[Music] thanks for joining us today i'm justin sears i run marketing at lucidworks and i'm joined today by brandon purcell principal analyst at forrester we're going to make the case for why personalization beats segmentation to create the next best experience to kick it off i'm going to hand it over to brandon to introduce himself and share his perspective on personalization great thanks justin and thanks to you and the lucid works team for having me and to everybody who's attending today i'm really excited to talk to you about personalization today i am a principal analyst at forrester research and i sit on our customer insights team so i help companies take their massive amounts of data and distill that data into insights help them win serve and retain customers if there's one thing that is different that has happened in the last 10 years or so from previous history of business is that we are firmly in the age of the customer and by this i mean that increasingly empowered customers have been able to disrupt entire industries if you think about uber and the transportation industry or you think about airbnb in the hospitality industry or facebook and democracy as we know it there's a little joke but um the onus is on us as as businesses to better understand our customers and anticipate their needs um the good news is that they're giving us a lot of bread crumbs right to better understand them we've been accumulating data since the middle of the 20th century financial data sales data and product data in the age of information in the 90s we saw the rise of um powerhouses like google and amazon with globally connected pcs and we had an explosion of data then with transactional data in first and third party customer data but today there's been an exponential increase in the amount of data we have whether it be social media data behavioral data on our customers mobile data or even data from the internet of things connected devices the customer insights professional who i write my research for is charged with finding these golden nuggets that are buried in data that are useful to win serve and retain these increasingly empowered customers especially as industries become increasingly commoditized and you have to differentiate through offering a more seamless convenient customer experience now in theory uh unearthing these insights should be pretty easy customers interact and transact with us and that creates a data footprint we apply analytics to that data to under these insights these golden nuggets and then this isn't an academic process we need to take action based on those insights to inform the customer experience so that we can actually win server maintained customers and finally this this insights life cycle is in fact the cycle because we need to learn from the efficacy of those actions so we saw that justin was likely to churn and we sent him a retention incentive did he or did he not stay with us that's important data to continuously optimize this model now unfortunately as you can see from the title of this slide this life cycle is broken companies struggle to turn data into insights insights into action and they especially struggle to close the loop on that so enter artificial intelligence ai refers to the theory and capabilities that strive to mimic human intelligence through experience and learning now i realize that many people have their own definition of ai this is forester's definition and there are two key facets to this definition the first piece is some sort of mimicry of a human faculty and you can see on the right here there are a.i technologies at this point everybody knows ai is not a single technology there are multiple different component technologies some of them can sense the world around them others can reason upon what's been fenced or think and then others actually can take action so that's one piece mimicking some human faculty the other piece and the piece that separates today's ai from your grandfather's ai is machine learning so ai today most ai today is using machine learning at its core to learn from data the best way to optimize processes now if this graphic looks a bit familiar it's because it actually mirrors that insights life cycle i just introduced you right um sensing the world around us i mean that is data one of the beauties of ai is that it makes data just data unstructured data is now data structured data has always been data um we think about that data to create insights and then of course we take action so the promise of ai and the reason that that i believe it's so hyped in the market is because it could very well automate and optimize this insights life cycle so that you're continually learning and getting better and better at retaining customers increasing lifetime value selling more to customers in the world of customer analytics that i cover at forester i like to talk about how customer analytics in theory should get the right message to the right customer at the right time but the truth is is that until recently customer analytics has really focused on that right customer piece maybe the right timepiece but the right message the right experience has been lacking and ai can help us to identify curate and deliver that right message or write experience so what are companies doing today to try to satisfy the demands of these increasingly empowered customers well most companies are using segmentation segmentation has been around forever i received more inquiries at forrester about segmentation than anything else more than ai more than any sort of predictive analytics uh our clients are interested in segmentation and it's it's too bad because it's the best of times for personalization especially given all of the advances in ai and all the data we have and the worst of times for segmentation and i realize that's a somewhat provocative statement so let me tell you why that is look at all of these different millennials we have 12 different millennials here demographic classic demographic segmentation would treat all of these people the same way now do these people look similar would you try to sell these ansari the same pantsuit as olivia pope or kerry washington i hope not this is probably not going to go over very well no we can't treat these people as one static monolithic demographic segment instead we have to use all of the data that we're collecting on them the behavioral data the transactional data to better understand their wants and needs and respond to them so how do i differ segmentation from her how does segmentation differ from personalization well there are a few different ways to think about this one is thinking about what your intent is as a business using these methodologies with segmentation the intent is traditionally been to drive desirable actions or behaviors with personalization you're thinking