McKinsey’s 2021 report revealed that companies managing to provide a personalized shopping experience earn up to 40% more revenue. In eCommerce, personalization can combine various tactics – tailored product recommendations, targeted newsletters, geo-specific offers and discounts, and even weather-sensitive content. If implemented in the right way, personalization helps attract and nurture high-quality leads and upsell or cross-sell products that the consumer might otherwise overlook.
Keeping in mind that 76% of shoppers get frustrated if they don’t receive the right amount of intimacy, personalization is table stakes in today’s retail. However, successful personalization greatly depends on the company’s ability to leverage massive amounts of real-time data acquired during different stages of the customer’s lifecycle in various parts of the omnichannel organization.
For many years, eCommerce giants employed both internal and third-party information for extensive audience research. Recent developments in laws and regulations around consumer data, such as the removal of third-party cookie support, however, have pushed many businesses to rely solely on zero-party and first-party data. Usually, it is gathered from the company’s owned channels, such as the website, email software, pop-ups, surveys, purchase data, etc.
If utilized well, internal data analysis can unlock valuable insights into the different types of customers and motivators behind their purchase decisions. On the other hand, analyzing behavioral tendencies only from internal data misses the context surrounding the customer’s willingness to buy, such as the psychological impact of bad reviews or powerful influencers.
For example, you noticed that a customer is continuously browsing through specific categories on your web but isn’t willing to buy any goods. Is it due to the price or the wrong product assortment? Should you come up with a personalized offer, and if yes, what could it be? Answering these questions is difficult unless you start exploring outside of the box.
The power of consumer sentiment
One of the reasons why relying only on internal data might easily lead a company down the garden path in their personalization strategy is that it’s hard to tell what lies behind certain behavioral tendencies. It can be tricky even with existing customers, but all the more so – with newcomers, as there’s no historical internal data about them. For the same reason, it can be challenging to integrate new products into recommendation algorithms as there are no prior engagement metrics.
Imagine that you cater to a broad female audience, including 18-25 YO women in the UK and 18-25 YO women in Japan. Data shows that young women from different countries tend to buy SomeRandomBrand T-shirts on your site. Still, when you place them among product recommendations, you notice that the sales growth in the UK is only incremental and goes down two weeks later. In Japan, only a specific oversized model is trending, and the shoppers mainly ignore others.
In this situation, there’s not much a retailer can read from internal data unless it has a review section where people can openly write their opinions about products and services. On the other hand, analyzing alternative data from external sources can unlock interesting insights. For example, it can reveal that specific T-shirt models in Japan are trending due to the powerful effect of a Tik-Tok influencer. Whereas in the UK, some negative reviews surfaced on the net, complaining about the quality of the T-shirts, driving the sales down.
To unlock such insights, you will need to tap into the power of sentiment analysis. This big data analysis method is based on natural language processing (NLP), text mining, and data mining. Through machine learning (ML) and artificial intelligence (AI), NLP enables data analysts to interpret massive amounts of textual information with encoded human emotions. Using sentiment analysis, eCommerce companies can sift through millions of anonymous reviews on different websites.
There’s plenty of scientific research showing that negative consumer sentiments can have a powerful impact on other people’s buying intentions and sales. According to Trustpilot, 9 out of 10 consumers use a review site before making a purchase. Sentiment analysis can help the company track its feedback (both positive and negative), translate it into data-driven personalization tactics, optimize marketing campaigns, and change unpopular product features.
Reviews are not the only factor, however, that can play into disappointing marketing results. Another common reason for poor sales is a bad product assortment. A PwC report revealed that nearly 30% of customers would change their usual retail store to another if it meets their needs better. Sentiment analysis that is based on data can show what is currently trending in certain target groups. This way, businesses can validate new product ideas, evaluate consumer sentiments towards competing brands, and improve their assortment.
Alternative-data-driven personalization
Alternative data offers eCommerce companies a vast pool of information that can be leveraged to build better business-to-customer relations. Utilizing currently available web scraping software, such as full-stack scraper APIs, retailers can gather various types of alternative data – information about pricing and changes in item stock, product descriptions, product listings, and so on. Based on it, companies can personalize even dynamic pricing, if necessary.
The more data the company can gather about its customers, the more optimized and targeted its marketing strategy becomes. For example, pricing can be changed according to the user’s demographic information, general profile (deal-seeker or quality-seeker), and competitor pricing and inventory details. Items that are trending or limited in stock in competing marketplaces might be sold for higher prices, especially during the holiday season.
An issue, however, that some companies might run into is scaling and data integration. Gathering various types of alternative external data is one part of the game – another is integrating it and achieving a holistic customer view that is based on both external and internal data insights collected through different channels. As the volumes of data increase, managing, processing it, and assessing the impact of different data points becomes possible only with machine learning.
Conclusion
It is estimated that the NLP market will grow from $20 billion in 2021 to over $127 billion in 2028, showing the undeniable potential of this technology in solving business challenges. It should propel a new generation of sentiment analysis techniques, such as microservice APIs that can measure emotion in audio and video content.
Alternative data harvesting and analysis haven’t even scratched the surface yet – businesses are yet to harness its power. There’s a joke on the internet that Spotify’s algorithm knows its users’ music tastes better than they do. Being ‘teased’ in such a way could be the goal of almost any eCommerce business. With AI- and ML-powered data scraping and analysis software, today’s retail companies can leverage big data to the level of an individual customer’s tastes, needs, emotions, and desires.
