FOR ECOMMERCE COMPANIES, ALGORITHM IS KING
Online marketplaces are
increasingly depending on algorithms to crunch data on customer behaviour and
turn profitable.
By the end of this year, m
commerce-to-wallet com pany Paytm will have all its category pages made by
machines. Paytm wants to respond to each customer who visits its website
looking for fashion wear or sports gear or iPhone covers in a personalised way.
The website will offer customer choices based on past usage and social media
posts. For example, if you recently went to Goa on a holiday and posted photos
of the trip on Facebook, you might get `beach-themed' iPhone covers.
The idea is to hook
customers with what they prefer. Given that the choice of iPhone covers run
into several hundreds, a customer visiting an online marketplace might not have
the patience to browse through all the pages and options. But by throwing up
just what he desires, the website might be able to coax him into buying.
Online marketplaces like
Paytm call this conversion rates -the number of visitors who end up buying
stuff. Helping them bump up conversion rates are algorithms. Algorithms are
responsible for customers browsing for goods being greeted with shopping
recommendations. Algorithms decide what to display for online marketplaces.
They keep track of what customer are browsing and buying.
“The goal is to improve
conversion rates and help the industry become profitable,“ says Vijay Shekhar
Sharma, founder, Paytm.
How does it work? Internet
merchants are swamped with mind-boggling flow of data -for example, Paytm has
about 30 lakh visitors every day with about 3 million page views daily.
Algorithms help it crunch data on customer preferences and increase sales.
“Algorithms are the base
for everything online -shopping, shipping, packaging, payments, price points
etc,“ says Sandeep Aggarwal, founder, Shopclues.com, an e-commerce marketplace.
The importance of
algorithms becomes stark looking at the current online marketplace conversion
rates. It is at less than 3% compared with that of offline retail at 22-25%.
Algorithms will also
underpin the future of ecommerce companies. There was a time when these
companies could live with that poor statistic. Not anymore.They are stacking up
$150-200 million in losses every month, throwing good money at customer
acquisitions and deep discounts. Profitability was not a priority. But now they
face a funding squeeze and pressure from investors to show profits.
Pragya Singh,
vice-president, retail, Technopak a retail consultancy, says the focus until
now was on topline growth.“In the last few months it's about how to come out of
deep discounting and show profits.“
Flipkart has been
downgraded twice in the last four months by investors Morgan Stanley and T Rowe
Price. In March, the Department of Industrial Promotion and Policy, the nodal
agency for investments, while allowing 100% FDI in pure marketplaces banned
deep discounts, predatory pricing and `big billion sales'.
With no room for
manoeuvring prices to attract buyers, the route to achieve better conversion
and reduce losses is big data analysis and algorithms.
Praveen Bhadada, partner
and practice head, Zinnov, a Bengaluru-based management consulting firm, sees
the reliance on algorithms as the second wave of ecommerce in India. “The first
wave was about getting the model right, getting people used to the idea of
shopping online. Now, a sizeable customer base is there (about 55-60 million
internet users shop online) and in the second wave companies are using algorithms
to improve profitability,“ says Bhadada.
Data as a Weapon
At any given time, there
are 3 to 4 million visitors online. They spend an average of seven minutes
viewing 8-10 pages. By the end of the day, about 15 million records are
generated. ComScore data for February for all etailers shows 52.98 million
unique visitors, 4.42 billion page views and about 55 minutes a visitor a
month.
The minutes spent on
e-shopping leave a trail and clues that companies want to dive into. What was
the shopper looking for? What are his previous purchases?
What device did he use? How many times has he visited the website? “We have to use this basic data -what did a person do -for strategic advantage. So, if a user has not logged in for 3-4 days the listing might be stale and the algorithm refreshes it. If a customer does a lot of cancellations, the cash on delivery option for him is automatically disabled (the customer might be doing it just for fun),“ says Aggarwal.
What device did he use? How many times has he visited the website? “We have to use this basic data -what did a person do -for strategic advantage. So, if a user has not logged in for 3-4 days the listing might be stale and the algorithm refreshes it. If a customer does a lot of cancellations, the cash on delivery option for him is automatically disabled (the customer might be doing it just for fun),“ says Aggarwal.
Generating traffic is not
the problem for etail ers. Getting customers to buy is. “We are super ambitious
about using data to help a person find what he is looking for. This will
increase conversion rate and improve profitability,“ says Rajiv Mangla, CTO,
Snapdeal. “We want to detect patterns in user behaviour to improve conversion.“
A number of companies are
already using algorithms to improve conversion rates. Ugam Solutions is a
Bengaluru based data analytics company whose clients include leading ecommerce
platforms such as eBay, LG and Staples. The company analyses data for clients
and offers signals--what inventory to carry, what models are trending, what are
users searching for and what competition is carrying. Say a marketplace wants
to dominate luxury watches segment, should it carry the whole inventory from
Rolex to Rado or focus on brands like Breitling or Chopard which have the more
likelihood of sales.
