A few weeks ago, Salesforce announced its new Artificial Intelligence (AI) functionality called Einstein. The new features promise to provide improved decision making based upon predictive scores and recommendations to sales, marketing, service, and other functions. Likewise, Microsoft announced yesterday that they have formed a dedicated AI group working on infusing Microsoft products with intelligent capabilities.
However, as AI and Predictive Analytics become key technologies for companies, it is important to remember the old GIGO maxim:
Garbage In, Garbage Out
These tools simply won’t work well if your information is inaccurate, out of date, or incomplete. Best case, bad data results in weak predictions that aren’t trusted. Worst case, they provide a false confidence that wastes resources and misdirects corporate activities.
John Bruno, an analyst at Forrester, described this problem well in a recent blog:
The future analytics-driven sales processes is bright, but the path ahead is not without its challenges. Current and potential Salesforce customers should be mindful that intelligent recommendations require a large volume of quality data. If poor data goes in, poor recommendations will come out. Cleansing data and iterating the fine-tuning of recommendations will be vital to long-term success. Another major hurdle is adoption. Many sellers still lack trust in “intelligent” recommendations. You will need to handhold these sellers until they form trust. This means starting with small recommendations and scaling from there.
The good news is that many of the sales intelligence companies are now offering data hygiene services for lead, contact, and account records. The processing can be performed via CRM or MAP connectors or by uploading files to their cloud services. The vendors match sales and marketing files against their reference datasets and then augment the files with firmographics, biographics, technographics, etc. Matching can be done both in real-time to support both list uploads and web forms and via batch processing to support on going maintenance of corporate data.
While no company and contact database is 100% accurate, they are far more accurate than most marketing automation platforms and CRMs. Furthermore, they have better field fill rates, standardized values (important for segmentation and analytics), and more rapid update cycles.
The predictive analytics companies are also beginning to provide enrichment services.
While there is a commonly cited statistic about contact data decaying at a 2.1% rate per month, the nature of this decay has been less reported. Predictive Analytics company Radius conducted a study of 10,000 businesses and assessed the rate of decay over three months. Data quality was assessed by external vendors in May and August 2016. The Move or Unreachable value of 27% is similar to the often cited annual decay rate of 25% for contacts.
Radius published only three month decay rates, but I annualized the data using a four-period compounding formula.
Radius three-month data decay rates with imputed annual rates calculated by GZ Consulting.
One statistic that I did not annualize is the “Emails become Invalid” rate. If 7.6% of contacts are not reachable after three months, then why are only 2.5% of emails becoming invalid? There are several reasons: First, approximately 8% of companies set their mail servers to not send bounce messages (or 0.6% of the three-month spread). Secondly, most companies do not immediately turn off email messages when a person leaves the firm. They generally forward the emails for a period of time to an administrative assistant or the individual who has assumed the departed person’s role. This tends to be a temporary situation, but it explains the 5% gap between the two rates. As one would expect companies to eventually decommission old emails, the annual rate of emails becoming valid should be closer to 25% than the non-displayed CAGR rate of 9.7%.
Radius is looking to address the decay problem in its database via leveraging their clients’ second-party data to obtain network effects for augmenting and updating their file. Customers opt into the network with their data immediately anonymized and aggregated, “providing additional points of validation and verification.” Customer contributions now cover 70% of the businesses in Radius’ Business Graph spanning one billion interactions.
Zoominfo has employed a similar model over the past few years for building out their contact file. Their Community network has lifted their coverage of active US B2B contacts to 80 million.
Radius claims that the network improves the accuracy, comprehensiveness, and freshness of their data. For example, phone connect rates improve from 84% to 93% when there are at least five data validation points. Likewise, physical address accuracy improves from 85% to 96% when there are at least five validation points.
The comprehensiveness of firmographic data also improves with additional members. Without the customer network, only 64% of records had full firmographic or contact attributes. The population of comprehensive records rises to 81% with fifty network members.
Finally, Radius claims it’s network is “up to 20 times faster” at updating the Business Graph “than with traditional, manual methods of data collection and validation.”
