Webinar: AI and Machine Learning for Omnichannel Retailers
•Download as PPTX, PDF•
1 like•1,623 views
Retailers pursuing the Omnichannel strategy now must deploy more advanced features to attract, retain, and service their most loyal customers. Advanced search, AI, and Machine Learning capabilities are core to helping retailers gain a competitive advantage. This webinar will explore: - Common uses of AI & ML in retail and future trends - How to capture user intent - Different types of recommendations and how they map to the customer journey - Taking AI a step further for your retail site
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Webinar: AI and Machine Learning for Omnichannel Retailers
Similar to Webinar: AI and Machine Learning for Omnichannel Retailers (20)
More from Lucidworks
More from Lucidworks (20)
Recently uploaded
Recently uploaded (20)
Webinar: AI and Machine Learning for Omnichannel Retailers
- 1. AI and Machine Learning for Omnichannel Retailers A search-driven approach
- 2. Based in San Francisco Offices in Bangalore, Bangkok, New York City, Raleigh, Munich Over 300 customers across the Fortune 1000 Fusion, an AI driven on premise solution Cloud, an end-to-end search app to search analytics solution 10 Leading provider of advanced relevant search for over 10 years Focused on ease of implementation and advanced artificial intelligence For highly personalized search and data discovery experiences
- 3. Site Search Site Search in a matter of minutes, ready to embed wherever you need it. Search all of your site’s content in one place. Ai-powered personalization Secure JSON rest Push API Embeddable UI code for search bar and results Enhanced Web Crawler for metadata extraction
- 4. Site Search
- 5. Enterprise Search Secure data ingestion Customizable search in a hosted environment Built-in Intelligence leveraging Fusion AI Connectors tailored for Enterprise Search
- 8. • Connects to hundreds of datasources to integrate all your data • Robust parsing framework to seamlessly ingest all your document types • Point and click Indexing configuration and iterative simulation of results for full control over your ETL process • Your security model enforced end-to-end from ingest to search across your different datasources
- 9. • Relevancy tuning: Point-and-click query pipeline configuration allow fine-grained control of results. • Machine-driven relevancy: Signals aggregation learn and automatically tune relevancy and drive recommendations out of the box . • Powerful pipeline stages: Customize fields, stages, synonyms, boosts, facets, machine learning models, your own scripted behavior, and dozens of other powerful search stages. • Turnkey search UI (AppStudio): Build a sophisticated end-to-end search application in just hours.
- 10. AI and Machine Learning for Omnichannel Retailers A search-driven approach
- 13. Among customers who lived farther away and had previously shopped only online, the online coupon generated twice as much profit as among the control group, and the flexible coupon increased profits by 800%. But when distant shoppers who’d previously bought only in stores were given online-only coupons, profits from them fell by 51%. In other words, encouraging online customers to visit a store increased profits, but incentivizing in-store customers to shop online decreased them. • People still shop in stores if they are close by • Online customers are more price conscious • In-store customers are more easily influenced https://hbr.org/2016/07/how-to-make-the-most-of-omnichannel-retailing — Omnichannel Couponing,” by Fue Zeng, Xueming Luo, Yifan Dou, and Yuchi Zhang The Research
- 14. The Moment of Truth • FMOT – You see the product • SMOT – You buy the product • TMOT – You love (and advocate for the product) • ZMOT – You actually research everything online first https://en.wikipedia.org/wiki/Moment_of_truth_(marketing)
- 17. “if retailers want to slow Amazon’s dominance, then they must integrate technology that creates frictionless experiences for their customers across channels. Amazon has a commanding lead, but retailer personalization and brand experiences can power a counterattack.” • The CDJ is still a very traditional view • 87% of consumers prefer to buy from brands that personalize the best • Most customers prefer to be recognized across channelshttps://www.forbes.com/sites/gregsa tell/2015/10/12/marketers-need-to- drastically-rethink-the-customer- decision-journey/#2e836737260a -Joelle Kaufman, head of marketing and partnerships for BloomReach. — https://www.bloomreach.com/en/resources/ blogs/2015/10/amazon-commands-nearly- half-of-consumers-first-product-search.html The Research
- 19. Key tools for personalization • Signal Capture • Better Search Results • Personalized Recommendations • Link in-store shopper data to online experiences • Link online experiences to in-store experiences
- 20. How do we Personalize? They’re COMING!!
- 22. We can beat them!
