Importance of Big Data and Analytics in eCommerce

Is Big Data just a passing trend or is it here to stay? With online commerce just 9% of the overall US Retail share, is Big Data even relevant for Retail businesses? Is my business Big Data ready? What does it mean to have a Big data mindset?

In this article I’ll take a closer look at these questions and more.

The relevance of Big Data

Let’s take a closer look at companies by valuation in 2008 — The list is largely dominated by Oil, Telcos and FMCG companies.

Microsoft is the lone ‘digital’ warrior making it to the Top 10. Valuation based on Data is irrelevant, still.

Moving on to 2017, data first companies including Amazon, Alphabet, Facebook (and Apple or Microsoft with their ever increasing data revenue), are consistently ruling the Top 10 list.

With top retailers including Amazon, Alibaba and Tencent shaping up the future of retail, it is time for the entire retail chain to catch up.

What it means to be Big Data ready

This scene from Moneyball truly sums up the way decision-making still happens in Retail.

https://www.youtube.com/watch?v=pWgyy_rlmag

Too many times, consumer eyeballs and mindspace — the premium retail inventory, are fed with irrelevant product recommendations from retailers based on intuition, stale data points, and copying what a successful competitor is doing.

The reason retailers fail in their targeting strategy is either because they have not yet embraced the datafirst approach, or, they are not making best use of their data infrastructure.

So, how do we as a business get Big Data ready?

It starts with recognizing data as an asset! Today’s technology enables us to collect data for every step of the retail transaction, starting with the first click that generated that visit on the website, to the Lifetime value (LTV) attributed to each consumer and product.

Further Reading: Calculate Your Customer LTV to Drive Repeat Purchases

Each of these data points can provide incredibly powerful and real-time insights into marketing, and selling both online and offline.

The success or the failure of the datafirst approach depends on the quality of data that is collected and consumed.

AI-driven eCommerce marketing platforms like OnlineSales.ai help you get Big Data ready for all your marketing needs.

A few tips to find the right platform here:

A customer profile is essentially a detailed description of your target demographic that includes:

Look for a platform that has direct API integrations with popular commerce engines including Magento, Shopify, BigCommerce, Vinculum and KartRocket.

You can then choose your preferred engine and have ready and immediate integrations, all from one place.

Find a platform that automates smart feeds creation and quickly maps your product E-Commerce with prominent shopping engines including Google, Facebook and Amazon.

A platform where you can define your business goals and one which provides you with a data-driven engine with intelligent options for audience selection, merchandising preferences and device options is highly recommended to ensure there’s no manual labour and errors to be spent on.

Make sure to place a tracking pixel. Find a platform with a proprietary tracking technology that’ll enable real-time cohort analytics for the recency-frequency of website visitors, product movement by audiences and cities, order seasonality and conversion rates split by funnel.

Trust me, you’ll need these numbers (a lot!)

Try to find a platform that enables integration of online marketing data with offline sales CRM data, ensuring an end-to-end view for the customer buying funnel.

The behavior of Big Data — a mix of correlation and causation

Big Data challenges the traditional approach in retail decision making — smaller dataset hypothesis, applied to the larger population. This approach is primarily based on analysing for causation.

The Big Data approach is to turn this philosophy on its head, and expand the sample set to n=all (or as close as possible).

The computing power has improved drastically where even large petabyte datasets can be analysed in a matter of seconds. With humongous amounts of data being processed, proving causation can be time-consuming and expensive in terms of lost opportunities.

Considering the nature of retail sales, with the Big Data approach, correlation is a highly effective technique that can drive incremental sales.

As an example, if the analytics prove that a particular brand of watches are regularly purchased by young females in California during a particular time of day, smart platforms like OnlineSales will raise its head and automatically adjust bids on those watches for that time and only that audience and geo.

Taking it a step further, platforms like OnlineSales deploys self-learning algorithms that help identify for newer combinations for best performing products and audience segments. (it’s a different story that OnlineSales is one of its kind ;))

Causation is very equally relevant as we analyse for cohorts of data.

If we are analysing for the conversion funnel on the website, it becomes extremely critical that any drop in conversion rates along the funnel be analysed thoroughly for the cause.

For example, the conversion rate between the add-to-cart and cart checkout for a client had dropped by 50%.

On further analysis it was identified that the drop was caused by moving the coupon code share at the start of the cart to just before checkout now.

The variables are endless but must be investigated and fixed immediately to ensure zero funnel leaks.

Power of Probability

Inherent to any large and complex problem is that the outcomes cannot always be predicted with a 100% accuracy.

When datasets are small, various dimensions can be sliced and diced to arrive at a concrete hypothesis, that can then be applied to alternate situations.

However, in case of retail marketing, the complexity increases significantly when we consider all the moving parts — volume of products, type of ad copies and formats, customer segments split by demographics, geographies and interests, devices to be targeted, timings and preferred days of the week et al.

As a retailer, I can keep waiting for the magic combination of product-ideal ad copy-customer, however the chances of hitting the bull’s eye will always be thin.

Big Data, combined with strong machine learning algorithms, allows you to arrive at a possibly winning combination within a certain probability range (let’s say > 85% probability).

The power of machine learning is such that ad copies can be analysed for the best performing images, titles, descriptions, colour backgrounds across millions of combinations of products and customers.

With customer segment experimentations across demographics, interests and geographies, it is possible to arrive at the next set of ‘Hero’ customer segments with 90–95% accuracy.

The risks with Big data

With great power comes great responsibility, and with massive data, comes massive responsibility for data collectors to ensure that the privacy and integrity of the data is maintained. Let’s take a closer look at the risks involved.

Data Privacy

When we are collecting data for any retailer, there are enough data points available that can link the purchase history to any individual — Names, Email IDs, CC information etc.

While 3rd party payment gateways allow the freedom to not maintain any CC/payment information on records at the retailer.

For any Personally Identifiable Information (PII) to be maintained on records, it is critical that proper data security guidelines are followed, and that the data is encrypted.

Additionally, strong policies must be in place to ensure data is not shared across third parties, unless with prior consent from customers and vendors.

Data Dictatorship

While data analytics can be invaluable in driving decisions, check points should be introduced to ensure there are no runaway scenarios.

Machine learning, with all the power built in, is still susceptible to break at those corner scenarios, and with the scale, the penalties can be significant.

Another gap to be careful about is to ensure the organization doesn’t become a slave for data and data only.

There have been instances in the past where data analysis has given a certain suggestion, however common sense dictates something else.

Let’s just say that although 95% of the airplane is on auto mode, that critical 5% is still human intelligence keeping us safe.

Big data is here to stay, and it is time that retailers take it seriously enough for their businesses to thrive and push to the next level.

The dynamic nature of the retail business dictates that organizations invest in technology and remain open for experimentation.

The entire supply chain is getting overhauled, with retail marketing taking a leadership position embracing data and driving significant value for retailers.

The possibilities on how we can leverage your data for improving marketing efficiencies are endless. Reach out to the OnlineSales team to dig deeper and solve.

Key Takeaways

• Big data + Machine Learning = Winning Combination
• With great power comes great responsibility, and with massive data, comes massive responsibility
• Don’t rely only on data. Listen to logic.

This article is part of a full series eBook: The Ultimate Guide on Starting Your E-Commerce Store. To read the full eBook click here

Originally published at onlinesales.ai.