Personalisation is all about relevance.
While this is a somewhat intimidating area to navigate, some brands have
already started implementing personalised communication, with huge success. 

With the amount of data that we are able to capture on our customers, there
really is no excuse for brands to not be using this to their advantage and
shifting the way they market altogether. 

Using the data we have to create a
unique and targeted experience for each of our shoppers, leads to huge
increases in sales, as seeded information is a lot more pertinent and therefore
welcomed.

According to stats from Accenture, 75% of shoppers are more likely to buy from sites
which use some form of personalization and although there may be challenges in
implementation (resources and know how) it has a proven success rate with the
vast majority of companies using it reporting an uplift in conversion rates.

 Q:
Have you experienced an uplift in conversion rates through any of these
channels since implementing personalization?

There are many ways to use personalization,
both on and off site using a range of different data sources:

Weather and customer location:

‘Very’ uses personalized
homepages which greet returning customers with products and content reflecting
their interests and previous behaviour.

 


Pages can be adjusted according to weather
changes, and using data against previous shopping habits, it can be hyper
personalised.

Personalised site searches:

‘Footwear
etc’
saw a 10% increase in revenue per visitor when
they implemented personalised search and navigation pages. The site would
deliver different results for different people, using the same search term,
based on previous history. For example, a search for leather boots, by a user
who had previously purchased mens items only, would serve results of mens leather boots, as opposed to all leather boots.


Size and Fit:

With the ever present challenge of reducing
sales return rates, due to incorrect fit, personalised sizing charts offer a
good solution.

‘ASOS’ asks customers to enter their height, weight and preferred fit so
it can deliver more accurate recommendations for the size to choose.

Personalised home pages:

Amazon definitely holds the top position
for serving relevant information, based on customer data. Amazon uses this data
to successfully market recommendations on products and categories.

 

 Personalisation is seen in every aspect of
their marketing. You are not only greeted on the home page by name and given an
overview of what was left in your basket, but Amazon also ensures that 50-60%
of the recommendations made are based on previous on-site behaviour. 

 Ask customers about their preferences:

It can take a significant amount of time to
build up knowledge on your customers. One way to speed up this process is to
ask. ‘Thread’ asks customers
directly, to choose a number of styles, via a few quick questions, that suit
their style, budget and clothing preferences. The result of which is a huge
amount of data, that can be used to personalise communication going forward.
And as customers stand to benefit from this (more relevant information means
less spam), they are more open to answering a few questions.

Post purchase emails:

Using data collected from buying behaviour,
post purchase emails can be delivered to customers, with cross sell
recommendations. ‘Matalan’ for
example, recommends products that are linked to purchased items, as a cross
sell opportunity.

 

Browse Behaviour:

Browse history can be used to create
personalised recommendations to customers. Browse abandonment emails can then
be sent recommending products that customers viewed, as well as similar
products.

Mobile Commerce Apps:

Apps offer a great way to build personalised
experiences for users and reward for loyal behaviour. Customers using apps
can generate a wealth of data for retailers, allowing them to
understand product trends and individual customer preferences. This information
can then be used to create a personalised shopping experience, as ‘Polyvore’ does here, using customer
likes and preferences to recommend products
.

Ref: blog.salescycle.com