Metaverse for Mobility and Sales

The Metaverse: Driving vValue in the Mobility sector and Sales

Mckinsey  January 4, 2023 | Article

This article is a collaborative effort by Kersten Heineke, Hamza Khan, Timo Möller, Dennis Schwedhelm, Shivam Srivastava, and Felix Ziegler, representing views from McKinsey’s Automotive & Assembly Practice.

Although a fully immersive, interconnected metaverse remains years away, mobility stakeholders can already capture real business value from the technologies designed to enable it.

Riding in a car is a very physical experience, from gripping the steering wheel to pressing down the gas pedal to feeling a jolt if the vehicle suddenly stops. One day, people may experience these same sensations by taking a virtual ride in the metaverse—the next iteration of the internet in which people can immerse themselves in a digital world that closely mimics reality.

The full-fledged metaverse will likely require at least five to ten years to materialize, but stakeholders in the mobility sector can already capture real business value from the “proto-metaverse.” This early incarnation relies on spatial computing and extended reality (XR), which is an umbrella term that includes augmented reality (AR), virtual reality (VR), and mixed reality (MR). The proto-metaverse has already enhanced both sales and operations within the mobility sector, and many leading OEMs and other stakeholders are launching metaverse initiatives to explore their benefits to the core business.

Mobility moves toward the metaverse

This article examines how selected technologies, available today or in the near future, can help expand and diversify revenue streams in the mobility sector, enhance brand loyalty, improve customer experience, and optimize production. (For a more comprehensive look at current and future technologies, see sidebar “Mobility moves toward the metaverse.”) Going forward, could OEMs increase brand awareness with applications that let potential car buyers participate in highly realistic virtual races at Le Mans? Or could technicians walk customers through easy repairs at home using virtual twins of engines? Such experiences could be possible if the metaverse continues to advance. But even before the advent of such immersive experiences, OEMs still have much to gain from metaverse tools and concepts.

Kicking virtual tires: Customers in the metaverse

In a recent McKinsey consumer survey, 59 percent of respondents say that they prefer to conduct at least one daily activity, such as socializing, shopping, fitness, or education, in the virtual world rather than in person.1 Another survey also indicates that excitement about the future metaverse is consistently high across demographic groups, regardless of region, age, or gender.2

These findings bode well for greater use of metaverse-related experiences and technologies over the next few years, as do other recent developments. For instance, the market value of XR devices is expected to increase nearly tenfold, from $28 billion in 2021 to more than $250 billion in 2028.3

The metaverse showroom: Just a click away

Many potential car buyers now research vehicles online before buying, which partly explains why US consumers in 2017 visited only two dealerships before making a purchase, down from five in 2007.4 The pandemic has reduced the odds that potential buyers will find their vehicle of choice on the lot, however, because of supply chain disruptions. OEMs may partly compensate for the lack of inventory by using immersive virtual experiences to help potential buyers envision and configure their desired vehicles.

These new technologies go far beyond websites that allow users to click on different vehicle features to get information or cycle through custom options. RelayCars, for example, offers a mobile application with AR and 3-D visuals that allow users to explore thousands of cars. But even with more sophisticated online tools, the average car buyer will still want to see an actual car before making a purchase, since even the best online images may not provide the clarity and detail that a car buyer needs. ... ' 

On Intent Data

 Exploring Intent marketing data. 

Making Sense of Intent Data for Sales and Marketing Pros

Fiona O'Connor, Content Marketing Manager    in TechTarget

Intent data promises different benefits depending on how it’s made, how it’s sourced and how you put it to use. Companies are adopting it for different reasons and solution providers are touting it heavily. Some forms of intent may only help to increase your advertising efficiency. Others can give you a suggestion of who might be considering a purchase because of an upcoming contract renewal, a change in personnel, or similar indirect indicators. And a few can provide meaningful access to significant pockets of undiscovered demand that can even drive more real opportunities into your pipeline. While many users have adopted one or more of these signal types, many organizations haven’t yet realized the major differences that could be important to their getting the most bang for their buck.

We’ve created this guide to help accelerate your own journey with intent data – whatever stage you’re at. It walks through how purchase intent data can help support every area of your organization, from Sales to ABM and Marketing – and we’ll provide additional resources to further expand your knowledge.

What is real intent data?

