Category: Blog

Here are some more insights we found in our analysis of online shopping behavior and tests:

• Targeted email and direct mail campaigns were most effective.  By analyzing prior purchases, you can for example determine if a customer prefers meat, poultry, fish or vegetarian. By targeting correspondence and promotions at a macro level, the engagement rate was significantly higher than generic promotions for protein.
• A significant number of customers arrived at the grocer’s site through organic searches.  Many of the organic searches are for broad categories like “fresh fruit online” or “fresh fish”.  This ultimately led to a search link on the site with little to no merchandising, so new customers didn’t understand their value prop.  In one experiment, we prominently displayed value messages in search results which we expected to have a higher conversion rate with new customers.
• New customers tend to browse the site categories, navigating through headers and surveying the assortment whereas existing customers tend to simply search for the items they want.  This leads to more opportunities to merchandize to new customers but if you try to merchandize too much to existing customers it simply slows the experience down and they buy less.

Building on the previous post analysis, we utilized Google Analytics to understand where the customers were coming from and their conversion rates for each acquisition path.  Using our insight from the previous analysis we again segmented the customers into existing users and new users from valid zip codes.

Here you can see that the majority of customers were arriving at the site direct.  For existing customers, the users would go directly to the URL or click on a link in their history such as the cart page.  A significant number of customers arrived from Organic Search.  Again, a significant number of existing customers would simply use Google search to find the site rather than clicking on their browser history.  A significant number of new users would also enter the retailers name in search and we suspected that the brand awareness came from marketing activity such as billboards, direct mail, and other advertising.

Channel	Type	Users (K)	Bounce Rate	Conversion Rate
Direct	E	415	16.12%	24.35%
	N	589	43%	10.75%
Email	E	105	29.32%	12.13%
	N	90	53.45%	5.27%
Organic Search	E	81	14.03%	24.34%
	N	258	36.67%	10.68%
Branded Paid Search	E	61	12.99%	22.58%
	N	103	21.28%	13.85%
Generic Paid Search	E	37	42.86%	9.98%
	N	398	81.64%	0.89%
Affiliates	E	36	13.64%	26.83%
	N	45	36.20%	12.93%

The glaring issue in this analysis was that Generic Paid Search was performing abysmally for new customers but had a significant number of sessions.

Further analysis on the Generic Paid Search found that customers were searching for individual items and would then abandon the site.  Our next step was to survey these customers to try and understand what the customers were looking for.  Using Qualtrics we could solicit the customers for feedback by offering a credit on their next purchase.  By doing that, customers that had no intention of buying would consider buying now that they were invested and had a credit.

K3Group can work with you to fine tune your strategy.  Again, we’ll send more insights in our next cadence and if you’d like to learn more about any of this, we’d be happy to share the information on a call.

We work with many grocery stores helping with eCommerce optimization, category management, and pricing.  At one customer we analyzed online customer behavior, researched customer segments, and tested hypotheses.  Here are a couple high level insights we found and our analysis associated with the insights:

• New customers behaved very differently than existing customers.  It’s obvious, but when looking at browsing patterns we saw that new customers want to explore, browse, and engage with the site while existing customers want to make the process as efficient as possible.
• Existing customers typically spend a set amount of time buying.  We saw in one test that existing customers spent 12 min shopping and added an average 21 items to the cart.  When we made the process more efficient, they still spent 12 min shopping but added 24 items to the cart.

Drilling in further, we used a tool called Content Square to perform the journey analysis.  The tool helps analyze the full customer experience from journey analysis to heat mapping and session recording.  To start the analysis, we looked at the customer journey for all customers.  The image below shows what that view looks like.

Some interesting questions came out of this.  Why are so many customers coming to the home page and then abandoning the site?  Also, what is driving them to the Product Details Page (PDP) and then abandoning the page.

By integrating with Google Analytics, we were able to utilize the segments across both GA and Content Square to support a deeper dive into the customer journey.  By segmenting new customers from valid zip codes, we were able to see a different picture of how those customers behaved.

It became clear that new customers were driving the abandonment rate.  Further analysis showed that the customers were being driven to the site through Generic Paid Search and Organic Search which we’ll dive into further in a subsequent post.  The other important insight in the journey analysis was that customers were frequently drilling down on the categories and section pages to explore the site.  They wanted to be sold to.  We used that information to further refine their experience to more prominently show the value proposition for the retailer when a customer arrived at the site.

Next, when looking at existing customers, a different behavior profile emerged.  As you can see below, the existing customers utilized reorder and search extensively.

Drilling into the existing customer behavior showed they simply wanted to build their cart and order their items.  As mentioned, they would visit the reorder page, place multiple items in the cart off the page and then search for more items from the search bar.  They rarely visited the category pages.  We ran an experiment to make the customer flow more efficient and found that existing customers originally would spend 12 min on the site and buy an average of 21 items.  When we made the process more efficient, those customers bought an average of 24 items.

K3Group can help you improve your customer experience leading to an increased order value and improved conversion rates.  We’ll drill into other insights we learned in subsequent posts.