- 23. Keys to Success • Better, more personalized experiences • Better Service • Niche • We’re right there…
- 24. -160 million annual website visits -2.61 million square foot distribution space -$2+ billion in annual revenue The products we make and sell are extremely high quality. When they come to our website, they have the same expectation. -Dale Smith, Lead Engineer https://youtu.be/a12beliwb3U?t=1s
- 25. We saw a 50-60% increase in conversions just from turning on Fusion” “ Jacob Wagner, Director of IT - Content, Bluestem Brands — -160 million annual website visits -2.61 million square foot distribution space -$2+ billion in annual revenue
- 26. Signal Capture Sources • Queries • Clicks • Add to cart • Returns • Purchases
- 27. AI Doesn’t matter for “most” of our customers
- 28. AI matters for our most important customers • The ones who can be influenced by something more than just price • The ones who will come to YOU first • The ones that buy the most
- 29. Signals online and off • Phone calls • Mobile App uses • Where you go in the store
- 30. Most users don’t scroll https://www.theleverageway.com/blog/how-far-down-the-search-engine-results-page-will-most-people- go/
- 31. In fact they don’t look past the first result https://conversionxl.com/blog/how-people-view-search-results/
- 32. Signal Boosting • Everyone who searches on “Cat” then clicks the second cat “tabby” votes for it to be boosted • If it gets clicked often enough, it gets boosted above the first result • Our search learns and gets better
- 33. Three steps and I give up • Each step on a web-task increases the chance a user gives up • If a user hits “back” the chance of them giving up increases dramatically • What if their first query doesn’t return what they want? https://www-cs.stanford.edu/~jure/pubs/navigation-wsdm14.pdf
- 35. Queries for Query • Influence the user to make a different search if they didn’t find it • Prevent a back click • Information for your search engineers
- 36. • Straight search on the words from the item’s description • Encourage users who found the wrong color, size or brand to click on something “really similar”
- 37. Recommendations • Influence the user • Use signal data • When other item did users click on? (Items for Item) • What items did similar users click on? (Items for User) • Which users might want this item? (Users for Item)
- 38. • Given a specific item: • What other items did users click on who clicked on that item? • Not just clicks. Use purchases, cart ads, returns, whatever. Items for Item
- 39. • Given a user • Hyperpersonalized towards user’s history • This is good for a user’s home screen • Drives personalized search, biased towards a user’s likely purchases Items for User
- 40. Users for Item • Given an Item, what users are likely to be interested in it? • Based on users who have similar click patterns • Useful for promotions
- 41. Document and Query Classification • Automatically categorize and tag items with the right departments (food, electronics, produce, etc) • Help users filter on the right department or category
- 42. Advanced • Head-n-Tail analysis • Query Expansion – did you mean • Clustering, deep learning and beyond
- 43. Bottom Line • AI and search are the key to an omnichannel strategy • We can win with Signals, better search results and Machine Learning tools like Recommenders, Query Classification, and Head-n-Tail analysis • Sources of behavior are everywhere • Customers want us to know them and personalize to their needs across all of our channels • Good search is good service
- 44. Thank You • Increase Retail Sales with Recommendations - https://lucidworks.com/2018/01/24/using-recommendations-to-increase- retail-sales/ • Omnichannel Retail Store of the Future - https://lucidworks.com/2018/01/29/omnichannel-retail-store-of-the-future/ • Keeping Retail sites up 24x7x365 – https://lucidworks.com/2018/01/22/keeping-your-retail-site-up-24x7x365/ • Providing Amazon-like experiences with Fusion - https://lucidworks.com/ebook/amazon-like-experiences-with-fusion/
Editor's Notes
- This is an executive to team-lead level talk. We’re going to go into the business of retail and how AI technologies can be used to better serve customers and drive both online and offline conversions. We’ll talk about where this fits in to an omni-channel strategy and how we as retailers can continue to delight customers and adapt to an environment of technological change.
- As mentioned I’m Andrew Oliver and this is Jake Mannix from Lucidworks. We’re going to delve into AI and Machine Learning for Omnichannel Retailers in just a moment. First I’d like to talk a little bit about Lucidworks, and our products. Lucidworks is San Francisco company focused on advanced search. We think that search should be easy to implement and maintain but also highly personalized. Our company has offices, employees and customers all over the world. We produce two main product lines. Fusion, a platform for creating search applications (which you can host yourself) and Cloud a solution built on fusion which allows you to just connect your data and WYSIWIG out an application.
- I mentioned our cloud product. It has two applications about to launch for public consumption. The first is site search. This allows you to connect your web resources for public search.
- Site search provides customers with personalized search and recommendations. This is extremely easy to get started and connect and because it is a Software as a Service solution, you don’t have to do any maintenance or system level tuning.
- Launching later in the year is our Enterprise Search solution which connects to your companies internal resources. It comes with all of the connectors you need to provide your employees with a cloud-based internal search solution to help them be more productive.