As originally coined, “purchase intent data” was a category of behavioral data (data created by analyzing the behaviors of people) that provided a strong indication of an impending product or service purchase. Recently however, not only has the term been abbreviated to simply the word “intent” alone, but some vendor players have worked hard to broaden its meaning to include any “signal,” regardless of whether it can be reasonably associated to an impending purchase.

As we use the term, purchase intent data must be able to guide go-to-market (GTM) teams first to significantly more active demand than they could otherwise see, and then, very precisely to specific people whose behaviors have signaled an interest in buying a given product. In a nutshell, it’s about maximizing visibility into the totality of current market demand and then optimizing the focus and precision of your actions on the available insights. Among the many different types of data or data sources now being labeled as “intent,” our definition is quite specific. Depending on the sourcing and methods involved, a given resource will have very different characteristics, and therefore, very different utility in supporting a GTM.

For a clear framework you can use to evaluate intent data sources and types, read Making Sense of B2B Purchase Intent Data and Putting It to Use.

Like any data source, intent adds more value to your GTM if you can effectively put it to use in support of multiple use cases and user groups. Next, we’ll take a look at how real purchase intent data can be useful in various ways for specific GTM teams. From strategy optimization through to one-to-one sales personalization, the right intent source can quickly provide essential guidance that yields meaningful competitive advantages.

Intent data for Sales Development

Though Sales Development has exploded in popularity especially at enterprise tech companies, it’s still a relatively immature discipline at many of them. Sales Development organizations are often assembled in a rush with the expectation that they will mature themselves as they go along. But the reality at many companies appears to be that the build process is leaving a lot of important gaps which can easily become chronic points of failure. Today, we still see many Sales Development organizations where such gaps continue to cause underperformance in their companies’ sales development capability. While the majority of these teams’ time is intended to be spent on better qualifying demand for more effective sales follow-up, in a recent survey of Sales Development organizations, most companies reported that their teams still fail to hit their objectives. While a range of issues contribute to this, the quality of data resourcing stands out as a key point of failure.

While intent data has not yet become a staple for Sales Development teams, it is growing in usage. (Of course, as discussed above, the utility of intent for these use cases very much depends on the data source and its available insights). At the time of the research, less than 25% of all tech Sales Development teams were using intent data in a rigorous fashion despite the fact that the right type of intent source has been shown to yield substantial improvements in conversations, meetings and opportunities. When Sales Development teams are trained and enabled to use an appropriate source of purchase intent data, they are able to execute better outreach and more effectively deliver meetings that progress through the pipeline.

For more insights on the challenges facing Sales Development teams and how purchase intent data can help, read How Does Your Sales Development Program Measure Up?   ....' 

P&G Admits What Worked, What Didn't in Pandemic

Some rare inside information re Digital details from my former colleagues at P&G.  Recall I have mentioned here that P&G was an early pioneer in AI based methods.  Its good you can admit to what did not work.  Lets you adjust your directions and methods in the future. 

P&G's Benjamin Spiegel Admits What He Got Wrong

The global chief digital officer also discussed AI, 'maskne,' virtual influencers and the lipstick effect.

By Paul Hiebert   in AdWeek

Benjamin Spiegel, global chief digital officer for Procter & Gamble’s beauty segment, is not afraid to admit that some changes in consumer behavior he thought might occur during the pandemic … well, did not occur.

“There were a lot of things that we assumed would happen early on that, in the end, didn’t quite pan out,” noted Spiegel while speaking at Adweek’s Elevate: AI summit.

One was an expected boom in direct-to-consumer shopping. Instead, Spiegel explained, retailers got creative and have updated their click-and-collect and last-mile delivery options. “That was a big one for me that I got totally wrong,” he said.

Another prediction was that people would struggle with working at home—that the ideas, creativity and collaboration just wouldn’t be there. While remote work certainly has its challenges, Spiegel said people have shown resilience and embraced new technology that enables workers to get the job done.

Spiegel said the company has begun thinking about campaigns as “always on,” as opposed to initiatives that start and stop. “The time[s] of day where people make decisions [and] consume content have definitely changed,” he said.

P&G has also tracked the popularity of certain search terms, such as “maskne” (i.e. acne caused by wearing a face mask), and responded with tailored messaging and educational content. Usually, trends come and go, Spiegel said, but trends during Covid-19 had a “big impact on how people thought about product and usage.”