“Network effects have long been a driver of business value and innovation across many industries, particularly for B2C companies,” said Radius CEO Darian Shirazi. “At Radius we are pushing the envelope on what B2B companies can come to expect from data. Now, leveraging customer network effects opens the door to further transform B2B data and develop new marketing innovations. By tapping into our predictive expertise and already robust data set, customer network effects can help marketers make smarter, faster decisions that drive revenue and growth.”
In the late 1980’s, when my career was first beginning, I worked on a technology helpdesk for an insurance agency automation system (Aetna’s Gemini platform). Many of the calls were routine with an easily road mapped set of resolution steps. So the firm decided to invest in artificial intelligence (AI) and began interviewing its most seasoned experts to identify the problem resolution path.
After several months of development, an AI module would be unveiled that walked the user through problem resolution. It was basically a set of if-then-else and case statements providing pre-coded branching logic. Support reps started with a category and were walked through a set of questions to ask and resolution steps to convey over the phone.
The solution was expensive and lacked the ability to learn. Thus, if new problems arose or the problem resolution changed due to new hardware or software being introduced, the rules no longer applied.
It was far from intelligent. Heck, I’d coded a twenty-questions game in a first semester programming class that was more intelligent than the service. At least my Q&A game had the ability to learn new questions to ask without requiring an expensive consultant.
Finally, it was only used by new hires as much of the routine steps were just that — routine.
Solutions like this quickly proved that Artificial Intelligence wasn’t intelligent and after a few years, the term AI fell from favor and returned to the realm of sci-fi killer robots.
Nearly three decades later, the term AI is once again being rolled out. But now it does convey an impressive level of intelligence which makes our devices feel smart. It’s why we call them smartphones. They are able to leverage vast amounts of data and make decisions in the blink of an eye. Whether it is asking Siri a question or having Google map the best route to a location subject to current traffic patterns and transportation mode, we expect our devices to be intelligent.
AI represents a massive change in technology. You might call it a “paradigm shift” or “disruption” or we could just stick with “massive change.” What we’re trying to say is, AI is kind of a big deal. And just like the arrival of the personal computer, cloud computing, and the mobile smartphone, AI is going to fundamentally change the way things work, forever.
So it was with a smile that I saw the term AI being used by Salesforce in positioning their new Einstein service. Each year at Dreamforce, CEO Marc Benioff discusses a new underlying technology or cloud. Most recently it has been Lighting (UI and workflows), Wave (analytics), and the Internet of Things Cloud. At Dreamforce 2016, it is Einstein, their artificial intelligence platform to assist with sales, marketing, and service.
Salesforce presents AI simply as
Lots of data + cloud computing + good data models = smarter machines
So while much of this technology has been provided as consumer applications for over a decade, businesses have been lagging behind when the scope goes beyond a mobile app or e-commerce portal.
Shouldn’t the full transactional and service history be available to help understand past purchases, preferences, and potential cross-sell and upsell opportunities?
Wouldn’t we want it delivered no matter the touch point?
That is the type of intelligence that Einstein is looking to bring to Salesforce customers. Einstein is “the world’s first comprehensive artificial intelligence platform for CRM. I’ve never been more excited about the innovation happening at Salesforce,” said Benioff.
Einstein is available both programmatically (for developers) and “declaratively for non-coders,” said Benioff. It is integrated directly into the SFDC platform and available across all of the clouds. For example, an Einstein widget displays a set of insights identifying competitor news, recommended actions, and account intelligence.
Einstein Insights alerts widget.
Einstein Insights surfaces insights both programmatically for developers and data scientists and declaratively for end users. It can even infer competitors from emails and deliver alerts within SFDC widgets.
Einstein builds models with no coding or initial training by users. For example, the system is able to determine which trigger events are important to sales reps and surface news about competitors without asking “who are your competitors?” The system also can make recommendations concerning high-scoring leads based upon both fit (firmographics, biographics) and behavior (e.g. recent viewing of a demo).
Einstein recommends actions to sales reps. In this case, it is suggesting an email requesting a meeting with the VP of Sales at a high scoring lead who recently viewed a product demo on the website.
Not only does the system recommend activity, but it then offers recommended email copy including a proposed call time.
