Accelerating ecommerce growth with predictive analytics

Accelerating ecommerce growth with predictive analytics

January 18, 2022

Roman Davydov

Ecommerce Technology Observer

Despite initial fears, many companies around the world have been able to withstand the immense complications caused by the pandemic. Restaurants, retailers, and grocery shops have managed to keep servicing customers with the help of ecommerce solutions. As a result, recent research by McKinsey shows that ecommerce sales have achieved 10 years’ growth in just three months. 

US ecommerce growth

Expectedly, large retailers with established ecommerce infrastructures have managed to get the biggest pieces of the pie, while those with rushed and immature ecommerce initiatives have struggled to achieve profit targets. This is also unsurprising, and as PwC found out in their recent Future of CX report, 32% of modern customers won’t return to a certain business after one bad experience.  

When do consumers stop interacting with a brand they love

Considering how fast customer expectations are changing, retailers are in dire need of tools for accurately anticipating them unless they want to lose their clients’ loyalty. This makes AI and virtual reality retail important prerequisites for ecommerce success. Let’s discuss how predictive analytics consulting can help ecommerce companies to optimize operations, increase customer loyalty, and boost profits. 

How predictive analytics works in ecommerce

Predictive analytics encompasses a combination of techniques and technologies like AI, ML, and statistical analysis. From forecasting stock market fluctuations to preventing equipment failure, predictive analytics has made it possible for companies to make informed decisions in a wide range of business areas. 

In an  ecommerce context, predictive analytics can be used to forecast market demands, predict customer behavior, enable dynamic pricing, and detect fraud. This is done by finding relationships between various customer data points including past purchases, demographic data, social media sentiment, web activity data, and more. 

Essentially, predictive analytics allows for continuous analysis of customer data, enabling advanced personalization capabilities, and in today’s increasingly competitive ecommerce landscape, personalization is one of the main differentiators. Recent research by McKinsey reveals that 76% of consumers are more likely to make a purchase when brands offer personalized experiences. With predictive analytics, ecommerce companies can make online shopping experiences unique for every customer. Let’s explore how companies can use predictive analytics to make this happen and discuss its implementation nuances. 

Recommendation engines

Brick-and-mortar stores have long relied on the only foolproof way of providing customers with unique product recommendations and hyper-personalized experiences. For centuries, in-store salespersons directly asked customers about their needs and wants and offered them relevant products in return. Until the emergence of machine learning-powered recommendation systems, this level of personalization couldn’t be matched.

Thanks to this powerful embodiment of machine learning in ecommerce, an online business can tailor the shopping experience of  every website visitor and establish more meaningful customer relationships. Here are the major types of recommendation systems: 

Collaborative filtering

In simple terms, the collaborative filtering approach to recommending products implies that customers who have bought similar items in the past are likely to buy similar products in the future. For example, if Bill bought items A and B and Jane bought items A, B, and C, Bill will have a high probability of being interested in item C. In this case, the recommendation system will recommend item C to Bill. The flaw of such systems is that new items won’t be recommended until they start to sell.

Collaborative filtering

Content-based filtering 

Content-based filtering takes product metadata and user profile data into account. For example, if a user has purchased skis and then bought a ski suit, he or she has a high chance of being interested in ski accessories like helmets, gloves, ski masks, etc. The recommendation system will determine what these products have in common, define customer profiles based on past purchases, and predict what product the customer might be the most interested in. 

Similar to how collaborative filtering models struggle to suggest new items, content-based filtering models can’t work with users without purchase history and definitive customer profiles.

Content-based filtering

Unsurprisingly, the aforementioned recommendation systems complement each other in many ways. So, to harness the advantages of both models, the majority of successful ecommerce projects utilize the hybrid approach. Developing recommendation engine from scratch can be costly for many companies. However, the majority of today's ecommerce SaaS platforms provide decent pre-built recommendation systems.  

Landing page optimization

One of the most easy-to-implement recommendation system applications is landing page suggestions . The idea here is to present the best product or service offerings to customers as soon as they open the online store. Given that the Pareto principle, or that 20% of products constitute  80% of sales, often rings true in the context of ecommerce sales, it’s always a good idea to show customers the most valued products first, as they have a higher chance of keeping new visitors on the website. This can also partially solve the problem of  new customers without a  purchase history and complete profiles. 

More  importantly, it’s not about showing the highest  selling products but the most valued ones. It may be the number of views, sales, the amount of times a product has been added to a  cart, social media sentiment regarding the product, current weather conditions, and a myriad of other metrics that determine the value of the product in each case. 

Advanced analytics 

It’s paramount to realize that recommendation engines shouldn’t be used for cross-selling and up-selling initiatives only. Otherwise, the brand risks looking like an annoying salesperson that persistently recommends products but doesn’t take into account customer opinion. In this way, instead of recommending similar items after a user has abandoned their cart, it might be better to offer a discount or purposefully recommend similar but lower-priced offerings. However, such decisions can be automated only if robust data analytics foundations are set. 

An advanced level of personalization is possible when an augmented analytic platform can analyze both structured and unstructured data from various sources. This requires setting up a customer data platform equipped with ML automation to process all the incoming customer data and synchronize analytics across devices and networks. The resulting system will deliver advanced levels of personalization regardless of the sales channel. 

