According to an Accenture study (available here in French), 44% of French consumers switched over to competitors in 2017 because their shopping experience was not personalised enough – and 66% of them are more likely to buy from companies that personalise customer experiences (based on their location, past interactions or preferences). This demand for ultra-personalisation, combined with highly volatile Internet users, is pushing digital marketing into the era of hyper-relevance. Consumer loyalty to a brand is becoming almost obsolete. Online shoppers increasingly take advantage of the best opportunity, regardless of where it comes from – and expect companies to offer them exactly what they need, at the right price and at the right time. So how do companies approach this modern conundrum? Welcome to Hyper relevance… And the most effective way to obtain it is to collect and analyse analytical data of impeccable quality.
In practice…
From experience, we have seen that many companies are still light years away from grasping the extent of the modern shifts in online marketing. Internet users often find themselves pursued by the advertising of a product they have already bought, and whose price goes down over time. This type of inconsistency fuels customers’ mistrust and is often caused by a lack of data quality which undermines a brand’s credibility and harms their business.
It’s all
about trust
In
practical terms, for marketers, it is a question of restoring relevance in
their every action, campaign and in the relationship they create with the
consumer. The challenge is to increase the level of trust in the brand over
time. Only quality data (accurate, complete, clean, timely, consistent
and compliant) can guarantee that errors are kept to a minimum during
its use (there is no such thing as zero risk).
Paradoxically,
47% of French people are concerned
that the new digital services will know too much about them and their family… 82% also say
that it is extremely important for companies to protect the confidentiality of
their personal information.
In addition
to advanced personalisation, corporate transparency and ethics also become a
weapon of persuasion, and therefore a necessary condition for restoring trust.
Hyper-relevance analytics
What can be
offered, for example, to a customer affected by a natural disaster? Or to a
user whose flight has been significantly delayed? These situations require a
high degree of relevance in the response you need to provide. With a highly
advanced knowledge of user expectations, it is possible to target precisely and
at the right time. You can achieve this by:
- Obtaining a perfect
understanding the customer’s entire digital ecosystem, and not just their
site-centric performance, which tends to be generalised by all their online devices
(mobile activity can often reveal specific, often varying behaviour). - Detecting a strong
interest in particular content through the analysis of requests made by the
Internet user in an engine, or by studying the frequency of their visits. - Measuring
underperformance: visitors’ exposure to error pages, queries without results,
etc. - Identifying consumer
trends and behaviours, the best buyers or the most volatile. - Anticipating traffic
variations influenced by external factors such as news, weather, etc.
Machine learning – the key to value
The most
mature companies already use enhanced analytical technologies. They use machine
learning algorithms to detect and anticipate anomalies or precisely segment
customer profiles according to various parameters. Investing in predictive and
prescriptive technologies is one way to address the issue of hyper-relevance. But
without ensuring data quality, all efforts are meaningless, and above all
risky.
Gartner refers to increased analytics as
the number one trend and priority for CDOs in 2020. Built using mathematical
algorithms and models, these tools must make it possible to describe and
predict the behaviour of Internet users. One of the Machine Learning
applications to improve data quality is to offer an automatic anomaly-detection
service. The idea is to record the time trends and any suspicious or abnormal
fluctuations in the metrics (human beings are unable to do this by themselves
on a large scale). These analyses help to explain the probable causes of these
anomalies.
For example:
if a robot passes over a site and causes a significant peak in traffic, an
anomaly is detected on the number of pages viewed. By automatically exploring a
whole set of related dimensions (source, device, browser, etc.), a causality
analysis can be established, and it can be concluded that this anomaly was
caused by an abnormal increase in traffic on the direct traffic segment in
Canada on the chrome 55 version. This type of tool makes it possible to deliver
a preliminary analysis, to better understand behaviours and to guarantee the
reliability of your analyses.
If you are
keen to find out more about Data Quality, please don’t hesitate to download our
latest free guide:
