Why keep consumers at arms-length? Consumer engagement leads to confidence, conversion and loyalty. Stop guessing, and start knowing!
As set out in this Retail Touchpoints blog, today’s technology-led approach to eCommerce personalisation sees consumer engagement as unnecessary friction. The focus is on guessing what the consumer might be looking for, using any data that is available (segmentation, observed behaviour, etc). But in the end, a guess is a guess…
Enabling the consumer to tell us what their needs are, is much more powerful.
We think it’s time for a change. What do you think?
Traverz is proud to launch a Conversational Commerce revolution by releasing an industry first: Multi-Channel Commerce. This capability brings genuine product discovery to messenger channels. Seamless platform switching between webshop and messenger channels, at will. And a unique capability to initiate consumer journey recovery, to tackle a huge industry problem: browse abandonment.
Imagine you are exploring products in an ecommerce store on your tablet over breakfast, and it is time to leave for work. During your train journey, the store pings you on WhatsApp – would you like to continue exploring here, using your earlier preferences? Yes please! You adjust some of your preferences after seeing more products, and create a shortlist. Later in the day you arrive home and transfer your search to your laptop, make a final selection and complete the purchase. Is this the future?
This is now. Conversational Commerce 2.0 has arrived. Traverz has turned it into a two-way product discovery conversation that naturally flows from device to device, channel to channel.
With just 10-15% of consumers globally adding products to the cart during their shopping journey, browse abandonment (where the consumer abandons the search journey) is a huge driver of lost sales opportunities. Cart abandonment (where the consumer adds items to the cart but does not check out) is a recognised ecommerce problem that has been successfully tackled through email and messenger-based solutions. Yet browse abandonment is an equally large opportunity that largely remains unsolved.
Traverz puts consumer preferences at the centre of the ecommerce experience, and makes the product discovery journey iterative and interactive. These foundation stones enable a natural transition of the consumer journey from messenger channels (such as WhatsApp), to the online ecommerce shop, and back to messenger channel.
Traverz has now released a fully interactive Multi-Channel Commerce solution for ecommerce, featuring Browse Continuation. This provides retailers with unparalleled flexibility to engage with consumers across all channels and platforms.
When browse abandonment is detected, the consumer can be prompted via messenger channels to continue their product discovery journey, in a productive manner that remembers their preferences and enables them to continue exploring. At any time, the consumer can choose at will to switch from shop to channel, and vice versa – to suit their desired device and moment.
The Traverz Multi-channel Commerce journey is a productive and interactive experience – just like the Traverz online shop journey. Patent-pending technology overcomes the limitations of search personalisation by encouraging consumers to communicate their real intent. Now extended across all platforms and channels through the Multi-channel Commerce API.
Ready to leverage Traverz for competitive advantage?
Contact us to supercharge your product search experience!
eCommerce retailers need to face up to an uncomfortable truth, which is that today’s search and discovery mechanisms are based on technical and user experience concepts developed over 20 years ago. They leave consumers frustrated and unengaged, resulting in churn or missed sales opportunities.
In this article in Retail Technology Review, Twan Vollebregt (CEO) looks at why (and how) eCommerce retailers need to rethink their approach to how consumers search for products on their websites.
We think it’s time for a change. What do you think?
eCommerce retailers need to face up to an uncomfortable truth, which is that today’s search and discovery mechanisms are based on technical and user experience concepts developed over 20 years ago. They leave consumers frustrated and unengaged, resulting in churn or missed sales opportunities.
Not true! Is the collective cry. So let’s analyse it and see why (and how) eCommerce retailers need to rethink their approach to how consumers search for products on their websites.
So much change – yet so little change
At the heart of product discovery is search, guided by facets and search-bars. These mechanisms reflect a technical search engine perspective – for good historical reasons. Once upon a time in the early days of the Internet, a software engineer was asked to fetch products from a database. The simplest way to achieve this was to ask the product designer to add some input parameters to the UI, which acted as database filtering parameters. Voila – the concept of ‘filtering’ was born – and it is still with us today, but not because it provides a great user experience.
Yes, both mechanisms have evolved over time. Facets are now being selected dynamically, and may be based on the specific user’s journey. Search bars have become more and more sophisticated, adding capabilities such as autocomplete, spellcheck, faceted search, synonyms, and boosting – as well as providing their results using a more appealing drop-down UI.
Read the full article here.
We are very excited to announce that Traverz will be present with a booth at this year’s Shoptalk Europe in London (6-8 June). Come and find us at Booth G21.
Shoptalk Europe is an important industry event and Traverz attending with a booth signifies an important milestone in our development. Our patented product discovery technology is garnering a lot of positive attention from search platforms, commerce retailers and marketplaces. By attending Shoptalk Europe we are creating an opportunity to engage with a wider audience.
We are looking forward to immersing ourselves with our peers into all things search and retail technology over the two days.
CEO of Traverz, Twan Vollebregt said. “Shoptalk is an incredibly important event and we are very happy to be attending in an official capacity this year. Our technology is creating a lot of interest amongst the established search platforms and retailers and the opportunity to meet face-to-face is invaluable.”
