Before the world of e-commerce, retailers had to rely on traditional methods of sourcing information about their customers.
Likewise, customers had to manually navigate their research in order to find the best deal.
Now, however, things have changed.
With the rapid advancement of technology, retailers and consumers have the intel available to make better-informed choices.
This intel paves a new way for digital retail, and it’s most prominent in using big data and algorithms.
In this post, we’re going to look at two things.
How retailers use retail algorithms to provide their customers with personalized digital shopping experiences.
How consumers use algorithms to support the way they discover and purchase items.
Let’s go.
Use of big data in retail algorithms
The use of big data is not distinct for tech gurus, it can be used in the realm of digital commerce too. Big data can now be applied throughout the entire retail process – for both the consumer and the retailer.
Retailers use sophisticated software to collect large amounts of data. They use this data to determine which products are most likely to become popular, see where there is demand for future products, and optimize pricing so that the retailer always has a competitive advantage.
On the consumer side, shoppers use their suite of tools to collect and organize masses of data that tells them more about the ranking of the products they’re hoping to buy and the suitable price they should pay.
This works well for most shoppers as, in recent times, there has been an increasing need for consumers to conduct multiple paired comparisons instead of making final judgment decisions about one product.
If you’re unfamiliar with this concept, think back to when you last purchased something.
Say, for example, you see two bottles of Scotch, one for $150 and one for $50, you’re probably going to go with the $50. Now let’s see you see another two, but this time one is $150 and one is $1999, the $150 no longer seems that out of price.
You see, consumers have made decisions about what they purchase for a long time.
The rise in AI, predictive analytics and big data makes that entire process easier for them.
Reviews for More Traffic
You’ll already be familiar with the concept of reviews.
Even before the digital commerce boom, people would happily and openly talk about products they liked (and disliked). In a 2019 survey, 92% of respondents stated that they read at least one review before making a purchasing decision.
A newer concept, however, is how retail giants like Amazon use reviews to tailor products for future customers.
When you search on Amazon, the first results usually have the highest positive reviews.
If we look at the last page of the search results (where no one goes), we will find that the items on that page have fewer reviews (if any).
In the future of retail, we’re likely to see review sites like Tripadvisor for travel that take an unbiased look at an aggregation of reviews from a range of sources.
You might not have any direct reviews on your Amazon profile. Still, you could’ve had several influencers talking about your product.
These should be taken into account too.
Big data will be able to create an algorithm that takes a consumer’s search query and shows them several reviews (from different sources) from around the web and beyond.
However, this has its limitations.
Example of Reviews
When we think about booking a flight, there are several tools to compare the different options.
This is relatively easy for flight companies because of the standardized nature of flying.
- You choose what time you want to fly
- You decide where you want to fly to
- You choose where you want to fly from
- You decide how much you want to pay
- You determine what extras you need
This is difficult in retail due to how the specifications differ, and there needs to be a way to write about or present your product. Because of this, the algorithm needs to understand what products are the same or similar, even when described in different non-standardized ways.
That’s not to say this shift can’t happen in retail. Some industries are more prone than others – technology for example.
It’s much easier to compare two types of laptops or headphones than two types of t-shirts.
Recommendation Engines to Drive Sales
Product recommendation engines are a common feature of e-commerce stores. You can use algorithms to surface your products for customers based on various factors such as customers’ purchasing habits or e-commerce trends.
It shows visitors products based on the data fed into the engine; customers are most likely to buy. For online shoppers, improved relevance means a better experience.
Netflix thinks its recommendation engine saves $1 billion annually to the company. This is because it helps users to find a show or a movie they want to watch quickly without entering a search button.
How can you use it?
Start by showing trending or best-selling items to your visitors. But product recommendations can be more advanced from personalized shopping history, which can influence the purchase. In this way, recommendation engines can produce their recommendations accurately, which would be impossible to do manually.
Personalization to Increase Customer Loyalty
Personalization can be used in various ways in e-commerce stores and email marketing. The aim is to improve customer experience and turn visitors or new customers into loyal customers.
When used well, personalization improves customer communication and offers advanced product recommendations. It also helps retailers provide easy and flexible shopping, making more purchases.
Personalization has often been implemented as using customers’ names in emails or recommending similar products based on customers’ previous purchases. These tactics can work, but AI algorithms offer the opportunity to personalize on different levels.
Retail algorithms can do different and unique email designs for each customer segment. These emails include personalized product and content recommendations to customers.
It’s a much easier and more effective way than traditional personalization methods in e-commerce.
Demand forecasting
If you want to stay ahead of the curve, you need to know where the curve is headed. Sure, you can make a guess as to what your customers might want next.
You may have even spoken to them to determine what products your product line still needs to include.
But one of the most effective ways to have a solid understanding of what your customers want in the future and how much of it they want is to use software that helps you predict trends.
Retail algorithms look at past data and chatter online to find out what the must-have items will be for this year, next year, and beyond.
You can conduct this research yourself simply by using the Google Trend tool.
Knowing what items people want to learn more about is vital to your success if you’re in the food and beverage industry.
Alongside trend research, retailers also use sentiment analysis that uses intelligent retail algorithms to understand better the context of when a product is discussed.
This tells us that these technologies cannot be used in silos.
It would help if you combined several intelligence software and tools to develop the best possible solution for predicting what your customers might want next.
The work continues once you know what they’ll buy.
You need to understand who will be buying it.
You need to gather a large number of demographic and economic data that helps you understand how people spend their money in your industry.
Price Optimization for Maximum Profits
If you use Google to shop, you’ll notice they provide many options at different prices. The same goes for the marketplace, Amazon.
This is helpful for consumers as you get to see various products at different price ranges.
Google will then direct the shopper to the best store for their query.
Please remember that shoppers are always looking for the best tool.
And as tools become available to them to help them work out the best price from the best retailer, you need to get more brilliant at pricing your products.
Suppose you set your prices once and never look at them again. In that case, you run the risk of your competitors altering their prices and taking a more significant portion of the pie.
An e-commerce pricing tool can help you identify your historical and current price points and your competitors.
You can use this to help navigate the complex pricing world to find the best solution.
This type of software looks at the price points for specific products across various competitors and alerts you when one price increases or decreases.
Chatbots
Implementing AI chatbots in your e-commerce store can streamline your customer service process easier and faster. They can answer customers’ questions efficiently and reduce operational costs.
Additionally, chatbots provide personalized shopping experiences that improve customer satisfaction leading to increased sales.
Since chatbots can be developed for better customer satisfaction you should consider working with them if you didn’t already.
Chatbot algorithms can work as a personal assistant which aims to find the right product your customer is looking for.
This type of software can guide your online shoppers who have any trouble in seconds with its possibilities.
Retail Algorithms Takeaways
Using sophisticated algorithms changes how we live our lives, especially in e-commerce.
The good news is that they’re only going to get more intelligent. As a result, retailers and consumers will have access to data that helps them make informed decisions.
With the algorithms available, you can use a data-first strategy to understand your customers better and match them with the products they want.
You will get left behind if you still need to implement tools or software within your business to accommodate these technology trends.
Put yourself in the best possible position and use big data, algorithms, and predictive analytics to your advantage.
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