo and see why successful retailers like Vans, The North Face, and Timberland trust us for their visual merchandising needs.