The Evolution of Retail Sales Strategies

The retail landscape as a whole has been growing, evolving, and becoming more streamlined. Specifically, the increasing prominence of e-commerce today has brought with it a new wave of development in retail. Adapting to new technological innovations and general market trends is pivotal if retailers want to stay competitive in the modern market.

In the past, various aspects of a company’s sales strategy would be reasonably difficult to correctly optimize. Things like cost-effective marketing, dynamic pricing, and brand loyalty are closer to a science now, compared to the relative guessing-game they were in the past. Artificial Intelligence plays a huge part in this ever-evolving environment, allowing retailers to maximize the use from every little detail.

Competitive Pricing

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E-commerce and online shopping give consumers considerable transparency when it comes to prices, as they can compare the best prices for a product within minutes. A Forrester Consulting study in 2014 concluded that 81% of American shoppers compared prices amongst different vendors before making a decision (you may learn here how price sensitivity is evaluated). This shift in consumer power requires retailers to adapt to their competition daily and even hourly. After all, being regularly edged out by competing retailers for price doesn’t bode well for the longevity of a business.

Before this pressure to have competitive prices became a cornerstone of the retail sphere, the act of scouting competitors’ prices was fairly simplistic. Manually checking prices and adjusting accordingly, while a fairly simple task, can quickly become time-consuming and is insufficient in combating things like brand loyalty and promotional pricing. Now, more than ever, there is a huge reliance on technology to fill that gap and do it effectively.

Competitive data scraping and web crawlers are one-way retailers can get a full picture of their competitors’ strategies with close to no effort. They can effectively generate a full report on a specific product’s prices and activity in minutes, giving businesses a look at not only their direct competitors but also any retailer selling similar products at all. Some competitive data software can even be integrated with the company’s goals and rules, offering more fleshed-out pricing data and giving a view into pricing decisions as a whole.

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A large portion of a retailer’s business can be attributed to well-timed and attractive promotions. Smart marketing and competitive discounts can leave a lasting impact on buyers and go a long way in building the holy grail of retail: brand loyalty. As a result, keeping track of competitors’ promotional strategies offers a more transparent view, while optimizing their own promotions helps retailers break-even and combat the less-than-lucrative price wars.

This evolving, data-driven landscape brings about some previously unfathomable aspects of retail. The need to continually stay competitive with pricing strategies leads to businesses changing their prices incredibly frequently; back in 2013, Amazon was already changing their prices 2.5 million times a day, magnitudes more regularly than its big-name competitors.

All in all, while dynamic pricing can be a burden on retailers in their mission to stay competitive, consumers today are taking the spoils. When practically all major retailers are trying to woo buyers with their prices, customers are more empowered now than ever.

Growing Importance of Consumer

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Because customers are more empowered now with the wide selection of prices and vendors, their importance is reflected in retailers’ business strategies. Rather than trying to sell one product to as many buyers as possible, the onus is now on getting the most value out of any single customer. While some data-driven marketing strategies might seem heavy-handed, they certainly go a long way in building loyalty.

If you’ve ever bought something on Amazon, you might have noticed personalized recommendations or the “people also bought…” tab on practically every product page. Put, rather than guessing. They are showing you what their algorithms believe you’re most likely to buy. Similarly, if you’re an active member of an online marketplace, you might get emails or messages with recommendations for future purchases. By cutting the product discoverability time altogether, it can become more comfortable for an e-retailer to increase brand loyalty through personalized recommendations.

Targeted advertising is a far more noticeable application of retailers (and websites as a whole) getting to “know” consumers. By assessing users’ browsing behavior, search history, and personal information, the algorithms behind online advertising come up with the ads that are most likely to be of interest.

On the other hand, promotions can be personalized, too. One such example is the personalized discounting of items that have been sitting in a customer’s cart or wishlist; this method is designed to increase conversion of wishlist items as a whole and build customer loyalty through a personalized offer. On a smaller scale, segmented personalized discounting groups buyers with similar interests or traits and offers each group a promotion tailored towards them.

Role of Artificial Intelligence

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Overall, the data explosion over the past decade has spurred the growth of technology able to make sense of it all. AI plays a massive role in today’s market, primarily in extensive data collection and analysis. All of the strategies mentioned above have been vastly enhanced and improved by AI, be it competitive pricing or targeted ads.

The truth is, people, don’t have time to meet and get to know every one of their buyers. However, analyzing millions of bits of data daily via machine learning tools is the next best thing. The use of AI in retail rises as more retailers begin to understand the value of these tools. Whether their goal is cost-saving, increased brand loyalty, or projecting a company’s mission better, AI is ever-present as the data-driven assistant.

There is a myriad of applications of artificial intelligence that haven’t been covered here, many of which aim to better the retailer-consumer relationship. Smart chatbots, email marketing, personalization of consumers’ product experience (Netflix’s recommendation algorithm comes to mind), and many others are changing the consumer’s retail experience.

The vast digitization of the retail sphere gave customers more options than ever before, and retailers began implementing machine learning tools to survive in an increasingly competitive environment. While it might be weird to conclude, artificial intelligence, of all things, has helped retailers understand their consumer base better.