They like getting offers tailored to their needs, they
value time and comfort – they are e-stores clients. Do the companies ignoring
their preferences stand any chance for survival?
You’re entering
one of the online bookstores. An offer of recommended products flashes on your
screen right away: recent Swedish crime stories, sports gadgets and a cycling
routes guide. How come one of the largest Polish shops knows that Scandinavian
whodunnits is my favourite pastime and my holiday plans involve travelling by
bike?
– This is
personalized recommendation – Mateusz Gordon, Gemius expert in e-commerce explains.
– The system is a well-established element of business strategies employed by
the largest e-shops in Poland. If it weren’t for it, e-buyers would be
presented with the shop’s entire product offer, which does not necessarily meet
their needs or interests – he goes on.
For example,
such solution is utilized by Spotify, a digital music site, which selects music
pieces, performers, playlists or albums based on our preferences and history of
portal usage. With this approach employed, discovering new music is easy and
fun.
– Customers
appreciate offers suited to their taste – comments Prof. Grzegorz Mazurek, from
Kozminski University, head of e-commerce post-graduate faculty. – Research
suggests that shopping recommendations are widely popular among e-stores
customers. Over half of internet users considers them useful, practical and
helpful – he adds.
According to
the Professor, customizing website content to buyer’s needs is no longer just a
marketing tool, but first and foremost, a great advantage for customers and a
time-saver.
From the
e-shop owner’s point of view, introduction of personalized recommendation
systems is a sink or swim situation. In the times when, as IIBR research
suggests, over 70 per cent of internet users declare that they did e-shopping
at least once, and 19 per cent intend to buy products or services this way more
often, it will be the fittest to survive, i.e. those that cater for the
customer needs best.
– How long
would an e-shop homepage have to be to feature all products on offer? – asks
Gordon. In his opinion, this may not be a problem for shops with limited range
of products, but in case of stores that broaden their spectrum and increase
their customer base the story is different.
– No
personalized offer on a website means a less satisfied and a less loyal
customer. And often an irritated one, as he/she cannot get what they are
looking for. A shop without a working recommendation system stays behind its
competitors – Mateusz Gordon sums up.
How does a personalized recommendation system work?
- A
recommendation system enables a shop to tailor an offer to concrete user needs
on any location (i.e. the homepage, product, product category or cart or
thank-you page). Taking the form of a recommendation frame, it displays those
products that the visitor may find interesting. This kind of profiling is
possible thanks to the fact that the system can ‘see’ particular types of
customer behaviour: which price range they go for, which categories they click
on, what they buy, but what they store in their carts, what their paths on the
website are. Based on such information, the algorithm used in the
recommendation system proposes the client such products that may be deemed
supplementary to their shopping choices made so far.
- Depending
on whether the user is a regular client, or if they are registered or maybe
they are first time visitors, the system will base on four types of
recommendation: alternative, complementary, generic and personalized. The
first involve product of the same category
or type, but of different brand. Complementary recommendation, in turn, puts
forward items that correspond with the product the client has chosen and that
increase the value of purchase, e.g. a remote to go with a TV set. Generic ones
appear on a website when a user visits the store for the first time. It
features bestseller proposals, products bought by other users or promoted by
the seller.
The most complex recommendations are the personalized
ones, suited to a customer taste based on his/her concrete behaviours on the
website: posting comments, liking, adding to cart or simply browsing through
the stock. These four types of recommendation may be
combined.
What is more, generic recommendations are no longer
generic upon logging in, as - based on a user visit pattern - the system will retrieve
his/her interests and then, in form of a frame, it will propose whatever may potentially
suit their needs. This is possible even when the customer deleted the cookie
files but then re-logged. But if the cookies were not removed, the system will
remember the preferences even if the last visit took place a month before.
An e-store not
taking advantage of recommendation systems records a lower conversion rate (or
the indicator showing the proportion of effected transactions to the number of
website visits). Even if every additional product exposition may rise the rate,
it is the increase generated thanks to presenting it in form of a
recommendation frame that brings far greater benefits. Such shop also has a
shorter user path (2-3 clicks) and lower CTR, as an implemented recommendation
system will extend it and provide a larger number of clicks.