Augmented Analytics For eCommerce
How eCommerce stores can use augmented analytics to grow their businesses
It’s a well-known fact that for businesses to remain competitive today, they require timely and actionable insights about their performance.
Not Data, Insights.
The competitive advantage now lies in how best/fast a business can generate and act on its insights.
With so many business integrations from marketing and advertising to supply and logistics, there’s no shortage of e-commerce data to track and collect.
And that’s exactly the problem.
Companies don’t struggle with collecting data.
The trouble is in drawing insights from the data they already have and taking the trackable actions for improved performance.
That is why data-driven e-commerce companies — such as Amazon and other fast-growing stores — are actively developing analytics systems and models that can enable them to take full advantage of their data in a resource and time efficient way.
One of these new developments is Augmented Analytics
The next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists — Gartner Report.
Augmented Analytics Is the Future of Data and Analytics — Gartner Data & Analytics Summit 2019
For the past two years, augmented analytics as a field has grown and more businesses are beginning to see the value in efficient data analytics.
It automates the process of discovering patterns in your data and communicates signals to the right people in real time.
With this kind of analytics power, businesses especially e-commerce stores are evaluating the browsing behavior of their users, to better understand their shoppers, their habits, and their needs.
The more you know about your prospects, the better your chances of successfully selling to them.
Many of the companies that are successfully implementing augmented analytics are using it to enhance their e-commerce features, functions, and performance.
Here are some of the ways augmented analytics can be used by e-commerce stores.
1. Enhanced Multichannel marketing and selling
Multi-channel marketing and selling is definitely the way of modern business.
Today, 86% of shoppers use at least two channels when shopping.
It’s an incredible ability to sell and market products across any platform, anytime.
It’s how you reach waves of new customers — shoppers that may never have laid eyes on your online store suddenly buying through Instagram, Pinterest or through Amazon.
Today multi-channel marketing and selling is no longer an option but a necessity for businesses because no single channel is fully dependable unless if you have total control.
SEO is unpredictable as Google is always releasing new updates affecting organic traffic, new and better content is always being published making it even hard to rank.
Facebook and Instagram shut down for hours without notice or explanation.
Bottom line is, you need more than one channel.
According to a DigitalCommerce360 study, companies that integrate marketing between each channel retains an average of 89% of their customers from channel to channel. Meanwhile, those with weak marketing integration only retain 33%.
Managing a multi-channel strategy and the analytics requirement that comes with selling on various platforms is near impossible because most of these platforms don’t share data.
It takes a lot of resources for companies to analyze data from all these platforms. Probably the reason why most e-commerce businesses are hesitant to start multi-channel selling and those who do, end up with inaccurate data.
Augmented analytics offers the only viable solution.
It allows for deep integration of all your data sources (Google, Facebook, Twitter, Shopify, and Pinterest), clean and analyzes all your data to provide you with valuable insights about you are currently performing across all your platforms.
These insights allow you to better manage inventory and make marketing and sales decisions, while also improving the overall buying experience for customers in the same way as if you were managing a single channel.
2. Advanced Personalization.
Customers have a lot of expectations, one of which is hyper-personalization.
They expect both the convenience and flexibility in where they can place orders from and how fast they receive them.
They would also like to see content and offers that cater to their needs.
Retailers that have implemented personalization strategies see sales gains of 6–10%, a rate two to three times faster than other retailers, according to a report by Boston Consulting Group (BCG).
With advances in augmented analytics and deep integration between platforms, new personalization techniques have entered e-commerce.
Deep integration allows stores to collect and analyze data from different channels through one dashboard to provide a unique, seamless and personalized customer experience across not just one but all channels.
Good personalization can increase engagement, conversions, and decrease time to a transaction. For example, online retailers can track web behavior across multiple touch points (mobile, web, and email).
After all, personalization in the recommended content or products is how we create engaged readers and customers.
3. Dynamic Pricing.
Dynamic pricing is a strategy based on which retailers change the price of the product based on supply and demand.
Augmented analytics unlocks access to more granular insights, allowing you to surge or drop the prices depending on individual customer’s tolerance just like Uber does.
Data-backed price management initiatives bring significant results in the short terms perspective: 2%-7% increase in business margins and a 200–350% average growth in ROI over a 12-month period according to Deloitte data.
While having fluctuating prices is not new (happy hours, the stock market, airline tickets), the insights generated from augmented analytics, we can now unlock this new potential.