The lipstick effect—the theory that shoppers still spend money on small indulgences during economic downturns—is still going strong during the pandemic, Spiegel said. For example, their research found that instead of buying a piece of clothing that costs $200, consumers would purchase a $40 beauty product.

“People went into saving money in certain places, but then they ended up spending more in other places,” he said.

For the quarter ending March 31, P&G’s beauty business, which includes the brands Olay, SK-II and Old Spice, saw net sales climb to $3.3 billion, up 9% compared to the same period in 2020.  ... '

Detecting and Using Emotion in Language Interaction

Interesting ideas implied here, but not proven,   We worked on two kinds of 'emotion'  Detecting emotion in consumer reactions, then classifying and determining its strength.   Also  embedding it in bot style interactions with consumers, in context,  and also classifying that interaction and strength.   Could 'style' be considered a kind of classification of use here?  I like the direction suggested, though hard to see how useful it might be.  Powerful potential idea in sales and marketing. 

Expert.ai adds emotion and style detection tools to natural language API  By Damon Poeter  in Venturebeat  May 10, 2021 

Enterprises and investors are increasingly excited about using natural language (NL) processing to assist in tasks like data mining for sales intelligence, tracking how marketing campaigns change over time, and better defending against phishing and ransomware attacks.

Still, AI products using natural language engines to analyze text have a long way to go to capture more than a fraction of the nuance humans use to communicate with each other. Expert.ai hopes the addition of new emotion- and behavior-measuring extensions and a new style-detecting toolkit for its natural language API will provide AI developers with more human-like language analysis capabilities. The company this week announced new advanced features for its cloud-based NL API designed to help AI developers “[extract] emotions in large-scale texts and [identify] stylometric data driving a complete fingerprint of content,” Expert.ai said in a statement.

Based in Modena, Italy and with U.S. headquarters in Rockville, Md., Expert.ai changed its name from Expert System in 2020. The company’s customers include media outlets like the Associated Press, which uses NL software for content classification and enrichment, business intelligence consultants like L’Argus de la Presse, which conducts brand reputation analysis with NL processing, and financial services firms like Zurich Insurance, which uses Expert.ai’s platform to develop cognitive computing solutions.

Freeing people up for higher-order tasks

Expert.ai’s software platform enables natural language solutions that take unstructured language data from sources like social media sites and emails, transforming it into more digestible, usable intelligence before human analysts look at it. An example of a basic NL capability would be to distinguish between different ways a word like “jaguar” is used contextually—-to signify the animal, the vehicle, or the name of a sports team. This allows for process automation steps to be introduced to text gathering, categorization and analysis workloads, freeing up human analysts to perform higher-order tasks with the data.... " ... ' 

New Sales Expert LLC

Met Sean the other day, had an enjoyable introduction to his company and methods.   

Drive New Sales  In Your Company

Sean O’Shaughnessey,  CEO and President,  New Sales Expert LLC

Sean@NewSales.Expert

From Pipeline to Bottom Line: How to Boost Your Sales Team Effectiveness

It might seem the only necessary indicator of sales success is the bottom line. If your sales team is contributing to healthy profits, it’s all good, right? Well, not necessarily. The sales process has many stages – from building the pipeline, to nurturing prospects, to closing deals and managing customer relationships. Along the way, there are milestones and pivot points where course corrections can occur if warranted. But to know when and how to make those corrections, it’s important to measure sales team effectiveness at every stage of the process. Here’s a quick look at how to assess sales, track sales productivity, and ensure your sales team is effectively building a solid pipeline and driving results.

The Big Picture First

Obviously, the best place to start assessing the effectiveness of your sales team is by looking at overall sales. Does the bottom line look healthy? How does it compare to past results? To targeted goals and objectives? To your competitors’ results?

Web Site:   http://newsales.expert/   With lots of additional information and pointers to methods and clients.

Publix Pandemic Sales Increase

 Interesting numbers, starting with increased sales.   A kind of behavioral measure of interest.  Much more at the link.

Publix Super Markets: For the full fiscal 2020 year, Publix saw net earnings rise by $1 billion to $4 billion.   Publix says pandemic boosted fiscal 2020 sales by 12.1% ... 

By Russell Redman ... '  in Supermarket News

Publix Super Markets tallied big sales and earnings gains for its 2020 fourth quarter and fiscal year, fueled by booming consumer demand for food and groceries amid the COVID-19 pandemic.