Shoptalk Europe takes place from 6th to 8th June at the ExCel London.
eCommerce merchants are in danger of forgetting the elephant in the room – the consumer. “Control” over the eCommerce search process naturally starts with the merchant but should quickly move to the consumer.
In this article in Multichannel Merchant, Twan Vollebregt (CEO of Traverz) shines a light on why customers are not being engaged on their own shopping journeys. And why the idea of giving up control could in fact be the key to better performance.
We think it’s time for a change. What do you think?
If you take a step back and remove your retail or trading or marketing or technology hat for a moment, doesn’t it seem ludicrous that we spend so much time trying to control what consumers see? Sure, we want to provide the consumer with guidance and support, but that is very different from the relatively prescriptive way that most platforms currently drive the customer journey. We extensively ‘merchandise’ and ‘personalise’ the search results, at significant effort. It is a case of “platform knows best”.
But is all of that effort actually paying off? Maybe our desire to control the customer journey is actually impeding our ability to deliver a better customer experience and achieve higher conversions. The idea of giving up control is not one that is often considered by eCommerce providers, yet we should look at why this could in fact be the key to better performance.
When consumers land on our site, we want to provide a positive initial experience by tailoring the products shown. For example, in summer we display bathing suits more prominently; in winter ski outfits. Modern eCommerce platforms include so-called merchandising capability to allow such boosting or burying of products based on a set of (generally manually-entered) rules. This is useful for the start of the consumer’s search journey, when we want to shape the search results in a direction that shows proven uplift, compared to simply using a random ordering.
On top of merchandising, modern eCommerce platforms use search engines with sophisticated ‘personalisation’ algorithms to tailor the results for the specific consumer. This uses third/second/first-party data and segmentation information (when available) to re-order the search results to be in line with an assumed purchase intent. In essence we try to guess what the consumer is most likely to be interested in.
All of this merchandising and personalisation effort is beneficial at the start of the customer journey. The platform’s efforts to control what the consumer sees leads to more useful results for the consumer and higher conversion for the platform.
But… we should remember that all these efforts are part of a consumer search journey. Merchandising and personalisation assist the search, but what about the consumer and the journey? We are in danger of forgetting the elephant in the room – the consumer – who is on a multi-step journey. At each step the consumer is learning about the available products and developing a better idea of their preferences, and thereby forming or reshaping their purchase intent.
This purchase intent is far more powerful information than pre-defined merchandising rules, or personalisation based on pre-assumed intent. Through consumer feedback, we should let their actual intent become the driving force behind the tailoring of the search results. Merchandising and guess-based personalisation should gradually take a back seat.
‘control’ over the search process therefore naturally starts with the merchant but should quickly move to the consumer. If merchants focus too much on controlling the search, they are actually in grave danger of getting in the consumer’s way – frustrating rather than delighting them.
Search in eCommerce is based on lots of implicit consumer data, allowing technology to build a picture in an attempt to predict what the user wants. There is only one problem. Consumers themselves have a much better understanding of their real purchase intent than technology will ever be able to guess. By ceding control, eCommerce providers can achieve the next level of conversion performance whilst simultaneously providing their consumers with better user experience.
Traverz isn’t just busy building a sector defining product, we are also getting ready to tell everyone about our great technology. Over the next few weeks, we will be telling you all about Traverz.
In March we launched our whole new website and brand. Now we are launching our first campaign video in a series of videos explaining the What, Why, How and Now of Traverz.
First up is the ‘What’ video. A slick 30 second taster into what Traverz is all about. The video sets out the problem that Traverz is solving, using our new visual language, it neatly sums up ‘what’ Traverz is.
View the video below:
Traverz is changing product discovery. For Good.
“The approach to product discovery that Traverz is taking is truly ground-breaking for consumers and platforms alike. No-one else is enabling consumers to share their preferences and interests to influence their discovery journey.” – David Newberry
Traverz is very pleased to announce that David Newberry is joining the Advisory Board with immediate effect. This appointment demonstrates the intent and impact the Traverz solution is gaining in the product discovery market place.
David brings with him incredible experience gathered by working in software innovation his entire career, most recently as Chief Marketing Officer of Attraqt, the product discovery search platform. David’s ability to bring his knowledge to bear for Traverz at this critical juncture of our growth is truly game changing.
David’s career has been spent helping companies understand their customers better and helping create the services and products they need to be successful in the marketplace. With stints at Attraqt, Portrait and Pitney Bowes, David brings a wealth of insight to the role.
Twan Vollebregt, CEO of Traverz commented ‘On a personal note, I could not be more pleased that David has agreed to join Traverz on our incredible journey. He brings so much experience and insight. Traverz is already benefitting from his presence. I am very much looking forward to working closely with him going forward’.
David added ‘I am very happy to have joined Traverz and to be able to support the team at this critical phase of their growth. They really have a great product that is unique in the market place. Current search mechanisms like filter-bars and search-bars are a single-shot technology-driven user experience that leaves little room for discovery, and platforms struggle to truly personalise the user journey. Traverz is a win-win – bringing improved user experience that not only reflects iterative and fuzzy human thinking, but is also super easy to implement because of its MACH architecture, meaning solutions can be deployed quickly to market.’
