30% Growth For Online Retailer By Mapping Customer Decisions

 By Mark A Carbone, Co-Editor, CaseStudiesOnline.com 

 A large online retailer who many of us buy from on a regular basis sells thousands of products online in dozens of categories.  They noticed a big drop in conversion rates and revenue per client and didn’t know why.

They went to McKinsey & Company for help, who did a 20,000 person study on consumer behavior and how selling and marketing to consumers has changed.  They identified where efforts should be directed to yield the highest return on marketing efforts.  Click here to read a great brief about the study – “The Consumer Decision Journey.”

Key Findings

  • They dug deep into existing online analytics to study the correlation between purchases and quantity of product per category
  • They used segmentation to calculate likelihood that customers in each category would “cross the aisle” and buy something in another category
  • After digging into the data, they found the lifetime value of a toy buyer increased greatly when they bought in other categories
  • Conversely, consumers who bought a lot of pet products did not buy frequently in other categories
  • After studying their consumer decision journeys they developed cross-selling and category penetration techniques to grow the lifetime value per customer
  • 6 months into this project yielded a jump of 25% in email conversions, 60% increase in on-site conversions, increase in overall sales of 20% and and overall ROI of 30%.

Click here to read full case study

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Using YouTube Insights for Demographics and Statistics Discovery

Now that you’ve got your YouTube video uploaded and live, it’s time to learn more about who’s watching your video, where they’re coming from, and what parts of your video they’re most interested in.

Measuring ROI and The Problem with Outliers

Jaremy Rich at Viralogy.com has posted a great article about social media ROI and makes the point that social media is, indeed, measurable — the problem, though, can lie in using that measurement model twice. For instance:

If one major influencer picks up your social media campaign and promotes it to their followers, you have a big outlier problem (not the worst problem to have, to be sure). Sure, you can measure and report on the results of that individual campaign, but moving forward, you’re going to have issues duplicating and predicting future success.

The problem is with data extrapolation. If one user with millions of followers retweets your post, it changes everything. It’s just like if a new post or viral video you’ve made lands on the Digg front page – an event that provides an exponential boost in traffic.

Though you can measure that one event, it will be extremely difficult to account for that when predicting future events. So if you’re running a contest that costs $5,000, it’s certainly something to keep in mind. If you consider each impression on Twitter to be worth one tenth of a cent ($0.001 – due the relatively small number of people that actually *read* each tweet), a retweet by @aplusk would put you immediately at break-even when your campaign might have otherwise lost thousands of dollars

It’s a great read.

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