For the quarter ended Dec. 26, sales climbed 14.8% to $11.2 billion from $9.8 billion a year earlier, with same-store sales rising 13.4% year over year, Publix reported yesterday. The Lakeland, Fla.-based grocer estimated that the pandemic’s impact lifted sales for the period by 8.7%, or about $850 million. ... ' 

Boosting Inbound Sales

This happened to come up in a conversation last week.   Good summary below and more detail at the link.

4 Behaviors that Boost Inbound Sales  by Matthew Dixon, Ted McKenna, and Tom Shepherd  in  HBR

Summary.   

Particularly during the pandemic, when face-to-face visits with customers have been constrained, inbound selling in calls centers has become more important to company revenue. New research uses recordings of millions of such calls, analyzes the way salespeople drive the conversation, and record whether the call results in a sale. This analysis shows four behaviors that play the biggest role in converting callers into buyers: Disqualifying callers who shouldn’t be dealing with a salesperson, prescribing a solution to the customer problem, digging into objections, and de-risking the purchase so callers don’t get off the phone to “think it over.” Only 1% of calls contained all four of these behaviors — but when they did, 70% of calls resulted in a sale.   ... '

Salesforce Doing Advanced Metric Analysis for NLP

Good to see interesting AI things in the sales-marketing domain, a place we played early on.

Salesforce researchers release framework to test NLP model robustness

Kyle Wiggers, @Kyle_L_Wiggers, January 13, 2021 6:00 AM in VentureBeat

In the subfield of machine learning known as natural language processing (NLP), robustness testing is the exception rather than the norm. That’s particularly problematic in light of work showing that many NLP models leverage spurious connections that inhibit their performance outside of specific tests. One report found that 60% to 70% of answers given by NLP models were embedded somewhere in the benchmark training sets, indicating that the models were usually simply memorizing answers. Another study — a meta analysis of over 3,000 AI papers — found that metrics used to benchmark AI and machine learning models tended to be inconsistent, irregularly tracked, and not particularly informative.

This motivated Nazneen Rajani, a senior research scientist at Salesforce who leads the company’s NLP group, to create an ecosystem for robustness evaluations of machine learning models. Together with Stanford associate professor of computer science Christopher Ré and University of North Carolina at Chapel Hill’s Mohit Bansal, Rajani and the team developed Robustness Gym, which aims to unify the patchwork of existing robustness libraries to accelerate the development of novel NLP model testing strategies. ... '

Shopping with YouTube?

 Though have used YouTube since its inception, only in the last month have been using all of its capabilities.   Have been thinking about how it was  a powerful engagement model, and how ads were interspersed.   Thinking the paths within different classes of content.  Here more:

Is YouTube a shopping powerhouse waiting to happen?   by Tom Ryan in Retailwire

YouTube is asking creators to tag and track products featured in their videos as part of an “experiment” in what is potentially a major step toward fulfilling the platform’s e-commerce ambitions.

Creators have largely monetized their YouTube content from advertisements served on their videos and from YouTube Premium subscribers watching their content. Some videos include links in their descriptions to Amazon or other retailers designed to drive affiliate sales.

The video tags that YouTube is now testing are linked to analytics and sales through Google, YouTube’s parent. A Shopify integration is also being explored, according to Bloomberg. The report stated, “The goal is to convert YouTube’s bounty of videos into a vast catalog of items that viewers can peruse, click on and buy directly.”   ... "

Valuing Spatio-Temporal Information

The valuation of data is a long time interest, have consulted with large companies on the topic.  Lately about automotive connections.  Technical detail on the topic at the link.

Computing Value of Spatio-temporal Information
By Heba Aly, John Krumm, Gireeja Ranade, Eric Horvitz
Communications of the ACM, September 2020, Vol. 63 No. 9, Pages 85-92
10.1145/3410387

Location data from mobile devices is a sensitive yet valuable commodity for location-based services and advertising. We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase. We present an algorithm to compute the expected value of location data from a user, without access to the specific coordinates of the location data point. We use decision-theoretic techniques to provide a principled way for a potential buyer to make purchasing decisions about private user location data. We illustrate our approach in three scenarios: the delivery of targeted ads specific to a user's home location, the estimation of traffic speed, and the prediction of location. In all three cases, the methodology leads to quantifiably better purchasing decisions than competing approaches.

