How applying technology to prices makes eCommerce businesses earn more
Big data are two of the words that are most used lately in the digital environment. Big data management, automation, and artificial intelligence are just some of its applications.
When it comes to sales techniques, big data was never going to be any less important. Big data is one of the key factors that make the implementation of dynamic pricing strategies possible. So, how do these two terms come together and how can they help you earn more money online?
Big data and dynamic pricing: technology in service of sales
Dynamic pricing strategies are the order of the day in digital sales. This technique allows you to adapt the price of each product according to the moment in which the market finds itself, in the function of supply and demand.
What’s the main advantage of this? It achieves new paths towards profitability and sales opportunities to offer the greatest benefit to each business.
Implementing a dynamic pricing strategy implies knowing the market in which you move inside and out, whether you are an eCommerce business, a marketplace, or a brand itself.
The point is that in a scenario such as the digital one, in which the participating actors rise to their maximum strength, the task of investigating each and every one of them can seem simply impossible.
Big data, meanwhile, is the tool that allows a dynamic pricing strategy to work. This technology is centered on the optimization of data processing (and all that doing so entails). It’s what simplifies the competition monitoring procedure for each online business, so that price fluctuations can be managed in an optimized way.
In big data, analysts, strategists, and eCommerce managers have found the answer to their prayers. It’s given them the necessary processes to correctly define the most appropriate pricing strategy for each business. Thanks to this type of technology, it’s been possible to bring something to the digital environment that isn’t really novel in itself.
Instead, it’s something that has been developed exponentially to adapt itself to the new times and new ways of buying. In the following, we’ll see the contexts in which big data acts on dynamic pricing.
How big data participates in dynamic pricing software
Dynamic pricing is a technique that begins with process automation. Even though this is an exercise that could be performed manually, the complexity and breadth of the factors involved make it so titanic that it becomes almost impossible.
Imagine for a moment that to extract the trends you need to obtain the prices for your own store, what reviewing each product in the catalog of each of your competitors – one by one – would entail. It’s not at all appetizing.
Monitoring each of the competitors for your eCommerce business results in a massive amount of data that must be stored, processed, and analyzed. Without a tool capable of doing so, it would be impossible to take into account the actions that need to be executed.
These are the dynamic pricing tools, the ones that allow the entire strategy to be managed comprehensively. To do so, they must have the ability to collect, manage, and structure data as well as to execute actions accordingly. For this, they must also allow as many variables and conditions to be registered as each business needs.
The function of big data is nothing more and nothing less than this: to tackle data processing. As we have already said, the main obstacle that must be overcome is that of a large number of records, rows, and variables that must be collected over time so that the strategy can take form and decisions can be made according to movements in the market.
The importance of technology within the world of dynamic pricing is such that it’s a value that must be considered, especially when it comes time to choose the best dynamic pricing solution for your eCommerce business.
The size of the catalog involved, the variables to be measured, and the number of competitors, are all factors that should come into play when choosing software that is powerful enough to cover all of your needs.
Here’s a practical example. Imagine that an online supermarket wants to expand its network of stores to other markets, having three variables that characterize its offerings:
- Fresh products whose price depends enormously on stock and seasonality
- A huge catalog of products of all kinds, not all of which have to be edible
- Offers and discounts of its own based on demand and the seller’s own supply, which at times needs to adjust its prices to offload merchandise.
For all of these, the competition analysis for an online supermarket will have thousands – if not millions – of pieces of data accumulated for each record.
The part of big data that dynamic pricing is concerned with embraces both this capacity to store data as well as to organize it correctly. The truth is that having millions of pieces of unstructured data can be as dangerous (or more dangerous) than having no information at all. Let’s go into detail about what power big data can bring to each field.
The pillar on which big data is based is data processing. Within what we can understand as the technology itself, three key elements can be identified: the data collection engine, the organization of the database, and the presentation of this to the client.
When it comes to dynamic pricing, as we have stated before, all three are crucial for making the best decisions at all times. For each online store, the intelligence part of big data must be easy to apply and to manage. If not, all of its extraction capacities will be of absolutely no use.
On the other hand, if we were to talk about data processing capacity as another characteristic of dynamic pricing software, the power of the analysis engine and the speed with which it can capture, transmit, and manage data comes into play.
Likewise, the volume of data that the software can work with could be key to what businesses choose it. Extensive catalogs with constant changes require greater storage power.
Imagine a case like that of larger sales platforms, such as Amazon or Alibaba, or even the example of an online supermarket that has thousands of products, each of which has its special characteristics.
In addition, the necessity of having an accumulated history or not also comes into play here. For many businesses, it’s essential to store data with a larger temporal dimension, which will require that both the data processing and storage are excellent.
