Bottom Line: Using AI to measure and predict revenue, costs, and margin across all Professional Services (PS) channels leads to greater accuracy in predicting payment risks, project overruns, and service forecasts, reducing revenue leakage in the process.
Professional Services’ Revenue Challenges Are Complex
Turning time into revenue and profits is one of the greatest challenges of running a Professional Services (PS) business. What makes it such a challenge is incomplete time tracking data and how quickly revenue leaks spring up, drain margins, and continue unnoticed for months. Examples of revenue leaks across a customers’ life cycles include the following:
Billing errors are caused by the booking and contract process not being in sync with each other leading to valuable time being wasted.
When products are bundled with services, there’s often confusion over recognizing each revenue source, when, and by which PS metric.
Inconsistent, inaccurate project cost estimates and actual activity lead to inaccurate forecasting, delaying the project close and the potential for bad debt write-offs and high Days Sales Outstanding (DSO).
Revenue leakage gains momentum and drains margins when the following happens:
Un-forecasted delays and timescale creep
Reduced utilization rates across each key resource required for the project to be completed
Invoice and billing errors that result in invoice disputes that turn into high DSOs & write-offs
Incorrect pricing versus the costs of sales & service often leads to customer churn.
Revenue leakage gains momentum as each of these factors further drains margin
Adding up all these examples and many more can easily add up to 20-30% of actual lost solution and services margin. In many ways, it’s like death by a thousand small cuts. The following graphic provides examples across the customer lifecycle:
Selling projects and the promise of their outcomes in the future create a unique series of challenges for PS organizations when it comes to controlling revenue leakage. It often starts with inaccurately scoping a project too aggressively to win the deal, only to determine the complexity of tasks originally budgeted for will take 10 – 30% longer or more. Disconnects on project scope are unfortunately too common, turning small revenue leaks into major ones and the potential of long Days Sales Outstanding (DSO) on invoices. When revenue leaks get ingrained in a project’s structure, they continue to cascade into each subsequent phase, growing and costing more than expected.
The SPI 2021 Professional Services Maturity™ Benchmark Service published by Services Performance Insight, LLC in February of this year provides insights into the hidden costs and prevalence of revenue leakage. The following table illustrates how organizations with high levels of revenue leakage also perform badly against other key metrics, including client referencability. The more revenue leakage an organization experiences, the more billable utilization drops, on-time project deliveries become worse, and executive real-time visibility becomes poorer.
How FinancialForce Is Using AI To Fight Revenue Leakage
It’s noteworthy that FinancialForce is now on its 12th consecutive product release that includes Salesforce Einstein, and many customers, including Five9, are using AI to manage revenue leakage across their PS business. Throughout the pandemic, the FinancialForce DevOps, product management, and software quality teams have been a machine, creating rich new releases on schedule and with improved AI functionality based on Einstein. The 12th release includes prebuilt data models, lenses, dashboards, and reports.
Andy Campbell, Solution Evangelist at FinancialForce, says that “FinancialForce customers have access to best practices to minimize revenue leakage by scoping and selling the right product and services mix to allocating the optimal range and amount of services personnel and finally billing, collecting and recognizing the right amount of revenue for services provided.” Andy continued, saying that recent dashboards have been built for resource managers to automate demand and capacity planning and service revenue forecasting and assist financial analysts in managing deferred revenue and revenue leakage.
By successfully integrating Einstein into their ERP system for PS organizations, FinancialForce helps clients find new ways to reduce revenue leakage and preserve margin. Relying on AI-based insights for each phase of a PS engagement delivered a 20% increase in Customer Lifetime Value according to a FinancialForce customer. And by combining FinancialForce and Salesforce, customers see an increased bid:win ratio of 10% or more. The following graphic illustrates how combining the capabilities of Einstein’s AI platform with FinancialForce delivers results.
Conclusion
FinancialForce’s model building in Einstein is based on ten years of structured and unstructured data, aggregated and anonymized, then used for in-tuning AI models. FinancialForce says these models are used as starting points or templates for AI-based products and workflows, including predict to pay. Salesforce has also done the same for its Sales Cloud Analytics and Service Cloud Analytics. In both cases, Salesforce and FinancialForce customers benefit from best practices and recommendations based on decades of data, which should be particularly interesting considering the “black swan” nature of 2020 data for most of their customers.
95% of the content essential for decision making in an organization is unstructured, residing in PDFs and various file formats that defy easy indexing and quick access, according to MIT Media Labs.
80% of typical organizations’ data is unstructured, slowing down work, often leading to less-than-optimal decision-making, according to an Accenture study published earlier this year.
Organizations use 35% of their structured data for insights and decision-making, but only 25% of their unstructured enterprise data, according to an Accenture study on how data is used for decision-making.
60% to 80% of employees can’t find the information they are looking for even when there’s content management or knowledge management system in place, according to IBM’s knowledge management study.
Bottom Line: Stravito is an AI startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable.
When It Comes To Finding Market Research Data, Intranets Aren’t Getting It Done
Facing tight deadlines to get a marketing plan together for a new product, channel, or selling strategy, market research and product marketing teams will give up looking for a report they know they’ve bought and re-purchase it. The tighter the deadline and the more important the plan, the more this happens.
When a quick call to the Market Research Analyst who has access privileges to all the market research subscriptions doesn’t have the reports a team needs, they either move on without the data or repurchase the report. Having spent the first years of my career as a Market Research Analyst, I can attest to the accuracy of IBM’s finding that 30% of a typical knowledge workers’ day is spent searching for information and understanding its context and original methodology. All reports our organization had distribution rights to internally went on the Intranet site. There were hundreds of reports available online on an Intranet platform with mediocre search capabilities.
The company was founded by Thor Olof Philogène and Sarah Lee in 2017, who together identified an opportunity to help companies be more productive getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (HQ), Malmö and Amsterdam.
Instead of settling for less-than-optimal market and industry data that partially deliver the insights needed for an exceptional product launch or sales campaign, marketing & senior management teams need to set their sights higher. It’s time to replace legacy Intranet sites and their limited search functions with AI-based search engines that auto-tag content and build taxonomies based on content attributes in real-time. Stravito combines AI, machine learning, NLP and Search on a single platform that can index every major file type an organization uses, creating a taxonomy that streamlines search queries.