more broadly about actually improving the customer experience you're thinking about what your customers want and expect from you and how you meet those needs and so your outcomes actually change instead of looking at traditional metrics like a higher response or conversion rate increased retention or sales instead you're also looking at customer focus metrics like increased customer satisfaction or cfat or you're looking at reduced effort scores and as we'll see in a second lifetime value becomes a very key um metric in all of this and finally um the movement from segmentation to personalization also impacts um um different parts of the experience so instead of just looking at traditional campaigns offers and recommendations and messages you're actually going to use personalization to impact functionality and you're going to surface different content for different types of customers and interact with them in different ways um justin i know lucidworks has a uh an interesting point of view here i'd love to hear a little bit from you about the lucid works perspective yeah thanks brandon i was just this is a great way to slide in this this great break it down i was nodding my head here because one of our biggest customers sells a lot of yoga pants online and they had a big sale in july with record breaking revenue it was because they were able to offer a personalized experience to their shoppers with fusion and anticipate the next best experience in the shop in their shopping experience and a couple other examples that also line up really well with this is a prominent discount retailer was using understands very well the difference between personalization and segmentation and their team was really focusing on how they could get the most out of personalization and when they turned on uh signals in fusion which i'll talk about in a little bit they saw their conversion rate double um over what they were seeing before because they were bringing in that ai piece that you alluded to before and they didn't have it um and before they turned that on and then the last example that really lines up well in the in the digital commerce space is a major online books bookseller and we are talking them um before they they purchase fusion they split the traffic between their status quo tool and fusion and they intended to run a test for a few months before the holiday season after watching the results for two weeks they said forget about it and shifted all of their traffic to fusion because they were seeing about 12 point improvement in conversion just because they were adding that personalization that this level of intelligence using ai spread across all those experiences of their thousands of shoppers that's great justin thanks for those examples especially the the yoga pants one um i'm not surprised that they experience a high rate of sales in in july um my hope is that um after we we go back to some semblance of normal we can continue to wear yoga pants uh during the work week that would be great very comfortable um so you know from an analytics perspective you know segmentation is one way to skim this gap but there are a number of analytical techniques that you can apply to your customer data to better personalize experiences this is kind of the cornerstone of my customer analytics research at forrester i'm not going to go into every single one of these techniques but i would like to just tell you the way to read this pinwheel diagram is in the outer circle in green we have the different applications of customer analytics and so there are analytical techniques that help you identify customer context to improve your marketing to them moving clockwise there are those that help you acquire customers every business is interesting in growing its customer base at six o'clock on the diagram you'll see techniques that align with retention and building loyalty moving along there are techniques that are fully focused on personalization what we're talking about today although arguably all of these techniques can be used to deliver personalized experiences and speaking of experiences customer experience is another application area for customer analytics identifying pain points in the experience improving the experience differentiating through experience so those are the application areas and then in blue we have the different techniques that align generally to those application areas for instance just if you go right down to six o'clock you'll see customer churn and nutrition analysis that's kind of the canonical customer analytics technique where you have data on customers who've historically turned and not turn you feed them into a supervised learning model and it can detect patterns amongst customers who have churns that differentiate them from non-turner's so that you can score your entire customer base based on their likelihood to churn now even in the best of times not all of these techniques are created equal meaning some of them are going to be more valuable for some industries or use cases than others but we did just see a very rapid shift in the prioritization of these techniques due to the pandemic and how it impacted customer behavior um what happened was and and you've all experienced this firsthand hand people stopped leaving their homes right and certainly weren't going into brick and mortar locations and were increasingly interacting solely digitally because of this companies had to reinvest in descriptive techniques like customer journey analytics which is there at 11 o'clock to understand the new volume of customers on digital journeys what those digital journeys were um the different touch points within them and whether the journeys were actually working or not so what were the kpis at the end of the digital journeys did some digital journeys uh result in higher conversion rates than others or higher drop-off rates than others well that gives you a sense of what you may need to fix or double down on at the same time companies that were analytically advanced and had predicted models in place experience what's known in data sciences data drift because all of a sudden customers behaviors shifted so drastically the past no no longer reflected the present and so those predictive models were far less accurate than they were in the past and so companies have had to scramble to try to retrain or rebuild those predictive models based upon new data now even in the best of times or today companies are going to be using many of these models in tandem and they need a way to orchestrate all right what experiences should we deliver based upon the insights that we're deriving from these models and that orchestration layer is something i like to call the next best experience and justin mentioned the next best experience before um i think of it as the holy grail of the customer