1. Introduction
As people carry and interact with their connected devices, they create spatiotemporal data that can be harnessed by them and others to generate a variety of insights. Proposals have been made for creating markets for personal data1 rather than for people either to provide their behavioral data freely or to refuse sharing. Some of these proposals are specific to location data.6 Several studies have explored the price that people would seek for sharing their GPS data.5, 13, 9 However, little has been published on determining the value of location data from a buyer's point of view. For instance, a Wall Street Journal blog says10:

"What groceries you buy, what Facebook posts you 'like' and how you use GPS in your car:
Companies are building their entire businesses around the collection and sale of such data. The problem is that no one really knows what all that information is worth. Data isn't a physical asset like a factory or cash, and there aren't any official guidelines for assessing its value."
We present a principled method for computing the value of spatiotemporal data from the perspective of a buyer. Knowledge of this value could guide pursuit of the most informative data and would provide insights about potential markets for location data.

We consider situations where a buyer is presented with a set of location data points for sale, and we provide estimates of the value of information (VOI) for these points. Because the coordinates of the location data points are unknown, we compute the VOI based on the prior knowledge that is available to the buyer and on side information that a user may provide (e.g., the time of day or location granularity). The VOI computation is customized to the specific goals of the buyer, such as targeting ad delivery for home services, offering efficient driving routes, or predicting a person's location in advance. We account for the fact that location data and user state are both uncertain. Additional data purchases can help reduce this uncertainty, and we quantify this reduction as well.

In the next section, we introduce a decision-making framework with a detailed analysis of geo-targeted advertising. We focus on the buyer's goal of delivering ads to people living within a certain region. We show that our method performs better than alternate approaches in terms of inferential accuracy, data efficiency, and cost. In Section 3, we apply the methodology to a traffic estimation scenario using real and simulated spatiotemporal data. We present our last scenario in Section 4, where we show how to make good data-buying decisions for predicting a person's future location. ... 
" 

Pandemic Shopping Habits

An interesting peek into corporate bahavior.

Our pandemic shopping habits are here to stay. Brands are racing to adapt  By Hanna Ziady CNN Business 

Three days a week at 7:00 am, senior Procter & Gamble executives check in with each other about their customers: what they're buying, how their needs are changing and whether the company's products are hitting the mark.  .. "

Amazon: Sellers must Identify Themselves

Will this create a major change?  As is said, creates half of Amazon's sales.

Amazon Is Making a Simple Change that Will Change the Relationship Between Sellers and Customers Forever.    Starting September 1, sellers will have to prove who they are and where they're located--and share that information publicly.
By , Jason Aten,  TECH COLUMNIST in INC.

This week Amazon announced a change that affects every one of the almost half-million third-party sellers on its U.S. marketplace platform. That's a big deal because those third-party sellers, along with the 1.7 million more worldwide, account for roughly half of Amazon's sales, according to the company. 

Beginning in September, Amazon will require U.S. sellers to include their business name and address on their seller profile. It seems like such a simple move, and to be honest, it's actually a bit odd that it wasn't already policy that sellers had to identify themselves. That seems like a no-brainer, but it's just another way Amazon has previously taken a hands-off approach.  .... " 

Kroger CEO Talks Now and Next

Good overall view by a major player.

Kroger CEO talks about booming sales, supply chain advantage, shopping habits in BizJournal

Sales are soaring at Kroger Co., but CEO Rodney McMullen said today in a wide-ranging discussion he's watching what the next phase of shopper behavior will be and has even talked with supermarket CEOs around the world to track consumer activity as coronavirus creates a new normal.

McMullen talked Thursday morning with Evercore ISI analyst Mike Montani for a fireside chat, or as McMullen called it, “a fireside chat without the fire” since it was done remotely. He covered everything from shopper behavior to in-demand products to consumers’ brand preferences for toilet paper. As head of downtown Cincinnati-based Kroger (NYSE: KR), the nation’s largest operator of traditional supermarkets, he should know.

McMullen said he has “regular calls” with CEOs of supermarkets in China, Italy, Hong Kong, Singapore, Australia and Canada.  ... " 

Super Saturday vs Black Friday

Another look, with some numbers, with further expert comment and opinions from Retailwire.

Is Super Saturday rivaling Black Friday in importance? in Retailwire   by Tom Ryan

Super Saturday’s sales this year reached $34.4 billion, becoming the biggest single day in U.S. retail history and surpassing Black Friday’s haul of $31.2 billion by 10 percent, according to Customer Growth Partners.

