Dmitry Azarov is Chief Marketing Officer at Itransition, Denver-based software development company.
Predictive analytics is a form of advanced analytics that helps to get information from existing data sets in order to dictate patterns and forecast future outcomes and trends. Marketing, insurance companies and financial services have also been users of predictive analytics, as have large search engine and online services providers.
The holiday season is almost here. For online retailers, this is a time to grow and beat the sales records set last year. But how does one achieve such hefty goals in a fiercely competitive landscape?
Predictive analytics is the answer.
Sometimes referred to as point-of-sale analytics, these powerful e-commerce tools are powered by big data, artificial intelligence (AI), and machine learning (ML).
As predictive analytics technologies can give marketers increasingly accurate answers to their mission-critical questions, it takes the guesswork out of the whole process while reducing exposure to risk. It also helps brands keep up with rapidly changing consumer demands.
This is why fast-food giant McDonalds acquired Tel Aviv-based startup, Dynamic Yield, for over $300 million. This acquisition promises to deploy smart algorithms to enhance drive-thru experiences.
However, before we get ahead of ourselves, let’s take a step back and define it.
1. What Is Predictive Analytics?
Predictive analytics can be described as a branch of business intelligence that leverages AI, ML, and big data analytics to predict future outcomes. It can also be described as a combination of correlation analysis, customer sentiment, impact analysis, and path analysis. This approach can be applied across industries, from healthcare to logistics to e-commerce.
These tools can also be used to efficiently manage inventory. For example, if the reports show that there will be an increase in demand for the Harry Potter Dobble card game, it’ll make sense to stock it to meet the demands of this shopping season.
What’s predictive analytics in marketing, specifically? What does it have to do with e-commerce?
When it comes to marketing, you can leverage data and ML to understand customer preferences and buying behaviors better. You can use this approach to reduce risks and boost sales by finding accurate answers to questions like these:
- What are the key characteristics of my ideal customer?
- What kind of products are they searching for?
- How do they engage with my brand?
- What kind of advertising or promotion will be most effective?
- What factors are contributing to desired outcomes?
- What will they want next?
- Which leads will convert?
The answers to these questions will help you formulate a “winning” sales and marketing strategy.
In e-commerce, you can take it a step further by analyzing customer data and behaviors to forecast the emotions customers might feel for your brand, or the future paths they might take.
The more information you have, the more accurate the prediction. However, all the data would be rendered useless without powerful ML algorithms underlying your predictive models and generating actionable insights in real time.
These smart algorithms have to be trained and updated continuously to keep pace with an evolving marketplace. That’s why predictive analytics existed in theory for decades, but it hasn’t been realized until recently.
Today, it comes as the next frontier of e-commerce marketing, helped by highly-specialized software and robust computing power.
2. How Does Predictive Analytics Boost Online Sales?
When ML drives predictive analytics in e-commerce, it can reverse-engineer customer behavior to drive enhanced experiences by analyzing data generated from multiple sources (including websites, mobile apps, social media, and the Internet of Things) in real time.
How does this work? Let’s take a look.
a. Predictive Analytics Enables Personalized Customer Journeys
The insights gained from customer data (past behavior, expectations, and desires) will help you tailor online shopping experiences to perfectly fit the profile of each customer. Delivering personalization at such a granular level can boost brand loyalty and improve customer retention rates.
For example, almost 80% of what’s watched on Netflix is based on recommendations. This approach has helped the company save as much as a billion dollars in value thanks to customer retention.
Online retail giant Amazon has been effectively leveraging its comprehensive collaborative-filtering engine for years in order to make accurate recommendations. This approach has helped the company up-sell and cross-sell successfully.
If we take Amazon’s category of DVDs, for example, recommendations of similar movies purchased by other customers have helped generate as much as 35% of the sales annually.
Now the company wants to take this to the next level by leveraging predictive analytics to ship products to customers even before they buy anything. While anticipatory shipping sounds like something out of a science fiction movie, it has the potential to enhance the brand’s free one-day Prime delivery service.
In this scenario, Amazon will push its one-day shipping offer with popular items and categories like beach towels, beauty products, cleaning supplies, and similar.
b. Predictive Analytics Helps Improve Customer Lifetime Value
When you have the insights allowing you to make highly accurate predictions, you can have a better understanding of the Customer Lifetime Value, or CLV. For example, if a customer spends $20 a year on your products for ten years, then their CLV will be $200.
You can calculate this based on past purchasing behavior and the products they are forecasted to buy in the future.
With the help of big data and smart algorithms, you can extend the CLV by doing the following:
- Enhancing customer experiences
- Funneling traffic from social media platforms
- Recommending products that complement past purchases
- Segmenting your email and SMS subscription lists
When you’re alerted to the early signs of customer dissatisfaction, you can set your customer retention protocols in motion and reduce the churn rate.
c. Predictive Analytics Allows Brands to Augment and Refine Products
When you have an in-depth understanding of your customers’ expectations, your business will be well-placed to adapt your offering to meet the demands of your target market. Essentially, this will mean centralizing your resources on the most highly-demanded SKUs while balancing your investments in market research and new product development.
You can apply predictive analytics in all the areas mentioned above, so that your efforts complement each other for maximum effect. But none of these benefits can be possible without oceans of data and highly sophisticated intelligent algorithms.
The good news is that you don’t have to be a multinational corporation like Amazon to afford predictive analytics technologies. Even small and medium-sized enterprises can access smart insights to get ready for Black Friday and beyond.
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