analytics a mature customer analytics practice and it obviously builds on the next best quote-unquote trilogy that preceded it next best product next best offer next best action um but it's a bit different and i use the the term experience intentionally because it encompasses not just the marketing experience but we can actually apply the next best experience across could be marketing but also customer experience customer service a product experience or some other operational area so the lens is much broader than just marketing and also the execution mindset is that personalization execution mindset we talked about before it's not inside out what do we want to get from the customer but we're actually striving to identify what the customer is trying to achieve with us we still want to be profitable and so we need to look at the long-term profitability of individual customers as opposed to short-term conversions or clicks and therefore as i mentioned before customer lifetime value being a measure of that future profitability of each customer is the metric that we will want to optimize in this next best experience model of course again this should be aspirational for you as you're building out your practice you can build each of the individual models that will eventually enable you to create this next best experience orchestration i'm going to turn it over to justin to talk a little bit about the lucidworks approach thanks brandon uh you know when i read your research and came across your concept of nespec's next best experience i said that's what we do at lucidworks and i also like the slide you just showed that it is a journey so we have customers that are at the first step of that journey and are just thinking about how they might create that experience and they're getting used to using machine learning and applying that to the experience then we have customers that are stage three or stage four of that journey that you just outlined and they're doing some really advanced things we offer the next best experiences to our clients so they can provide that to their customers or their employees through two types of solution the digital commerce solutions for shoppers and digital workplace solutions for employees and you just referred to the best of times and worst of times which of course comes from the dickens classic a tale of two cities my grandfather was a big fan so that book was off quoted when i was a kid um and here's a quote from your paper that summed up really well what we strive to offer with our platform lucid works fusion personalization delivering the right experience to the right customer at the right time is not easy but it's de rigueur for companies that wish to thrive in the age of the customer the next best experience that you described in that research is proof to the consumer of how well a company can personalize for them and of course i mentioned digital workplace at lucidworks we're on a mission to bring that same level of best-of-breed consumer personalization to the workplace so we can experience that as we work and some people may be wealthy enough to spend most most of their time shopping uh rather than working but most of us for most of us the opposite is true and work can be a lot better a lot more satisfying if we get to use the tools that augment our human intelligence and give us more time doing and less time finding the information we need so this statement really sums up the challenge of personalizing for employees to offer them the next best experience at work just like you might offer that next best experience to a consumer on an e-commerce website organizations possess lots of data style load inside disconnected applications and unavailable to employees at their moments of need so brandon we're really seeing a theme here it's that moment the moments change from instant to instant and ai really allows the system to be aware at some level what type of moment the user finds him or herself in and at lucidworks we know how challenging it can be to personalize that moment of finding because we began working on it 12 years ago we began with a deep understanding of search and apache solar the open source project for distributed search on big data and we wrote code to ingest data generated by systems humans and applications you see it on the left side of this slide we've developed advanced functionality for indexing clustering classification fasting filtering relevancy ranking analytics all of those different steps in the middle and we've bundled it all into the digital commerce and digital workplace solutions available with lucifer's fusion now because we've done the work and become experts at making finding personal for shoppers and workers at the world's largest organizations our customers our new customers can begin using the fusion platform without diverting time attention and money away from their core business so that they can build or maintain uh their business rather than caring for platforms and applications in-house so what we say is focus on your business your customers and your employees instead now to abstract it a little bit um i showed the spaghetti chart on the last slide this is how we want our customers to experience fusion um because they don't need to know about the details so let's work fusion incorporates ai and machine learning throughout the platform to intelligently ingest explore and curate the data when you ingest data fusion uses ai to prepare documents data records files for discovery it clusters those records and classifies organizes content and identifies entities within the content using natural language processing so it's it's looking at the data comes in and identifying people places products or some other entity that that you might be interested in then once a user interacts with the system fusion invokes ai again to predict the user's intent then we can match that intent with the most relevant content and personalized search results browsing options or make proactive recommendations that are on point with what the system believes the user might might need at that particular moment fusion application studio allows teams to quickly create new personalized applications that constantly tune themselves to offer the next best experience and all this can either be deployed on premises or can be self-hosted on public cloud services or managed entirely by lucid works in a cloud platform as a service so there's lots of flexibility in how our customers can can deploy fusion this has been a brief conversation about how personalization can really make life better for your customers and how fusion excels at personalization hopefully you're curious to learn more about both topics so please join brandon and myself for a q a session following this talk and we can dive more deeply into these important topics
2021-03-22