The team at Traverz are very excited to have David on board and to be further redefining the product discovery and search market together.
Among Airbnb’s “100+ innovations and upgrades” released last month was a number of extensions to its accommodation search mechanism. These are designed to provide renters with an ability to search more flexibly, in a clear effort to counter the impact of ‘over-filtering’.
Airbnb is not alone. Several of the major product search platforms have been on a journey of making their search mechanisms more flexible and user-friendly. In each case, the focus has been different. Some have started to enable user feedback during the search journey, while others (like Airbnb) have tackled the rigidity of their filter system.
As yet, these feel like a somewhat ad-hoc set of adjustments. Each one is for sure improving the User Experience, and it is good to see clear recognition that it is time to move on from the traditional one-shot filter-based search mechanism. However, will the incremental adjustments implemented by players like Airbnb, Google, and Rightmove provide a solid basis for a longer term evolution?
The case of Netflix’s feedback mechanism suggests that it does not. A more holistic conversational and preference-based approach (such as Traverz Conversational Recommendation technology) would provide a superior search foundation that encompasses all these ad-hoc adjustments while enabling a natural super-set of deeper solutions.
Lets review the changes made by three key players: Airbnb, Google, and Rightmove, and wrap up with a look at what went wrong when Netflix sought to incorporate user feedback into its recommendations.
Airbnb – adding ‘flexibility’
Last month, Airbnb released “a whole new way for guests to search on Airbnb”. One of the key changes made was the addition of a new feature called “I’m Flexible”. This allows renters to search for flexible dates, property features, and property types.
Flexible Dates is a pretty cool feature that includes the ability to adjust not only the length of the stay, but also to search for any weekend, week, or month throughout the year. It avoids users having to run several searches for different time periods, in order to see which Airbnb listings are available. For example, you can search for a weekend in either May or June and see the results immediately.
Flexible Matching is designed to help prevent the problem of “over-filtering” that plagues users of classic filtering mechanisms. For example, if you filter locations by search parameters (like Wi-Fi, parking, or hot tub), you may not get so many options to choose from – or indeed none at all. Flexible matching will then show you locations that are just outside of your search parameters – i.e., they are missing one or more of the desired features – and label those features as being missing. As Airbnb stated in its release notes: “This way, you never miss out on a great stay that falls just outside what was specified in a search”.
Images courtesy of Airbnb
The concept of Flexible Matching is absolutely an improvement on the classic filtering approach, where each criteria is rigidly applied. It avoids the frustration of getting few or zero results (and not knowing which criteria is the driver!).
At the same time, it feels like Airbnb has implemented a half-solution. The new matching flexibility is only available for the property features that are listed in Airbnb’s filter UI, which are pre-defined by Airbnb and represent a limited set of common criteria. Users also cannot ‘respond’ right away when they see something that they like in a property listing – they have to tediously go back to the filter menu and see if that feature is indeed available as part of the search criteria available or not. Unless you only have common requirements and are pretty familiar with Airbnb’s range of available criteria, chances are that this will feel like a modest step rather than a large leap forward.
Google image search – recognising that search is iterative
When you enter an image search, Google presents a series of context-specific buttons above the image results list with additional search terms that make your search more specific. So if you search for “dog images”, it adds buttons for “puppy”, “labrador” etc. Once you select one of these, it is displayed in blue background as the first button, with the remaining buttons being adjusted based on the content of the new set of image results.
This is effectively a form of Conversational Recommendation and demonstrates the feedback loop of that search mechanism. The platform presents the user with context-relevant options for feedback, and then reacts upon user feedback with a new set of recommendations.
This works great, and it illustrates the benefits of being able to iteratively build a search. As yet the same functionality is not available for standard searches, even though the rationale and use-case is very much the same.
A logical extension is to not only show feedback buttons at the top of the results list, but to show them alongside each individual result. This approach (as taken by Traverz) encourages and enables the user to respond directly to a specific item rather than having to scroll back to the top of the page and find the relevant tag.
Rightmove – preference based search but only for keywords
Rightmove is the UK’s leading property search portal, and has held a dominant position in that market for very many years. Around 18 months ago, Rightmove introduced a “Keyword Sort” feature that was billed as the way to personalise your property search.
What Keyword Sort adds to the standard search is a mechanism to prioritise properties whose listings contain user-entered key words. Up to 3 of such key words can be entered, and the standard results list (based on the main filter settings) is then ordered to first show properties that meet all keywords, then those that meet one less keyword, etc.
Rightmove demonstrates the power of a preference-based search mechanism, which allows users to truly personalise the recommendation order of the results list. However this capability is available only for the user-entered text. Standard filter bar features such as price, number of bedrooms, and property type do not have such a prioritisation method. And as a result, there is no impact on the ‘over-filtering’ problem – if you search for a detached house in York with max 2 bedrooms and a garden, you get just 1 result (a cottage that clearly is not detached). The user is then left to determine which of those criteria is problematic by manually adjusting them one by one.