Data segmentation as needed
As with the virtues in terms of data processing, using big data techniques to carry out your dynamic pricing strategy will also help you to resolve another of the great analyst dilemmas: data segmentation.
As we said before, unstructured data, without any kind of organization, can become even more dangerous for your company than having no data at all. A figure that dances can put more operations at risk than an unknown question.
Thanks to the implementation of big data, it’s possible to organize the data collected according to the criteria defined by each business, grouping them by type of consumer, by product, by purchase phase, by the type of price variation, by scaling, by the percentage of variance, and so on.
Even the season and the time of day in which the changes take place can be recorded. The intersection of all of these factors will resolve everything that an eCommerce manager needs to define.
Nevertheless, it’s also important to think about all of the data which, in many cases, seems to be one step beyond monitoring since the multiplicity of variables makes them practically unattainable.
Traffic sources, references, visit frequency – all of that extra information could be useful for extracting increasingly specific data, which has a specific space thanks to big data.
With all of that, the vision of the market and trends that affect the business are much more realistic, allowing you to adjust your prices with greater success and, therefore, find the best sales opportunities.
Big data + machine learning: the winning combination for pricing
If big data can be considered the brute force behind dynamic pricing, the technology that makes it move is machine learning. As such, this technology, an indispensable part of artificial intelligence (AI) systems, is the true executor of the changes defined within the framework of the dynamic pricing strategy. This, together with big data, forms the team that is necessary for dynamic pricing to be as practical as it’s effective.
Defining a dynamic pricing strategy requires the precise establishment of variables between specific events and the response to this as an action that the system ought to perform.
It’s thanks to machine learning that dynamic pricing software can identify patterns, read casuistry, and execute a response in line with the values set in place by each eCommerce business. This ensures that the optimization of the pricing strategy is continuous and directed towards extracting the greatest benefit from sales within the business.
What is its direct relation with big data? That’s simple. One technique extracts and the other executes. In reality, machine learning is the element that activates the requests to the dynamic pricing software after having read and processed all of the collected data.
Heading towards the algorithm
When these two technologies that participate in dynamic pricing come together is when you can say that the strategy joins the technique and that, therefore, there are actions to be executed.
What measures and controls these technologies? That is the job of the algorithm of each piece of software. This optimization algorithm is what each tool depends on and for what they have been conceived. Its objective is nothing more than to help you know, in each case, how the market that you are selling in is functioning, what the price at which you should be selling your products is, and how predisposed the users are to buy under these conditions.
The algorithm calculates and optimizes the strategy based on the data collected; the prices of the competition; the eCommerce business’s inventory, sales, and promotions defined over time (even when that time has passed); and the volume of sales achieved in each of these cases. With this complete panorama, the dynamic pricing software will be able to:
- Set the new best price for each product, within the established margins.
- Make predictions and allow them to be analyzed by eCommerce managers before specific dates arrive.
- Find the best starting price for new products that are being launched for the first time.
Where are the analytics?
Until now we have talked about everything that concerns technique, the most technological version of everything that big data in itself implies. However, there’s a part beyond that which doesn’t stop requiring human intervention.
To all of those worried about machines replacing humans, there’s nothing to worry about. You have to see big data as what it is: a tool that facilitates the task of data management and the automation of the extraction, categorization and storage processes. Nevertheless, any analysis will always depend on the human factor.
Big data as a technique indicates what the results will be when faced with certain queries. This entire set of data is collected and filtered according to each need but is never interpreted at the decision-making level.
In regard to dynamic pricing, the application is simple. Even though with the aid of big data, it will be possible to collect the distinct prices over time, the dates when prices are changed, and the activity of specific competitors along with other data, it will remain the task of each analyst to draw their own conclusions and understand how the market acts, how to prevent future changes, and how to define the best dynamic pricing strategy.
Big data technicians, the eCommerce world needs you
One of the most sought-after technological profiles in the last five years is that of the big data technician. Engineers and mathematicians are the principal candidates to form part of this cast of professions but computer scientists and economists are also looked upon favorably.
The key is to have a broad and practical vision of how to bring all of this data, which is already an essential part of digital business, to fruition.
The truth is that big data experts were already essential for eCommerce businesses, not only for dynamic pricing. They are also needed for everything from audience segmentation to the analysis of the channels through which it’s more profitable to launch a marketing campaign.
In this age of data, it’s not enough to simply know how to use a tool like Google Analytics in its most basic version. Now, the need is centered on having a profile that is able to see things where others only see numbers and labels. What’s required of those in this role?
- The ability to manage formulas and design algorithms
- The ability to extract conclusions as a data analyst
In short, big data is one of the pillars that will help you optimize the results achieved by your eCommerce business by participating in your pricing strategies. Without a doubt, having the techniques and tools that make the development of your business more effective is the key to following the path to success. This applies, above all, when we talk about increasing your profits.