The next biggest shopping days were Dec. 14 ($28.1 billion) and Cyber Monday ($19.1 billion). With only expedited shipping options left, Super Saturday tends to be more of an in-store than online event.

While foot traffic was down at most malls, the conversion rate, or the percentage of people who go to malls and make a purchase, increased, Customer Growth Partners said.

Super Saturday was expected to gain a boost from the compressed holiday selling season. The season saw six fewer days between Thanksgiving and Christmas and one less Saturday this year versus 2018.

NRF’s survey predicted 147.8 million planned to shop on Super Saturday in the U.S., up from 134.3 million last year.  .... " 

Sales Organizations and AI

Which AI and how is this measured?

Now Is the Time for Sales Organizations to Embrace AI

By Kayleigh Halko • CRM Buyer • ECT News Network

Artificial intelligence is arguably the most disruptive technology to emerge over the last few decades. Consumers are producing data at record levels. It's estimated we'll produce 463 exabytes per day by 2025. Yet humans aren't equipped to process that complex information.

We're starting to rely more on AI to interpret massive amounts of consumer and third-party data in real time, and to make it relevant for our uses. As a result, businesses can create personalized and tailored interactions for their customers at every step of the buying journey, delivering unforgettable experiences.

These AI-powered moments aren't just for business-to-consumer engagement anymore. Business-to-business buyers now expect the same experiences and buying processes as consumers. They want a company to know them -- to engage with them seamlessly on their preferred channels in a way that adds value at their moment of need.

Just like consumers, B2B buyers do the majority of research on their own before contacting companies -- but when they need support from your company to make a decision, they will reach out. You'll need to be ready when they do.  ..... '

McDonald's Use of Sales AI

Note the use of sensor acquisition to drive sales category with AI methods.  Lots of data there, and many different kinds of components.   Have always respected McDonald's tech work.

Would You Like Fries With That? McDonald's Already Knows the Answer
The New York Times
By David Yaffe-Bellany

McDonald's is acquiring companies that develop artificial intelligence and machine learning to make the company more like Amazon. Its incorporation of technology is aimed at reversing its recent loss of customers, resulting in restaurants closing and sales declining. The company is incorporating technologies such as digital boards that promote its products, taking into account environmental factors like the weather and the length of the wait for service. The company has tested algorithms at its drive-throughs that capture license-plate numbers, so the restaurant can list recommended purchases personalized to a customer's previous orders, as long as the person agrees allow the fast-food chain to store that data. McDonald's also recently tested voice recognition at certain outlets, with the goal of deploying a faster order-taking system. Regarding the use of new technologies, the company’s CIO, Daniel Henry, said, “You just grow to expect that in other parts of your life ... We don’t think food should be any different than what you buy on Amazon."  ... '

On Scoop and Conversation Analytics

Remembering having this idea for interacting with people conversationally on a chatbot, but the technology was not even close then.   And then also using the conversation to route to the write human or machine resource.  It was Suggested that a 'context' map could be set up for a particular customer.   The technology at the time could not support the possibility.

On Scoop.ai: Conversation Intelligence for Maximum Sales Conversion

Close more deals with AI-powered Conversation Analytics.
Because bottom of the Sales funnel is the moment of truth
Scoop mines insights from conversations to prioritize opportunities and coach reps
CRMs don’t fully capture the reasons behind a deal’s win or loss. The details are hidden in conversations a Sales rep has with customer. Scoop uses ML to uncover the critical elements of winning sales calls.

From DSSResources.com:
AI-powered conversational analytics platform, Scoop to help brands glean unique insights from all customer conversations

Scoop has been incubated by Netcore Solutions, a global Marketing Automation provider

NEW YORK, Aug. 28, 2019 /PRNewswire/ -- In a recent development that highlights how rep-customer interactions can be leveraged to gain market intelligence, Scoop, a Netcore incubated startup, has developed an AI-powered conversational analytics platform designed to help Inside Sales teams to maximize sales conversion optimization.

Scoop leverages machine learning to mine insights from customer conversations across voice, video, and text sources to discover sales opportunities. The insights gleaned by the AI can be utilized to coach sales representatives, helping them win more deals with more efficient conversations over time.

