Machine learning for Sales

Whether you realize it or not, machine learning has been driving sales for a long time now. While ML techniques are not necessarily available to smaller companies and teams directly, global players like Amazon and eBay are investing heavily in AI already. From automatic translations to demand prediction and fraud detection, machine learning is becoming vital for sales operations. Retail giants also have the ability to create AI tooling and adapt it to their merchant base, so that ML algorithm advantages can be trickled down to the sellers of the platforms. This gives valuable tools to the merchants who typically do not have the skill set or capital to invest in ML. While the use of algorithms can be debated, their effectiveness seems to be completely dominating and clearly points us towards a future where it is a major sales driver.

What is the situation with offline retailers though? Are they affected by AI? While brick and mortar stores may have been a bit late to the party, ML techniques are affecting them significantly as well. Robots are finding their way more often for various tasks – including serving customers and performing supportive functions as managing shelves or inventory. Virtual reality backed by ML is also making its way in these stores, allowing for richer, more engaging consumer experience. Not to mention the fact that machine learning techniques have been used in the back office for a long time for analyzing consumer behaviour, demand planning and product success predictions. In some ways, machine learning is the tool that brick and mortar stores could use to compete and get back at least some of the market share, that they lost to the online world.

One key aspect where ML has been quite useful in both worlds though is understanding user sentiments. Whether online or offline, sales team do want to know how consumers view their products or services. While focus groups have been a traditional way to understand how consumers see their merchandise, today’s users post comments on websites, write review on blogs and make social media posts about the products they buy. Consequently, the focus it seems moves from testing the product to a small sample of people to finding the tools to scrape the Internet and find out directly what user think about your products and services. This task is not without challenges, but the benefits are clearly there to see.

So can we provide some interesting ML trends that online or offline sales would go through? Here are three of them:

  • Personalization. While simple algorithms have long existed to show specific products that a user might be inclined to buy online (e.g. has searched for them recently), we do expect this trend to become much more sophisticated and targeted in the near future. Online sites may change completely the look, feel and product displays depending on the user. While brick and mortar stores cannot do that for obvious reasons, what they can do is personalize promotions and create targeted offers to get the most of their user base. We believe for both the online and offline world the ability to create attractive offers and offer products of interest in the short time span of user interest will become ever more important.
  • Automation. While automation technologies have long existed, surprisingly little automation can be seen in the small online retailers even as compared to a modern brick and mortar chain store. The reasons become obvious when you look at the scaling. Companies like Amazon itself do invest immensely in machine learning backed automation for tasks like storage handling, planning, sales demand planning, etc., making them leaders in the field. However merchants on the online marketplace platform are often small companies, that do not have the skill set and capital to invest heavily in automation. We believe however this will change in the future, mostly driven by standardized tools, provided by the marketplaces themselves or by their partners, that continue to increase automation in sales.
  • Individualized pricing. Online shops have long had the option to charge different prices for different visitors. They can change princes depending on a number of parameters, including user profile, past behavour, current supply and demand, so that sales results are optimized. While brick and mortar retailers clearly cannot do that directly, they have means available to do that. They may use custom discounts, coupons, individualized promotions to get the user to the store and make the sale. All of this is possible with artificial intelligence. Combining vast amounts of data and making decisions is not only achievable with AI, but will likely become an ever more important factor for sales in the future.