The Data-driven
eCommerce Workbook
The Data-driven
eCommerce Workbook
A process for designing solutions based on insights from customer behavior.
Get the FREE Google Slides workbook
Get the FREE Google Slides workbook
Make a copy, print it out, make it your own.
Identify, analyze, and design solutions for all aspects of your eCommerce site experience from Path to Purchase, Product Discovery, Drop-off, Retention, and Mobile Optimization.
Make a copy, print it out, make it your own.
Identify, analyze, and design solutions for all aspects of your eCommerce site experience from Path to Purchase, Product Discovery, Drop-off, Retention, and Mobile Optimization.
Take a pulse check on your entire site's experience
The workbook is divided into 5 sections, casting a wide net to cover areas inherent to most eCommerce businesses. Taking a week to go through the entire workbook can help the team take a step back and recalibrate, zoom out on the holistic experience, then zoom in to the areas that need the most attention. This can result in:
Go deep on a specific area
You might already know that the navigation is confusing, or that retention is dropping. Jump directly to each relevant section.
For confusing navigation, jump to Path to Purchase.
Are there products that aren't catching on? Is your retention strong, but have difficulty getting the first-time customer to Discover?
Issues with Drop-off or Retention?
Noticing spikes in mobile traffic, especially from social marketing campaigns, and not sure if your Mobile strategy is prioritized?
When you're feeling stuck, overwhelmed, or not sure where to start
We all have our own standard operating procedures for approaching our work —— a comfortable routine that we've settled into because it has served us well. If you find yourself stuck, the question-based prompts could help lend a fresh perspective to help you attack the problem or approach the analysis from a different angle.
It's also a way to help add structure when looking through your analytics tool, customer recordings, or piles of customer interview notes. If you find yourself getting overwhelmed because there's so much data everywhere that nothing feels actionable, having a goal in mind can help you drill in to just the areas that matter, and avoid the time drain of going down distracting rabbit holes.
To steer product decisions away from personal preferences and back on the user
I've also used this workbook as a workshop tool to help align stakeholders who all may have different ideas for what to build or ideas that need help unpacking. "The breadcrumbs should be bigger!" "Everyone's doing personalization —— we need it too." "The navigation is messy —— let's clean it up."
There's potential merit in all of these ideas, but what is the problem that each of these is addressing? Is the problem worth addressing? How is the problem currently affecting customers? Based on that, is this the best solution?
As an example, one executive felt strongly that their site needed bigger, better breadcrumbs on product detail pages (PDPs). It's an eCommerce best practice they've read in several articles, and when they use the site themselves, they find themselves using the breadcrumbs to navigate. The team had mixed feelings about this request and asked for my thoughts. Looking at heatmaps of the site usage, it was clear that customers were in fact using breadcrumbs to navigate —— in fact, clicks on the breadcrumbs exceeded clicks on the main navigation on product detail pages. But, the footer was also getting a large percentage of clicks. Based on these observations, we could conclude that since breadcrumbs are used so often that we should invest in bigger, better breadcrumbs. Another interpretation is that the rest of the navigation is failing the user that they're resorting to breadcrumbs and the footer to continue their journey or complete their shopper mission. Drop-off was also highest on Product Detail Pages (PDPs), and people landing on a PDP had the lowest engagement (page views per session and session duration) of any page type. Are breadcrumbs the best way to improve PDPs to make them more engaging and to help cross-navigation to the right products or categories?
By unpacking an idea back to the problem, then rooting it in objective observations of what's actually happening, teams can lead productive discussions on how to interpret what they saw, then brainstorm solutions based on that shared understanding of the problem. This helps avoid the trap of starting with a solution, then data-mining your way to justify it, or loss of morale when one hunch overrules another hunch.
Each prompt is broken into three sections to help guide your analysis from the objective data observed, your interpretation of the data and insight, and finally your proposed solution to test.
Discovery & observations
What data did you analyze and what behavior did you observe?
This is the quantitative or qualitative evidence for each prompt. This might be a snapshot of a Google Analytics report, quotes from a customer interview, observations or recording of a customer using the site, a graph, data table, or any other observable evidence.
Interpretation & insights
What’s your interpretation of this behavior? What insights can you extract?
This is the qualitative analysis. Why might the data present in this way?
Stretch your thinking in how you interpret these observations—for example, observing many footer navigation clicks might mean bigger, better footer nav! Or, it could be a consequence of the overall navigation being so challenging that people are resorting to the footer nav.
Decisions & Actions
What are your proposed next steps, changes, or tests from these learnings?
Based on the data observed and analysis, what are the resulting action items?
This may include brainstorming solutions, running tests to confirm that the interpretation of the data is correct, or further studies to be done.
1 Path to Purchase
Especially for conversion rate optimization, we like to look for high-impact opportunities fitting these three criteria:
In this case, 20% of revenue was influenced by interaction with filters, which only 5% of sessions used. If filters are helpful in narrowing the inventory to help people browse for the right products, why aren't more people using filters? Could we make filters more accessible and useful? What else does this tell us about how our customers are shopping?