Having AI as the foundation of the Stravito platform delivers the following benefits:
AI-powered fast search gives individuals the ability to find and share insights and information quicker than any legacy Intranet technology could. With everyone working from home and self-service being a goal every marketing, business planning and IT department is trying to achieve today, Stravito’s architecture is designed for simple queries and requests anyone can quickly learn to create.
Relying on AI and machine learning to alleviate the need to manually upload and tag hundreds of market research reports and analysis. Stravito’s approach to data categorization using AI also identifies and removes duplicate report copies and can be configured to filter out any reports past a specific date. Search perimeters, auto-tagging and in-PDF search options are all configurable. Stravito will rank PDFs by the percentage of relevant content they have for a specific search term, providing a bar graph designating which pages have the most relevant content.
Stravito’s design team has successfully combined AI, machine learning and advanced user interface design to produce an application comparable to Spotify, Google and Netflix. Developing and launching an enterprise-level search engine designed for usability first is noteworthy. Many enterprise applications still aren’t achieving this design goal despite being mentioned as a first priority by enterprise software vendors. As can be seen from their search results screen, Stravito’s approach is to combine information discovery and collaboration:
Stravito deserves credit for finding new ways to use AI and machine learning to accomplish drag-and-drop integration of any commonly used file format in an organization – and then have it assigned to a taxonomy in seconds. Stravito’s innovative use of AI, machine learning and auto-tagging provides its customers with a simple drag-and-drop interface that supports bulk uploads. The platform has API integration designed with any market research or advisory service with an API library compatible with their platform. Their customer base actively relies on Euromonitor and Mintel today, for example.
Conclusion
Stravito fills the gap legacy Intranet technologies and current generation collaboration platforms are not addressing. That’s the need to provide a more powerful search engine, one capable of continually adapting to new information and documents. Supervised machine learning has proven effective for taking on challenges related to creating and keeping taxonomies current. Stavito’s product strategy of providing personalized recommendations for the content of interest is a natural progression of their platform. For organizations overwhelmed with research data yet can’t seem to get the reports to decision-makers fast enough, the Stravito platform is worth checking out.
AI and Machine Learning are on track to generate between $1.4 Trillion to $2.6 Trillion in value by solving Marketing and Sales problems over the next three years, according to the McKinsey Global Institute.
Marketers’ use of AI soared between 2018 and 2020, jumping from 29% in 2018 to 84% in 2020, according to Salesforce Research’s most recent State of Marketing Study.
AI, Machine Learning, marketing & advertising technologies, voice/chat/digital assistants, and mobile tech & apps are the five technologies that will have the greatest impact on the future of marketing, according to Drift’s 2020 Marketing Leadership Benchmark Report.
Chief Marketing Officers (CMOs) and the marketing teams they lead are expected to excel at creating customer trust, a brand that exudes empathy and data-driven strategies that deliver results. Personalizing channel experiences at scale works when CMOs strike the perfect balance between their jobs’ emotional and logical, data-driven parts. That’s what makes being a CMO today so challenging. They’ve got to have the compassion of a Captain Kirk and the cold, hard logic of a Dr. Spock and know when to use each skill set. CMOs and their teams struggle to keep the emotional and logical parts of their jobs in balance.
Asked how her team keeps them in balance, the CMO of an enterprise software company told me she always leads with empathy, safety and security for customers and results follow. “Throughout the pandemic, our message to our customers is that their health and safety come first and we’ll provide additional services at no charge if they need it.” True to her word, the company offered their latest cybersecurity release update to all customers free in 2020. AI and machine learning tools help her and her team test, learn and excel iteratively to create an empathic brand that delivers results.
1. 70% of high-performance marketing teams claim they have a fully defined AI strategy versus 35% of their under-performing peer marketing team counterparts. CMOs who lead high-performance marketing teams place a high value on continually learning and embracing a growth mindset, as evidenced by 56% of them planning to use AI and machine learning over the next year. Choosing to put in the work needed to develop new AI and machine learning skills pays off with improved social marketing performance and greater precision with marketing analytics. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.
2. 36% of marketers predict AI will have a significant impact on marketing performance this year. 32% of marketers and agency professionals were using AI to create ads, including digital banners, social media posts and digital out-of-home ads, according to a recent study by Advertiser Perceptions. Source: Which Emerging Tech Do Marketers Think Will Most Impact Strategy This Year?, Marketing Charts, January 5, 2021.
3. High-performing marketing teams are averaging seven different uses of AI and machine learning today and just over half (52%) plan on increasing their adoption this year. High-performing marketing teams and the CMOs lead them to invest in AI and machine learning to improve customer segmentation. They’re also focused on personalizing individual channel experiences. The following graphic underscores how quickly high-performing marketing teams learn then adopt advanced AI and machine learning techniques to their competitive advantage. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.
4. Marketers use AI-based demand sensing to better predict unique buying patterns across geographic regions and alleviate stock-outs and back-orders. Combining all available data sources, including customer sentiment analysis using supervised machine learning algorithms, it’s possible to improve demand sensing and demand forecast accuracy. ML algorithms can correlate location-specific sentiment for a given product or brand and a given product’s regional availability. Having this insight alone can save the retail industry up to $50B a year in obsoleted inventory. Source: AI can help retailers understand the consumer, Phys.org. January 14, 2019.
