Trevor Legwinski is chief strategy officer of SearchSpring
By Trevor Legwinski There is a lesson in the numbers for online retailers — one that they are likely not hearing for the first time. Consumers are continuing to more strongly embrace mobile devices, particularly smartphones. To date, mobile has emerged as the number one platform for browsing and researching products, but it still lags desktop platforms when it comes to driving conversions. The next few years are likely to change this as more retailers move to mobile-first commerce. Double take Forrester Research predicts that purchases from mobile devices will double by 2020 to reach $250 billion worth of transactions by the end of the decade. Not surprisingly, product purchases within applications is one particularly hot growth area, buoyed by the lightning-fast nature of simply pulling out a phone and clicking “buy.” Retailers looking to maximize mobile purchases must ensure that their customers’ experiences – on both mobile sites as well as in apps – are as slick and seamless as possible. There are numerous factors contributing to this, including fast load times, intuitive interfaces and easy payment options. However, mobile search is emerging as a critical element in creating the types of customized, deeply satisfying mobile interactions that drive conversions and revenues. Smarter search, combined with more profit-aware merchandising and machine learning-based behavioral analysis, are a powerful combination for increasing mobile conversions. Intelligent mobile search Some retailers may be frustrated that customers continue to use their mobile sites and apps primarily for searching and browsing, rather than actually shopping. However, there is a major silver lining to this trend. Google recently announced that mobile search queries have officially eclipsed desktop search queries. Organizations can leverage this to their advantage, by emphasizing a smarter type of search that engages and personalizes during those first few make-or-break seconds of an interaction. In a mobile world, customers prize speed and convenience above all else. Intelligent mobile search leverages machine learning to grasp user context and past behavior, and tailor mobile search results accordingly. This is known as real-time personalization, and it is quickly emerging as a pillar of the mobile user experience, giving users content tailored to their interests, needs, and location. Intelligent mobile search can actually harness data across various touch points – desktop, mobile site and mobile apps – to provide the most relevant mobile search results that incite customer action. Intelligent mobile search can also deliver other features that time-pressed mobile users appreciate, such as type-ahead autocomplete. It can predict and display the most relevant facets for particular search results – for example, price, color and size for a pair of shoes. By highlighting only the most relevant facets, mobile sites and apps become better equipped to strike the critical balance between giving users the information they need and the ability to personalize their product choice, while avoiding information overload that can be perceived as a nuisance, or detract from the overall speed and quality of experience. It is also important for faceted navigation technologies to be responsive-design compliant. Given the challenges of mobile interfaces – limited screen size, fewer horizontal pixels – maintaining mobile-friendly design is imperative. Profit-focused merchandising Helping mobile users quickly find what they are looking for is only the first step in enhancing mobile’s conversion-driving capabilities. Once this is done, mobile sites must merchandise more effectively in a profit-aware manner that enables them to maximize visibility for those products with the highest conversion and revenue-generating potential. For example, let us say a customer searches for “black dress, adult medium, priced under $50.” The site obviously wants to prioritize for display the most relevant, profitable products that the customer is most likely to act upon. There are numerous possible criteria for determining what this product should be, including social likes, conversion rates and click-through rates. Product boosting has evolved as a technique that allows sites and apps to deploy specifically defined rules to automatically determine the order of search results. All else being equal, product boosting will favor one black dress over another, if the former historically has had a higher click-through rate, conversion rate or other metric suggesting a higher probability of conversion. Desktop sites have long understood the correlation between search rankings and higher product profitability – the higher up a product is ranked, the more likely it is to be clicked. This trend is evident in Google’s own search rankings and, in fact, this was the reason behind the “mobile-geddon” panic of 2015, when Google announced a new algorithm that would alter search rankings. Given the combination of fleeting attention spans and limited, precious mobile real estate, this ability to merchandize with profits in mind – through techniques such as product boosting – are especially critical in mobile. There are additional benefits as well, including greater marketing consistency across mobile and desktop sites. Machine learning to identify key behavioral patterns Like on their desktop sites, ecommerce managers must constantly be on the lookout for ways to do things better on mobile, which means analyzing the data that they have at their disposal. In mobile, search is far and away the greatest repository of this actionable data, due to the prevalence of search on mobile devices, as well as the fact that mobile users tend use features such as product recommendations and product finders less frequently than on desktops. Analyzing mobile search data can provide insights into critical questions. For example, which search terms are most prevalent, showing the types of products in which customers are most interested? What search terms are generating zero results, such that product findability can be improved? With the rise of ad blockers, mobile sites need to be judicious with regards to their volume of advertising content. Search data analysis can help mobile sites understand which products may be overexposed as well as underexposed, helping guide advertising decisions. Today’s customers are extremely fickle and their tastes are always changing. Retailers must be able to sense and appropriately respond to these changes as close to real-time as possible, across all of their digital properties – Web sites, mobile sites and in apps. FIFTEEN YEARS AGO, we did not envision a day when such a huge proportion of retail sales would be driven by ecommerce sites. That has happened, and we expect the next evolution will be mobile shopping – moving beyond browsing and researching, to actually driving an equally, if not greater, volume of purchases than desktops. There is still work to do to make mobile sites and apps more conducive to driving conversions. Better personalization from the very beginning, at the point of search, will prove to be critical. Mobile sites and apps should also take a page from the desktop book, merchandising for profit and applying machine learning to a rich treasure trove of behavioral data to amplify and extend the benefits of greater personalization. Taken together, these capabilities can help maximize the revenue-generating capabilities of mobile sites and apps, while supporting a more profitable, efficient and consistent omnichannel strategy overall. Trevor Legwinski is chief strategy officer of SearchSpring, Providence, RI. Reach him at trevor@searchspring.com.