Another feature worth noting on Rightmove’s platform is the more-like-this button, labelled as “See similar properties”. Clicking this button sets up a search using the property’s postcode, a search radius within 0.5 miles, a price range bracketing the price of this property, and minimum number of bedrooms equal to that of the selected property.
As is somewhat predictable, the new search criteria tend to result in a very small number of results (if any). This is due to the inflexibility of the filter criteria (location, price, bedrooms) that are set, again illustrating that the more-like-this and key-word features are fighting with an underlying filter system that is inflexible.
Another thing to note is that this “see similar properties” button is only displayed if you open the property directly (e.g. from a Google search). We presume that the idea behind it is to provide a way for you to broaden out to other properties, given that you do not yet have an existing results list to go back to. Clearly the same mechanism of adding/setting search criteria could also be provided when the user was already browsing on Rightmove, but this has not been implemented. Which is a shame because such a more-like-this functionality (which is always present in Traverz) is a great way for users who are browsing a diverse results list to indicate their interest and be given a very relevant set of recommendations.
Netflix – insufficient recommendation data to… recommend
A final example of a major platform that is exploring ways of improving the search mechanism is Netflix, the popular streaming service. Although Netflix already changed its five-star rating system to a thumbs-up / thumbs-down approach several years ago, it is nevertheless instructive to briefly review this change and its consequences. In particular, it is a good example of why feedback mechanisms must provide some meaningful clarity as to the user’s intent – or it becomes counterproductive.
Netflix CEO, Reed Hastings, is famous for having cultivated a culture of candid feedback. “Frequent candid feedback exponentially magnifies the speed and effectiveness of your team”, he says. Yet the same cannot be said for the consumer’s ability to provide input and feedback to the Netflix platform. Netflix has always had a very basic filter and search system, and its dominant UX can largely be summed up as “here is a long list of recommended movies/series – go browse”. Those recommendations are based on your viewing history (including anything it can glean from your platform interactions) and the cohort that you are in. Here is a link to a brief explanation from Netflix.
Originally, Netflix provided a 5-star rating system which acted as a key input into their recommendation algorithms. Several years ago this was modified to a simple thumbs-up / thumbs-down feedback system.
The original 5-star system had some clear issues, with users confusing the purpose of the rating (to provide input for individual recommendations) with an external or objective rating for each title. However, given that the user was only ever able to provide one rating for each title without any nuance regarding various aspects of that title (actors, length, genre, etc), then it is not surprising that moving from a 5-tier to a 2-tier system was likely to be fraught with issues.
And indeed, users complained that the new feedback system was close to meaningless, and did not allow them to express what kind of titles they liked. Many stopped using it after discovering that they were being recommended titles that they did not consider interesting. Of course, it is highly likely that they experienced similar recommendation issues under the 5-star system, but the new approach likely led users to feel a strong reduction in their control over the recommendations.
This highlights the importance of providing users with clear feedback mechanisms that are granular enough to be meaningful, and to be transparent about what elements of a recommended product do and do not match their indicated preferences. As many users have reported, the claim “when you look at your Netflix homepage, our systems have ranked titles in a way that is designed to present the best possible ordering of titles that you may enjoy” feels like rather a bold statement when there is so little meaningful input.
The Age of Conversational Recommendation
Airbnb has clearly recognised that searching for properties requires more modern and flexible mechanisms than the traditional filter system. With the Covid pandemic putting the travel industry under severe pressure, it is looking to innovate around the property search process in new ways. Improved search mechanisms that provide flexibility around dates, property features and destinations are a strong sign that preference-based search is entering the mainstream.
Others, such as Google and Rightmove, have already made moves to provide users with mid-search feedback mechanisms. When taken together, these disparate and often apparently ad-hoc adjustments are pointing to the emergence of preference-based Conversational Recommendation. However in each case there has only been one aspect tackled, and/or the implementation has been limited. This is partly due to the difficulties posed by attempting to ‘layer’ further flexibility and interaction on top of a rigid one-shot filter system. For many of these platforms, the search paradigm will need more substantial change to enable further steps to be taken.
At Traverz we have spent several years developing and fine-tuning this search technology, and we look forward to exploring it further with the above-mentioned platforms and their competitors.
Ready to leverage Traverz for competitive advantage?
Contact us to supercharge your product search experience!
Online property search platforms are now the first place that most of us turn to when we are looking for a new home. Yet the vast number of listings provided by these platforms results in the problem of overchoice, and finding the right property remains far from easy. Traditionally, home-buyers would turn to real-estate agents, who understand the local market and engage with the specifics of the buyer’s requirements. Today, that human element is missing on property search platforms. As a result, many home-buyers report that their online searching and browsing experience is impersonal and unproductive.
In this article we explore how leading platforms can excel in the face of tough competition, by making the shift from delivering mere listings to meaningfully guiding home-buyers in their big life decisions.