The platform is built on a series of recent acquisitions including RelateIQ (rebranded SaleforceIQ), MetaMind, Implisit, PreductionIO, and TempoAI. The firm now has a team of 175 data scientists “stitching together this amazing platform,” said Benioff.
“The new platform will “democratize artificial intelligence” and “make every company and every employee smarter, faster and more productive,” continued Benioff. “This is going to be a huge differentiator and growth driver going forward as it puts us well ahead of our CRM competition once again.”
The new platform infuses their sales, cloud, and marketing platforms with AI capabilities for “anyone” regardless of their role or industry. According to Salesforce, Einstein lets employees “use clicks or code to build AI-powered apps that get smarter with every interaction.”
Einstein is positioned as having your own data scientist focused on applying AI to customer relationships. Einstein has access to a broad set of intelligence including CRM data, email, calendar, social, ERP, and IoT to “deliver predictions and recommendations in context of what you’re trying to do. In some cases, it even automates tasks for you. So you can make smarter decisions with confidence and focus more attention on your customers at every touch point.”
Several predictive analytics companies used the launch to shout, “hey wait, we’ve already mastered AI for sales and marketing.” LeadSpace CEO and former Salesforce CMO Doug Bewsher stated, “B2B marketers need a complete solution that works across multiple channels, in their existing marketing stack.”
“Bad data is the Achilles heel of AI,” continued Bewsher. “AI is only as good as the data available to it. Marketers who want to get the full benefit of AI need to address their data problems first, or they’ll see the same diminishing returns as with traditional marketing automation.”
Shashi Upadhyay, CEO at Lattice Engines was a bit more diplomatic in welcoming Einstein. “After having led the market for several years, we are really excited to see the mainstream attention shifting towards AI-based solutions for marketing and sales. The Einstein announcement from Salesforce is a great step forward, as it will serve to educate the market and signal that predictive solutions are here to stay.”
Mark Kovac of Bain and Company wrote an interesting piece on the topic of digital exhaust in Harvard Business Review. The short piece, titled “Using Digital Exhaust to Improve Sales,” provides three examples of how software vendors are combining big data and analytics to provide new tools to sales management.
Kovac defines digital exhaust as “the data generated from the regular activities of a sales force or their customers, to change the behavior of frontline sales representatives in ways that dramatically improve sales productivity and effectiveness” and provides examples from three firms including Lattice Engines.
The first firm, Volometrix, was acquired by Microsoft last year. Volometrix performs resource analyses to determine where sales reps are spending their time and which behaviors are positively correlated with sales performance. Unfortunately, the use case Kovac provides is about a firm that realigned company priorities providing more time for selling and therefore “too much sales capacity.” While efficiency is desirable, if software solutions result in efficiency improvements without much improvement in efficacy (ability to sell), then they will be resisted. This isn’t too say that Volometrix doesn’t provide efficacy gains (I only know them from the story), but case studies which focus on cost savings (efficiency) over revenue gains (effectiveness) may create situations where sales reps refuse to cooperate.
The second vendor discussed is GoToMeeting which is performing voice-to-text semantic analysis and discerning which phrases and approaches are more effective. The data gathered is anonymous (though I still see issues with recording calls, particularly in certain jurisdictions) and provides insights in how to more effectively sell. The software sounds a bit like SalesforceIQ but instead of focusing on email analysis, GoToMeeting is using conversations.
Lattice Engines stratifies leads by probability of closing with the highest probability leads immediately sent to sales and the remainder held for nurturing.
The final case study was predictive analytics company Lattice Engines which helps firms improve “call response rates, close rates and average order value.” Predictive analytics for sales and marketing is a growing class of recommendation software with a broad set of competitors including Leadspace, Infer, 6Sense, and Mintigo. Lattice Engines combines first-party and third-party data sets to score leads and recommend sales approaches. Unfortunately, Kovac focuses on the matched third-party data instead of the digital exhaust captured by the firm (he does mention loyalty scores and product purchase history).
There is a growing set of SalesTech vendors that are applying digital analysis to previously inaccessible datasets. While sales remains an art, these solutions are shifting the sales profession from a craft to a science. If vendors are to be successful, they need to focus more on efficacy versus efficiency. While sales could certainly become more efficient by reducing non-productive administrative activities (and reps are always happy to reduce dead weight time spent tracking problems, navigating contract signatures, and entering data), the focus should be on top-line growth and exceeding quota, not cost reduction. Otherwise, sales reps will resist their adoption.