5. Disney is applying AI modeling techniques, including machine learning algorithms, to fine-tune and optimize its media mix model. Disney’s approach to gaining new insights into its media mix model is to aggregate data from across the organization including partners, prepare the model data and then transform it for use in a model. Next, a variety of models are used to achieve budget and media mix optimization. Then compare scenarios. The result is a series of insights that are presented to senior management. The following dashboard shows the structure of how they analyze AI-based data internally. The data shown is, for example only; this does not reflect Disney’s actual operations. Source: How Disney uses Tableau to visualize its media mix model (https://www.tableau.com/best-marketing-dashboards)
6. 41% of marketers say that AI and machine learning make their greatest contributions to accelerating revenue growth and improving performance. Marketers say that getting more actionable insights from marketing data (40%) and creating personalized consumer experiences at scale (38%) round out the top three uses today. The study also found that most marketers, 77%, have less than a quarter of all marketing tasks intelligently automated and 18% say they haven’t intelligently automated any tasks at all. Marketers need to look to AI and machine learning to automated remote, routine tasks to free up more time to create new campaigns. Source: Drift and Marketing Artificial Intelligence Institute, 2021 State of Marketing AI Report.
7. Starbucks set the ambitious goal of being the world’s most personalized brand by relying on predictive analytics and machine learning to create a real-time personalization experience. The global coffee chain faced several challenges starting with how difficult it was to target individual customers with their existing IT infrastructure. They were also heavily reliant on manual operations across their thousands of stores, which made personalization at scale a formidable challenge to overcome. Starbucks created a real-time personalization engine that integrated with customers’ account information, the mobile app, customer preferences, 3rd party data and contextual data. They achieved a 150% increase in user interaction using predictive analytics and AI, a 3X improvement in per-customer net incremental revenues. The following is a diagram of how DigitalBCG (Boston Consulting Group) was able to assist them. Source: Becoming The World’s Most Personalized Brand, DigitalBCG.
8. Getting personalization-at-scale right starts with a unified Customer Data Platform (CDP) that can use machine learning algorithms to discover new customer data patterns and “learn” over time. For high-achieving marketing organizations, achieving personalization-at-scale is their highest and most urgent priority based on Salesforce Research’s most recent State of Marketing survey. And McKinsey predicts personalization-at-scale can create $1.7 trillion to $3 trillion in new value. For marketers to capture a part of this value, changes to the mar-tech stack (shown below) must be supported by clear accountability and ownership of channel and customer results. Combining a modified mar-tech stack with clear accountability delivers results. Source: McKinsey & Company, A technology blueprint for personalization at scale. May 20, 2019. By Sean Flavin and Jason Heller.
9. Campaign management, mobile app technology and testing/optimization are the leading three plans for a B2C company’s personalization technologies. Just 19% of enterprises have adopted AI and machine learning for B2C personalization today. The Forrester Study commissioned by IBM also found that 55% of enterprises believe the technology limitations inhibit their ability to execute personalization strategies. Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.
10. Successful AI-driven personalization strategies deliver results beyond marketing, delivering strong results enterprise-wide, including lifting sales revenue, Net Promoter Scores and customer retention rates. When personalization-at-scale is done right, enterprises achieve a net 5.63% increase in sales revenue, 10.26% increase in order frequency, uplifts in average order value and an impressive 13.25% improvement in cross-sell/up-sell opportunities. The benefits transcend marketing alone and drive higher customer satisfaction metrics as well. Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.
CMOs and their teams rely on AI and machine learning to iteratively test and improve every aspect of their marketing campaigns and strategies. Striking the perfect balance between empathy and data-driven results takes a new level of data quality which isn’t possible to achieve using Microsoft Excel or personal productivity tools today. The most popular use of AI and machine learning in organizations is delivering personalization at scale across all digital channels. There’s also increasing adoption of predictive analytics based on machine learning to fine-tune propensity models to improve up-sell and cross-sell results.
Bibliography
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State of Marketing, Sixth Edition. Salesforce Research, 2020.
Software companies continue to deliver the highest growth rates for the 25th straight year, representing 71% of the entire list, the highest-ever percentage in the history of the rankings.
353 of the 500 fastest-growing companies in North America are in the software industry according to Deloitte’s 2020 Tech Fast 500, the most ever in the history of their rankings and a 3% increase over last year.
Two of the ten fastest-growing companies over the last three years specialize in cybersecurity, OneTrust and Transmit Security.
Biotechnology/pharmaceutical companies are the second most prevalent sector, comprising 14% of all companies, followed by digital content/media/entertainment (5%) and medical devices (4%).
It’s fascinating to look at the emerging trends in Deloitte’s 2020 North America Technology Fast 500 Rankings as leading predictors of innovation. This year’s report is a quick read and provides a glimpse into the fastest-growing companies between 2016 and 2019. Deloitte chooses Technology Fast 500 awardees based on percentage fiscal year revenue growth from 2016 to 2019. Overall, the 2020 Technology Fast 500 companies achieved revenue growth ranging from 175% to 106,508% over the three-year time frame, with a median growth rate of 450%.
Key insights from the rankings include the following:
Five of the top ten winners are software companies, includingBranch Metrics, OneTrust, Transmit Security, Drift and CharterUP. It’s noteworthy that cybersecurity is well-represented in the top ten fastest-growing companies between 2016 and 2019. OneTrust and Transmit Security is in the top five fastest-growing companies between 2016 and 2019, accentuating how critical cybersecurity is becoming in all businesses. The following graphic lists the top ten Deloitte 2020 North America Technology Fast 500 winners.
Deloitte’s 2020 North America Technology Fast 500 Rankings
Digital platform and enterprise infrastructure & productivity dominate software companies are dominating software sub-sectors with 56% of all companies. Deloitte’s ranking reflects the increasing urgency all organizations have to launch, scale and excel at new digital selling channels. The pandemic accelerated the urgency faster than the most compelling business case ever could. Having over 50% of all software companies in these categories quantifies the cloud as the platform of choice across enterprises.
Deloitte’s 2020 North America Technology Fast 500 Rankings
Electronic devices/hardware, energy tech and software & SaaS are the three sectors generating the fastest growing businesses over the last three years. Edge computing and the quick pace of innovation in intelligent sensor development and adoption for the Internet of Things (IoT) and Industrial Internet of Things (IIoT) use cases are catalysts driving the 683% growth rate. Sustainability’s bottom-line benefits, including its positive impact on lean manufacturing, help drive to 525% growth rate in energy tech. Software and SaaS median growth rate of 465% shows enterprise software’s evolution is nascent and just getting started.