- In many companies losing your bookmarks folder is a career limiting event. There are like 12 places to search but no place to put together the information people need. Lucidworks Cloud based Enterprise search App allows them to have a single, attractive, AI-powered personalized search interface that actually finds things.
- All of this is powered by our flagship platform, Lucidworks Fusion.
- You can host Fusion on premise or in the cloud. It can pull from hundreds of data source types from Oracle, DB2 and SQL Server to Box, sharepoint and salesforce. Heck, reddit uses it to pull from and search…well...reddit. This is a highly flexible, secure and extensible search solution to develop any search application imaginable.
- Included in fusion is our engine for highly relevant search, AI and a turnkey solution for quickly developing search applications as well as analyzing search patterns and customers. Gone are the days of months of development just to make the UI. Gone are the days of figuring out what search analytics to pull. Fusion makes delivering highly personal search experiences easy to deploy and maintain.
- So let’s get to the topic at hand.
- There used to be just retail and mail order. Then there was retail and online retail. Then retailers up websites and ran those as largely separate businesses. Get this photo from istock https://www.istockphoto.com/photo/female-manager-working-on-tablet-in-factory-gm527045000-92709157
- Buy this https://www.istockphoto.com/ie/photo/hand-holding-network-marketing-connection-omni-channel-gm513311532-87537919 The next wave was Omnichannel where we would hit multiple channels for customers.
- However, research and experience has shown us that the naieve form of omnichannel, encouraging your in-store customers to go online and your online customers to go in-store isn’t necessarily a great idea. For the most part customers who live close to a store tend to shop in-store. Customers who live further away tend to shop online. Research has shown that when we encourage our online customers to come into our stores, profits rise. Research has also shown that when we encourage instore customers to shop online, profits tend to fall. Online customers are more price conscious and in-store customers are more easily influenced to buy more and often make impulse purchases.
- Proctor and Gamble came up with the concept of the Moment of truth. In short you come into contact with a product or brand and then you see it in the store and buy it and then you hopefully like it and even advocate for it. That was nearly 20 years ago. Google responded a few years back after mobile and the web changed consumer behavior with the idea of the Zero Moment of Truth. The zero moment of truth is where you research everything on the Internet first.
- The Zero moment of truth is really all about search.
- In response to all of this McKinsey developed what they call the Customer Decision Journey. Something makes someone realize they want to buy a product, they have an initial set of brands they consider, they go search on them, buy things and then have a good or bad experience and hopefully buy from the same store or brand again.
- But the Customer Decision Journey is still a kind of traditional view. Its mainly advertising content moved online, you buy something or from someone and hopefully it went well so you keep coming back. In reality this is probably still behind the times of consumer behavior. People like personalized experiences. They us to recognize them in the store, online, on their phone and in their marketing materials.
- So how do we personalize to customers?
- The first step in personalization is capturing behavior. Customers tell us something every time they come to our website or search on something absolutely. They also tell us something anytime they come into our store, leave our store (empty handed or otherwise) and anytime they make a purchase. We need to capture these customer events called “signals” use them to make better recommendations and to link both online and instore experiences. It isn’t an expectation to have a personal shopper follow you around a store but the personal shopper should be embedded in the store itself as well as the website and any communications we send.
- So while we have all of the tools to make a more personal experience, we need to be aware that we’re not the only ones thinking about this and the enemy is thinking about our next move. The empire will strike back. While the jedi returns, the first order is relentless.
- If you needed validation of Omnichannel or that having a store near the customer is a key advantage, Amazon just bought whole foods. Meanwhile, they’ve created an automated store. Everyone laughed, it didn’t really work. So they created another one, no RFID tags, just cameras. This time it actually works. There is only one person in the store to check IDs for booze.
- This may work in some places but if I’m going to pay whole foods prices, I want to be able to ask a question and help finding something. Amazon beat us with better search and computer generated recommendations based on our purchases. We can fight back with better search, better service and a store that is closer.
- When I think of my buying patterns I think about REI. I’ll buy a lot from Amazon. I once thought I wouldn’t buy clothes but I tend to buy the same style jeans every time, I don’t really need to try them on, the closest store is at the mall, I buy jeans from Amazon. However, when I buy cycling gear, I have questions, as a MAMIL (middle aged man in lycra) I want to make sure my Lycra isn’t too scratchy.
