Socializing Insights with end users: Analytics for masses – Amazon vs. LinkedIn

This blog post is about comparison of amazon.com and linkedin.com in terms of similarities across dimensions of analytic maturity & use of data shared by their customers. As Thomas Davenport mentions in his book “Competing with analytics”, amazon.com is one of the few companies which was built on the foundation of data, the so called “Analytically mature” company. LinkedIn has joined the list, with lot of new features available to their users.

              As customers interact with the site, they generate data about their liking towards certain products or feature. Companies like amazon.com and LinkedIn clearly understand how to leverage this information to make the interaction between the customer and the site even more valuable & relevant. Users who are ready to share more data with site about their likes/dislikes, the better would be the site’s recommendation for the user.  The companies need to instil this confidence in the customers mind, and hence have the users share data by will.

               Amazon.com & LinkedIn makes available every little fact about the consumer’s behaviour and interaction with other users or products to help change their behaviour in terms of decision they make to buy or not-buy a product or whether to look for an employer change, etc.

LinkedIn has amazing insights about the companies, profiles which is all available to the users freely. In a interview with linkedIn CEO, Reid Hoffman by Andreas weigend, Reid talks about every individual as a small business and every individual thinks of their reputation in terms of number of new connections, who viewed their profiles, how many times their profile came up in the search results and stats of similar kind.  Andreas Weigend, a social data expert talks behavior change brought about by features like ‘who viewed your profile in the last 15 days’ in end users and in the way companies like LinkedIn treats the users.

(i)                  Insights about companies( lets say we are researching the company mu-sigma):

• Employee switching patterns between companies. Employees moved from ‘xyz’ to mu-sigma.
• Employee switching patterns between companies: Employees  moved from mu-sigma to “abc”.
• Gender distribution: M to F ratio at mu-sigma.
• By years of experience, how does mu-sigma differ from other companies. Similar statistics is available by job function, educational qualification & university. Similar company benchmark is available for comparison.
• People who looked at “mu-sigma” also viewed – other list of companies?
• Where employees of mu-sigma call home
• Most recommended at mu-sigma.
• Time trend of employees who got a change in title.

(ii)                Insights about profiles/users

• Who viewed my profile in the last 15 days?
• How many times did your profile show up in search results?
• Recommendation about other profiles/users you  might know.
• Companies which user might be interested in following.
• Relevant jobs for every user with functionality to apply for it.
• Work recommendations by colleagues and customers.

LinkedIn

Here’s a look at what amazon.com offers. When purchasing a product at amazon.com, the user would be presented with stats related to
• How many users who searched for the book “The outliers by Malcolm Gladwell” (say) ended up purchasing it or ended up purchasing “The tipping Point” , “The Blink” , “What the dog saw”, etc.. in the same order. However I feel the need to quantify the same would help. I mean calling out that 80% of people who searched for “A” ended up purchasing “B”. Or 80% of people who searched for “A” ended up purchasing “A”.
• “Frequently brought together items” for a given product.
• Review statistics: How many rated 5-star, 4-star and so on, as a bar chart.

We are moving towards an era of socializing data with end users to make every little decision they possibly make is data driven. WordPress, Netflix, glassdoor, etc are some of the other companies geared towards this trend. The intention of collecting data has truly gone beyond marketing purpose.

Some of the insights have stood best, because they were simple!

 In this blog post, i talk about 3 scenarios where there had been highly valuable insights derived, yet remaining simple.

1. Customers shopped online returned via stores Randy Lea, VP product & service marketing Teradata talks about one of their clients, who had tagged their e-com customers as best customers based on web sales they were generating and reaching out to them with various promotions. However, on integrating their web data with Enterprise data( store data) they found most of the customers were buying things online in multiple units and returning them through stores.

        For example, some customers brought 4-5 shirts of different colors, however they reatined one of them they liked the most and returned the rest of them visiting the stores. Effectively customers were buying through one channel(web) and returning them through another channel(store).Hence the web customers, whom they believed best not actually best rather average shoppers and shouldnt have been sent offers.

Source: Teradata ( Video) 

2. In the United States, if you live more than two miles from a pharmacy store, you probably don’t shop there!In the book data-drien marketing , Mark Jeffery talks about the case of how walgreens optimized their marketing spend using simple geo-spatial visualization. The pic on the right, is a picture of three stores of the Walgreens pharmacy chain on a map.Walgreens is a $59 billion annual revenue pharmacy company with 6,850 stores throughout the United States.

Geo spatial visualization of Walgreens stores

This geospatial picture shows dots that are the customers and where they live and are coded by shape depending on which of the threeWalgreens stores they shop. The ‘‘diamond’’ customers shop at Store 1; the ‘‘square’’ customers, at Store 2; and the ‘‘star’’ customers, at Store 3. This pharmacy retail chain predominantly markets using flyers in newspapers. The way they pay for the marketing is by zip code, denoted by the dashed line, for example, in the picture. Mike Feldner, the marketing manager who first created these pictures, noticed something interesting: the circle on the picture is two miles in radius, and after looking at many pictures throughout the United States, he noticed that there are no dots (customers) for a store more than two miles from the store. He concluded that in the United States, if you live more than two miles from a pharmacy store, you probably don’t shop there. At that time,Walgreens treated each U.S. locale equally; allocating equal dollar amounts for newspaper advertising in each zip code across the United States. But the data show that if there is no store within two miles of the zip code, customers do not shop at the store. Based on these data, Walgreens ultimately stopped spending advertising dollars in all zip codes without a store within two miles of the zip code. As you might guess, the impact to sales revenues was exactly zero. The impact to marketing, however, was a cost saving of more than $5 million, for a total cost of collecting the data and creating the plots of approximately $200,000. This multimillion-dollar saving in marketing did not require a lot of money, and the analysis was done on a personal computer (PC). This is yet another example of being simple in approach, yet making the impact.

Source: “Data-Driven Marketing” by Mark Jeffery

3. We won because we understood the science of incentivizing people to cooperateLate last year the Pentagon’s mad-scientist research wing, Darpa, announced the Network Challenge, a $40,000 prize for the first group to find and report the locations of ten red weather balloons that the agency would set aloft one day in secret locations around the country. Most of the thousands of groups that signed up quickly realized that crowdsourcing was the way to find the 8-foot spheres. So, naturally, they offered bounties to balloon hunters. But Pentland’s crew at MIT’s Human Dynamics Lab–part of the MIT Media Lab–took their crowd control a step further. “It was trivial for us to slap together the balloon thing,” says the 58-year-old Pentland. That’s because other groups’ tactics were based on guesswork, he argues. His were based on lessons learned through data-mining research. “We won because we understood the science of incentivizing people to cooperate.”

Read the entire article here: Mining Human Behavior at MIT

 

Any insights you derived had high impact in your organization?

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