Deloitte’s 2020 North America Technology Fast 500 Rankings
62% of the fastest-growing companies of the last three years say the Covid-19 pandemic has a low- to moderate impact on their business. It’s understandable how most high-growth companies are seeing a moderate impact of the pandemic as entire industries rely on their software, services and products to survive. Take software, for example. Manufacturers producing Personal Protective Equipment (PPE) need ERP and MES systems to get healthcare workers much-needed supplies. Retailers need e-commerce software to drive sales online. Bolt’s success with its Checkout Experience Platform and it now being the platform of choice for Authentic Brands Group’s Forever 21 e-commerce relaunch are a case in point.
Deloitte’s 2020 North America Technology Fast 500 Rankings
Online conversion rates increased 8.8% in February, reflecting a level of shopping urgency typically seen during Cyber Mondays, according to QuantumMetric.
Just over 306 million Americans are affected by stay-at-home orders, nearly 95% of the U.S. population. COVID-19 will forever change retailing, and its initial impact on e-Commerce is creating challenges to online selling & service no one imagined in January. The following graphic from COVID-19 Commerce Insight, an Emarsys initiative in cooperation with GoodData show how year-over-year revenue growth comparing the last seven days to the same period last year:
Mobile devices are the most popular device for online shopping by a wide margin. 72% of consumers are using mobile devices to shop in stores according to the latest PYMNTS’ 2020 Remote Payments Study. E-Commerce and online retailers’ supply chains, order management, and fulfillment systems are all being tested by the triple-digit order and revenue growth going on today. And best of all, more energy and intensity is being put into improving customer experiences online.
E-Commerce’s Time Savings and Efficiency Are Here To Stay
Stay-at-home orders will eventually be lifted state by state, but in the interim, there are millions of consumers creating and reinforcing new online buying behaviors and habits. In many families, online grocery, apparel, and entertainment shopping will replace store and mall visits permanently until a vaccine is available.
Paradoxically, quarantines have helped alleviate the severe time shortages, so many families and friends have had in their lives. Many are reluctant to go back to old shopping habits for fear of getting sick. A recent Morning Consult study found that 24% of consumers said they wouldn’t feel comfortable shopping in a mall for more than six months, 16% said they would feel comfortable in the next three months. The results are based on surveys with 2,200 U.S. adults between April 7 and April 9. Consumers are more motivated than ever to stay home and shop online, creating the ideal market conditions to fast-track, test, and launch new experience-driven mobile apps, sites, and touchpoints across their platform.
Eliminating Friction Is Key; Customers No Longer Have to Trade Experience for Security
Just as the latest approaches to personalizing web content, offers, pricing, and promotions adapt to each customers’ unique preferences and buying history, account control and security must do the same. Customizing security for every online customer eliminates the friction of having the most loyal, VIP-level customers go through the same authentication steps as a new one. Instead of one-size-fits-all account verification, there needs to be a more adaptive approach to managing friction customers experience. And the best place to start is by expanding the dataset used for defining and personalizing adaptive friction approaches by customer, all in real-time. Knowing the user type, device specifics, IP risk, geolocation, custom data, and more can be taken together to define a micro-segmentation based strategy.
One of the more innovative approaches to solving this challenge uses AI and Machine Learning algorithms to customize every e-commerce consumers’ experiences, reducing friction by identifying and segmenting users based on common characteristics. It’s called Kount Control – Account Takeover Protection and what’s noteworthy about this approach is its ability to identify returning customers even if they are logging in from a new location or a new device. Rather than instantly blocking their access, e-commerce businesses can give their customers an appropriate login response, such as step-up authentication or frictionless experience.
Kount Control enables an e-commerce companies’ fraud agents to see the health of the logins and take a quick pulse of the number of blocks and challenges as well as the failed login attempts. They’re also able to see the normal ebbs and flows of a business, which can help analysts identify when something is amiss. The following dashboard is crucial for showing location patterns to analyze logins during specific periods, or even filter on a particular user to identify attempted fraud.
Knowing the patterns of failed login attempts helps identify and stop credential attacks, while also helping to improve user’s login and purchase experiences. What’s noteworthy about Kount’s approach to doing this is how they identify potentially risky IP addresses and feed those to Security Operations while also identifying potential users that are under attack so they can be contacted via customer service outreach. The following is an example of a Kount dashboard tracking and analyzing failed login attempts in real-time.
Conclusion
Millions of online customers changing their behavior at the same time to opt for more time saving and convenience puts a considerable strain on e-commerce and online retailers today. How they choose to react will define the future of e-commerce. Many are choosing to remove the friction that stands in the way of turning occasional customers into the most loyal. And they’re starting with securing online identities and protecting accounts from takeover control. Once state by state stay at home orders are lifted, e-commerce sales may stabilize at a lower growth rate than they are today, yet the behavioral changes are already in motion to completely change the retailing landscape and commerce overall for years to come.
Worldwide enterprise application software revenue totaled more than $193.6B in 2018, a 12.5% increase from 2017. CRM made up nearly 25% of the entire enterprise software revenue market.
72.9% of CRM spending was on software as a service (SaaS) in 2018, which is expected to grow to 75% of total CRM software spending in 2019.
Worldwide spending on customer experience and relationship management (CRM) software grew 15.6%, from $41.7B in 2017 to $48.2B in 2018, and is projected to reach $55.2B in 2019.
CRM revenue in 2018 is comprised of software and services revenue from Customer Service and Support (35.7%), Sales (25.9%), Marketing (25.4%), and Digital Commerce (13%). These four categories together comprise the customer experience and relationship management market, according to Gartner.
Details Of What’s New In Gartner’s Hype Cycle For CRM, 2019
Four new technologies are on the Hype Cycle for CRM, reflecting enterprises’ need for greater integration of diverse systems and the demand for more predictive and prescriptive analytics-based insights. The four technologies include the following:
Blockchain for lead generation. Gartner sees the potential for blockchain to provide a decentralized peer-to-peer network model that supports exchanging data to the highest bidders using smart contracts. Gartner predicts this approach reduces or in some cases eliminates the need for a centralized authority such as a data intelligence solution. It also allows for a new ecosystem of managing, sharing and monetizing data for revenue-generating purposes.
