Measuring and Improving Cross-Sell and Upsell
Jun 19th, 2009 by admin
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 groups 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 groups 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 Dont know conversion rates – 44% of retailers Dont 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 Dont know conversion rates – 50% of retailers Dont 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, its 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 youre using. If youve built your solution in-house or your commerce platform came with cross-sell/upsell out of the box, youll need to figure out how the data will feed into your analytics tool. If youre 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.
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 theres 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 doesnt tell me if customers are buying more items per sale. I dont know which suggested products are most successful to refine our merchandising strategy. I dont 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) theres 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 dont know their conversion rate just dont know where to access the reports from the vendor.
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.
Youve read this far in this article. We think youll also love last years Get Elastic post Cross-Selling Tips for Online Retailers for a list of Dos and Donts, along with retailer examples.
Elastic Path also did a webinar on effective merchandising with Mike Svatek, formerly of Baynote. Mike offered this advice:
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.
Dont 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 its unlikely someone adds a lens to cart and then all of a sudden wants to buy a camera. Same with sports tires – you wouldnt try to upsell a Porsche.
Be careful that you dont 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 dont goof your directional selling.
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 important attributes are different (red vs. blue dress) or when you show items that dont 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 tools 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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