Importantly, setting up a customer data platform calls for marketing and IT teams to work in tandem. When it comes to non-digitally-native businesses, it’s paramount to hire additional talent that can make data science accessible to marketing teams. This is why companies are increasingly hiring analytics translators who can communicate business goals to engineers and data insights to business users and stakeholders.

Demand prediction 

Conventionally, retailers predict demand based on historical data and their experience, and under stable conditions, this approach can deliver good results. However, in any ecommerce niche, there are hundreds of factors that can impact demand requirements on a daily basis. Everything from price fluctuations to promotions to even weather conditions can change the demand for certain products. 

With the volumes of data that ecommerce companies have at their hands, it’s only logical that ML-powered predictive analytics models are used to forecast demand. Such models can ensure that every accessible data point is considered in demand prediction, helping to ensure an adequate amount of stock is available at all times. For instance, if your ecommerce stack is equipped with different types of POS, these solutions can fulfill a role of such data points, providing you with detailed inventory reports. If needed, you can even set up automatic replenishment of those goods that are running short (for example, if your ML-solution is integrated with eSourcing software).

How demand prediction works

The nuances of selling long-tail products 

It’s still crucial that your predictive analytics platform should account for items that don’t sell on a regular basis because they have considerably higher prices or belong to a product niche. 

The long-tail model

Figuring out the demand for such low-selling products can prove to be a wild guess, as there is a lot of random variability in the data associated with them. In other words, there are often insufficient amounts of data for a predictive analytics engine to accurately forecast demand for low-selling products. Given that the majority of ecommerce stores have such products on display, it’s crucial to tune predictive analytics models to account for long-tail products. 

First, you need to minimize the number of factors that influence the model output. In this context, all non-essential low-weight variables should be ruled out, as they can create unnecessary ‘noise’ which skews predictions in the wrong direction. Second, you need to analyze data from external sources to detect regional or global demand patterns for a particular product. For example, this can be done by communicating with product distribution centers.

Embrace the human-in-the-loop approach

Regardless of how sophisticated and advanced predictive analytics models can be, human judgment should still be a crucial part of demand prediction initiatives. This is especially relevant in the context of industry-specific disturbances or global events like the COVID-19 pandemic. Also, quite often decisions about stock replenishment are not solely about filling the demand but also about business risks and long-term goals, which are outside the expertise of predictive models. 

This calls for the adoption of systems that can provide demand planners with easy-to-access forecast reports. Understanding why the system has suggested a certain decision is the most important factor in establishing trust between humans and machines. ‘Black box’ systems where you can’t understand why certain decisions are made are notoriously less trusted and adopted. Furthermore, system transparency allows demand planners to see what improvements can be made to improve the model’s accuracy.

Dynamic pricing

Predictive analytics can also be used to dynamically adjust prices based on many factors including historical pricing data, supply and demand, market trends, competitors’ prices and promotional activities, as well as  consumer habits. Dynamic pricing strategies allow companies to respond to demand fluctuations in real-time, attract more customers, and, ultimately, boost profits. By applying dynamic pricing in ecommerce, companies can also ensure that their pricing strategies remain competitive at all times. Here are a few suggestions to realize the full benefits of dynamic pricing strategies.

Difference between static and dynamic pricing

When it comes to dynamic pricing, there is a thin line between customer loyalty and customer dissatisfaction. A common trap that ecommerce companies fall into is adjusting prices for products that are supposed to remain relatively stable over long periods of time. Imagine a customer who has been purchasing the same car tires for years, only to discover that their prices have spiked for no apparent reason. 

This is why it’s critical to define which item prices can easily fluctuate over short periods of time and which should remain the same. For example, in general, prices of expensive items like TVs or refrigerators should remain relatively the same. Given that some consumers tend to research these high-priced items for months, frequent price changes will most likely frustrate them. On the other hand, prices of trendy fashion items can change almost daily. It’s paramount to consider products’ purchase cycles and customer expectations when developing pricing strategies. 

Similar to demand prediction strategies, it’s important to have dedicated personnel that can routinely review the dynamic pricing model’s output. In some cases, it’s important to assess factors that predictive analytics systems can’t account for. For example, last year during the pandemic’s peak, some retailers significantly increased the prices of cleaning products. In the majority of cases, it ended poorly, with retailers losing customer loyalty and dealing with significant consumer backlash. Most importantly, many dynamic pricing models also automatically raised prices for these products due to the apparent demand spike.  

Regardless of your brand’s technological maturity, dynamic pricing requires continuous testing and refinement. For this technology to bring tangible benefits, it’s paramount to develop definitive adoption frameworks that can track progress and measure the impact. 

Conclusion

After decades of figuring out customer needs relying on intuition, industry experience, and conventional statistical models, businesses finally have the means to address customer needs in real-time using granular insights about their target audiences. At this point, relying on traditional approaches leaves an ecommerce business competing with the rest of the market with a significant disadvantage. 

Importantly, tapping into predictive analytics or virtually any other data-driven technology implies a revamp of business operations and approaches. Especially for those falling behind on digital transformation, establishing solid data organization frameworks and developing data culture are cumbersome but essential processes for long-term business success.