Knowledge graphs for sales. The ability to build an AI-enabled knowledge model of real-world entities and their relationships to one another, expressed in a data schema, shows potential to increase sales effectiveness. Gartner predicts this emerging technology provides organizations with the ability to create data-driven sales organizations using graphs arranged in a network of nodes rather than in tables of rows and columns. The significance is the ability to correlate sales activities and benchmark against performance metrics in a more digestible and insightful way, which is often too complex for human analysis.
Digital adoption solutions. Gartner sees potential in this technology to improve the adoption of multiple tools across a selling and marketing organization. Digital adoption solutions enable sellers to onboard more quickly and improve productivity.
Relationship intelligence. By relying on machine learning, sales organizations can map out their universe of network connections, both internal and external, to identify potential avenues of engagement with any prospect or client. Gartner sees this as useful in its ability to provide warm introductions or even referrals for revenue-generating activities while reducing sales cycles.
Gartner predicts the following five technologies will deliver the most significant transformational benefits to selling organizations in 2 years or less. The five most transformation technologies in the near term are the following according to Gartner:
CPQ Application Suites
Digital Content Management for Sales
Lead Management
Partner Relationship Management (PRM)
Price Optimization and Management for B2B
The following CRM technologies have gained wide usage and adoption in the last year, as reflected by their position on this year’s Hype Cycle. Data intelligence solutions for sales, CPQ application suites, digital content management for sales, and sales KPI analytics are among the most adopted mature technologies on the hype cycle today.
Visual configurators have moved at a much faster pace to mainstream adoption along the Hype Cycle this year. Gartner credits visual configurators’ rapid adoption rate to how the majority of them are now embedded or easily integrated with configure, price, quote (CPQ) applications, or in digital commerce sites. State-of-the-art visual configurator are enabling engineering, production, and sales to become real-time collaborators in creating new products. For additional insights into visual configurators, please see How To Make Complex CPQ Selling Simple With Visual Configurators published earlier this week.
Algorithmic guided selling is now listed as obsolete. Gartner has re-assigned this technology as it’s now an embedded core capability in many CPQ and sales force automation (SFA) applications. By doing this, Gartner is saying it is doubtful algorithmic guided selling applications will be sold stand-alone in the future.
Social for sales and predictive B2B marketing analytics are off the CRM Hype Cycle. Gartner has chosen to merge them into the data intelligence solutions for sales market. Social for sales is more of a process, not a technology market. The majority of social for sales-based strategies are executed over social networks that have the audience and scale to make them succeed, with LinkedIn being an example. Gartner believes the predictive B2B marketing analytics vendor landscape has shrunk and is not a viable market long term, as they have seen inquiries regarding market share in this area steadily drop in this area since 2016.
Gartner is seeing two main drivers of investment and innovation in CRM in 2019 and beyond. The first is digital optimization or a process and program of using digital technology to maximize existing operating processes and business models. The second is predictive/prescriptive-enabled technology or technology using capabilities such as machine learning that provides predictive signals and prescriptive “next best action” recommendations. Please see their research note, 4 Key Insights from the Gartner Hype Cycle for CRM Sales Technology, 2019, for additional details.
Bottom Line: Realizing visual configurators’ full potential starts by enabling engineering, production, and sales to become real-time collaborators in creating new products.
2D, 3D, Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) visual configurators are proliferating across the Configure, Price, and Quote (CPQ) landscape today. Manufacturing marketing teams say they are the most effective lead generation technology they have, responsible for 40%+ growth in Marketing Qualified Leads (MQLs) this year alone. Sales VPs and Chief Revenue Officers (CROs) are seeing from 9% to 30% improvements in deal close rates and over 90% increases in quote accuracy. Visual configurators deliver shock-and-awe to prospects and drive more leads and deals.
Product Models Need To Scale, Driving Greater Collaboration
The good test of any product configurator is whether it can scale from assemble-to-order (ATO) to Engineer-To-Order (ETO) while enabling real-time collaboration between engineering, production, and sales. A given products’ many attributes and options defined by engineering in their PLM system need to be consistent with manufacturing’s work instructions and Bill of Materials (BOM) in their ERP system. And the visual configurator sales & marketing is using needs to reflect, in real-time, what engineering defined in PLM and what manufacturing’s ERP system can build. Product models serve as the master data that enables real-time collaboration between engineering, manufacturing, and sales.
Visual configurators need to push beyond the veneer of delivering shock-and-awe and enable real-time collaboration between PLM, ERP, and CRM & CPQ systems to achieve their full potential. Visual configuration providers need to pursue the goal of enabling engineering, manufacturing, and sales to be collaborators in creating accurate products and challenge themselves to deliver the following:
Improve sales performance while increasing margin per deal by providing only the options that are the most buildable at the lowest cost.
Eliminate disconnects between what engineering designed and what manufacturing can produce leads to more sales at higher gross margins.
Close product configuration gaps and improving fulfillment speed and product quality, creating greater customer loyalty and follow-on sales.
Automatically propagate product and design changes across all functional areas to accelerate new products to market while improving product quality.
Real-time fine-tuning of new product features to the model level that specific customers want becomes possible when engineering, manufacturing, and sales are collaborating in real-time.
Update work instructions and BOMs in real-time based on changes customers make in product visualizations.
Improve the balance of revenue across configurable products to sell higher-margin models based on real-time collaboration between PLM, ERP, CRM, and CPQ systems.
See in real-time how changes in product design, Bill of Materials (BOM), and delivery dates impact the financial performance of a manufacturer.
Predicting Visual Configuration’s Future
Shortening cycle times from product concept to completed product is the secret to succeeding with visual configuration. And when each manufacturing cycle time has its cadence or speed depending on how little or much a customer wants a product customized, visual configurators need to flex and deliver what customers want when.