Mihir Kittur, co-founder
& CEO, Ugam Solutions, says India is a growth market where the belt has
tightened.
To be sure, companies are
looking at data with renewed interest. Saurabh Vashishtha, vice-president Paytm
says his company “stores everything“.
“There's a huge push to
dynamic content from static a year back.“
So if six months back all
visitors saw the similar content on each category page, now Paytm has a better
idea and displays content based on what the algorithm picks up.
Deepali Tamhane, senior
director, product management, Flipkart, says the company is working towards
achieving the next level of personalisation. “We want to provide our users with
what they want, even before they know they want it, of course with their
consent to use their data.“
Finding the Sweet Spot
Adds Bhadada, “in the small
window the user logs in the goal should be to understand what she wants and not
carpet bomb. At furniture e-tailer Pepperfry, if a popular product, like the
Disney almirah for kids becomes too common, more people may not buy. “There comes
a point when it should be moved out. That point is not determined by humans but
machine learning software,“ says Sanjay Netrabile, CTO, Pepperfry.
Pricing is another aspect
where algorithms become handy. Consultancy PricewaterhouseCooper's (PwC) leader
data and analytics Sudipta Ghosh says humans decide on pricing products for
offline retailers. In the online world, with millions of simultaneous
transactions, this decision is taken by data analytics. “If price point is too
low people might perceive it as too cheap to buy and abandon purchase. This
point is determined by algorithm,“ says Ghosh.
According to Bhadada, all
types of data is useful and outside the platform as well, in logistics,
shipping, warehouses 5-10% can be saved if data is correct. Algorithms help a
logistics firm to decide on the best delivery route.
Most companies use
Hadoophbase (server software) to analyse big data, machine learning tools like
R & Paython, which use data to create business models and web traffic data
analytics from Alexa, Google Analytics or Adobe's Omniture. Besides the big
data analytics tools, inhouse teams write codes for specific outcomes. Snapdeal
has a 25 people data engineering team--which essentially determines what kind
of data to collect and a 25 people data science team which analyses the data
collected and tweaks the algorithm.
At a broad level, it could
be to push cricket memorabilia or IPL gear in the current season and at micro
level, it could mean wooing a Kolkata Knight Riders fan with a KKR T-shirt, a
taste picked up from Facebook.
“We have to create that
intelligence in conversion; else it could misfire,“ says Mangla of Snapdeal.
For example the goal could be to maximise sales. So “shoppers see products from
sell ers whose returns are lower. The software could also note that certain
brand of shirts at a price point of `800 are selling fast, but all sellers are
not getting orders. It could determine the reason as poor quality of the
catalogue and alert the seller“, says Vashishtha. He says conversion rate has
gone up 50% in the last six months due to better intelligence.
Adds Aggarwal of Shopclues,
“Data analytics is science and delivers better return on investment than any
other system like marketing or advertising.“
Shopclues transactions have
improved ten times in the last 12 months thanks to algorithms compared with the
traditional approach of mass advertising.
Pepperfy uses a sorting
algorithm that detects a potential shopper in Mumbai or Delhi who have
different needs based on the character of the cities they reside in. The former
gets to see contemporary styles and space saving furniture while the latter
gets options in solid wood, with less concern on space saving designs.“The goal
is to get to know the sweet spot,“ says Netrabile.
Kittur of Ugam Solutions
believes data analytics can lead to 3-7% improvement in bottom line and at
least 40% improvement in conversion rates in the short term. At present there
are 50-60 million online shoppers and 400 million internet users. With rising
internet users, more shoppers are expected to come online.
Karthik Bettadapara, CEO,
Dataweave, says, “The industry is shifting from blind discounting to targeted
analysis.Now funding is tight, marketplaces have to be smart about spending
money.“
Dataweave, funded by Google
honcho Rajan Anandan, Blume Venture and others, looks at even external data to
create intelligence--like what is the competition selling, at what price
points, what products are people buying and so on.
As companies like Paytm
move to completely automated systems, the goal is to be like Uber--do real time
analytics to predict where demand is and multiply the chance of success. Sharma
of Paytm says Uber has among the best data science teams in the business. “We
would eventually like to do it real time--meet a buyer's demand at almost all
times.“
Shelley
Singh
|
ET21APR16
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