SalesLoft, DemandBase, Datanyze, and Leadspace made Gartner’s “Cool Vendors in Tech Go-to-Market, 2016” list. According to the report, “Marketing and sales enablement leaders should consider these software-as-a-service applications to complement existing CRM tool investments.” Gartner also recognized predictive analytics firms Everstring and Radius in the data-driven marketing category.
Providers are doing a better job in responding to the changing B2B technology buying cycle and the higher expectation that buyers (both prospects and customers alike) have when they look to make a purchase. Some of this involves process and training improvement, improved messaging and positioning. But there is also a technology element, particularly as it relates to things like data, analytics, content, targeting, personalization and engagement. And clients are increasingly leveraging the latest tools that allow them to make better, smarter decisions.
Gartner Research Director Todd Berkowitz
Here is what Berkowitz said was cool about the firms:
Datanyze – The technology tracking firm helps identify “when a particular piece of SaaS or mobile software (say from a competitor) is added and fire off alerts.” This feature helps SDRs and sales reps see “which companies are in market and engaging with them.” Datanyze also offers a “cool” Chrome browser extension called Insider which displays firmographics and technographics from a company website, performs on demand email detection, and uploads this information to Salesforce.
Demandbase – The firm supports Account Based Marketing (ABM) marketing with IP-based advertising and personalization tools. The firm delivers “a unique ‘one-two punch’ of real-time IP identification and technology that makes it possible to deliver company advertising (targeting and retargeting) and website personalization to help marketers increase awareness, drive up conversions, generate net-new and upsell/cross-sell leads from named accounts, and measure program effectiveness across the funnel.”
LeadSpace – The predictive analytics vendor helps customers “generate demand, enrich and prioritize accounts/leads from companies with a higher propensity to buy.” Berkowitz also commended their “virtual data management platform that drives their models and recommendations.”
SalesLoft – The Account Based Sales Development (ABSD) firm assists sales development reps (SDRs) with lead qualification and prospecting. “Their suite of templates, an integrated dialer and real-time analytics are a lot cooler for SDRs than the old way of working. And they work much better.” Their Cadence tool helps streamline prospect communications such that “some of their customers reported more than doubling the number of successful connections, appointments, demos and sales-qualified leads (SQLs), while reducing follow-up time from leads by more than 75%.” Their new Sales Development Cloud provides prospect intelligence to SDRs from DiscoverOrg, Crystal, Owler, InsideView, Datanyze, RingLead, Sigstr, and ExecVision.
Everstring – A more recent entrant to the predictive analytics space (founded in July 2014), Everstring offers predictive demand generation and scoring models at both the lead and account level. Everstring covers eleven million B2B companies and 20,000 different attributes with “rapid deployment of models across many points in the funnel.” The firm is also a strong proponent of ABM and helps marketers identify accounts. “EverString’s predictive account models enable marketers to identify high-potential ABM candidates and then push them to third-party ABM platforms for use,” said Berkman.
Radius – The predictive company was lauded for its segmentation tools which help SMBs “determine total available market, create attractive segments and identify accounts to target.” Radius was also praised for its “clean interface,” “data and analytics tools,” and the ability to train and validate models within one business day. Berkman warned that Radius has faced little competition in the SMB market to date but is likely to face stiffer competition as both Radius and the market for predictive analytics solutions grow.
Berkowitz noted that these tools are all focused on making it easier for marketers and down-the line sales to make better decisions” noting that they all rely on the “heavy use” of data and analytics.
Radius provides easy to interpret segmentation, success analytics, and net-new prospect acquisition tools from within SFDC.
While many of these firms provide predictive analytics, Berkman contends that vendors will soon be offering “prescriptive analytics” that help firms decide what should be done. Thus, analytics will shift from predictions based upon historical analysis to recommendations concerning which actions to take. Prescriptive analytics utilizes graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.
