Data Science can help e-Commerce beat stagnation & achieve their true Potential

August 18, 2021 at 1:36 PM

For a moment, imagine yourself climbing a treacherous mountain in the stunning Himalaya’s. Your progress is as good as you hoped and you’re already more than half way up.But suddenly, the clouds close in around you. Visibility is now no more than your outstretched arm. The maps that have served you well are now useless because you can’t see which way to go. It’s a hopeless situation and one that’s forced many before you to give up and accept that this as high as they can get.

And that’s like Digital Marketing?

While they may not be literally stuck on mountain, digital marketers can often feel just as frustrated and desperate. That’s because they too may have a reached a point where they think they cannot do any better. All their campaign optimization with ad platform data, tweaks and tests is no longer delivering the returns they want. The gains are minuscule or non-existent.

This situation is the definition of a local maximum: the point where you hit peak performance with the digital assets, touchpoints and campaigns you currently have. Even if you make hundreds of tweaks, improvements will only be marginal. The campaigns are as effective as they can be with the current underlying foundation.

Making the Shift from Local to Global Maximum

When the optimization fails to make a noticeable improvement, we need to focus on making gigantic changes in the right places to move from the local maximum to the global maximum — the ultimate optimal point where digital marketers have all the resources they need for campaigns to perform at their peak and achieve their potential.

Generally, the first step in this process is to increase ad spend for a quick advantage. By doing this, you can easily add more visitors to the top of the funnel. If you get the same types of users at the same type of conversion rate you usually get, you’ll definitely grow your revenue. However, this is rarely the case as our as this assumption is usually wrong.

Data Science to the Rescue

Today’s web Analytics tools capture over 250 different data points, which translates into millions of possible combinations. It is humanly impossible to process all this data and find which combinations are most likely to convert or give you a better return on ad spend. Therefore, companies need to employ data science to achieve this.

Using data science, you can strategically increase volume and get closer to the global maximum without risking return on ad spend (RoAS) or cost per acquisition (CPA). You can look at your existing campaign successes using Machine Learner (ML) algorithms and plot the attributes that have a positive impact on conversions. In other words, find your most profitable cohorts of users.

For example, a ML algorithm may factor where a visitor where came from, what technology they used and what they are interested in from multiple data points within your analytics. This blends both click and behavioral data to find cohorts. You can use these cohorts to expand and find similar audiences to scale to minimize any risk on returns.


Author Bio
Dinesh V Dino is a Co-Founder of, an artificial intelligence and data science application that helps e-commerce businesses grow and scale online profitably. Prior to Alavi, Dinesh worked with many organizations around the world to help them understand and embrace factors affecting digital transformation, innovation, consumerism and digital lifestyles.