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, both retailers and consumers alike have the intel available in order to make better-informed choices.
This intel paves a new way for digital retail and it’s most prominent in the use of 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 in order to collect large amounts of data. They use this data to work out which products are most likely to become popular, see where there is demand for future products and optimize pricing in such a way that the retailer always has a competitive advantage.
On a consumer side, shoppers use their own suite of tools in order to collect and organize masses of data that tells them more about the ranking of the products they’re hoping to buy as well as a suitable price they should be paying for it.
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 absolute 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
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 conduct any search on Amazon, the first results are usually the ones with the highest amount of positive reviews.
If we were to look at the last page of the search results (where no one really goes), you’ll find the items on that page have fewer reviews (if any at all).
What we’re likely to see them in the future of retail is 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, but you could’ve perhaps had a number of 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 a number of reviews (from different sources) from around the web and beyond.
However, this is not without its limitations.
When we think about booking a flight, there are a number of 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 choose where you want to fly to
- You choose where you want to fly from
- You choose how much you want to pay
- You choose what extras you need
This is more difficult in retail due to the nature of how the specifications differ. There’s no one-way to write about or present your product and because of this, the algorithm needs to develop an understanding for 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 different types of laptops or headphones, than it is to compare two different types of t-shirts.
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 find out what products your product line is missing.
But one of the most effective ways to have a solid understanding of not only what your customers
Retail algorithms look at past data, as well as chatter on the web 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.
You’ll notice over the past five years, there has been a significant increase in the number of people talking about the health food drink: Kombucha.
If you’re in the food and beverage industry, knowing what items people are keen to learn more about is vital to your success.
Alongside trend research, retailers also use sentiment analysis that uses intelligent retail algorithms to better understand the context as to when a product is discussed.
It’s all well and good understanding that Kombucha has increased in popularity over the years, but what if that is because there was an epidemic where people died because of it?
What this tells us is that these technologies cannot be used in silos.
You need to combine a number of different intelligence software and tools in order to come up with the best possible solution for predicting what your customers might want next.
Once you know what they’ll be buying, the work doesn’t stop there.
You need to understand who will be buying it.
You need to gather a large number of demographic and economic data that helps you get a clearer idea as to how people spend their money in your industry.
Price optimization
If you use Google to shop, you’ll notice they provide you with a number of different options at different price points. The same goes for the marketplace, Amazon.
This is helpful for consumers as you get to see a range of different products at different price ranges.
Google will then direct the shopper to the best store for their query.
The thing you need to remember is 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 smarter at how you price your products.
If you set your prices once and never look at them again, you run the risk of your competitors altering their prices and taking a bigger portion of the pie.
Using an e-commerce pricing tool can help you identify not only your own historical and current price points, but your competitors too.
You can use this to help navigate the complex world of pricing to come up with the best solution.
This type of software looks at the price points for specific products across a range of different competitors and alerts you when one price increases or decreases.
Retail Algorithms Takeaways
The use of sophisticated algorithms change the way we live our lives, especially in the world of e-commerce.
The good news is that they’re only going to get more intelligent and as a result, both retailers and consumers alike will have access to data that helps them make more informed decisions.
With the algorithms available, you can use a data-first strategy to better understand your customers and match them with products they really want.
If you’re not yet implementing tools or software within your business to accommodate for these technology trends, you’re going to get left behind.
Put yourself in the best possible position and use big data, algorithms and predictive analytics to your advantage.
e-commerce marketing
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