“We have seen it [prescriptive] used a little bit on the sales analytics side already,” said Berkman. “It is likely that we will get to that stage with marketing so the marketer will know not only who is most likely to buy and what they will buy.”
For marketing, prescriptive analytics will assist with prospect identification, optimizing customer communications, and improving prospect offers.
Another trend you will find amongst the cool vendors is the heavy citation of ABM and ABSD tools amongst these vendors. Everstring, SalesLoft, and DemandBase are all strong proponents of ABM while Leadspace recently partnered with ABM vendor Engagio.
The Sales Intelligence (SI) space has been undergoing some rapid change over the past year. This evolution in functional scope and content sets has resulted in an expansion in the number of companies I cover as well as the categories (ABSD services, PE/VC funding databases). There is also a movement of sales intelligence vendors into marketing intelligence as the traditional SIs look for additional revenue opportunities and a broader value proposition.
A year ago, Account Based Marketing (ABM) was discussed mostly by DemandBase, a top of the funnel programmatic marketing vendor, but the predictive analytics vendors and Zoominfo began discussing the methodology. Thus, a year ago, ABM meant anti-ballistic missile or activity based management to all but the most well-versed marketers. Now the term is commonly found in corporate blogs and collateral and has spawned ABSD (Account Based Software Development) which follows ABM down to the middle of the funnel in the sales development function. There are now several ABSD vendors which I have begun to include in my newsletter including SalesLoft and QuotaFactory. ABSD shifts the sales development focus away from “smile and dial” calling towards targeted messaging into a set of top prospects. Since the prospecting activities are targeting higher value opportunities, there is a benefit to personalizing calls and emails. SalesLoft refers to this activity as “sincerity at scale.”
What is even more impressive about SalesLoft and QuotaFactory is that they are both less than two years old and yet they have already grown in commercial stature to the point where they are building out partner ecosystems with traditional SIs and other vendors. SalesLoft rolled out their Sales Development Cloud at their customer conference last month with nine partners including DiscoverOrg, InsideView, Datanyze, and Owler. At the same time, QuotaFactory announced partnerships with Bedrock Data, Ambition, HG Data, and InsideView.
A second area of rapid growth is the technology sales intelligence vendors. DiscoverOrg and RainKing have grown revenue and capabilities, transforming what was historically a sleepy niche into a significant sub-category. Both vendors have posted high multi-year growth rates, internationalized their datasets, expanded their technology trigger events, and developed CRM and marketing automation connectors. While they continue to gather rich profiles of IT execs, they are broadening their functional coverage to include non-IT functions that are significantly investing in IT cloud solutions such as marketing and finance. DiscoverOrg is continuing this functional expansion with product management (the recently released TEDD dataset), HR, and Sales. Furthermore, their databases, which once focused on the Fortune 1000, now cover nearly 50,000 top global companies and 700,000 executives. Both firms announced significant funding events in the past six months.
Aberdeen Group, which was spun off of Harte-Hanks last year, has begun to invest in the AccessCI database. Once the leading source of technology profiles and leads, the AccessCI (aka CiTDB and CITDS) dataset had received little investment from Harte-Hanks over the prior decade. Under new ownership, the product is once again receiving management attention.
The SIs have also increased their coverage of technographics. Avention acquired SalesQuest two years ago and integrated their Crush profiles into their products while other vendors have licensed vendor/product data from HG Data or mined technographic intelligence. HG Data has become so adept at collecting vendor/product data that DiscoverOrg and Aberdeen Group have begun licensing content from them.
Several firms that began as fundings databases found that Business Development was a logical extension of their value proposition and have since repositioned themselves as sales intelligence solutions. Firms such as DataFox and Mattermark are focusing more on sales intelligence functionality while CB Insights has launched a sales intelligence solution (with technographics) while retaining its focus on the PE/VC space.
For the most part, the SIs have avoided the predictive analytics space. The exceptions are Avention, which supports business signals and ideal profiles, and Radius which morphed from an SMB SI into a predictive analytics company. Meanwhile, the predictive analytics companies are beginning to offer a subset of SI features such as net-new leads.