We can now analyze customer data, competitive pricing data, and sales transaction data to predict when to discount, what to discount, and dynamically calculate the minimum amount of discount needed to ensure a successful transaction.
Automation tools can also enable better on-the-spot decision making, for instance showing your sales teams how discounting a certain product line will impact your profitability or how likely customer segment A will respond to a 15% discount.
Early adopters are already leaving competitors behind. While other online retailers are experimenting with dynamic pricing, Amazon has it mastered.
It’s a key reason why they are market leaders, they’ve managed to price their commodities lower than others.
4. Predictive Product Recommendations.
We can now collect and analyze volumes of data to append mass-consumer purchase behavior to an individual’s purchase history to offer relevant and helpful product recommendations.
This information is then put to good use to ensure the business maximizes its profits. The data also gives the company the ability to display products that specific users will be more likely to order and purchase.
Amazon is a common example of how to track buying behavior by account to recommend related products.
5. Predictive Behavior Modeling.
E-commerce tech alone does not drive sales.
You need a successful sales and marketing strategy to support the engine.
And the success of your sales and marketing plans are largely dependent on how well you understand your target customers.
Today we use our own experiences working with customers, past purchase behavior, market analysis, and personas to better understand how our customers may behave in the future.
With access to more data from different sources and the ability to process all this data, augmented analytics is enabling e-commerce leaders to understand their customers and new trends in behavior better than ever.
Now, we can access a multitude of structured and unstructured data sources like social media, loyalty cards, sales, and market research to create deep psychographic profiles of our known customers to spot emerging trends and predict unknown customer’s demographics.
6. Higher Revenues from Cross-Sell and Up-Sell Campaigns
The typical customer buying journey is no longer linear — they switch between website, search Google for promo codes and, according to Konstruct Digital, drift to trusted online sources for reviews, before returning to your website and making a purchase through another device.
Capturing and analyzing all those interactions can be challenging for any business even with a number of data analysts in place.
But it hardly presents any difficulties with augmented analytics.
By gauging and churning all those online behaviors, new-gen analytics tools can compile comprehensive user personas — data-rich profiles of different audience segments.
The depth of such profiles goes beyond the general demographics data. They capture all the interactions a user previously had with a brand — products viewed, clicks, past purchases etc. — and deliver personalized product recommendations based on everything the system knows about a particular customer.
Predictive recommendations can significantly improve your business bottom line.
Analyzing how one purchase corresponds to another could really help derive an added insight on what to upsell next.
Amazon’s product recommendation engine drives 35% of cumulative company revenue.
7. On-page improvements
E-commerce companies these days are tracking everything customers do when they’re on the website, including mouse movements, keystrokes and so much more.
This data is then analyzed to not only predict the next possible move but also to make the necessary improvements for better customer experience.
Once the likeliest move is figured out, businesses can execute specific actions to provide the customer with the right information at the right time, prevent the customer from moving away from the page with custom-made promotional items.
This is the basis on which customer churn predictive models are built.
With more data and the right analytics in place, patterns can be identified for a more robust trend to be established for which appropriate strategy on how to improve the store can be put up.
8. Data-Driven Product Research and Product Development
Deciding on new products to sell or develop is never an easy task for e-commerce brands.
The idea may look good on “paper”, but eventually flop due to poor market research and product positioning. According to HubSpot, 66% of products fail within the first two years and 80% of new products stay on the shelves for less than two years.
A lot of new e-commerce entrepreneurs tend to capitalize “on-the-moment-product” trends, rather than develop a 360-degree industry outlook and plan ahead.
But every hunch should be backed by solid data, showing you exactly what people are buying, what price they are ready to pay on average and so on.
Most believe that you need to pay at least five figures to some consulting company for such research. But that’s no longer the case.
Data analytics platforms like DataSlinger can supply you with all those insights for a fraction of the cost.
Consumers are being prominently vocal online with their demands and preferences.
Brands that manage to capture that data and apply it to product development succeed better in the long run.
At Humanlytics, we are the leading provider of Augmented analytics services.
Our Dataslinger tool is designed to help you bypass any analytics hurdles and make analytics at your business effortless.
We currently support integration with Google Analytics, Facebook Ads, and Google Ads, and if you are integrated, don’t hesitate to reach out to us at mike@humanlytics.co, or visit our website at www.humanlytics.co for more information about what our product can do for your business.
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