Discovery & observations
Interpretation & Insights
Decisions & actions
2 Product Discovery
One client's niche product assortment was successful in driving organic traffic to their product detail pages (PDPs). However, PDPs were the worst-performing page type (compared to the homepage and product list pages)—failing to engage traffic in page views per session and session duration. In an effort to help shoppers discover other products offered and increase engagement, a recommendations widget was added to all PDPs that paired similar products together and exposed more options for the shopper to continue their journey. How did this perform and what can we learn?
Discovery & observations
Interpretation & Insights
Decisions & actions
Understand why
Are customers adding bundles because they misunderstand or assume thereʼs a bundle discount? Then remove when they realize thereʼs no additional benefit to the bundle in their cart?
Improve cross-navigation
An important function the PDP must serve is to enable shoppers to cross-navigate to similar products and the parent product category (browse while remaining close to the target product).
Instead of constraining shoppers to 3 select products (which likely arenʼt a perfect match), consider adding links to the brand, product category, styles, properties, and other parent categories.
Improve cross-sells
By simplifying decisions
Consider testing a bundle that shows the PDP product + a set of matching glassware of the same brand. This removes the additional complexity of deciding if the unit price on the beer + glassware set is a fair value.
By separating alternative & supplementary
Consider separating alternative and supplementary products in this cross-sell section, since it caters to two distinct and opposite use cases.
Alternative: products that replace the main product (similar configuration, different brand).
Supplementary: complements or improves the experience of the main product (merchandise, accessories).
3 Drop-off
It's (almost) always more expensive to attract a new customer than it is to retain an existing one. Customer interviews and surveys can be some of the most insightful ways to understand why some people become loyal customers and advocates, while others bounce and churn. But to take a quicker, broader view at your business, running a cohort analysis can reveal patterns that you can further probe in your customer research. In this example, we discovered customer habits that are statistically significant across time that can't be dismissed as anecdotal evidence.
Discovery & observations
Interpretation & Insights
Decisions & actions
Use sale events to strategically reactivate customer segments
Week 7 shows a steep drop-off in re-engagement with sale messaging. Consider messaging dormant customers differently than newly-acquired customers. Perhaps the price discount announcement alone isn’t enough to trigger purchase for an older or dormant cohort.
Reward and encourage loyalty
How could sales events be even more effective at reactivating existing customers and building loyalty? Early access for the best selection? Newsletter-only product discounts? Sneak peek at Cyber Monday selection? Contests?
Post-sale workflow
Beyond monitoring the effectiveness of sales events to attract new customers, monitor its impact on retaining existing customers.
4 Retention
One client sells specialty hobby starter kits and refills. It's a trendy hobby to take up, but requires hours of dedication to get started—and you're not guaranteed that your first try is successful. It was also unclear to the team if these kits were actually being used, or if they were purchased aspirationally and spent their lives gathering dust in the garage.
Discovery & observations
Interpretation & Insights
Context mapping: potential solutions to unmet needs
Decisions & actions
Identify customer-centric solutions
Conduct interviews to understand the customer's unmet needs, pain points, barriers, concerns at each stage of the customer journey.
Hypothesize solutions to help address each unmet need
For each unmet need at each stage, brainstorm possible solutions to address the need. This could include looking to your existing channels (including offline resources if you have them) and analogies with other markets/problems (how have others addressed a comparable problem?).
Prioritize solutions
Which of these needs are most painful? Which ones are we best positioned to solve? Which assumptions can we validate cheaply and quickly? One way to quickly evaluate the long list of ideas generated in the previous step is to categorize solutions by their execution effort and impact. [Based on the Action Priority Matrix]
5 Mobile Optimization
5 Mobile Optimization
Okay first, why is mobile even a category on its own? Doesn't it make this whole thing not MECE (mutually exclusive, collectively exhaustive)? I get it, but as long as there's a bias toward desktop experience first, it's worth emphasizing that mobile isn't desktop, but smaller. With differences in distractions, times of day, physical locations, marketing mix, patience level, mobile behavior can sometimes be surprisingly different from desktop and can be optimized differently.
For example, one client I was working with had entirely different shopping behavior on mobile:
Discovery & observations
Interpretation & Insights
Decisions & actions
Get the FREE Google Slides workbook
Get the FREE Google Slides workbook
Make a copy, print it out, make it your own.
Identify, analyze, and design solutions for all aspects of your eCommerce site experience from Path to Purchase, Product Discovery, Drop-off, Retention, and Mobile Optimization.
Make a copy, print it out, make it your own.
Identify, analyze, and design solutions for all aspects of your eCommerce site experience from Path to Purchase, Product Discovery, Drop-off, Retention, and Mobile Optimization.
Credits
Thank you to my analytics partner Lea Masatsugu, who taught me everything I know about approaching analytics in a thoughtful way. Also thank you Dana Lee, who worked with me to stress test this approach and format. And finally, thank you Brandon Tadesse, for all of the feedback along the way.