Instead, the SIs have focused more on marketing analytics, data enrichment, and data hygiene which allows them to leverage their databases without investing in data scientists. Dun & Bradstreet acquired NetProspex last year for its contact database and the Workbench cloud data hygiene platform. They have also begun to offer Hoover’s concierge services including enrichment, segmentation reporting, and email delivery. Avention launched its DataVision customer data platform earlier this year while Zoominfo, Data.com, and InsideView have placed equal weight upon marketing services and sales intelligence services.
Social Selling continues to be a core element of positioning for InsideView and LinkedIn Sales Navigator. Artesian Solutions, a UK vendor that is launching a US product later this year, also focuses on social selling. A significant product gap across the SIs is the lack of social tools built into their offering. I can understand why SIs have shied away from Who Knows Who tools (the exceptions are InsideView and DueDil), but it is perplexing why most SI vendors have only limited sets of social media links and little social media content displayed in their services. Only InsideView, Artesian, and Owler have put much emphasis upon social media content.
Europe is also becoming a home of new services. DueDil has evolved into a UK challenger to Avention and BvD Mint while IKO System and Sparklane (formerly Zebaz) have an established presence in France.
When I started my newsletter four years ago, many of the companies and products either had not been launched or weren’t on my radar. I mostly focused on Avention, Hoover’s, InsideView, DiscoverOrg, BvD, Sales Genie, Data.com, and RainKing. While these companies continue to innovate, much of the energy is coming from new entrants. The rapid growth and diversity of sales intelligence functionality has been exciting to observe.
Credit: Darwin’s Finches are in the public domain. Charles Darwin, 1845.
French based predictive analytics company IKO System recently closed on a Series A funding round of 2.5 million euros to help speed up its European development. Investors include Naxicap Partners, 3T Capital and Bpifrance.
The firm addresses two problems: generating qualified leads and automating meeting planning. The Predictive Prospecting service begins at 12,000€ per year ($13,200) and provides unlimited lead scoring, lead engagement insights, and CRM/MAP integrations (Hubspot, Marketo, Salesforce, Pardot, and Oracle). IKO System describes lead engagement insights as “Buying signals and context for each lead: competitors or complementary vendors in place, references to quote, key signals, email / tel / social.”
IKO System claims that its predictive analytics increase conversion rates by a factor of four to eight.
The Revenue Automation service bundles Predictive Prospecting with programmatic email campaigns, multi-user support, and engagement messaging consultations to “optimize lead response rates.” Revenue Automation pricing was not disclosed.
The IKO database covers 9 million European companies and 30 million professional contacts. Contacts, however, are focused on a few countries: the UK (8 million), France (7 million), Germany (3 million), and Benelux (3 million).
The firm gathers insights from 60,000 data sources including Bloomberg, Reuters, Zoominfo, LexisNexis, Experian, and DataMonitor. Licensed content is married with monitored company websites. IKO data is then matched against corporate datasets housed in CRM, marketing automation platforms, and other corporate assets. IKO System constructs a lead scoring model for each company that includes firmographics, job position, data quality (e.g. fill rates on contact information), and buying signals. IKO looks both at corporate level signals such as technology and product launches and “The lead’s buying signals for an offer similar to yours (public discussions, interviews, preliminary talks with your competitors).”
“Marketing teams contribute up to 40% of the leads needed by their sales force,” said IKO Systems CEO Marc Rouvier. “At the same time, salespeople need to organize themselves to prospect and generate 60% of leads to hit their targets. By putting IKO System in place, our clients give their sales teams a way of structuring and automating their prospection in order to generate 5 times more meetings.”
IKO has over 200 clients including Oracle, Adobe, Citrix, McAfee, HP, and TIBCO.
Social media job site Glassdoor recently published its second annual ranking of the top jobs in America and, of the top twenty-five jobs, ten were in technology. The top ranked position was data scientist which jumped from ninth last year. Other high ranked positions were Solutions Architect (#3), Mobile Developer (#5), and Product Manager (#8). Glassdoor bases their rankings on three variables: the number of job openings, salary, and career opportunities rating.
The Median Base Salary for a data scientist is $116,840. Other tech base salaries can be seen in the above graphic.