Companies defining the future of visual configuration today include CDS, DERWID, and SAP Visual Enterprise. These three companies are defining the future of visual configuration by enabling real-time integration between PLM, ERP, CRM, and CPQ systems. I recently spoke with John Major, CEO of CDS to get his insights into what’s driving visual configuration’s success today. “What we’re seeing in the marketplace now are two things. One is the clients want to understand how our visual configuration solution is going to fit into their change management as it’s rooted in PLM, because to any manufacturer, PLM reigns supreme,” he said. He continued, “The second is about staffing. When you’re a manufacturing company, and you buy a visual configurator toolkit that requires you to create your app, a few things happen. You need to staff up a software team to now run that toolkit and write development. So your long-term cost is fairly significant versus a company that can deliver an entire solution at scale.”
CDS is partnering with eLogic, who is regarded as the leading system integration partner in CPQ and product configuration and is considered a global leader in delivering business solutions for manufacturers across SAP configuration technologies and Microsoft Dynamics 365, Power Platform & Azure. Together they are delivering next-generation visual configuration solutions for their shared clients. Examples of the work they are doing are shown below:
Real-time model updates keep engineering, manufacturing, and sales in sync. When customers are designing a new product in a CPQ session, the model is updated in real-time and saved, so engineering, manufacturing, and sales can see how their changes affect the product. An example of this is shown below:
When the product is configured “to scale,” 2D proposal drawings are automatically generated, and the product model is updated in real-time, making augmented reality visualizations possible. 3D models are also made available in a variety of CAD formats. Additionally, an Augmented Reality model is created that can be placed in any virtual environment. What’s noteworthy is that while the model’s appearance is changing, all relevant changes to the work instructions and BOM are happening in real-time using the SAP Visual Enterprise
When product models are the catalyst enabling real-time collaboration between engineering, manufacturing, and sales, selling into the aftermarket becomes profitable. Aftermarket selling has a complexity all its own. Taking on the challenge of shortening cycle times from product concept to completed products in the market is what’s needed today. The example below shows a piece of equipment selected in CPQ, then rotated, zoomed in, and exploded to see the internal components. Internal parts can now be selected, quoted, ordered and delivered for replacement.
Conclusion
Visual configurators are capable of so much more than they are delivering today. It’s time to graduate beyond the shock-and-awe stage, which has been very successful in driving leads, generating MQLs, and closing deals. It’s time to get down to the hard work of making all those impressive models buildable at scale and profitable. And that comes by doubling down efforts at shortening cycle times from product concept to completed product. That’s the true north of this market and the secret to succeeding. Getting engineering, manufacturing, and sales collaborating using product models as a single source of truth is the best place to start.
Manufacturers can get more than their fair share of channel sales and margins by improving price management for every dealer, distributor, and reseller they sell-through. It’s possible to expand earnings by 50% on slight increases in volume when pricing is consistent channel-wide. McKinsey’s latest research on the topic, Pricing: Distributors’ Most Powerful Value-Creation Lever, shows how the highest performing distributors use pricing to create value. For manufacturers competing for more sales through distributors, they share with competitors, improving their channel partners’ margins is the single best strategy to win more sales and long-term loyalty.
A 1% price increase yields a 22% increase in Earnings before Interest & Taxes (EBITDA) margins for distribution-based businesses.
It would take a 7.5% reduction in fixed costs to achieve the same 22% increase in EBITDA that a 1% increase in pricing achieves.
A distribution-based business would need to increase volume by 5.9% while holding operating expenses flat to achieve the same impact as a 1% price increase.
Channel partners are more loyal to margin than manufacturers, which is why price management needs urgent attention on CPQ roadmaps.
CPQ Strategies Need To Deliver More Margin Back To The Channel
The typical manufacturer who has over $100M in sales generates 40% or more of their sales through indirect channels. The channel partners they recruit and sell through are also reselling 12 other competitive products on average. Which factors most influence a distributor or channel partner’s decision to steer a sale to one manufacturer versus another? The following are the steps manufacturers can take now to improve price management and drive more channel sales:
Upgrade the pricing module in CPQ to deliver more than configurable price lists to include pricing waterfalls, automated approval levels for pricing requests, and discounts. Distributors drive more deals to manufacturers whose CPQ systems are designed to give them greater freedom in tailoring pricing to every customer and selling situation they have. Automating approval levels using machine learning-based supervised algorithms that serve as pricing guardrails on every quote a channel partner creates is proving effective at delivering a 1% price increase which drives margin back to resellers. The more a manufacturer can make margins flow back to its channel partners, the faster the channel partners can grow. The following graphic from McKinsey’s latest pricing research illustrates why.
Distributors will drive more deals to manufacturers who automate pricing approvals, guiding their sales teams to the largest and most profitable deals first. One of the best ways to compete and win more deals through channel partners is to achieve the ambitious goal of delivering pricing approval within seconds on a 24/7 basis. Pricing needs to provide guardrails that guide channel sales reps to the largest, most profitable, and most ready-to-buy new and aftermarket sales opportunities. Manufacturers capturing more channel sales are relying on machine learning-based pricing systems that optimize price approvals while recommending only those new and aftermarket deals that will drive a 1% or greater price increase. Machine learning is making solid contributions to automating pricing approvals. It’s proving most effective when it is balanced with the flexibility of responding to subjective competitive situations where pricing on specific products need discounts to win deals in aggregate. The following workflows from Deloitte explain how this is being accomplished today:
Helping distributors solve sales compensation problems by improving price management drives more deals in the short-term and keep distributors in business long-term. Distributors start out building their sales comp plans on volume and growth alone. The problem is comp plans reward revenue growth at the expense of profits. That’s making it harder every year for distributors to stay in business. Manufacturers delivering new pricing management and optimization apps in their CPQ platforms need to provide real-time guidance on margin potential by the deal, pricing waterfall logic that includes margins, contract pricing overrides for margins and more if they are going to help their distributors stay in business.
Conclusion – Pricing Is the Engine Powering CPQ’s Market Growth Today
Manufacturers who excel at growing indirect product and services revenue through channels realize that every one of their channel partners is more loyal to pricing and margins than any specific vendor they resell. Providing a CPQ application or platform they can personalize, and automate workflows is just the beginning. The bottom line is that manufacturers need to put more intensity into improving pricing today if they’re going to hold onto the distributors they have and attract new ones.