- I go to REI. Someone is there to help me. I often search their website first. I tend to buy different things from them online than I buy in the store. Amazon will baffle me with hundreds of choices at varying degrees of quality. The reviews are either glowing or one star and barely readable. REI’s site has reviews that tell me something. Moreover, the organization and search is much better for my needs whenever I want camping or cycling gear. For myself if I’m buying cycling shorts I go in the store, I’ll buy bike tubes often in the store but I’ll often buy camelback bags or tubes and such online. If I don’t know what I need I ask. REI came to our last conference and spoke about how they used Fusion to deliver this experience.
- While today we’re talking about Omnichannel, these technologies are also very helpful for online retailers. Bluestem brands who you may know as Fingerhut, is using Fusion and saw a major benefit. Search is just key to modern retail and they’ve seen that in their Fusion adoption.
- So what are sources of customer signals which go into this search? When a customer queries something, what do they click on? What things do they add to their cart? What do they return? What do they buy?
- So if you look at our analytics I’m sure you’ve noticed that anyone coming in from a price comparison tool tends to just buy that thing and leave and only if our price is lowest. A large number of people coming in from google do much the same thing. AI won’t matter for *most* of those people. However, what about the people who DO come back, over and over again. If we make those people happy and optimize for our most loyal customers, that tends to make a bigger difference. Racing to the lowest price may not be our best path to profitability.
- Buy this https://media.istockphoto.com/photos/target-audience-picture-id157606359 The people that come back over and over again are the people we have the best information on. We “know” these customers and can make suggestions. We can influence them. While our AI tools can make our overall site search better, it will make it the best for our best customers.
- Our best personalized shopper strategy is to connect not just our web signals but all of the signals a customer gives. This includes phone calls, mobile, some stores are now even able to track where a customer goes in the store.
- For our website it is critical that our best results are on the first page. Most users wont’ scroll. If you look at this graph, most people click on one of the first 5 results. Anything after that might as well not be on the page.
- In fact if you look at an eyeball tracking heat map, most people don’t even look past the first result…at all. So our search needs to ‘self-heal’ if it isn’t working. If our only clicks are on the second result then it should definitely be our first result. If we’ve got more than a few product we can’t do this manually quickly enough. This needs to “just work”
- Fusion has this built in by default. You capture your signals from your search application and it automatically boosts the results that get the clicks. Our best result might not be the first one (probably is because our search is good), but it will be very soon after.
- So you have some great search result but customers don’t always make great queries or know exactly what they want. However, if they don’t find it within 3 clicks they will probably never find it. The research says that each step increases the chance they give up. If they hit the back button they are more than 3x as likely to give up. We don’t want them to ever hit the back button.
- One way we can help them is tell them what they might have queried on instead. Google Trends is an example of this. If I search trends.google.com on retail, I get related queries and topics.
- Fusion lets us have this on our site related to our products as well. If a customer searches on one thing we can tell them other things that they might have searched instead. That’s just one more way to keep them moving forward and never hit the back button. IT is also great information for our search engineers. If a large number of people click on an alternative query after searching on something, maybe that should just be the result to the first query.
- Fusion can also recommend an item “more like this” when a customer clicks on an item’s detail page. Maybe I wanted a red one or a different size or the EXACT SAME THING but I had a bad experience from the first brand I clicked on. This is a straight up similarity search on the item’s description. Not AI but super useful.
- The point of our AI recommendations is to influence the user. We use their signal data in order to understand their preferences. Based on this we can recommend items based on what other users clicked on.
- When you look at an item, and it isn’t what you want, Fusion can recommend items based on what other users who clicked on that item either clicked on, added to their cart or purchased (or all of the above).
- Most visitors to our site don’t even click on anything. The Internet’s default bounce rate is like 70% on the low side. So for our best customers we want to show them what they probably want anyhow. Fusion can recommend items for a user based on similar users and their past behavior.
- I have absolutely no desire for an Amazon Echo. I know that makes me different. However, Amazon spams me with it relentlessly. I realize they want to sell those things, but surely after awhile they realize that not only am I not interested but I don’t open their promotions that mention the echo and there are probably a lot of other people like me that don’t find utility in the echo. Why don’t they know me? Or rather why haven’t they connected their promotions to a recommender like Fusion’s Users for Items. When I have an item I want to promote, which users are most likely to be interested in that item? Fusion knows.
- Customers aren’t always specific. Frequently items fit into multiple categories. Document classification automates this for our inventory but Query Classification also to helps provide better results for our customers.
- Some use case and bla bla and head n tail
- AI and search are central to an omni-channel strategy or modern retail in general. We can win customer relationships with signals, better search results and machine learning tools. Sources of behavior are everywhere. Good search is just good service.
- Thank you so much for attending and letting me share some of the research that has been influencing me for the past few years. If you liked this presentation then you might enjoy some of the additional resources on our website. We’d also be happy to answer any questions you may have.