Conversations between a sales representative and a customer are a storehouse of valuable information that usually lies untapped. These conversations usually happen across channels and mediums. Scoop ensures that all the conversations are transcribed and searchable, which are then mined by ML algorithms to uncover unique market insights.

The platform provides a clear and timely view of customer intent, while revealing the gaps and strengths of the representatives. This information can thus be analyzed to prepare a personalized coaching plan, shared with each representative, to unravel the mystery of why deals are won or lost. Scoop aims to enable enterprises to leverage conversational analytics, understand the key problems and eventually skyrocket their conversions.  .... "

Gartner Blog: We are Very early in Predictive Analytics

Here an excerpt.    Interesting thought, and our forecasting will become much better, in some areas spectacularly so.  But still, it will be operating with some of the same data. Old, faulty, in the wrong context, gathered haphazardly.  So you still cannot expect exact predictions.  If you are not exactly right there is the risk of implemented error.  And that risk itself cannot be perfectly tagged.   So there again a caution.   And I would certainly not use the position of a technology on a wavy line as predictive input data.  So please, save your pennies, but caution all around.

Start Saving for Predictive Analytics
by Steve Rietberg  |  August 8, 2019  | 

When I was young and fresh out of college, people urged me to start contributing to a retirement fund as soon as I could. They explained that since I had time on my side, even modest investments would pay significant dividends. I just needed the discipline to start saving early.

We’re all young, with respect to predictive analytics technology. According to Gartner’s Hype Cycle for CRM Sales, sales predictive analytics (which includes predictive forecasting, upsell/cross-sell recommendations and opportunity scoring) is still in its adolescence. This market is expected to grow quickly in the immediate future.

How can we take advantage of our youth, and get a head start on preparing for advances in predictive pipeline analytics?

Your CRM Thinks Too Linearly

Consider this. Your historic pipeline data suggests a correlation between sales stage and an opportunity’s likelihood to close. In many organizations, that correlation–directly or indirectly–informs seller coaching and forecasting decisions. But there is a disconnect between the sequential sales stages in a typical CRM system and the nonlinear buying process that customers actually follow. To improve the validity and accuracy of your pipeline analytics, you need to track more than sales stage. For this reason, measuring buyer behavior along with seller-provided sales stage and probability clears a path to improved predictive analytics. .... " 

Nestle and Enterra for Intelligent Daily Business Decisions

We had talked to Enterra regarding supply chain applications.  Intriguing company and solutions.  Nestle's decision to automate complex decision making for CPG is also interesting.  Like the idea of emphasizing key processes and decisions.  Good move towards the intelligent enterprise. 

Press Release.    Supporting Video.

Nestlé Selects AI-Driven Analytics Firm Enterra To Build Platform for Marketing, Autonomous Sales

David Bloom Senior Contributor in Forbes

Nestlé USA and Pennsylvania cognitive-computing company Enterra Solutions are working together to use artificial-intelligence tools to transform the way the consumer-packaged-goods giant makes daily business decisions. Following.  

The companies are deploying two Enterra software packages that provide advanced sales and marketing insights and automate complex decision-making. The companies also are creating and staffing a center for advanced analytics on the Nestlé campus.

Enterra’s cognitive computing platform will be a crucial part of Nestlé USA's Intelligent Enterprise project. The shift to advanced artificial-intelligence applications that can transform decision-making and business processes is happening with companies across sectors such as entertainment, transportation and manufacturing.  

Enterra is also building the Center for Advanced Analytics and Insights at Nestlé’s Virginia headquarters, and staffing it with an advanced-analytics team of mathematicians, artificial intelligence and data scientists, consumer-packaged-goods experts and data-management and -visualization specialists.

The new initiatives will focus on gathering a wide range of pertinent data and integrating it to make smarter, faster decisions and break silos between Nestlé, its suppliers and customers.  ... "

Predicting Sales Behavor in Real Time

Sales Prediction in real time.

6sense raises $27 million for its marketing and sales predictive analytics tool  By Manish Singh @REFSRC

6sense, a San Francisco-based startup that uses big data to predict in real time when people are looking to buy products, has raised $27 million to grow its marketing analytics tool.

The funding round was led by Industry Ventures, with existing investors Bain Capital Ventures, Battery Ventures, Costanoa Ventures, Salesforce Ventures, and Venrock also participating. 6sense, which described the new round as “growth funding following series B,” has raised $63 million to date.  .... "