Guiding home-buyers with discovery, recommendation, and support
Real estate has seen an increasing shift online and home-buyers in most countries now have one or more well-established, high traffic home-search marketplaces to choose from. In fact, studies reveal that in the UK, a full 98% of potential home-buyers are using an online marketplace as one channel to look for their next home. Through these online platforms, potential buyers have access to a vast number of listings that they can browse from the comfort of their home.
Yet, there is one major challenge that still causes frustration – finding the right property. A US trends report concludes that 56% of people agree that this is the most arduous step in buying a home.
This is not entirely surprising. The first phase of the online revolution delivered breadth, convenience and price transparency. That approach has, however, done little to assist home buyers in finding the right match between the available properties and their requirements. What is largely missing is an element of personalisation into this matching process, which is traditionally one of the key roles that real-estate agents fulfilled. While property search platforms today are able to offer nationwide real-estate coverage and reach potential homebuyers with marketing ingenuity, “finding the right property” still remains a challenge for buyers.
Following the first phase of the online revolution (breadth, convenience and price transparency), we are now moving into the second phase, which is all about discovery, recommendation and support. This is where leading platforms are now focused – delivering a buyer journey experience that brings in many of the elements of human understanding and assistance. Let’s explore the steps involved in meeting today’s home-buyer’s expectations and remaining the destination of choice.
The human dimension
The benefits of an online marketplace are clear – they can offer a huge range of listings that the buyer can look through at their own pace, with the ability to view photos, video, descriptions, floor plans and more. But one of their key plus points is also a negative. The sheer volume of listings puts the buyer in a position where they become overwhelmed and have to do a lot of hard and time-consuming work to make progress on their search before they start seeing the properties that they would be interested in exploring further. The marketplace is only really assisting with a small (though important) aspect – listings. The buyer is left trying to evaluate the pros and cons of each of these properties, understanding the trade-offs they might need to make, with little to no help from the marketplace.
Estate agents have traditionally performed this “helper” role. A good real-estate agent can make the search process easier by supporting the buyer in their key decisions. They ask meaningful questions to understand what a home-buyer wants, pinpointing their preferences and identifying nuances that the home-buyer may not have even thought about. The agent builds comfort and trust because they are able to offer very personalised recommendations based on a deep understanding of the buyer’s preferences.
Providing a more human experience is a natural evolution of property search platforms.
Helping the buyer make smart decisions
The most advanced platforms utilise modern technology to provide the buyer with the type of assistance that an estate agent can offer. In particular, preference-based and AI-driven approaches deliver a significantly improved home-buying experience that enables it to be top of the game.
Let’s look at examples of some common marketplace home buying issues and identify how technology can help to bring the real-estate agent benefits to the online property search:
A family might be looking for a home in a good school district and may be willing to make certain sacrifices (e.g. having a smaller garden) to make this happen. This balancing of preferences is something an estate agent can pick up on during a conversation with the buyer.
Existing online marketplaces, however, will push the buyer into a filter-based search system to try to find this sort of property. The binary nature of a filter system struggles with such nuance. The buyer might be able to set a filter that is tagged as “near a good school”, but from there, they will have to put in the extra time and effort to look through the listings to see where the compromises may lie. They could select “No garden” to narrow their search, but this rules out any properties that are near a good school and do have a garden! This is one of the biggest frustrations for buyers using online marketplaces: The filter-based search system, by its very nature, rules out homes that check a lot of the boxes of the buyer.
This issue can be addressed by changing to a preference based search system. The buyer simply indicates that they want a home near a good school and also would like a garden, but sets their preference for a good school as more important than a garden.
The platform is then able to show them properties near a good school, with a garden if there are any, but without discounting those properties near a good school that are without a garden. They can extend this preference list as much as they want with additional requirements and desires, without the fear of eliminating properties that might be of interest to them. The platform balances the importances of their preferences and pushes the properties that best match their criteria to the top. Offering a system that allows the buyer to take control of their property search in this way leads to greater insights into the pros, cons and trade-offs within the available properties, and enables them to make smart decisions.
Bringing a virtual estate agent experience to the online marketplace
As highlighted above, the conversation between the buyer and a traditional estate agent is ever evolving towards a set of preferences and trade-offs that helps lead to the type of property the buyer is most interested in. As the buyer outlines their needs and wants, the agent will ask follow up questions to further understand their preferences.
AI, NLP and smart UX allow us to introduce a virtual agent that monitors the buyer’s search actions and interacts with them when they have smart suggestions that will help the buyer, much like the estate agent would. By offering pro-active, intelligent ways for the buyer to interact with the marketplace, their search becomes targeted to properties they would be more likely to follow up on.
This level of helpful interaction can be taken a step further. Often, when the buyer is looking through properties with a real-estate agent, they will see something they like, or dislike, that they hadn’t really been thinking about. They can simply say to the agent “Hey, that open plan kitchen is nice” or “I’m not keen on this one, it has on-street parking”. The agent takes a mental note of this before showing them more properties. Online though, if a buyer sees something they like or dislike, they are forced to then go hunting for the relevant filter for that feature (if it’s even available). A smart marketplace will make those features interactive wherever the buyer is focused at that point. They can quickly click the feature and set their preference without interrupting what they are currently doing or, if they want to go into more detail about that feature, open the virtual agent and get helpful suggestions and insights. Offering the ability to add features and get insights as they progress on their journey will encourage the buyer in the exploration phase of their search.
