Data Scientist at Flutura Decision Sciences & Analytics at almaBetter
With the accelerating adoption of mobile — US smartphone penetration exceeds 40 percent today and is projected to reach nearly 60 percent in three years — digital commerce is poised to explode, bringing shopping quite literally into the palms of many consumers’ hands.7 For some retailers, mobile is already a huge factor: at designer-fashion retailer Gilt, for instance, mobile accounts for about 50 percent of daily traffic and more than 30 percent of total sales. Mobile technologies will increasingly influence every stage of the customer’s shopping journey — from personalized promotions prompted by geotargeting to in-store research and price checks, as well as to payment capabilities that offer checkout options beyond waiting in line. A recent McKinsey survey of digital shoppers highlights how mobile technology can complement the in-store experience; for example, almost half of the consumers who conduct research on their mobile phones have done so while in stores, and half say they’re open to the idea of in-store mobile payments.8 Indeed, while just two years ago mobile accounted for only 3 percent of e-commerce sales, that figure will probably rise to 15 percent by the end of 2013.9
Habits of consuming content have changed dramatically. US consumers doubled their spending on digital newspapers in the past seven years, for example, while halving their spending on print newspapers.10 As more consumers abandon print media for digital media, marketers follow: 44 percent of them now allocate at least half of their marketing budgets to digital media, up from only 31 percent in 2009.11
We’re already seeing that direct mail and newspaper circulars are playing a diminished role in retail marketing. Mass advertising will not disappear overnight, but its influence is certainly waning. Ads are shifting toward not just digitization but also personalization, powered by increasingly sophisticated algorithms and predictive models that analyze transaction data and digital-media trends (for example, what topics are hot on social networks). Already, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations based on such algorithms.
Company-directed marketing is also competing for attention with peer recommendations through social networks, user reviews, and the like. Our research shows that for the average consumer, peer recommendations carry ten times more weight than recommendations from salespeople. Indeed, social media could well make up 22 percent of marketing budgets in five years as retailers increase their spending to facilitate and influence peer connections about brands through paid ads and branded pages on social-media platforms such as Facebook, Ibotta, and Pinterest.
Amazon already offers same-day delivery in ten cities and guarantees one- to two-day ground delivery in the continental United States. It is not unreasonable to think that consumers will expect comparable shipping speeds from all retailers — we expect same-day delivery to become available soon in at least the top 150 metropolitan statistical areas, which hold nearly 75 percent of the population.12 Furthermore, we believe retailers will offer shipping free of charge to their most loyal and profitable customers, as opposed to providing it only for those who make minimum purchases. We also expect to see third-party distribution services evolve and expand. Some companies may make big investments in distribution infrastructure and sell it as a service to other retailers, as Amazon and eBay do now. Others are beginning to invest in infrastructure to provide convenient and secure package-delivery locations: lockers and pickup boxes are appearing in groceries, convenience stores, and drugstores nationwide, and new services are sprouting up to let retailers ship packages for pickup at other retail locations or self-storage facilities.
Consumers have come to expect simple and seamless processes not only for receiving the products they’ve purchased but also for returning unwanted products. Free and easy returns — including the ability to return or exchange online purchases in stores — are becoming table stakes.
New retail business models
No doubt, retail competition just keeps getting tougher. Consider the ongoing blurring of lines between formats and sectors as retailers try to steal shopping trips and share from one another (for instance, fresh food is no longer the dominion of supermarkets alone but is also increasingly found in warehouse clubs, convenience stores, pharmacies, and even dollar stores). Furthermore, players across the value chain are encroaching on what used to be the exclusive turf of retailers. More manufacturers are selling directly to consumers; examples include Apple, Nike, and — via Vitacost.com — several consumer-product manufacturers. Tech players are also fighting for consumer retail dollars: Google offers more than one billion products for sale on Google Shopping and may soon open retail stores.13 Additionally, companies such as craigslist, eBay, and Etsy (home to almost a million small businesses) are creating marketplaces where individuals and entrepreneurs can sell their wares to the masses. Finally, rental and aftermarket-circulation models, such as Chegg for textbooks or Rent the Runway for designer fashion, are eating into traditional demand for retail goods.
Competition is coming from near and far as technology makes retailing much more global than it has ever been. UK online retailer ASOS.com, for example, offers free two-day shipping worldwide for a relatively small membership fee, and at times as a promotional offer to all customers. Until recently, retailers didn’t have to worry much about global competition until stores started sprouting down the street — nor did they have an opportunity to access global consumers from North America — but that is changing as technology helps break down barriers and generates new retail business models.
We need cross-selling and upselling because there is a very high probability of an existing customer buying a similar product at around 60–70% and around 5–15% for a new customer. If used effectively, they can increase your sales and, therefore, profits. And if backed with machine learning for marketing, cross-sell and upsell campaigns can significantly improve your business’ position on the market.
Let us explain this with an example. If a customer has bought an iPhone 10, he may also be interested in buying AirPods. This would be an example of cross-selling. If he has an iPhone 10, he might be interested in buying an iPhone 12. This would be a classic example of upselling. Conversion to YouTube Premium would also be a very good example of upselling.
1. Machine Learning is a key to personalized recommendations and improves cross-selling and up-selling opportunities
Once we have already collected more than enough data about our customers we have data about your customer’s age, location, gender, hobbies, buying history, marital status, etc. And even did customer segmentation for a better offering. That’s great, but machine learning algorithms can significantly improve our offer of personalization. Thanks to data-driven recommendations, customers will get the right offers at the right time, and, therefore, purchase more products. One example is — Amazon identifies which items are often purchased together. After that shows users potential complementary products. This trick ensures a better customer experience. As users receive exactly those recommendations which they may be waiting for.
Machine learning algorithms are usually divided into two categories: collaborative filtering and content-based filtering. However, combining both of these approaches is also a popular way of building a recommender system.
2. Machine Learning sharpens sales prediction
One of the best things about machine learning is that it never stops learning from new data. This ability allows forecasting our customers’ behavior and expectations in the future. On the basis of historical and new data, a machine learning model can increase the accuracy of the sales forecast. This is especially important and useful when you need to predict how our customers will perceive new products or services. Cross-selling and upselling strategies, and reduces the risk of inefficient marketing.
Apart from this, using machine learning and customer predictive analytics, it is possible to identify the most effective sources of contact with clients. Again, this can help us to polish your selling strategy and gain more profits.
3. Machine Learning makes dynamic pricing possible
Dynamic pricing is among the latest pricing trends — it implies continuous altering of product prices, in reaction to real-time demand and supply. This model allows better control of the pricing strategy, gives flexibility without reducing the brand value, and saves budget over the long run. And, dynamic pricing can be a great help when we are cross-selling or upselling our products.
However, it is virtually impossible to efficiently monitor other items and follow the real-time demand and supply manually, since there is too much data to check and analyze. But machine learning can solve the problem — a properly built model will take into account a lot of factors. It will take much more than we would be able to consider without such an algorithm, provide us with precise data, and ensure much faster responses to demand fluctuations.
We have tried to give you a touch of the ML. We hope you have a great learning through this article.
It feels good to work with a awesome team. I would like to thank Sanchita Paul & Priyanshi Singh for her invaluable contribution to this article.
That’s all folks, Have a nice day ????