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.