Executive Summary
eCommerce has become an extremely
important part of the marketing mix with the increase of online shopping and
access to technology and the internet. eMarketer tracks 180 companies and have
reported a total annual retail eCommerce sales figure of $201 billion. Walmart
is currently ranked third in total annual eCommerce sales at $13.5 billion (Zaczkiewicz,
2016). “From online activity to in-store purchases and social mentions, Walmart
has more customer connections than any retailer in the world. According to
Daniel Thorpe, Senior Director of Analytics for Global Customer Insights, the
company sees 300,000 social mentions per week (Four ways Walmart uses
analytics, n.d.).” This case study will highlight findings in relation to Walmart’s
use of digital analytics and recommendations for their strategy moving forward.
Findings
DeZyre highlights key big data
points for Walmart:
- 245 million customers visiting 10,900 stores and 10 active websites across the globe
- Close to 300,000 social mentions every week
- Walmart collects 2.5 petabytes of unstructured data from 1 million customers every hour
- Walmart has exhaustive customer data of close to 145 million Americans of which 60% of the data is of U.S. adults and they track and target each customer individually
But how does Walmart use the big
data? DeZyre provides the following insight:
- The analytics systems at Walmart analyze close to 100 million keywords on a daily basis to optimize the bidding of each keyword
- By incorporating big data into their overall strategy framework Walmart observed a 10% to 15% increase in online sales for $1 billion in incremental revenue
Bridg further breaks down exactly
how Walmart tracks their customer’s lifecycle from their web data:
Mining
Sales Data
“Data mining helps Walmart find
patterns in their sales data that can be used to provide personalized product
recommendations and promotional offers. They use a matching algorithm to
discover products commonly purchased together or purchased in a particular
sequence (The Walmart Big Data Case Study: Think Large, Act Small, n.d.).”
Connecting
In-Store and Online Customer Behavior
“Walmart pulls data from common
sources like clickable actions on their website, contact information from their
e-club, and POS transactions, as well as from one-of-kind programs developed by
Walmart Labs (The Walmart Big Data Case Study: Think Large, Act Small, n.d.).”
Personalized
Email Marketing
“Walmart’s extensive inventory of
actionable data has given their marketing teams incredible power in driving new
revenue through best-in-class email marketing (The Walmart Big Data Case Study:
Think Large, Act Small, n.d.).” Their email marketing includes personalized
recommendations, time of spend and personalized subject lines creating a more intimate
experience with their customers.
Conclusion
Walmart is a leading retailer for
many reasons and their recent success can be attributed to their use of web
data to drive strategy resulting in more online sales and conversions. One of Walmart’s
strength is that they are open to new data and find new ways to use and
manipulate what they have to find the right people at the right time while maintain
personal communication with existing customers.
Recommendations
I think one of Walmart’s great
opportunities (and most retailers) is within predictive analytics. I know most
are using these analytics in some capacity and starting to scratch the surface
but this information is so, so valuable.
Improve
Customer Engagement and Increase Revenue
Predictive analytics allow
retailers to manipulate variables to create environments that present the engagement
most likely by a consumer. “This could mean signing up for a newsletter, clicking
on a promotion or or some other form of engagement (Mehra, n.d.).”
Launch
Promotions That Are Better Targeted For Your Customers
Predictive analytics help take
multiple touch points into consideration – personalize promotions, targeted
customer segments, browsing behavior – to provide the right promotion to the
right customer (Mehra, n.d.).
Minimize
Fraud by Proactively Detecting It
Fraud has been a huge issue with
retailers in recent years with more hacks happening every day compromising
valuable customer information. Incorporating predictive analytics can help
utilize the information their website is gathering to actually prevent fraud.
There are solutions available that take into account browsing patterns, payment
methods and purchasing patterns to detect and reduce fraud (Mehra, n.d.).
Although predictive analysis is a
newer solution and requires additional plugins the outcomes are extremely
beneficial to any retailer in the eCommerce world. I think Walmart could
benefit tremendously by continuing to evaluate these tools and implement when possible.
References
Four ways Walmart uses analytics. (n.d.). Retrieved November
21, 2016, from http://www.sas.com/en_us/insights/articles/analytics/four-ways-walmart-uses-analytics.html
How Big Data
Analysis helped increase Walmarts Sales turnover? (n.d.). Retrieved November
21, 2016,from https://www.dezyre.com/article/how-big-data-analysis-helped-increase-walmarts-sales-turnover/109
Mehra, G.
(n.d.). How Predictive Analytics Is Transforming eCommerce & Conversion
Rate Optimization.Retrieved November 21, 2016, from http://conversionxl.com/predictive-analytics-changing-world-retail/?hvid=352IDw
The Walmart
Big Data Case Study: Think Large, Act Small. (2015). Retrieved November 22,
2016, from http://bridg.com/blog/walmart-big-data/
Zaczkiewicz,
A. (2016, March 7). Amazon, Wal-Mart Lead Top 25 E-commerce Retail List.
Retrieved November 21, 2016,
from http://wwd.com/business-news/financial/amazon-walmart-topecommerce-retailers-10383750/