Pricing is the primary catalyst driving the CPQ market’s growth as well. According to Gartner, the CPQ grew 36% in 2017, reaching $1.084B with the majority of growth attributable to cloud-based solutions. It’s no wonder CPQ is considered one of the hottest CRM technologies for the foreseeable future, projected to grow at a 25% Compound Annual Growth Rate (CAGR) through 2020. Supervised machine learning algorithms capable of providing guardrails in real-time for every potential deal a reseller sales representative has is what’s needed to protect a distributor’s margins. Winning more deals with channel partners starts by respecting how vital margins are to their success and improving pricing management as part of a broader CPQ strategy that delivers results.
Bottom Line: Adding new features to your CPQ channel selling platform directly benefits your resellers and channel partners, driving greater revenue, channel loyalty, and expansion into new markets.
Personalization Is Key To CPQ Succeeding In Channels
Sustaining and strengthening relationships across all indirect selling channels succeeds when dealers, multi-tier distributors, resellers, intermediaries, and service providers each can personalize the CPQ applications and platforms they use. Larger dealers, distributors, and resellers are adept at personalizing CPQ selling portals by the various roles in their organization. Personalization combined with a highly intuitive, configurable interface improves CPQ applications’ ease of use, enabling channel partners to get more done. The more intuitive and easy a CPQ application is to use, the more channel partners rely on it to place orders. When distributors are representing, on average, 12 different manufacturers, the one with the most intuitive, easily used CPQ system often gets the majority of sales.
Another aspect of personalization is defining levels of resellers. When many organizations first launch their CPQ channel selling strategies, one of the first requests they have is to organize all channel partners into performance categories. Differentiating channel partners on sales performance, customer satisfaction, and aftermarket revenue then gamifying how every one of them can move up a level is proving to be very effective at increasing channel sales. Competing with one another to be the top reseller for the manufacturing and service companies lifts an entire channel network to higher performance.
Every dealer, multi-tier distributor, reseller, intermediary, and service provider also has a unique way of selling that works best for their business. Another must-have feature on any CPQ channel roadmap is greater workflow flexibility to support increasingly complex, IoT- and AI-enabled configurable products. Smart, connected products are the future of manufacturing and channel sales. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020. Workflows like the one shown below of an internal sales rep using a multichannel CPQ system to order a customized product are due for a refresh to support even greater flexibility for more channels and greater product options.
Most Valuable Features For A CPQ Channel Roadmap In 2019
There’s a direct link between how effective a CPQ platform is across multi-tier distribution networks and the productivity of sales teams using them. 83% of sales teams are using CPQ apps today based on Accenture Interactive’s recent study, Empowering Your Sales Force: It’s Not Just Automation, It’s Personal (8 pp., PDF, no opt-in). There’s ample evidence that the more effective a CPQ platform is at equipping dealers, multi-tier distributors, resellers, intermediaries, and service providers, the greater the sales they achieve. The 2019 B2B Buyers Survey Report, by DemandGen in collaboration with DemandBase, found that B2B buyers are more likely to purchase from sales representatives who demonstrate a stronger knowledge of the solution area and the business landscape (65%) compared to competitors. B2B buyers also give high praise for sales teams who can provide quotes quickly and respond to their inquiries promptly (63%), in addition to providing higher-quality content (61%). Each of these benefits is derived from a CPQ platform that can scale across every phase of the selling lifecycle.
The following are the key features needed on CPQ channel roadmaps in 2019 to stay competitive and scale sales and revenue on pace with market growth:
Greater personalization for each type of partner portal supported by real-time integration to CRM and ERP systems, designed to scale for sales team turnover across multi-tier distribution networks. Channel partners’ sales teams tend to churn quickly, and it’s best to design in intuitive, easily configured portals by sales role to help new hires get up to speed fast. Channel sales associates are typically the fastest-churning area of any selling business. With greater personalization comes the need for greater integration to provide the data needed to enable partner portals to have a greater depth of functionality. The following graphic from Deloitte’s recent study, Configure, Price, and Quote (CPQ) Capabilities illustrates this point:
Support for multi-tier pricing, price management, price optimization, price enforcement, and special workflows, including Special Pricing Requests (SPR). Baseline CPQ platforms support price management and have successfully transitioned multi-tier distribution networks off of Microsoft Excel spreadsheets to a single pricing model that scales across all products and channels. Consider adopting advanced pricing logic to support SPRs so sales operations teams don’t have to do this process manually. In manufacturers who have transitioned from manual to automated SPR approvals, average deal sizes have increased over 60%, and productivity jumped over 76% according to a recent Gartner survey.
Augment advanced product configuration tools by making them more intuitive and easier to use to sell the more advanced products in your catalog. It’s time to push the boundaries of CPQ channel selling systems to sell more complex products and drive greater revenue and margins. Forward-thinking manufacturers are taking a virtual design and 3D-based design approach to accomplish this. Enabling channel partners to take larger orders for more complex products is paying off.
Upgrade guided selling strategies to be more than catalog-based selection systems, mining customer data using machine learning to see which products they have the greatest propensity to buy when. It’s time to migrate off of the guided selling systems that are selecting products from catalogs that may deliver the best gross margins or have a traditionally high attach rate with the product the customer is buying. Machine learning is making it possible to provide greater accuracy and precision to recommendations than ever before.
Improve the usability of sales promotions, rebates, and most importantly, Market Development Funds (MDF). It’s amazing how much time manufacturers are spending manually handling MDF claims today. It’s time to automate this area of the CPQ channel roadmap and save thousands of hours and dollars a year while enabling resellers to get reimbursed faster or get the funds they need to grow their businesses.
Contract management is a must-have for CPQ channel roadmaps today. Integrating a cloud-based contract management system into a CPQ platform is vital for taking one more step towards an end-to-end quote-to-cash workflow being in place. Real-time integration to contract management can save days of waiting for contract approvals, all leading to more closed deals and faster, more lucrative sales cycles.
