Measuring and Improving Cross-Sell and Upsell
Cross-selling and upselling is a popular tactic among online retailers in hopes of increasing average order value, items per sale and improving customer service with relevant suggestions. Amazon shared that cross-sells were responsible for 35% of its sales in 2006! According to the e-tailing group's 8th Annual Merchant Survey Report (of 190 ecommerce executives), 55% of retailers will include cross-selling and upselling in their merchandising activities this year.
But cross-selling and upselling is one of the most difficult activities to do well and effectively measure, as evidenced in the e-tailing group's findings:
Cross-sell/Upsell in Shopping Cart, Conversion Rates:
- Less than 1% conversion - 8% of retailers
- 1%-4% conversion - 16% of retailers
- 5%-10% conversion - 9% of retailers
- More than 10% conversion - 3% of retailers
- Don't know conversion rates - 44% of retailers
- Don't merchandise in shopping cart - 20% of retailers
Cross-sell/Upsell on Product Pages, Conversion Rates:
- Less than 1% conversion - 5% of retailers
- 1%-2% conversion - 15% of retailers
- 3%-4% conversion - 5% of retailers
- 5%-7% conversion - 6% of retailers
- 8%-10% conversion - 2% of retailers
- 11%-15% conversion - 1% of retailers
- More than 15% conversion - 2% of retailers
- Don't know conversion rates - 50% of retailers
- Don't merchandise on product pages - 14% of retailers
The only overwhelming statistic here is that most retailers have no clue how product associations convert. With 92% of retailers citing web analytics as the number one data source for merchandising decisions, it's disturbing that many retailers are not measuring the outcome of these decisions.
Of course, measuring conversion rates for cross-sell/upsell can be ridiculously complicated, and depends on what kind of cross-sell/upsell solution you're using. If you've built your solution in-house or your commerce platform came with cross-sell/upsell out of the box, you'll need to figure out how the data will feed into your analytics tool. If you're using a third party Software-as-a-Service like RichRelevance or Baynote, analytics might be provided for you, but it might not provide the depth and detail that you want.
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Measuring the Right Thing?
For example, your merchandising tool might not break out conversion rate by shopping cart vs. product page. It may not be able to show you detail like product category cross-sell/upsell conversion, or tell you "conversion rate for cross-sells in price range $X-Y in relation to product price $A-B is xyz."
Then there's the question of "what does conversion rate mean?" Does it mean the product is viewed, added to cart, or a sale is made by a customer who is shown cross-sells on his/her visit? The merchandising tool we use on the Vancouver 2010 Olympic Store tells us that their tool lifts conversion by 140% and average order value by $14.94 (with A/B split testing). That doesn't tell me if customers are buying more items per sale. I don't know which suggested products are most successful to refine our merchandising strategy. I don't know which products and categories have the highest conversion rate.
These problems and questions are common among online retailers, and while tracking these detailed events is possible (with complicated analytics mashups, for example) there's often not enough IT resources or budget to make it happen.
Though you may not have access to all the data that would be helpful, at minimum, a global conversion rate is a start. I wonder how many retailers who "don't know" their conversion rate just don't know where to access the reports from the vendor.
Improving Cross-Sell/Upsell Conversion
If you do know at least your conversion rate for pages with cross-sells vs. pages without, you have a benchmark you can work on improving.
"You've read this far in this article. We think you'll also love..." last year's Get Elastic post Cross-Selling Tips for Online Retailers for a list of Dos and Don'ts, along with retailer examples.
Elastic Path also did a webinar on effective merchandising with Mike Svatek, formerly of Baynote. Mike offered this advice:
When do cross-sells work?
Cross-sells work well for considered purchases (high involvement rather than impulse - typically higher cost) provided they are lower cost accessories related to the product. They also work for smaller purchases with small accessories like Barbie and an outfit. You want to keep the cross-sells at half the price or less. When they are more than 1/2 the price of the item considered, the attach rate is low.
Products with natural bundling are also good, like cameras with lens, cleaner, memory cards and warranty.
When do cross-sells fail?
Don’t try to push higher priced items together with lower priced. People who buy a camera may buy a camera lens at the same time, but it’s unlikely someone adds a lens to cart and then all of a sudden wants to buy a camera. Same with sports tires - you wouldn’t try to upsell a Porsche.
Be careful that you don’t just look at correlation in your analytics data - but consider the primary and secondary intent. You may want to manually add constraints to your rules engine so you don’t goof your directional selling.
When do up-sells work?
Upselling (suggesting a similar item instead of the item being viewed) must have a small difference in dollar value or a small nominal percentage difference - 10-20% max. You need to show some incremental value for the increase in price.
When do up-sells fail?
When important attributes are different (red vs. blue dress) or when you show items that don’t have the features the customer is looking for. You can also fail by showing different brands. If a customer owns a Nikon he needs Nikon accessories, not Pentax or Canon.
You also need to consider any contractual agreements you have with suppliers and brands. For instance, you may not be allowed to show certain brands next to each other.
Effective merchandising often requires tweaking your tool with custom rules, rather than a "set it and forget it approach." Make sure you fully understand your tool's ability to set constraints, blacklist products and create custom associations. Also understand how to review any available analytics data your solution provider collects.
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