Advances in natural language understanding, AI and UX improve how the user interacts with a forward thinking marketplace to better explore their needs and wants. Of course, a human agent may be hard to beat in some situations, but technology is now at a point where a more insightful, exploratory search experience can be achieved at scale, bridging the gap between the benefits of the online marketplace and the human touch of the local real-estate agent. And this is what the most advanced and dynamic home-listing marketplaces are integrating into their platform – leaving their competition for dead.
Driving conversions & loyalty with more meaningful experiences
Every buyer has unique preferences for their home search and a limited set of strict on/off filters simply doesn’t meet today’s home-buyer expectations. Traverz has developed Conversational Recommendation technology that solves this issue at scale by introducing a preference system and virtual agent driven by cutting edge AI and UX. Traverz not only helps the home-buyer with flexible search capabilities but also saves them time and keeps them engaged in their search. The iterative, personalised and human-like interactivity allows home-buyers to make informed decisions that they feel happy about, building marketplace loyalty and greatly increasing clicks on the property contact forms.
Are you ready to leave your competitors behind?
Contact us to find out how Traverz can supercharge your property search experience!
“It’s been the first time in a long time that I’ve seen a startup put together those solutions in the right way – making it simple, but very effective. And that’s what drew me to the guys.”
Barry Smyth, esteemed professor and researcher in the field of Conversational Recommender systems, joined the Traverz advisory board in October. Agnayee, from our marketing team, sat down with Barry to learn more about his rich background in academia and business, what drew him to Traverz, and how he sees the future of online search transforming in the coming years. Read the full interview below.
Barry, I’d like to begin our session by asking you what you believe are some of the biggest challenges facing online stores and online retailers today?
It’s bringing the experience we’ve come to expect and like from a physical store, into an online setting. So you know, sometimes it’s just very convenient to use an online service, and you don’t care that you’re not getting the level of personal service that you might get in another store in the real world. But increasingly, then, as more and more of our transactions take place online, there is an increasing subset of those transactions where we do need help, and where it is useful to get the informed wisdom and have a well informed sales assistant, for example, to help us. By and large, we’re not getting that at the moment. And that makes our online experiences less appealing. It’s okay when you’re buying a book or music. But if you move up to larger strategic purchases, then then we need more help.
Would you say the biggest challenge in not being able to achieve this is because technologically, we’re not there yet? Because technologically stores don’t have solutions to implement that can deliver this level of assistance?
I think that technologically, we’re closer than we think. But I think a lot of stores are still using e-commerce platforms that are over a decade old – they’ve focused on inventory and payment systems and maybe a little bit of recommendation to try and upsell products, but they haven’t focused on the user experience. And that’s not because the technology doesn’t exist, it’s because they have been busy. And so I think what we’ll find now is that increasingly, stores will start to take advantage of technology that is out there, such as the technology that Traverz has developed, which will allow them to move to that next level.
Got it. Can you think of any brand at all that’s been able to get close and has maybe invested in technology to bridge that gap?
It’s a difficult one. I’m struggling to think of any online experience that goes far enough these days. The big players like Amazon certainly do a good job. But the onus is still very much on the end user or shopper, for them to find what they are looking for. And they have to do a lot of the hard work when they want to find out answers to questions! To be fair, Amazon, and TripAdvisor, and others, they provide you with reviews. They sometimes even provide questions and answers that I can review. But it’s still work on my part. I think that they should know the sort of questions that are relevant to me. And they probably do know the questions that are relevant, and they could prioritise those, but they haven’t yet. But certainly those big players Amazon, TripAdvisor and booking.com, they’re getting close, but they still have some distance to go.
I think I just saw recently that Amazon added an “Alexa join the conversation” feature and are trying to do something where Alexa can be more conversational. I don’t know if you’ve seen that yet.
I haven’t actually seen that, but I’d be interested to take a look at that. That must be quite new, and I think it’s a good start. My instinct is that it will probably still retro caged what is largely available online – “tell me the price of this”, “what is its average review rating”, etc – rather than more insightful answers. The way I look at it is that oftentimes, especially when we’re making a new type of purchase, part of what we’re doing online is educating ourselves. So we don’t really have a fully formed picture of what we’re looking for, and we use an e-commerce site as part of our education. It would be nice for the service to partner with us in that.
And that segues nicely into the next question. So, we know conversational recommendation could be improved, and we can start bringing in new contextual experiences. How else can developments in AI, ML, smart connected devices lead to an evolution? Do you think we are lagging behind when it comes to the product search experience?
I think there’s certainly a lag, certainly it takes time for new innovations to find their way into what are now reasonably mature markets. And no doubt you’ll find that some stores don’t yet recognise that it’s broken, so they don’t want to fix something that they don’t think is broken.
