monetate-电子商务报告——正确的推荐（英文）.docx
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1、之 monetat 苞E02 心018(1 in iiN iit RRSISI3Return Purchase Rate for Items Added to Cart,by Recommended Status20.0%15.0%UO-S-UOU10.0%5.0%0.0%Non-Recommended ProductRecommended ProductFormarketers,then,thetakeawayshouldbeclear: Recommendingthe right products isnt just a single-session boon. It has a long
2、-tail effect that even out performs non-recommended items that have been left behind, suggestingthat product recommendations, whendoneright,can improve cross-selling initiatives and increase AOV even after a customers immediate urge to buy wanes.Theres a catch, though.When customers see recommended
3、products, theyre only engaging with them 11% of the time. But when they do, they show strong intentby adding to cart or purchasing in-session5% of the time.That meansthat nearly 50% of click-throughs result in customers moving toward purchase. Given the high likelihood that a customer will convert a
4、fter initial engagement, but still relatively low levels of engagement with recommended productsasa whole,ifsevenmoreevidenthowmuch retailers have togain by showing them the right recommendations to inspire interest.It seems that marketers are doing a great job with recommendations when theyre getti
5、ng them right, but theyre not getting them right often enough. Whats missing, then? For one, the overall sophistication of many brands9 approaches to product recommendations lags behind that ofthe rest of their personalization efforts. Andthatsuggeststheneedforanewapproach.The discoveries above-that
6、 simply getting customers to click on a recommendation can lead to value downstream, and that irrelevant recommendations have about the same impact as not recommending anything at all-are eye-opening. But ifs hardly surprising when you consider recent trends in customer expectations.The 2017 State o
7、f Personalization Report of US adults surveyed forthe report expressed a level offrustration with impersonal shopping experiences.Its the flipside of that equation, though, thafs most promising and most relevant to ouranalysis of product recommendations: 49% of those same survey respondents also rep
8、orted that they had madean impulse buy based on a personalized recommendation from a brand. Whats more is that 85% of those impulse buyers were happy with their purchase.Its become commonplace to say, personalization is table stakes.99 That may be true, but it makes it sound like theres little room
9、to surprise customers in a positive way. What our data shows and what this survey confirms is both that customers are still delighted by serendipitous discovery, and that marketers have plentyofuntapped opportunities todeliveron that.And they should be eagerto do so, given how much potential revenue
10、 is at stake. But they need to be smarter about how they facilitate that discovery.For too long, marketers have used product recommendations either as a stand-in for personalization (treating the one as synonymous with the other), or as a completely separate appendage from their other personalizatio
11、n efforts. This is a wasted opportunity. Instead, marketers need to treat their recommendations solution as an integral component of their personalization programs. Because the key to delivering the right recommendations is about more than choosing between viewed, also viewed99 or purchased, also pu
12、rchased“ algorithms. Its about understandingand acting on thedynamiccontextof thecustomer. Ideally, a recommendations solution can bejust as data-driven as a brands testing, targeting, and individualization efforts-taking into account historical and behavioral data that is specific to each customer,
13、 ratherthan making a recommendation decision based solely on which product is currently on the shoppers screen.An added boon ofthis approach: Recommendations may also serve as another key data point in that dynamic context of the customer, to inform better decisions in other touchpoints as well.Amon
14、ctMeChccMl。- G wjyt eo know tlE to rconlcter your protftaa recommendations MiutlonAre your recommendations making the most ofyourcustomerdata?Downloadourchecklist,M6Waysto KnowIts Time to Reconsider Your ProductRecommendations Solution”Product recommendations, like personalization, are now nearly ub
15、iquitous in ecommerce. And while the impact both provide is clearcut, they have rarely been deployed together as part of a holistic strategy. The data inthis report demonstrates that the potential is there, but perhaps the most striking data point is one that shows how much potential lies ahead: Cus
16、tomers are declining to engage with 88% of all product recommendations they see.Whether a brand is focused on using recommendations to move product, or to improve the overall customer experience, those poor rates of engagement can only be seen as an area forimprovement. Afterall, you cant accomplish
17、 either goal if your tactics are largely being ignored.And therein lies the opportunity for for marketers.Toimprovetheperformanceoftheirrecommendations,marketersneedto approach product recommendations in the samewaythey9re approaching their personalization strategy: as an evolving solution that cons
18、iders the customers historical and in-the-moment data with as much weight as it gives to the brands product catalog.The art of making recommendations, then, should balance the traditional rules-based, product-centric approach of product recommendations with the more customer-centric approach thafs t
19、aken hold in other areas of a brands personalization strategy. Doing so can help put the right product in front ofthe right customer, improving both the short-term impact that product recommendations have been known to deliverand the long-term benefits of improving customer experience.To us, that co
20、mbination of recommendations and personalization feels just right.DOWNLOAD DATASHEETBenchmarksThequarterlylVIonetateBenchmarkReportcontainstheindispensableKPIsyou need to gauge how your performance stacks up, and transform the ideas in this report from concepttoaction.Consultthemetrics within tostar
21、tbuildingaplanto advance your business today.15 Website Visits17 Conversion Rate20 Add-to-Cart Rate23Average Page Views26Average OrderValueWebsite Visits by DeviceTraditionalSmartphoneOtherTabletQ2 2017Q3 201740.25%37.44%48.09%51.56%0.30%0.31%11.35%10.69%Website VisitsbyDevice(US)TraditionalSmartpho
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