Manufacturers can realize greater revenue potential through their channels by combining machine learning insights to find those aftermarket customers most ready to buy while accelerating sales closing cycles with CPQ. Manufacturers want to make sure they are getting their fair share of the aftermarket. Using a machine learning-based application, they can help their resellers increase average deal sizes by knowing which products and services to offer when. They’ll also know when to present upsell and cross-sell offers into an account at a specific point in time when they will be most likely to lead to additional sales, all based on machine learning-based insights. Combining machine learning-based insights to guide resellers to the most valuable and highest probability customer accounts ready to buy with an intuitive CPQ system increases sales efficiency leading to higher revenues.
Conclusion
Now that the solutions exist for resellers to simplify CPQ selling strategies, it’s up to each manufacturer to decide how competitive they want their channel partner roadmap to be. Any given manufacturer’s quoting and configuration tools today are competing with 11 others on average for a reseller’s time, it is clear that roadmaps need a refresh to stay competitive. Suggested options include offering greater personalization, multi-tier pricing and a more thorough approach to price management, advanced product configuration support, revamped guided selling strategies and improved usability of sales promotions, rebates, and Market Development Funds (MDF). Manufacturers need to prioritize each of these features relative to their product- and revenue-specific goals by channel. A fascinating company who has deep expertise in designing, implementing, and scaling analytics, service, sales, IoT, and CPQ solutions for manufacturers is eLogic. The company’s mission is to enable manufacturers to achieve the highest value customer engagement and product & service lifecycle performance. eLogic is regarded as the leading system integration partner in CPQ and product configuration and is considered a global leader in delivering business solutions for manufacturers across SAP configuration technologies and Microsoft Dynamics 365, Power Platform & Azure.
Top-performing companies are more than twice as likely to be using AI for marketing (28% vs. 12%) according to Adobe’s latest Digital Intelligence Briefing.
Retailers are investing $5.9B this year in AI-based marketing and customer service solutions to improve shoppers’ buying experiences according to IDC.
Sales and Marketing teams most often collaborate using Configure-Price-Quote (CPQ) and Marketing Automation AI-based applications, with sales leaders predicting AI adoption will increase 155% across sales teams in two years.
Artificial Intelligence enables marketers to understand sales cycles better, correlating their strategies and spending to sales results. AI-driven insights are also helping to break down data silos so marketing and sales can collaborate more on deals. Marketing is more analytics and quant-driven than ever before with the best CMOs knowing which metrics and KPIs to track and why they fluctuate.
The bottom line is that machine learning and AI are the technologies CMOs and their teams need to excel today. The best CMOs balance the quant-intensive nature of running marketing with qualitative factors that make a company’s brand and customer experience unique. With greater insight into how prospects make decisions when, where, and how to buy, CMOs are bringing a new level of intensity into driving outcomes. An example of this can be seen from the recent Forbes Insights and Quantcast research, Lessons of 21st-Century Brands Modern Brands & AI Report (17 pp., PDF, free, opt-in). The study found that AI enables marketers to increase sales (52%), increase in customer retention (51%), and succeed at new product launches (49%). AI is making solid contributions to improving lead quality, persona development, segmentation, pricing, and service.
The following ten charts provide insights into how AI is transforming marketing:
21% of sales leaders rely on AI-based applications today, with the majority collaborating with marketing teams sharing these applications. Sales leaders predict that their use of AI will increase 155% in the next two years. Sales leaders predict AI will reach critical mass by 2020 when 54% expect to be using these technologies. Marketing and sales are relying on AI-based marketing automation, configure-price-quote (CPQ), and intelligent selling systems to increase revenue and profit growth significantly in the next two years. Source: Salesforce Research, State of Sales, 3rd edition. (58 pp., PDF, free, opt-in).
AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service is the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption use cases. Just over 52% of small businesses with sales of $25M or less are using AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).
22% of marketers currently are using AI-based applications with an additional 57% planning to use in the next two years. There are nine dominant use cases marketers are concentrating on today, ranging from personalized channel experiences to programmatic advertising and media buying to predictive customer journeys and real-time next best offers. Source: Salesforce’s State of Marketing Study, 5th edition
Content personalization and predictive analytics from customer insights are the two areas CMOs most prioritize AI spending today. The CMO study found that B2B service companies are the top user of AI for content personalization (62.2%) and B2B product companies use AI for augmented and virtual reality, facial recognition and visual search more than any other business types. Source: CMOs’ Top Uses For AI: Personalization and Predictive Analytics. Marketing Charts. March 14, 2019
45% of retailers are either planning to or have already implemented AI to improve multichannel customer engagement as a core part of their marketing mix. Reflecting how dependent retailers are on supply chains, 37% of retailers are investing in AI today to improve supply chain logistics, supply chain management, and forecasting (37%). Source: AI and Machine Learning use cases in the retail industry worldwide as of 2019, Statista.
Personalizing the overall customer journey and driving next-best offers in real-time are the two most common ways marketing leaders are using AI today, according to Salesforce. Improving customer segmentation, improving advertising and media buying, and personalizing channel experiences are the next fastest-growing areas of AI adoption in marketing today. Source: Salesforce’s State of Marketing Study, 5th edition
81% of marketers are either planning to or are using AI in audience targeting this year. 80% are currently using or planning to use AI for audience segmentation. EConsultancy’s study found marketers are enthusiastic about AI’s potential to increase marketing effectiveness and track progress. 88% of marketers interviewed say AI will enable them t be more effective in getting to their goals. Source: Dream vs. Reality: The State of Consumer First and Omnichannel Marketing. EConsultancy (36 pp., PDF, free, no opt-in).
Over 41% of marketers say AI is enabling them to generate higher revenues from e-mail marketing. They also see an over 13% improvement in click-thru rates and 7.64% improvement in open rates. Source: 4 Positive Effects of AI Use in Email Marketing, Statista (infographic), March 1, 2019.
How Top Marketers Use Artificial Intelligence On-Demand Webinar with Vala Afshar, Chief Digital Evangelist, Salesforce and Meghann York, Director, Product Marketing, Salesforce
The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).
