Big Data: Is it a passing trend? Is
Big Data relevant for Retail businesses, with online ecommerce accounting for
only 9% of the US Retail share? Are you a Big Data-ready business? What does it
mean for a business to have a Big Data mindset?
This article will take a closer look
at some of these questions.
What
does it mean to be Big Data Ready?
Too often, consumers' eyes and minds
- the premium retail inventory - are fed irrelevant product recommendations by
retailers based on intuition, stale information points, and copying what a
competitor is doing.
Retailers fail to implement their
targeting strategy because they don't yet embrace the data first approach or
they don't make the best use of their data infrastructure.
How
can we, as a company, get Big Data ready for our business?
Recognizing data as an asset is the
first step. The technology of today allows us to gather data at every stage of
retail transactions. This includes the initial click that brought you to the
website and the lifetime value (LTV), that each product or consumer is
attributed.
These data points can give you
powerful, real-time insights into your marketing and sales efforts online and
off.
Data quality is a key factor in the
success or failure of the data-first approach.
Here
are some tips to help you find the perfect platform.
A customer profile is basically a
description of your target audience that includes:
You should look for platforms that
integrate directly with popular ecommerce engines such as Magento and Shopify.
Then, you can choose the engine that
suits your needs and get ready-to-go integrations from one location.
You can find a platform that
automatically creates smart feeds and maps your product eCommerce strategy with popular shopping engines such as Google and
Facebook.
It is recommended that you have a
platform where you can set your business goals and one which offers data-driven
options to select audience preferences, merchandising preferences, and device
options. This will ensure that there is no manual labor and errors.
A platform that uses a proprietary
tracking technology will enable real-time cohort analytics to track the
recency-frequency and product movement by audience and cities. It'll also allow
for seasonality and conversion rates by the funnel.
These numbers will be very important
to you. You should look for a platform that allows you to integrate your online
marketing data with your offline sales CRM data. This will ensure that customers
have a complete view of the buying process.
The
Behavior of Big Data: A Mix of Causation and Correlation
Big Data challenges traditional
retail decision-making methods, which rely on a smaller set of data points
applied to a larger population. This approach is primarily focused on causation
analysis.
Big Data is a way to flip this
approach and increase the sample set to (or as close to) n=all.
Computing power has greatly improved
to the point that large data sets of petabytes can now be analyzed in seconds.
Due to the sheer volume of data that is being processed, it can be difficult
and costly to prove causation.
Correlation is an effective technique
to drive incremental sales considering the nature of retail sales.
Online sales use self-learning
algorithms to help them identify new combinations of products and audiences.
Online sale is a unique platform in its field.
As we analyze for data cohorts,
causality is equally important.
It is crucial that we analyze any
decrease in conversion rates on the website's conversion funnel for the cause.
Further analysis revealed that the
drop in sales was due to the move of the coupon codeshare from the beginning of
the cart to the checkout page.
There are many variables that can be
affected, but they must all be addressed immediately to prevent funnel
leakages.
The
power of probability
Any large or complex problem has one
thing in common: the outcome cannot be predicted with 100% accuracy.
If data sets are small, it is
possible to slice and dice them to reach a concrete hypothesis that can be used
to create alternate scenarios.
Retail marketing is complex because
of all the moving parts: volume of products, ad formats, customer segments,
demographics, geographies and interests, targeted devices, timings, preferred
days of the week, etc.
Combining Big Data with powerful
machine learning algorithms allows you to find a winning combination within a
given probability range (let's call it > 85% probability).
Machine learning has the power to
analyze ad copy for the most effective images, titles, descriptions, and color
backgrounds across millions upon millions of customers and products.
Big
Data: The Risks
Great power comes with great
responsibility. Data collectors have to take enormous responsibility for
ensuring that data integrity and privacy are maintained. Let's examine the
potential risks.
Privacy
of data
We have enough data points to link
any retailer's purchase history with any individual. Names, Email IDs, and CC
information are all possible.
Third-party payment gateways permit
retailers to remove any CC/payment information from their records.
It is essential that any Personally
Identifiable Information, (PII), is kept on records. This includes data
encryption and security guidelines.
Strong policies are required to
ensure that data is not shared with third parties without the consent of
customers or vendors.
Data
Dictatorship
Data analytics is a powerful tool for
driving decisions. However, it's important to establish checkpoints to avoid
runaway situations.
Machine learning can still be broken
even with all its power, and the consequences can be severe.
Another thing to watch out for is
that the organization does not become a slave to data.
In the past, data analysis gave a
suggestion but common sense dictated something different.
Let's just say, even though 95% of
the plane is in auto mode, the critical 5% represents human intelligence and
keeps us safe.
Big data is here to stay. It is time
for retailers to take big data seriously in order to help their businesses
thrive and reach the next level.
Retail's dynamic nature requires that
technology be invested in and open to experimentation.
The supply chain is being
reorganized. Retail marketing is taking the lead in embracing data and driving
significant retail value.
There are many ways we can use your
data to improve marketing efficiency. For more information and assistance,
reach out to the online sales team.
Key
Takeaways
- Big
data + Machine Learning = Winning Combination
- Great
power comes with great responsibility. And massive data comes with a huge
responsibility.
- Do not
rely on data alone. Listen to logic.