When Network World interviewed data scientists about their position, they noted the pleasure of discovery as a key benefit. A common complaint amongst data scientists was the headache involved with data preparation. “At times, munging [parsing] through data can get tedious,” said data scientist Jeff Baumes at Kitware. “The worst times are when I realize the quality, quantity, or other aspect of the data simply prevents me from gaining the level of insight that I hoped to gain from the data.”
The McKinsey Global Institute found there is a growing shortage of analytics talent in the United States. By 2018, they projected a shortfall of 140,000 to 180,000 professionals with analytical expertise. They also projected a deficit of 1.5 million analytics trained managers and analysts.
Data scientist talent acquisition and retention are a significant problem for organizations, particularly amongst firms looking to initially establish data science capabilities. In an article in the MIT Sloan Management Review, Ransbotham, Kiron and Kirk Prentice found that 55% of analytically challenged firms had a problem recruiting and retaining analytical talent while firms described as innovators had much less difficulty. Only 29% of innovators reported difficulty recruiting with 24% reporting difficulty retaining. Innovators also are much more confident that they have the appropriate skill levels in house. While 74% of Innovators believe they have hired the appropriate analytics talent, only 17% of the analytically challenged felt the same.
One advantage of partnering with sales predictive analytics companies such as Lattice Engines or Leadspace is the ability to bypass hiring of in-house data scientists and instead work with their resources and tools. While it is still important to understand the results and train staff in data interpretation, much of the complexity is removed.
Furthermore, the strategic advantage accruing to analytics capabilities is declining as more firms develop such capabilities. In 2012, 67% of surveyed respondents believed analytics capabilities conveyed a strategic advantage. By 2014, the percentage had dropped to 61%. The authors posited two reasons for the decline: an increase in the number of firms investing in analytics and a difficulty in converting analytical insights into business action. Half the respondents noted difficulty in translating insight to action.
“Technology is no longer the main barrier to creating business value from data: The bigger barrier is a shortage of appropriate skills,” said Ransbotham et al. “Companies with appropriate analytical skills are far more likely to say that analytics is creating a competitive advantage in their organization than are other organizations.”
Stephanie Kong, Product Marketing Manager at Radius, recently compared dirty data to rotten food. Working with either consumes more expertise and results in sub-par results:
Handing dirty data over to data scientists is tantamount to passing rotten ingredients to a chef and expecting that he/she transform the inputs into a gastronomical masterpiece. In both instances, the quality of the inputs impacts not only the quality of the outcome, it also impacts the experience and efficiency of the professional– how much time can be spent experimenting and applying the artistry for which the professional was hired versus overcoming hurdles to get to a sufficient baseline.
Bottom line: the quality and state of your internal data can impact– and even worse, impede– the ability of even the most talented data scientist to generate breakthrough ideas. Many turnkey data solutions can help you maintain data, even enhancing accuracy and comprehensiveness, in addition to extracting insights. It’s not simply a means of “killing two birds with one stone”; accurate and complete data is a critical first step. In other words– and without being too macabre– good data is the essential and necessary “first kill.”
Marketers are becoming more strategic in their approach to data as they realize the limitations and costs of poor data. Predictive Analytics systems are only as good as your underlying data. Bad data is simply noise (or as Kong would call it, “rotten ingredients”) that obscures the underlying signal. Without accurate data, how can you expect your predictive systems to give you anything more than random nonsense?
Likewise, the shift to Account Based Marketing requires strong firmographics for identifying the companies you wish to target. Furthermore, strong linkage is necessary for targeting subsidiaries and branches. Whether you are extending an MSA or looking to establish a beachhead, you need a holistic view of the organization across industries, regions, and job functions. You also need an accurate set of contacts spanning all functions, levels, and locations.
When evaluating B2B content vendors offering predictive or DaaS solutions, ask about their
Data Processes: Data sourcing, update cycles, verification and validation, feedback processes
Hygiene Services: Do they offer email, phone, and address verification, field standardization, deduplication
Matching Capabilities: Is it a direct match or probabilistic match based upon multiple fields? Are fields standardized prior to matching? Is the focus on company or contact matching?
Connectors / Integrations: CRM, MAP, DaaS cloud, API, etc.
Ongoing Data Refreshes: Frequency, Cost, Level of Automation
Contact Coverage: Emails, direct dials, functions, levels, bios,
Company Data: Scope, depth, firmographic fill rates, identifiers, linkage, etc.