The sort of thing that I expect to see more and more of in the next few years is Voice. As you said, Alexa now has a “shopping skill” as the Alexa people call it. But I think the next step after that will be to move beyond superficial interactions. For a long time, I’ve been able to search for movies using my voice. But what if I try to form complicated queries with my voice – that requires a fundamentally different shopping engine? You need to be able to provide users with new ways to express their queries, rather than just saying “show me movies that are recommended to me tonight”. I want to be able to say “show me movies that are like this one, but maybe a little more lighthearted, that are less than 90 minutes in duration”. I want to form those complicated queries.
That’s interesting. Do you think adopting new technology can be overwhelming for an e-commerce brand, besides large players like Amazon? Could they be thinking “Oh, these are really big problems to solve”? Or are they actually trying to already solve these problems based on your experience?
Lots of the big players are actively trying to solve these problems in a way. My instinct is that it’s not so much that it’s overwhelming – because certainly, for those organisations that have been in retail for many, many decades, long before the internet, these were the sort of interactions that they understood were important to their end users. So I think it should be quite natural for them to think about and engage their customers in these deeper, more meaningful conversations. I think what’s hard is ramping up the technology putting the skillsets in place that can take advantage of that right now. It’s the larger companies that have their own machine learning and natural language processing divisions that can do a good job. And it takes a few years for the technology to be commoditized. I’m seeing now that the technology that Traverz has developed is the first step to commoditising that, so that other e-commerce players can take advantage of it even though they don’t have the in-house skills to build a system like that.
What are the factors that will play a dominant role in defining the search experience for evolving online consumers? As you said, voice is important, and as we can see, more and more people are using voice as preferred communication method. Then there’s personalization, which may manifest in many different forms. What are some other things that say Gen Z and the new age of consumers expect? I read that while Gen Z wants personalized experiences, there’s that privacy concern. It is a thin line where they want you to know what they want and have all information on them. But then they also want you to be careful about privacy and data security. So, what are some of these nuances that we need to be mindful of?
I think that’s an important thing to get right, that balance between privacy and personalization. So on the one hand, I want the stores I trust to pay attention to what they know about me and to use that information to serve me well. On the other hand, I only want that information available to the brands that I trust. So I think we’re going to see how that will play out over the coming years, because there’s increased regulation, which is going to insist that more care is taken when it comes to user data. But I think the other side of this is that these stores will have to understand their products better. I think that’s lost in most e-commerce settings at the moment. They all focus on here’s a picture of the product, here’s its price. And here’s some reviews for it. And the stores don’t really understand what that product is. Now, all the information is contained within the data that they have on that product. But they haven’t developed the engine that allows them to recognise that there are some important features of this product that will matter to one customer, and that there are different features that matter to a different customer. So now, I want to prioritise those features, and choose. Surfacing that type of deeper product information, and making it available to the end user and allowing the product search to be personalised based on what you’ve learned about the user – all of that is part and parcel of what’s going to be important in the next experience. It will make shopping appear a lot more fluid and a lot more responsive. I think that there will be a sort of magic to the shopping engines that we will see. Because they will be much better able to adapt to our needs, even within a single session, they will realise Oh, look, they seem to be heading in this direction. Let me get it out of them. And let me present them with some answers.
We see that consumers are saying that they want that in-store human experience. Do you think we can get to a stage where they’re happy enough with just the online experience and wouldn’t crave the human experience anymore?
I think the pandemic has suggested that might actually be the case – people have probably been pleasantly surprised at how they’ve been able to survive using online stores. I suspect clothes buying has skyrocketed. And people have been surprised at how good that has been. The physical stores are still there, especially your local stores. But I suspect a lot of it will move online, yes.
Now, lets dive into the technicalities. We understand that conversational recommendation is going play a key role in product search. Are there similar new technologies that can improve the product search experience?
I can’t talk too much about the Traverz engine and the technology, but I think in general AI and machine learning are going to be the big enabler there. The deep ability of systems to learn about you and to predict what you’re going to want before you might even though it yourself, that’s the big weigh-in here. Increasingly, you’re going to find a kind of hyper personalisation, that allows these stores to reconfigure themselves for each individual user, and much more than we see today.
So prediction is going to be a big thing, being able to predict beforehand what people might want.
I think so. And, you know, we’ve seen examples of that for many years, Amazon does this routinely. But I think it will be a lot deeper. Other interesting innovations that are relevant to niche product spaces are things like clothing and automatically determining the size of your clothes using your camera. For example, there are now services that allow you to do that, or at least experiment with that.
You’re saying that companies like Amazon, they’re able to predict what you want, and that’s the goal. Is this really possible for everyone? I mean, Amazon can do it, because they are serving us across so many different verticals, we’re buying, maybe my groceries, my household items, gifting items, all of that. But for a standalone e-commerce store that is serving one vertical, is it possible to build that kind of loyalty? And because, you know, you have so many options as a consumer? So how do you then, from that competitive standpoint, how do you build that loyalty?
I think that’s a good question. In order to personalise, you need to know a lot about your customers – they have to be returning customers, and they have to trust you. And so there has to be that level of loyalty. In order to build that level of loyalty, you need to focus on the experience, and you need to offer them an experience that they can’t get somewhere else. Many of these technologies we’re talking about, begin by offering consumers a better experience even before they’ve learned much about them. One of the things that has impressed me about the Traverz technology is that it can support a brand new user, a user that you’re seeing for the first time. And so there are benefits even before you know a lot about that user.
So to answer your question, I think that it will be possible to attract users to increase loyalty and then to begin to personalise even within niche market verticals.
Okay, that makes a lot of sense. From your research and published papers, is there an interesting finding that has stuck with you and might be relevant for, say, our readers and for the product search experience, something that you can share from your past work?
One of the things I’m best known for in this space, I suppose, is the importance of diversity. So in the first generation of recommender systems, we very much focused on finding the best products for the user. So you would identify a set of relevant products and you would give them to the user as recommendations. And that worked well. Then we realized that a lot of these products could be very like each other. So there’s no point in recommending 10 products that are all variations on the same theme, you would be better off using the available space to recommend maybe four or five different categories of products. So that was a very novel way of looking at things in the recommender space. My research group was among the first to describe algorithms for how you could increase the diversity of recommendations, while still preserving the relevance to the end user. And since then, most recommender researchers followed suit and diversity is today an important constraint on recommendation.
As an example, let’s say you’re talking about TripAdvisor and their recommendation list. They might all be different rooms in the same hotel. Instead, maybe you’d be better off recommending three rooms in different hotels, to give them an option. So in that context, diversity there is not only about how relevant the product is to the user, but also about similarities to other things that could be recommended.
Understood. So we have covered technology and product, and your experience in this domain. Now we’d also like to understand your thoughts and vision as a leader. How would you describe yourself as a leader in just one word?
I’d say vision, because it’s an easy first word. I tend to have a good instinct about where things are going. So I tend, in the past, to have seen opportunities that are just around the corner. Like the importance of diversity, for instance, and recommendation. And so I tend to have a good foresight for what’s happening.
Other than innovation, having the right technology and analytical skills, what are some other important elements that can drive competitiveness of technology? When we are trying to bring something so disruptive into the market, what are some things that we have to have as an organisation?
I think it can be easy to get caught up in the algorithmic details. Their importance and having the technology is important, but how you put it all together is really, really important. Like the ability to present a very intuitive experience to the user, that’s what people would have called the user interface and in the past, but the general user experience has to be simple and intuitive. So there are lots of examples of really good technologies, but they haven’t taken off because they’ve been presented in the wrong way, or it’s been too complicated to use. Instead the technology should be almost invisible to the end user, they shouldn’t have to worry about what’s happening behind the scenes. And they should just be able to go about their business in a way that’s very natural to them.
I was just wondering if you can share some highlights from your experiences at ChangingWorlds and Haystacks?
I suppose looking at those companies and other companies that I have been involved in, one of the experiences that I’ve had is, it’s less about the technology, and much more about how you put the technology to use. Again, it’s back to that last point I made about how you put these things together, to figure out the right way to present it to the user. Oftentimes, you’ll find that innovative companies are too early, they’re ahead of their time. So in my experience, even though you know how to do something, it might not be the right time to do that. And you may have to develop a plan for how you can incrementally roll out your technology, rather than just trying to give too much to the customer or too much to your clients too early on. And so we saw a lot of that in ChangingWorlds, we were starting a little bit ahead of time when there was no mobile on the internet, for example. And we were developing mobile internet solutions. So many of the techniques that we developed wouldn’t really find a home for two or three years from when we started. So we have to figure out what is appropriate, given the current state of the art at a given time, and work from there. So I think you have to resist the urge to try and show everything all at once.
And did you have a process for doing this kind sanity check? To understand what do we take to the user – what are they ready for?
From my experience, it’s about working closely with a couple of early trusted customers. At ChangingWorlds it was very much a b2b play, wo we were working with enterprise clients. And it’s similar in many e-commerce scenarios, you’re working with large online retailers. So it’s about having a trusted customer like that, that you can go to that will be willing to partner with you as you try to develop these new technologies and figure out the right way to roll them out. And not every early client is a good fit for that sort of task. But if you have one or two of those clients, they can be really valuable.
Can you share a message for the team at Traverz?
Oh, let me see. Well, I think most one of the most important qualities in my experience is to stick with it. Stick with it. There are lots of bumps along the road, and you need to be determined and you need to trust in your instincts. I think trusting in your own instincts and having the belief to stay the course that those instincts take you.
That’s great. Any final thoughts or anything that you want to share about joining the Traverz team?
No, I think that covered everything very well. I guess I would just say that the reason that I was drawn to the team is that I liked what I saw, I could recognise some of the challenges that I felt existed in the e-commerce space, and some really interesting solutions to those challenges. And it’s been the first time in a long time that I’ve seen a startup put together those solutions in the right way, you know, making it simple, but very effective. And that’s what drew me to the guys. They were able to demonstrate how they thought about the space and where that led them. So I think they’re on the right track.