Other Data: Intent data, technology platforms, business signals, etc.
Data quality is a strategic asset so your content and technology partners need to be thoroughly vetted. It is important to understand the strengths and weaknesses of each offering during both the vendor selection and implementation stages. Otherwise, you may only partially address your “rotten ingredients” problem.
I generally avoid playing in the prognostications game. After all, it is difficult enough to understand what happened over the past year without projecting it forward or relying on simple extrapolation. Nevertheless, nothing prevents me from relaying a few of the more interesting sales and marketing related technology predictions during the Predictions Season (and you thought it was the holiday season!).
I will begin with James Cooke and others at Nucleus Research who wrote:
We’ve all heard “data science” creep into the CRM conversation over the past few quarters, but mostly as it relates to marketing. Although marketing was the first area to get predictive, the future for all three pillars (sales, marketing, and service) of CRM is predictive – taking advantage of the intelligence of the software to look forward, not just track progress. Some of the most interesting opportunities are in customer service, where better data about customer’s habits and the products and services they use can be used to proactively support them (think predictive maintenance of automobiles, for example). The concerns about customer data are less prevalent on the service front than in sales and marketing because they tend to opt in. The challenge for companies will be in addressing the human barriers to adopting process changes driven by more predictive and proactive CRM.
While I agree that marketing has taken the lead on predictive analytics with a focus on lead decisioning (e.g. scoring; nurturing vs. promoting to sales; customizing campaigns at the lead level based on behavioral, firmographic, and biographic forecasting) and best customer cloning (determining the “ideal customer” and providing similar leads), predictive has helped close the gap between sales and marketing.
For example, predictive scoring allows marketing to limit promotion to the top N percent of leads, resulting in much better set of Marketing Qualified Leads (MQL) being sent to sales. While fewer leads are being distributed over the Sales/Marketing Wall, they are of a much higher quality. Furthermore, because the leads are scored and the predictive companies are beginning to provide insights around those scores, sales can begin to develop confidence in the MQLs passed to them. The funnel attrition rate from marketing qualified leads to sales qualified leads should decline sharply. This is a point made by Lattice Engines and reinforced by today’s strategic partnership announcement between Infer and InsightSquared.
The Infer Activity Scorecard within InsightSquared stratifies leads by Infer score. The tool answers the question, “How are different quality leads getting worked?”
You also will hear fewer arguments between the parties about lead quality. Marketing has long complained that sales ignored their leads while sales complained that marketing sent too many junk leads resulting in sales quickly cherry picking the lists. Predictive tools should help eliminate this infighting by emphasizing lead quality over volume while providing insight into which leads are dormant.
The Infer Lead Aging report within InsightSquared helps answer the question “Are our best leads getting overlooked?” According to Infer, “this report lets leadership quickly zoom in on high quality leads and ensure that no good lead gets left behind.
Likewise, nothing prevents sales reps and sales ops from playing the cloning game and identifying similar companies of their own. Historically, this was done via peer searching where reps took a single customer and found companies of a similar size in the same industry. Ideal Profiles allow for a similar process, but one based upon the attributes of a group of customers. These profiles are much more sophisticated in that they build their models from many customers instead of a single profile and utilize potentially thousands of business signals to determine the ideal profile. Furthermore, many of the business signals may have been previously unknown because they were not available.
We are also seeing sales acceleration tools that
Make sure that customer questions and requests aren’t ignored.
Provide updated profiles and alerts to reps prior to meetings.
Warn service departments of pending customer sales calls so that the problem is given a higher priority.
Deliver messaging advice to reps around what to say and which products to lead with.
Thus, predictive analytics and machine learning are becoming more proactive and prescriptive. The next few years are likely to transform marketing, sales, and customer support.
The Sales Intelligence (SI) space has been undergoing some rapid change over the past year. This evolution in functional scope and content sets has resulted in an expansion in the number of companies I cover as well as the categories (ABSD services, PE/VC funding databases). There is also a movement of sales intelligence vendors into marketing intelligence as the traditional SIs look for additional revenue opportunities and a broader value proposition.
